Miracle-dz/UniFAR-FTUs huggingface dataset:
Galaxy cluster virial masses and Omega

To re-examine the rich cluster $\Omega$ value the CNOC Cluster Survey has observed 16 high X-ray luminosity clusters in the redshift range 0.17 to 0.55, obtaining approximately 2600 velocities in their fields. Directly adding all the K and evolution corrected $r$ band light to $M_r(0)=-18.5$, about $0. 2L_\ast$, and correcting for the light below the limit, the average mass-to-light ratio of the clusters is $283\pm27h\msun/\lsun$ and the average mass per galaxy is $3.5\pm0.4\times10^{12}h^{-1}\msun$. The clusters are consistent with having a universal $M_v/L$ value (within the errors of about 20\%) independent of their velocity dispersion, mean color of their galaxies, blue galaxy content, redshift, or mean interior density. Using field galaxies within the same data set, with the same corrections, we find that the closure mass-to-light, $\rho_c/j$, is $1160\pm130h\msun/\lsun$ and the closure mass per galaxy, $\rho_c/\phi(>0.2L_\ast)$, is $13.2\pm1.9\times10^{12}h^{-1}\msun$. Under the assumptions that the galaxies are distributed like the mass and that the galaxy luminosities and numbers are statistically conserved, which these data indirectly support, $\Omega_0=0.20\pm0.04\pm0.09$ where the errors are, respectively, the $1\sigma$ internal and an estimate of the $1\sigma$ systematic error resulting from the luminosity normalization.
Non‐axisymmetric relativistic Bondi–Hoyle accretion on to a Kerr black hole

In our programme of studying numerically the so-called Bondi–Hoyle accretion in the fully relativistic regime, we present here the first results concerning the evolution of matter accreting supersonically on to a rotating (Kerr) black hole. These computations generalize previous results where the non-rotating (Schwarzschild) case was extensively considered. We parametrize our initial data by the asymptotic conditions for the fluid and explore the dependence of the solution on the angular momentum of the black hole. Towards quantifying the robustness of our numerical results, we use two different geometrical foliations of the black hole space–time, the standard form of the Kerr metric in Boyer–Lindquist coordinates as well as its Kerr–Schild form, which is free of coordinate singularities at the black hole horizon. We demonstrate some important advantages of using such horizon-adapted coordinate systems. Our numerical study indicates that regardless of the value of the black hole spin the final accretion pattern is always stable, leading to constant accretion rates of mass and momentum. The flow is characterized by a strong tail shock, which, unlike the Schwarzschild case, is increasingly wrapped around the central black hole as the hole angular momentum increases. The rotation-induced asymmetry in the pressure field implies that, besides the well-known drag, the black hole will experience also a lift normal to the flow direction. This situation exhibits some analogies with the Magnus effect of classical fluid dynamics.
The influence of circumnuclear environment on the radio emission from TDE jets

Dozens of stellar tidal disruption events (TDEs) have been identified at optical, UV and X-ray wavelengths. A small fraction of these, most notably Swift J1644+57, produce radio synchrotron emission, consistent with a powerful, relativistic jet shocking the surrounding circumnuclear gas. The dearth of similar non-thermal radio emission in the majority of TDEs may imply that powerful jet formation is intrinsically rare, or that the conditions in galactic nuclei are typically unfavorable for producing a detectable signal. Here we explore the latter possibility by constraining the radial profile of the gas density encountered by a TDE jet using a one-dimensional model for the circumnuclear medium which includes mass and energy input from a stellar population. Near the jet Sedov radius of 10$^{18}$ cm, we find gas densities in the range of $n_{18} \sim$ 0.1$-$1000 cm$^{-3}$ across a wide range of plausible star formation histories. Using one- and two-dimensional relativistic hydrodynamical simulations, we calculate the synchrotron radio light curves of TDE jets (as viewed both on and off-axis) across the allowed range of density profiles. We find that bright radio emission would be produced across the plausible range of nuclear gas densities by jets as powerful as Swift J1644+57, and we quantify the relationship between the radio luminosity and jet energy. We use existing radio detections and upper limits to constrain the energy distribution of TDE jets. Radio follow up observations several months to several years after the TDE candidate will strongly constrain the energetics of any relativistic flow.
Contribution au développement de méthodes d'optimisation avancées pour la conception électromagnétique de circuits et dispositifs microondes

