Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic order in a spin- (1)/(2) interpolating square-triangle Heisenberg antiferromagnet

Using the coupled cluster method (CCM) we study the zero-temperature phase diagram of a spin-half Heisenberg antiferromagnet (HAF), the so-called J1? J2? model, defined on an anisotropic two-dimensional lattice. With respect to an underlying square-lattice geometry the model contains antiferromagnetic (J1>0) bonds between nearest neighbors and competing (J2? >0) bonds between next-nearest neighbors across only one of the diagonals of each square plaquette, the same diagonal in every square. Considered on an equivalent triangular-lattice geometry the model may be regarded as having two sorts of nearest-neighbor bonds, with J2? J1 bonds along parallel chains and J1 bonds providing an interchain coupling. Each triangular plaquette thus contains two J1 bonds and one J2? bond. Hence, the model interpolates between a spin-half HAF on the square lattice at one extreme (? =0) and a set of decoupled spin-half chains at the other (? ), with the spin-half HAF on the triangular lattice in between at ? =1. We use a N�el state, a helical state, and a collinear stripe-ordered state as separate starting model states for the CCM calculations that we carry out to high orders of approximation (up to eighth order, n=8, in the localized subsystem set of approximations, LSUBn). The interplay between quantum fluctuations, magnetic frustration, and varying dimensionality leads to an interesting quantum phase diagram. We find strong evidence that quantum fluctuations favor a weakly first-order or possibly second-order transition from N�el order to a helical state at a first critical point at ? c1=0.80�0.01 by contrast with the corresponding second-order transition between the equivalent classical states at ? cl=0.5. We also find strong evidence for a second critical point at ? c2=1.8�0.4 where a first-order transition occurs, this time from the helical phase to a collinear stripe-ordered phase. This latter result provides quantitative verification of a recent qualitative prediction of and Starykh and Balents [Phys. Rev. Lett. 98, 077205 (2007)] based on a renormalization group analysis of the J1? J2? model that did not, however, evaluate the corresponding critical point.
EXACT POSTERIOR SIMULATION FROM THE LINEAR LASSO REGRESSION

The current popular method for approximate simulation from the posterior distribution of the linear Bayesian LASSO is a Gibbs sampler. It is well-known that the output analysis of an MCMC sampler is difficult due to the complex dependence amongst the states of the underlying Markov chain. Practitioners can usually only assess the convergence of MCMC samplers using heuristics. In this paper we construct a method that yields an independent and identically distributed (iid) draws from the LASSO posterior. The advantage of such exact sampling over the MCMC sampling is that there are no difficulties with the output analysis of the exact sampler, because all the simulated states are independent. The proposed sampler works well when the dimension of the parameter space is not too large, and when it is too large to permit exact sampling, the proposed method can still be used to construct an approximate MCMC sampler.
Metal-poor stars towards the Galactic bulge - a population potpourri

We present a comprehensive chemical abundance analysis of five red giants and two horizontal branch (HB) stars towards the southern Galactic bulge, at (l,b)$\sim$(0$^{\rm o}$,-11$^{\rm o}$). Based on high-resolution spectroscopy obtained with the Magellan/MIKE spectrograph, we derived up to 23 chemical element abundances and identify a mixed bag of stars, representing various populations in the central regions of the Galaxy. Although cosmological simulations predict that the inner Galaxy was host to the first stars in the Universe, we see no chemical evidence of the ensuing massive supernova explosions: all of our targets exhibit halo-like, solar [Sc/Fe] ratios, which is in contrast to the low values predicted from Population III nucleosynthesis. One of the targets is a CEMP-s star at [Fe/H]=-2.52 dex, and another one is a moderately metal-poor ([Fe/H]=-1.53 dex) CH star with strong enrichment in s-process elements (e.g., [Ba/Fe]=1.35). These individuals provide the first contenders of these classes of stars towards the bulge. Four of the carbon-normal stars exhibit abundance patterns reminiscent of halo star across a metallicity range spanning -2.0 to -2.6 dex, i.e., enhanced $\alpha$-elements and solar Fe-peak and n-capture elements, and the remaining one is a regular metal-rich bulge giant. The position, distance, and radial velocity of one of the metal-poor HB stars coincides with the old trailing arm of the disrupted Sagittarius dwarf galaxy. While their uncertain proper motions prohibit a clear kinematic separation, the stars' abundances and distances suggest that these metal-poor candidates, albeit located towards the bulge, are not of the bulge, but rather inner halo stars on orbits that make them pass through the central regions. Thus, we caution similar claims of detections of metal-poor stars as true habitants of the bulge. (Abridged)
Neighbourhood inequality - Do small area interactions influence economic outcomes?

