Miracle-dz/UniFAR-FTUs huggingface dataset:
Tonometry and biomechanics of the cornea in contact lens wear

Aims: Research on use of contact lenses as drug delivery systems continues. Disposable lenses are often used to treat corneal injuries. Accurate intraocular pressure (IOP) measurements with lenses in situ will enhance patient care and save valuable chair time.Inter- and intraobserver reliability of rebound tonometer (RBT) and intraobserver reliability of ocular response analyser (ORA) with and without contact lenses of 50 (15 male, 35 female) healthy well adapted contact lens wearers between 18 – 55 years (M = 38.90, SD = 9.23) were examined. Clinical comparisons of IOP measurements with ORA and RBT were done. Accuracy of IOP measurements with four commonly prescribed disposable contact lenses (Acuvue Oasys, Frequency XC, Acuvue 1-Day Moist, and Pure Vision with powers -6.00 − +6.00 D) in situ was evaluated. Physiological and physical factors influencing IOP measurements with both instruments were determined. Findings: Intraobserver reliability of RBT without and with lenses was excellent (ICC > 0.88; > 0.92 respectively). Interobserver reliability of RBT was excellent without or with lenses (ICC 0.81; 0.88 respectively). Intraobserver reliability of ORA was good for all metrics measured except for corneal hysteresis (CH) (ICC: CH 0.63; corneal resistance factor (CRF) 0.79; corneal compensated IOP (IOPcc) 0.77; IOPg 0.87). RBT and ORA IOPg (Goldmann equivalent IOP) measurements were clinically and statistically comparable without or with lenses (differences < 0.6 mmHg). ORA IOPcc and RBT were less comparable (differences < 1.45 mmHg). Accurate RBT and ORA tonometry (within 2 mmHg) was possible with low minus power (range -0.50 to -6.00 D); moderate modulus of elasticity (< 0.75 MPa); thin silicone hydrogel (Acuvue Oasys) and hydrogel (Frequency XC; Acuvue 1-Day Moist) contact lenses in situ.Multiple regression analyses showed biomechanical metrics CRF and CH affected RBT and ORA (IOPcc and IOPg) measurements strongly (p < 0.0001). Therefore, cornea’s biomechanical properties had greater influence on accuracy of IOP measurements with these two instruments than other variables examined.
A Cooperative Optimal Control Framework for Connected and Automated Vehicles in Mixed Traffic Using Social Value Orientation

In this paper, we develop a socially cooperative optimal control framework to address the motion planning problem for connected and automated vehicles (CAVs) in mixed traffic using social value orientation (SVO) and a potential game approach. In the proposed framework, we formulate the interaction between a CAV and a human-driven vehicle (HDV) as a simultaneous game where each vehicle minimizes a weighted sum of its egoistic objective and a cooperative objective. The SVO angles are used to quantify preferences of the vehicles toward the egoistic and cooperative objectives. Using the potential game approach, we propose a single objective function for the optimal control problem whose weighting factors are chosen based on the SVOs of the vehicles. We prove that a Nash equilibrium can be obtained by minimizing the proposed objective function. To estimate the SVO angle of the HDV, we develop a moving horizon estimation algorithm based on maximum entropy inverse reinforcement learning. The effectiveness of the proposed approach is demonstrated by numerical simulations of a vehicle merging scenario.
Investigation of the Antibacterial and Antibiofilm Activity of Selenium Nanoparticles against Vibrio cholerae as a Potent Therapeutics