Ce memoire est dedie a l'amelioration des methodes d'optimisation classiquement utilisees pour la conception electromagnetique des circuits et dispositifs micro-ondes. Les ameliorations apportees par ce travail ont concerne deux axes : - L'utilisation de modeles electromagnetiques parametres permettant d'accelerer les procedures d'optimisation en court-circuitant les simulations numeriques. Le travail est consacre a la parametrisation de la geometrie et a son application a l'optimisation des dispositifs micro-ondes. Il est montre qu'en utilisant la parametrisation geometrique, la boucle d'optimisation classique peut etre fondamentalement modifiee, de sorte a limiter au strict minimum le nombre d'analyses electromagnetiques. La parametrisation du maillage est une etape essentielle pour la parametrisation geometrique efficace du modele electromagnetique: plusieurs techniques sont alors presentees et illustrees avec quelques exemples. - L'utilisation de methodes evolutionnaires efficaces comme strategie d'optimisation pour reduire les risques de divergence ou d'optimum local traditionnellement rencontres avec les strategies de type gradient. Une nouvelle technique evolutionnaire, tres recemment introduite dans le domaine de l'electromagnetisme est la methode des « essaims particulaires » (particle Swarm Optimization - PSO). Les details de la technique sont tout d'abord donnes puis appliques a l'optimisation de circuits et dispositifs micro-ondes a travers plusieurs exemples.
THE GEOMETRY OF RATIONAL SURFACES AND HILBERT FUNCTIONS OF POINTS IN THE PLANE

We study the structure of the set of numerically effective divisor classes on a rational surface and apply this to study hilbert functions of the homogeneous coordinate rings of 0-cycles on curves of low degree in P 2 . For example, for a nonnegative 0-cycle mipi + � � � + mnpn of points p1,. ,pn on an irreducible conic in P 2 , we show that the hilbert function depends only on the coefficients mi, as conjectured by Davis and Geramita (DG). We also determine for each such 0-cycle the degree at which the hilbert function stabilizes (first equals its hilbert polynomial) and we characterize all such 0-cycles having a generic hilbert function, a generic hilbert function being one which is equal to the hilbert function of the ring of P 2 up to the point at which it stabilizes.
Accuracy of Deterministic Extended-Path Solution Methods for Dynamic Stochastic Optimization Problems in Macroeconomics

The deterministic extended-path method for solving dynamic stochastic optimization problems approximates conditional expectations instead of approximating a model's complex non-linear dynamics. We show that this straightforward approach provides similar accuracy to the best results reported for alternative methods, and gives uniform performance across the entire state space. Our implementation requires roughly 4 fold more computer time than Galerkin projection, but the method has offsetting simplicity and generality that make it an attractive choice.
Selecting Classification Features for Detection of Mass Emergency Events on Social Media

The paper addresses the problem of detecting eyewitness reports of mass emergencies on Twitter. This is the first work to conduct a large-scale comparative evaluation of classification features extracted from Twitter posts, using different learning algorithms and datasets representing a broad range of mass emergencies including both natural and technological disasters. We investigate the relative importance of different feature types as well as on the effect of several feature selection methods applied to this problem. Because the task of detecting mass emergencies is characterized by high heterogeneity of the data, our primary focus is on identifying those features that are capable of separating mass emergency reports from other messages, irrespective of the type of the disaster .
Evolution of the Transverse Density Structure of Oscillating Coronal Loops Inferred by Forward Modeling of EUV Intensity