Over the last 25 years neighbourhood economic outcomes have become increasingly polarised in Australia (Badcock, 1997; Hunter, 2003). The growing spatial dimensions of this inequality have generated discussion about the existence of ‘neighbourhood effects’, localised externalities and other endogenous processes, leading to underinvestment in education, lower levels of job-creation and economic activity, than might be expected in disadvantaged neighbourhoods. Recently there has been renewed interest in economic models incorporating social interactions, dependence among economic agents and spatial spillovers. In failing to acknowledge the interdependence between neighbouring regions and individuals, traditional economic models ignore an important aspect of space. A range of spatial regression techniques have been developed to measure latent forces of interaction and handle data that violate statistical assumptions of independence. This paper explores the drivers of changing labour market outcomes within Metropolitan Sydney in 1996 and 2001 (POA level). Moran statistics will be used to determine whether these represent statistically significant clusters of employment and other demographic variables, known as ‘hot spots’ (high unemployment) and ‘cold spots’ (low unemployment). Spatial econometric techniques are them employed to ascertain the key drivers of these ‘hot’ and ‘cold’ spots; and the extent to which, independent of traditional explanatory factors, location-specific interactions within or between regions might be responsible for any observed polarisation.
SDSS DR7 WHITE DWARF CATALOG

We present a new catalog of spectroscopically confirmed white dwarf stars from the Sloan Digital Sky Survey (SDSS) Data Release 7 spectroscopic catalog. We find 20,407 white dwarf spectra, representing 19,712 stars, and provide atmospheric model fits to 14,120 DA and 1011 DB white dwarf spectra from 12,843 and 923 stars, respectively. These numbers represent more than a factor of two increase in the total number of white dwarf stars from the previous SDSS white dwarf catalogs based on DR4 data. Our distribution of subtypes varies from previous catalogs due to our more conservative, manual classifications of each star in our catalog, supplementing our automatic fits. In particular, we find a large number of magnetic white dwarf stars whose small Zeeman splittings mimic increased Stark broadening that would otherwise result in an overestimated log g if fit as a non-magnetic white dwarf. We calculate mean DA and DB masses for our clean, non-magnetic sample and find the DB mean mass is statistically larger than that for the DAs.
Improving Informational Bases of Performance Measurement with Grey Relation Analysis

Performance measurement (PM) needs objective empirical data with causal relevance in order to steer and control financial performance generation. In business practice, there is often a lack of such objective data. A surrogate might be collected subjectively based on data generated by questioning corporate experts. Such an involvement of subjects can rapidly lead to an immense extent of data that (partially) imply incomplete information. To handle this imperfection of data, the Grey systems theory (GST) and especially its element, the Grey relation analysis (GRA), seem to be methodologies able to improve informational bases for PM purposes. Therefore, GRA is able to reveal those performance indicators that considerably influence the corporate financial performance, the key performance indicators. GRA is able to supply valid results with only four data points of a time series. Hence, the GST provides an improvement of the PM framework in situations of incomplete information, which is demonstrated in the following.
VRML SceneGraphs on an Interacti veRay Tracing Engine

With therecentadvancesin ray tracingtechnology, highquality image generation at interactiverateshas nally becomea reality. Asa consequence ray tracingwill likelyplay a larger role in visualizationsystems, enablingtheconceptionandcreationof completelynew interactivegraphicsapplications. In thispaperwedescribethedesignanddevelopmentof a ray tracing-basedVRML browserand editor as a case study of such an application. It exploits all advantages of ray tracing, including physically-correct plug and play shadingandsupportfor largemodels. In particular we demonstr ate how to overcomethe limitations that resultfromexistingscenegraph librariesand dataexchange formatsstill beingtargetedtowards rasterizationtechnology. Alsoweshowhow to extendtheVRML lighting modelandto optimizescenegraphhandlingfor ray tracing.
Model-Based Control for Industrial Robots: Uniform Approaches for Serial and Parallel Structures