Vibrio cholerae is a major cause of severe diarrhea, which is ecologically flexible, and remains as a major cause of death, especially in developing countries. Consecutive emergence of antibiotic-resistant strains is considered to be as one of the major concerns of the World Health Organization (WHO). Nanoparticles as a new nonantibiotic therapeutic strategy have been widely used in recent years to treat bacterial infections. The present study aimed to investigate the antibacterial and antibiofilm effect of selenium nanoparticles (SeNPs) in vitro against V. cholerae O1 ATCC 14035 strain. SeNPs were prepared and characterized using ultraviolet-visible (UV-Vis) spectroscopy, DLS (dynamic light scattering), zeta potential measurement, and Fourier transform infrared (FTIR) analysis. The concentration of SeNPs was calculated by ICP (inductively coupled plasma) method. Also, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay was employed to assess the cytotoxic effect of SeNPs on Caco-2 cells. Antibacterial and antibiofilm activity of SeNPs was determined by broth microdilution and crystal violet assays, respectively. The average particle size of SeNPs was 71.1 nm with zeta potential −32.2 mV. The SEM images supported the uniform spherical morphology of the prepared nanoparticles. The antibiofilm effect of SeNPs was evident at concentrations of 50–200 μg/mL. This study results provided evidence that SeNPs are safe as an antibacterial and antibiofilm agent against V. cholerae O1 ATCC 14035 strain.
Connection of GeV Dark Matter with ( g − 2) µ Anomaly in U (1) L µ − L τ

In this work, we investigate the impact of recent anomalous ( g − 2) µ measurement with ∼ 4 σ deviation from the SM, probing its eﬀect on a light GeV scale fermionic dark matter (DM). The ( g − 2) µ anomaly can be readily explained in the a beyond the SM (BSM) U (1) L µ − L τ scenario, where only a portion of hitherto allowed parameter space can explain the anomaly. This constraint impacts the enhancement of the neutrino ﬂoor, the neutrino background in the DM direct detection experiments. The GeV scale DM is severely constrained by the ( g − 2) µ result as it restricts the Z (cid:48) mass in the range of 20 − 200 MeV. That restriction results in absence of s-channel resonant annihilation of the GeV scale DM, therefore resulting in over abundance of the DM relic. Even if t-channel annihilation aided by large couplings can bring the relic density in the observed range, direct detection cross section shoots up. Super-GeV DM gets almost ruled out where as sub-GeV DM gets severely constrained.
Public Information Disclosure under Private Information Acquisition∗

A policymaker discloses public information to interacting agents who also acquire costly private information. More precise public information reduces the precision and cost of acquired private information. Considering this effect, what disclosure rule should the policymaker adopt? We address this question under two alternative assumptions using a linear quadratic Gaussian game with arbitrary quadratic material welfare and convex information costs. First, the policymaker knows the cost of private information and adopts an optimal disclosure rule to maximize the expected welfare. Second, the policymaker is uncertain about the cost and adopts a robust disclosure rule to maximize the worst-case welfare. Depending on the elasticity of marginal cost, an optimal rule is qualitatively the same as that in the case of either a linear information cost or exogenous private information. Full disclosure is robust if and only if it is optimal under some information costs, even when no disclosure is optimal under other information costs. JEL classification: C72, D82, E10.
Non-Orientable Lagrangian Fillings of Legendrian Knots

Abstract We investigate when a Legendrian knot in the standard contact ${{\mathbb{R}}}^3$ has a non-orientable exact Lagrangian filling. We prove analogs of several results in the orientable setting, develop new combinatorial obstructions to fillability, and determine when several families of knots have such fillings. In particular, we completely determine when an alternating knot (and more generally a plus-adequate knot) is decomposably non-orientably fillable and classify the fillability of most torus and 3-strand pretzel knots. We also describe rigidity phenomena of decomposable non-orientable fillings, including finiteness of the possible normal Euler numbers of fillings and the minimisation of crosscap numbers of fillings, obtaining results which contrast in interesting ways with the smooth setting.
An Insight-HXMT Dedicated 33 day Observation of SGR J1935+2154. I. Burst Catalog