Recent developments in the observation and modeling of kink oscillations of coronal loops have led to heightened interest over the last few years. The modification of the Transverse Density Profile (TDP) of oscillating coronal loops by nonlinear effects, particularly the Kelvin–Helmholtz Instability (KHI), is investigated. How this evolution may be detected is established, in particular, when the KHI vortices may not be observed directly. A model for the loop’s TDP is used that includes a finite inhomogeneous layer and homogeneous core, with a linear transition between them. The evolution of the loop’s transverse intensity profile from numerical simulations of kink oscillations is analyzed. Bayesian inference and forward modeling techniques are applied to infer the evolution of the TDP from the intensity profiles, in a manner that may be applied to observations. The strongest observational evidence for the development of the KHI is found to be a widening of the loop’s inhomogeneous layer, which may be inferred for sufficiently well resolved loops, i.e., >15 data points across the loop. The main signatures when observing the core of the loop (for this specific loop model) during the oscillation are a widening inhomogeneous layer, decreasing intensity, an unchanged radius, and visible fine transverse structuring when the resolution is sufficient. The appearance of these signatures are delayed for loops with wider inhomogeneous layers, and quicker for loops oscillating at higher amplitudes. These cases should also result in stronger observational signatures, with visible transverse structuring appearing for wide loops observed at the resolution of current instruments.
Examining the Relationship between Food Insecurity and Students’ Academic Performance in Public Universities

This study examines the relationship between food insecurity and academic performances among university students in Selangor, Malaysia. Food insecurity exists whenever people have limited food access at all times for an active and healthy life. In the case of Malaysians, their multicultural identity has expanded their palate and surrounded them with various food choices in their daily life. However, only a few are well-informed of the story behind food production and its quality. Limited knowledge and understanding of health, particularly daily dietary food intake, might slowly make people descend to food insecurity. Therefore, it is essential to understand the consequences of this issue. A total of 400 survey data using a self-administrated questionnaire were collected among students across public universities in Selangor, Malaysia. Statistical analysis revealed that food quality significantly influences students’ academic performances. Students at a lower risk of food insecurity included those who reported living with their parents and those who received monthly allowances from government agencies and their parents. In addition, food-secure students were more likely than food-insecure students to report a higher GPA versus a lower GPA. These findings compel relevant authorities, including university and food-related service providers, to consider the importance of food quality and students' amount of food intake as these factors significantly influence their academic performances. Therefore, this study suggests further investigation with a combination of various study settings and scope probably
Mechanism of fucoxanthin biosynthesis in Phaeodactylum tricornutum under different light intensities via FCP complex formation

Fucoxanthin (FX), a primary carotenoid, is associated with the fucoxanthin-chlorophyll a/c binding protein (FCP) complex integrated into the thylakoid membrane (TM) which functions as a light-harvesting complex in Phaeodactylum tricornutum. Here, we aimed to elucidate the mechanism of FX biosynthesis under different light intensities via FCP complex formation. High light (HL) accelerated P. tricornutum growth more than low light (LL). The maximum values of FX content and productivity obtained under LL (1.7 mg/g and 2.12 mg/L/day, respectively) were substantially higher than those obtained under HL (0.54 mg/g and 0.79 mg/L/day, respectively). Notably, SDS-PAGE and photosynthetic pigment analyses revealed the enrichment of FCP antennae in the LL culture TM fractions but not the HL culture. Semi-quanti�cation of FCP antenna protein using LC-MS/MS and RNA transcriptome analyses revealed that PtLhcf5 and PtLhcf8 played crucial roles in FCP biosynthesis under LL. P. tricornutum cultured under light transition exhibited FCP formation only in the early growth stage to meet the increased photosynthetic activity requirements under LL. Meanwhile, FCP degradation could be triggered by HL throughout the cultivation period. Therefore, FX production was highly correlated with FCP formation, and LL conditions in the early growth stage were critical for higher FX productivity.
Exploiting Cross-Domain Sequence Data for Cross-Domain Recommendation