Nowadays, there are still two major challenges for industrial robotics in automated production. These are enhancing manufacturing precision and reducing cycle-times. Beside the advances made in the classic robotics over the last decades, new technologies are emerging in the industrial field aiming more flexible high-speed and accurate manufacturing. Robots with parallel structures are attracting the attention of automati on industry as an innovative product with high dynamic potentials. Such robots, like the tricpet are integrated nowadays by BMW, Volvo or Airbus in their manufacturing lines. Compared to each other, the classic serial (or open) chain robots and the parallel (or closed) chain robots have their specific benefits and suffer from own drawbacks. The proposed chapter gives a comparison of the two types in the scope of their suitability for solving modern problems in industrial robotics. Additionally, appropriate approaches are proposed to remove drawbacks of classic industrial control solutions. Hereby, it is focussed on model-based strategies for ameliorating control accuracy at high dynamics and therefore to expose solutions towards high-speed automation. One of the main purposes of the proposed chapter is to contribute to extending the state of the art in industrial robotics by the innovative class of parallel robots. Furthermore, classic and advanced model-based control approaches are discussed for both robot types. Uniform methodologies for both classes are given. It is focused on crucial issues for practical application in the industrial filed. The first aspect is surely the modelling of kinematics (see section 2) and dynamics (see section 3) for serial and parallel robots. Here, an opposite duality in formalism is shown. By appropriate choice of minimal coordinates and velocities, the inverse dynamics of the two robot classes can be derived by the principle of virtual power. This yields computational highly efficient models that are well appropriate for real-time applications. Since the success of such feedforward control depends on the estimation quality of the model parame
Interactivity And Mental Arithmetic: Coupling Mind And World Transforms And Enhances Performance

Abstract Interactivity has been linked to better performance in problem solving, due in part to a more efficient allocation of attentional resources, a better distribution of cognitive load, but perhaps more important by enabling the reasoner to shape and reshape the physical problem presentation to promote the development of the problem solution. Interactivity in solving quotidian arithmetic problems involves gestures, pointing, and the recruitment of artefacts to facilitate computation and augment efficiency. In the experiment reported here, different types of interactivity were examined with a series of mental arithmetic problems. Using a repeated-measures design, participants solved series of five 11-digit sums in four conditions that varied in the type of interactivity: (i) no interactivity (participants solved the problems with their hands on the table top), (ii) pointing (participants could point at the numbers), (iii) pen and paper (participants could note interim totals with a pen), and (iv) tokens (the sums were presented as 11 numbered tokens the arrangement of which participants were free to modify as they proceeded to the solution). Performance in the four conditions was measured in terms of accuracy, calculation error, and efficiency (a ratio composed of the proportion correct over the proportion of time invested in working on the sums). These quantitative analyses were supplemented by a detailed qualitative examination of a participant’s actions in the different conditions. The integration of artefacts, such as tokens or a pen, offered reasoners the opportunity to reconfigure the physical presentation of the problem, enacting different arithmetic strategies: the affordance landscape shifts as the problem trajectory is enacted through interactivity, and this generally produced better “mental” arithmetic performance. Participants also felt more positive about and better engaged with the task when they could reconfigure the problem presentation through interactivity. These findings underscore the importance of engineering task environments in the laboratory that offer a window on how problem solving unfolds through a coalition of mental and physical resources.
Graphene against Other Two-Dimensional Materials: A Comparative Study on the Basis of Photonic Applications

The evolution of the electronics industry since almost 75 years ago has depended on the novel materials and devices that continuously are introduced. In first decades of this century, 2D materials are impelling this development through materials such as graphene, graphane, graphone, graphyne, graphdiyne, silicene, silicane, germanene, germanane, stanene, phosphorene, arsenene, antimonene, borophene, hexagonal boron nitride (hBN), transition metal dichalcogenides (TMDs), and MXenes. In this work, the main strategies to modify electrical properties of 2D materials are studied for obtain‐ ing dielectric, semiconducting, or semimetallic properties. The effects of doping, chemical modification, electrical field, or compressive and/or tensile strains are considered. In addition, the light‐matter interaction to develop optoelectronic applications is analyzed. In next three decades, a lot of scientific research will be realized to completely exploit the use of 2D materials either as single monolayers or as stacked multilayers in several fields of knowledge with a special emphasis on the benefit to the electronic industry and ultimately our society.
Pre Print Version Managerial Challenges of Publicly Funded Principal Investigators

Principal investigators (PI) are at the nexus of university business collaborations through their leadership of funded research grants. In fulfilling their multiple roles, PIs are involved in a range of different activities, from direct scientific supervision of junior scientists, the organisation of new scientific avenues to engaging with industrial partners. With the increased impetus for public research to produce wealth through science commercialisation, research is increasingly orchestrated through programmes which seek to connect research avenues and markets. The role of PIs is of growing importance. The extent of managerial challenges encountered by scientists in the context of their PI role has not been the focus of any empirical studies. This paper examines the managerial challenges experienced by publicly funded PIs. Our study, set in the context of the Irish research system, found three foci of PI managerial challenges – project management, project adaptability and project network management.
The Gut Microbiota as Target for Innovative Drug Development: Perspectives and a Case Study of Inflammatory Bowel Diseases