Magnetars are neutron stars with an extreme magnetic field and sometimes manifest as soft gamma-ray repeaters (SGRs). SGR J1935+2154 is one of the most prolific bursters and the first confirmed source of a fast radio burst (FRB; i.e., FRB 200428). Encouraged by the discovery of the first X-ray counterpart of FRBs, the Insight-Hard X-ray Modulation Telescope (Insight-HXMT) implemented a dedicated 33-day-long Target of Opportunity observation of SGR J1935+2154 since 2020 April 28. With the HE, ME, and LE telescopes, Insight-HXMT provides a thorough monitoring of burst activity evolution of SGR J1935+2154, in a very broad energy range (1–250 keV) with high temporal resolution and high sensitivity, resulting in a unique valuable data set for detailed studies of SGR J1935+2154. In this work, we conduct a comprehensive analysis of this observation, including detailed burst search, identification, and temporal analyses. After carefully removing false triggers, we find a total of 75 bursts from SGR J1935+2154, out of which 70 are single pulsed. The maximum burst rate is about 56 bursts day−1. Both the burst duration and the waiting time between two successive bursts follow lognormal distributions, consistent with previous studies. We also find that bursts with longer duration (some are multipulsed) tend to occur during the period with relatively high burst rate. There is no correlation between the waiting time and the fluence or duration of either the former or latter burst. It also seems that there is no correlation between burst duration and hardness ratio, in contrast to some previous reports. In addition, we do not find any X-ray burst associated with any reported radio bursts except for FRB 200428.
Transmogrifiers: Bright of the Exomoon

Though it may be a behavior that has been observed and documented for millennia, and despite the connection between it and the full moon, the astronomical community has afforded very little attention to lycanthropy. We hope to address this deﬁciency by using the population of known exoplanets as a natural experiment to better characterize what properties of the moon are necessary to trigger a transformation into a werewolf. We additionally investigate which exoplanets are most likely to have exomoons which may cause werewolves, with a particular focus on LHS 1140 b. We also propose a new mission called the Werewolves From Infrared Radiation and Spectral-typing Telescope, or WFIRST, in order to better characterize exoplanetary systems. This will allow us to explore the impact of stellar type on lycanthropy more than it has traditionally been considered. We believe this represents a major step forward in our understanding and recognition of the burgeoning ﬁeld of exocryptozoology.
Mid-infrared Studies of Dusty Sources in the Galactic Center

Mid-infrared (MIR) images of the Galactic center show extended gas and dust features along with bright infrared sources (IRS). Some of these dust features are a part of ionized clumpy streamers orbiting Sgr A*, known as the mini-spiral. We present their proper motions over a 12 yr time period and report their flux densities in N-band filters and derive their spectral indices. The observations were carried out by VISIR at the ESO’s Very Large Telescope. High-pass filtering led to the detection of several resolved filaments and clumps along the mini-spiral. Each source was fit by a 2D Gaussian profile to determine the offsets and aperture sizes. We perform aperture photometry to extract fluxes in two different bands. We present the proper motions of the largest consistent set of resolved and reliably determined sources. In addition to stellar orbital motions, we identify a stream-like motion of extended clumps along the mini-spiral. We also detect MIR counterparts of the radio tail components of the IRS 7 source. They show a clear kinematical deviation with respect to the star. They likely represent Kelvin–Helmholtz instabilities formed downstream in the shocked stellar wind. We also analyze the shape and orientation of the extended late-type IRS 3 star that is consistent with the Atacama Large Millimeter/submillimeter Array submillimeter detection of the source. Its puffed-up envelope with a radius of ∼2 × 106 R ⊙ could be the result of the red-giant collision with a nuclear jet, which was followed by tidal prolongation along the orbit.
Type-directed program synthesis for RESTful APIs