The recommendation techniques based on the sequence data of user actions attract much research at-tentions recently, which can mine the short-term item dependency for recommending users their interested items. Till now, these techniques are mainly analyzed in the single domain. The application of them for cross-domain recommendation is still limited. In this work, we ﬁrst integrate the sequence data of a part of common users be-tween domains, and then exploit the integrated cross-domain sequence data for the cross-domain recommendation. There are now many domains (e.g., companies) partnering with each other to improve user experience and expand themselves, some of which have realized common userID or userID linkage. Therefore, the common user information can be obtained between domains. In addition, it is also possible to share the data of a part of common users between domains with users’ consent. We thus can integrate the sequence data of these users in order of time, based on which it is able to directly mine the short-term cross-domain item dependency. Then, we can recommend the items of one domain to the users of the other domain based on the mined short-term cross-domain item dependency. We evaluate the above approach on the Retailrocket data and further deploy it to our practical use scenario, both of which conﬁrm its eﬀectiveness.
IMPLEMENTATION OF LOW POWER CONSUMPTION USING MITCHELL’S APPROXIMATE LOGARITHMIC MULTIPLICATION FOR CONVOLUTIONAL MULTIPLEXER

: This paper proposes implementation of low power consumption using Mitchell’s approximate logarithmic multiplication for convolutional multiplexer image classification, taking advantage of its intrinsic tolerance to error. The approximate logarithmic multiplier is based on the Mitchell algorithm that converts multiplications to additions in the logarithm domain and achieves significant improvement in power and area while having low worst case error, which makes it suitable for neural network computation. Our proposed design shows a significant improvement in terms of power and area over the previous work that applied logarithmic multiplication to neural networks, reducing power compared to exact fixed-point multiplication, while maintaining comparable prediction accuracy in convolutional neural networks for MNIST and CIFAR10 applications. The recent breakthroughs in multi-layer convolutional neural networks (CNNs) have caused significant progress in the applications of image classification and speech recognition. From the milestone network LeNet for the MNIST handwritten digit recognition, CNNs have been continually studied and improved to perform well even for the large-scale image classification.
Preliminary Evaluation of a Novel Point of Care Diagnostic Device for Sports-Related Concussion

Objective: Visinin-like protein 1 (VILIP-1) is a neuron-specific calcium sensor protein rapidly released into blood after mild traumatic brain injury (mTBI) and may be a suitable biomarker for identification of sports-related concussion (SRC). The objective of the study is to test if quantification of a specific post-translationally modified (ubiquitinated) form of VILIP-1 (ubVILIP-1) from a fingerstick blood sample using a point of care (POC) lateral flow device (LFD) can be used to rapidly identify athletes with SRC. Design: Prospective cohort study. Setting: Side-line blood collection at football, soccer, and volleyball games/practices. Participants: Division I athletes with/without SRC. Main Outcome Measures: Blood ubVILIP-1 concentrations. Results: Data collected over 2 athletic seasons from non-SRC athletes (controls) show a small but statistically significant elevation of ubVILIP-1 over an individual season for male athletes (P = 0.02) dependent on sport (P = 0.014) and no significant changes in ubVILIP-1 levels between seasons. For SRC athletes, the data show ubVILIP-1 levels substantially increase above baseline as soon as 30 minutes postdiagnosis with peak concentrations and times postinjury that vary based on injury severity. Conclusion: Results of the study suggest quantification of blood ubVILIP-1 levels measured using an LFD may provide an objective identification of athletes with SRC, setting the stage for further study with a larger number of SRC patients.
Wicked problems – resilience, adaptation and complexity

This chapter draws together an overall understanding of the social-natural-technical-policy frameworks within which emBRACE work can be considered. It explains why deciding upon policy interventions to support community resilience presents us with both a ‘wicked’ and a ‘messy’ problem, and one which we will argue – both here and in other chapters (see ‘The Need to Structure Qualitative Data’) – calls for ‘clumsy’ policy solutions and interventions. Notwithstanding, the chapter argues that the structured, multi-sectoral, and multi-level approach anticipated by – and piloted in – the emBRACE project allows us to deal with the necessary complexity in planning. Further, we also need a multi-stressor, multi-exposure approach (cf. Kelman, Gaillard and Mercer 2015).
Who Benefits from Pharmaceutical Price Ceilings? Evidence from India∗