For many centuries scientists have been intrigued by the function of the intestine in human health and have tried to explain the role of the intestinal tract in our body, with sometimes rather original interpretations of its function. Leonardo Da Vinci for instance, who described the anatomy of the human body in great detail, concluded that the digestive system is in fact a part of the respiratory system, supporting its functioning. Indeed, he stated the following, giving a quite original interpretation on the function of the intestines: "The compressed intestines with the condensed air which is generated in them, thrust the diaphragm upwards; the diaphragm compresses the lungs and expresses the air" (O'Malley & Saunders, 1982). So gasses produced in the intestine would help to breath. In general, until quite recently it was believed that the main function of the intestine would be to dispose waste materials and reabsorb water from the intestine. Since the work of Antonie Van Leeuwenhoek it is known that the intestine contains an extensive microbial community (Smit & Heniger, 1975). Nowadays, it’s clear that the intestine is much more than an organ for waste material and absorption of water, salts and drugs, and indeed has a very important impact on human health, for a major part related to the specific composition of the complex microbial community in the colon. This microbial community composition is governed by age, diet, environment and phylogeny (Ley et al., 2008; Zoetendal et al., 1998; Zoetendal et al., 2001) and contains all three domains of life: Bacteria, Archaea and Eukarya (fungi, yeasts and protozoa). The human colon harbors a highly complex microbial ecosystem of about 200 g living cells, at concentrations of 1011 microorganisms per gram gut content, in total numbers which outnumber the amount of somatic and germ cells in the human body with a factor 10. Together, all 6.5 billion humans on earth represent a gut reservoir of 1023-1024 microbial cells, which is just five orders of magnitude less than the world’s oceans (1029 cells) (Ley et al., 2006). Therefore, the human gut constitutes a substantial habitat in our biosphere and we can in fact consider the human body as a mix of human and bacterial cells. Despite such high numbers, the microbial diversity is however relatively limited. Although 55 and 13 divisions of respectively Bacteria and Archaea have been described, only 8 bacterial
Haircut Cycles

This paper contributes to the literature on the effect of financial frictions on business cycle activity. We follow the "leverage cycles" approach in the spirit of Geanakoplos (2010) which argues that equilibrium fluctuations in collateral rates (equivalently haircuts, margins, or leverage), rather than just in interest rates, are a key driver of persistent fluctuations in economic activity. In particular, we focus on how adverse economic shocks can be amplified and prolonged by endogenous variations in haircuts in the standard macrofinance framework à la Kiyotaki and Moore (1997). In our model, collateral constraints are motivated by no-recourse loans, and the interest rate and the haircut are jointly determined as general equilibrium objects. We highlight the difference between the risk and the illiquidity of the collateral in determining the credit market equilibrium: an increase in risk increases both the interest rate and the haircut, while an increase in illiquidity increases the haircut but decreases the interest rate. Compared with the previous literature, our model allows us to decompose the transmission of adverse shocks through the credit market into the interest rate channel and the haircut channel, and evaluate their relative importance. The numerical exercises illustrate that risk shocks can generate sizable business cycle fluctuations through the credit market, and the haircut channel is dominant in times of low market liquidity.
ROHATYN CENTER for GLOBAL AFFAIRS

Although the United Nations and other multilateral treaties have established a chain of protocols to address the issues of refugee flows and the rights of refugees, states have increasingly become wary of the impacts of international migration policies on domestic policies. States have tightened their borders and created multilateral organizations of their own to bypass previously signed commitments to refugees. Those persecuted by violence find it harder to flee and instead vote to seek refuge in urban centers within the borders of their nations. Even when cities may provisionally relieve the plight of displacement, urban IDPs find it hard to access the formal economy and must face structural, personal, and gender and race-based challenges. States must adopt greater legal measures to grant IDPs institutional rights and access to greater opportunities for the improvement of their livelihoods.
A Generally Semisupervised Dimensionality Reduction Method with Local and Global Regression Regularizations for Recognition

The insufficiency of labeled data is an important problem in image classification such as face recognition. However, unlabeled data are abundant in the real-world applica‐ tion. Therefore, semisupervised learning methods, which corporate a few labeled data and a large number of unlabeled data into learning, have received more and more attention in the field of face recognition. During the past years, graph-based semisu‐ pervised learning has been becoming a popular topic in the area of semisupervised learning. In this chapter, we newly present graph-based semisupervised learning method for face recognition. The presented method is based on local and global regression regularization. The local regression regularization has adopted a set of local classification functions to preserve both local discriminative and geometrical informa‐ tion, as well as to reduce the bias of outliers and handle imbalanced data; while the global regression regularization is to preserve the global discriminative information and to calculate the projection matrix for out-of-sample extrapolation. Extensive simula‐ tions based on synthetic and real-world datasets verify the effectiveness of the proposed method.