With the rise of software-as-a-service and microservice architectures, RESTful APIs are now ubiquitous in mobile and web applications. A service can have tens or hundreds of API methods, making it a challenge for programmers to find the right combination of methods to solve their task. We present APIphany, a component-based synthesizer for programs that compose calls to RESTful APIs. The main innovation behind APIphany is the use of precise semantic types, both to specify user intent and to direct the search. APIphany contributes three novel mechanisms to overcome challenges in adapting component-based synthesis to the REST domain: (1) a type inference algorithm for augmenting REST specifications with semantic types; (2) an efficient synthesis technique for "wrangling" semi-structured data, which is commonly required in working with RESTful APIs; and (3) a new form of simulated execution to avoid executing APIs calls during synthesis. We evaluate APIphany on three real-world APIs and 32 tasks extracted from GitHub repositories and StackOverflow. In our experiments, APIphany found correct solutions to 29 tasks, with 23 of them reported among top ten synthesis results.
Electron scattering of light new particles from evaporating primordial black holes

Primordial black holes are a possible component of dark matter, and a most promising way of investigating them is through the product of their Hawking evaporation. As a result of this process, any species lighter than the Hawking temperature is emitted, including possible new particles beyond the Standard Model. These can then be detected in lab-based experiments via their interaction with the Standard Model particles. In a previous work, we have first proposed and studied this scenario in the presence of an interaction between the light new species and nucleons. Here we extend this discussion to include the case of interaction with electrons. We show that the simultaneous presence of primordial black holes and species lighter than about $100$ MeV can be constrained by the measurements of direct detection experiments, such as XENON1T, and water Cherenkov neutrino detectors, such as Super-Kamiokande. Our results provide a complementary and alternative way of investigation with respect to cosmological and collider searches.
Global and Asymptotically Efficient Localization From Range Measurements

We consider the range-based localization problem, which involves estimating an object's position by using <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> sensors, hoping that as the number <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> of sensors increases, the estimate converges to the true position with the minimum variance. We show that under some conditions on the sensor deployment and measurement noises, the LS estimator is strongly consistent and asymptotically normal. However, the LS problem is nonsmooth and nonconvex, and therefore hard to solve. We then devise realizable estimators that possess the same asymptotic properties as the LS one. These estimators are based on a two-step estimation architecture, which says that any <inline-formula><tex-math notation="LaTeX">$\sqrt{m}$</tex-math></inline-formula>-consistent estimate followed by a one-step Gauss-Newton iteration can yield a solution that possesses the same asymptotic property as the LS one. The keypoint of the two-step scheme is to construct a <inline-formula><tex-math notation="LaTeX">$\sqrt{m}$</tex-math></inline-formula>-consistent estimate in the first step. In terms of whether the variance of measurement noises is known or not, we propose the Bias-Eli estimator (which involves solving a generalized trust region subproblem) and the Noise-Est estimator (which is obtained by solving a convex problem), respectively. Both of them are proved to be <inline-formula><tex-math notation="LaTeX">$\sqrt{m}$</tex-math></inline-formula>-consistent. Moreover, we show that by discarding the constraints in the above two optimization problems, the resulting closed-form estimators (called Bias-Eli-Lin and Noise-Est-Lin) are also <inline-formula><tex-math notation="LaTeX">$\sqrt{m}$</tex-math></inline-formula>-consistent. Plenty of simulations verify the correctness of our theoretical claims, showing that the proposed two-step estimators can asymptotically achieve the Cramer-Rao lower bound.
Convex Parameterization of Stabilizing Controllers and its LMI-based Computation via Filtering

Various new implicit parameterizations for stabilizing controllers that allow one to impose structural constraints on the controller have been proposed lately. They are convex but infinite-dimensional, formulated in the frequency domain with no available efficient methods for computation. In this paper, we introduce a kernel version of the Youla parameterization to characterize the set of stabilizing controllers. It features a single affine constraint, which allows us to recast the controller parameterization as a novel robust filtering problem. This makes it possible to derive the first efficient Linear Matrix Inequality (LMI) implicit parametrization of stabilizing controllers. Our LMI characterization not only admits efficient numerical computation, but also guarantees a full-order stabilizing dynamical controller that is efficient for practical deployment. Numerical experiments demonstrate that our LMI can be orders of magnitude faster to solve than the existing closed-loop parameterizations.
Doubly-Robust Estimation for Unbiased Learning-to-Rank from Position-Biased Click Feedback