With the goal of driving down drug costs, governments across the globe have instituted various forms of pharmaceutical price control policies. Understanding the impacts of such policies is particularly important in lowand middle-income countries, where lack of insurance coverage means that prices can serve as a barrier to access for patients. In this paper, I examine the theoretical and empirical effects of one implementation of pharmaceutical price controls, in which the Indian government placed price ceilings on a set of essential medicines. I find that the legislation resulted in broadly declining prices amongst both directly-impacted products and competing products. However, the legislation also led to decreased sales of price-controlled and closely related products, preventing trade that would have otherwise occurred. The sales of small, local generics manufacturers were most impacted by the legislation, seeing a 14.5% decrease in market share and a 5.3% decrease in sales. These products tend to be inexpensive and important for consumer access, but I use novel data to show that they are also of lower average quality. I provide evidence that the legislation impacted consumer types differentially. The benefits of the legislation were largest for quality-sensitive consumers, while the downsides largely affected poor and rural consumers, two groups already suffering from low access to medicines. ∗I thank Guy David, Mark Pauly, Patricia Danzon, and Heather Schofield for their advice and guidance with this paper. I gratefully acknowledge funding from the Leonard Davis Institute of Health Economics and the Wharton Risk Management and Decision Processes Center. †Department of Health Care Management and Economics, The Wharton School of the University of Pennsylvania. 3641 Locust Walk, Philadelphia, PA 19104. E-mail: eboswell@wharton.upenn. edu.
A hidden Semi-Markov model for inferring the structure of migratory bird flyway networks

Every year, more than 50 million shorebirds migrate from overwintering habitat in Australasia to breeding grounds in Siberia. This migration is threatened by development pressures at stopover sites along the migratory route. Prioritising the conservation of critical bird habitat requires knowledge of the routes followed by birds but prioritisation remains diﬃcult without knowing how sites are connected. We propose to use statistical modelling and eﬃcient inference tools to determine the most likely network of migration routes based on observed counts at stopovers. In practice, available count data are noisy due to irregular collection intervals and detection errors. We model the network, noisy data and duration of stopover at sites as a set of Hidden Semi Markov Models (HSMM) factored by the observations. The semi-Markov framework allows us ﬂexibility to consider a sojourn time distribution other than the geometric distribution. This allows us to better ﬁt to expert knowledge of sojourn times of birds. In the FlywayNet model, individual birds sojourn in stopover sites for a random period of time (according to a shifted Poisson distribution) before moving to other sites, according to an unknown multinomial distribution that we aim to estimate. Each observed count, at a given site and a given time, is assumed to follow a Poisson distribution with mean equal to the true number of birds. For this kind of HSMM, exact application of existing estimation methods based on the likelihood is not possible for even a small number of sites. It is due to the dimension of the hidden state and and to the dependencies between the hidden chains of each bird’s trajectory conditionally to the observed counts. We designed two simulation-based methods to estimate the model’s parameters, based on Monte Carlo EM [1] and Approximate Bayesian Computing (ABC, [2]) methods. We compared the eﬃciency and quality of estimation of these algorithms on synthetic data simulated with the FlywayNet model
Fingerprinting as a Tool in Fish Biology

DNA fmgerprinting is a relatively new technique by which a set of polymorphic markers can be simultaneously detected, resulting in a pattern unique to a species, strain or individual (DNA fmgerprint). The DNA fingerprinting probes applicable to fish include human minisatellite DNAs, bacteriophage Ml 3, mouse sequence related to Drosophila Per gene and some oligonucleotide sequences such as (GATA)4 and (GACA)4. The fingerprints can also be obtained by combination of a number of hypervariable single locus markers. The methods employed to reveal DNA fingerprints include hybridization and the polymerase chain reaction (PCR). With arbitrarily-primed PCR, DNA fingerprints can be obtained from a wide range of species. The technique has proven to be a powerful tool in various fields of fish biology, such as individual and stock identification and breeding.