Clicks on rankings suffer from position bias: generally items on lower ranks are less likely to be examined – and thus clicked – by users, in spite of their actual preferences between items. The prevalent approach to unbiased click-based Learning-to-Rank (LTR) is based on counterfactual Inverse-Propensity-Scoring (IPS) estimation. Unique about LTR is the fact that standard Doubly-Robust (DR) estimation – which combines IPS with regression predictions – is inapplicable since the treatment variable – indicating whether a user examined an item – cannot be observed in the data. In this paper, we introduce a novel DR estimator that uses the expectation of treatment per rank instead. Our novel DR estimator has more robust unbiasedness conditions than the existing IPS approach, and in addition, provides enormous decreases in variance: our experimental results indicate it requires several orders of magnitude fewer datapoints to converge at optimal performance. For the unbiased LTR field, our DR estimator contributes both increases in state-of-the-art performance and the most robust theoretical guarantees of all known LTR estimators.
Similarity between the response of memristive and memcapacitive circuits subjected to ramped voltage

Abstract. We report a similar feature in the response of resistor–memristor and capacitor–memcapacitor circuits with threshold-type memory devices driven by triangular waveform voltage. In both cases, the voltage across the memory device is stabilized during the switching of the memory device state. While in the memristive circuit this feature is observed when the applied voltage changes in one direction, the memcapacitive circuit with a ferroelectric memcapacitor demonstrates the voltage stabilization effect at both sweep directions. The discovered behavior of capacitor–memcapacitor circuit is also demonstrated experimentally. We anticipate that our observation can be used in the design of electronic circuits with emergent memory devices as well as in the identification and characterization of memory effects in threshold-type memory devices.
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning

Lung cancer is the leading cause of cancer-related mortality worldwide. Lung cancer screening (LCS) using annual low-dose computed tomography (CT) scanning has been proven to significantly reduce lung cancer mortality by detecting cancerous lung nodules at an earlier stage. Improving risk stratification of malignancy risk in lung nodules can be enhanced using machine/deep learning algorithms. However most existing algorithms: a) have primarily assessed single time-point CT data alone thereby failing to utilize the inherent advantages contained within longitudinal imaging datasets; b) have not integrated into computer models pertinent clinical data that might inform risk prediction; c) have not assessed algorithm performance on the spectrum of nodules that are most challenging for radiologists to interpret and where assistance from analytic tools would be most beneficial. Here we show the performance of our time-series deep learning model (DeepCAD-NLM-L) which integrates multi-model information across three longitudinal data domains: nodule-specific, lung-specific, and clinical demographic data. We compared our time-series deep learning model to a) radiologist performance on CTs from the National Lung Screening Trial enriched with the most challenging nodules for diagnosis; b) a nodule management algorithm from a North London LCS study (SUMMIT). Our model demonstrated comparable and complementary performance to radiologists when interpreting challenging lung nodules and showed improved performance (AUC=88\%) against models utilizing single time-point data only. The results emphasise the importance of time-series, multi-modal analysis when interpreting malignancy risk in LCS.
Suffix tree-based linear algorithms for multiple prefixes, single suffix counting and listing problems

Given two strings T and S and a set of strings P , for each string p ∈ P , consider the unique substrings of T that have p as their preﬁx and S as their suﬃx. Two problems then come to mind; the ﬁrst problem being the counting of such substrings, and the second problem being the problem of listing all such substrings. In this paper, we describe linear-time, linear-space suﬃx tree-based algorithms for both problems. More speciﬁcally, we describe an O ( | T | + | P | ) time algorithm for the counting problem, and an O ( | T | + | P | + #( ans )) time algorithm for the listing problem, where #( ans ) refers to the number of strings being listed in total, and | P | refers to the total length of the strings in P . We also consider the reversed version of the problems, where one preﬁx condition string and multiple suﬃx condition strings are given instead, and similarly describe linear-time, linear-space algorithms to solve them.