Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear scaling electronic structure methods

Methods exhibiting linear scaling with respect to the size of the system, the so-called O(N) methods, are an essential tool for the calculation of the electronic structure of large systems containing many atoms. They are based on algorithms that take advantage of the decay properties of the density matrix. In this article the physical decay properties of the density matrix will first be studied for both metals and insulators. Several strategies for constructing O(N) algorithms will then be presented and critically examined. Some issues that are relevant only for self-consistent O(N) methods, such as the calculation of the Hartree potential and mixing issues, will also be discussed. Finally some typical applications of O(N) methods are briefly described.
Enhanced Parallel Multicolor Preconditioning Techniques for Linear Systems

When solving a linear system in parallel, a large overhead in using an incomplete LU factorization as a preconditioner may annihilate any gains made from the improved convergence. This overhead is due to the inherently sequential nature of such a preconditioning. Multicoloring of the subdomains assigned to processors is a common remedy for increasing the parallelism of a global ordering. However, the achieved degree of parallelism is still limited since diierent colors must be processed sequentially. Further reductions of the parallel overhead are possible. Here we suggest several strategies to decrease the idle time in the multicolor block Gauss-Seidel preconditioning.
Using Neural Networks for Click Prediction of Sponsored Search

Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE). click-through-rate (CTR) estimation plays a crucial role for ads selection, and greatly affects the SE revenue, advertiser traffic and user experience. We propose a novel architecture for solving CTR prediction problem by combining artificial neural networks (ANN) with decision trees. First we compare ANN with respect to other popular machine learning models being used for this task. Then we go on to combine ANN with MatrixNet (proprietary implementation of boosted trees) and evaluate the performance of the system as a whole. The results show that our approach provides significant improvement over existing models.
Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging

It has long been recognized that the diffusion tensor model is inappropriate to characterize complex fiber architecture, causing tensor‐derived measures such as the primary eigenvector and fractional anisotropy to be unreliable or misleading in these regions. There is however still debate about the impact of this problem in practice. A recent study using a Bayesian automatic relevance detection (ARD) multicompartment model suggested that a third of white matter (WM) voxels contain crossing fibers, a value that, whilst already significant, is likely to be an underestimate. The aim of this study is to provide more robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor‐derived analyses, using large, high‐quality diffusion‐weighted data sets, with reconstruction parameters optimized specifically for this task. Two reconstruction algorithms were used: constrained spherical deconvolution (CSD), and the ARD method used in the previous study. We estimate the proportion of WM voxels containing crossing fibers to be ∼90% (using CSD) and 63% (using ARD). Both these values are much higher than previously reported, strongly suggesting that the diffusion tensor model is inadequate in the vast majority of WM regions. This has serious implications for downstream processing applications that depend on this model, particularly tractography, and the interpretation of anisotropy and radial/axial diffusivity measures. Hum Brain Mapp 34:2747–2766, 2013. © 2012 Wiley Periodicals, Inc.
A scaling medium representation and its implication for acoustic wave propagation

The main subject of the research reported in this paper is to u nderstand how the complexity of the earth’s subsurface is tra nsferred to the wavefield. It will be shown that the apparent com plexity can be captured by means of a scaling medium represen tation. In this multiscale representation the Hölder expo nents and the singularity spectrum constitute useful order of mag nitude estimates mathematically quantifying the local and gl obal aspects of the singularity structure delineating the scali ng. At this point the question arises whether the current formulat ion for acoustic wave motion is capable of describing the transport of the singularity structure, displayed by the medium’s heter og neity, to the acoustic wavefield and vice versa. The ultimate go al of this new approach is to come up with an alternative descrip tion for the global dynamic effects, the dispersion, and the local dynamic effects, the frequency dependent AVO, both of which can be seen as manifestation of the scaling complexity displ ayed by the heterogeneities in the constitutive parameters as th ey are being observed from well-logs. Introduction As a consequence of the ever increasing demand for hydrocarbons and the steadily decline of newly explored resources an uprise has taken place in the production oriented seismic te chnology. The ambitions set by this new orientation are far mor e ambitious since they aim for the petrophysical and litholog ical information, sometimes even in a 4-D setting. For this reaso n the integration of different types of data, acquired within the disciplines of seismology and petrophysics, will be all importan t in the quest to further optimize the current production techni ques. Given this ambitious goal it will be necessary to interrelat measurements obtained at varying scales. This has been and stil l is the primary goal of this research project where I try to improve my understanding of the dynamics of waves interacting with a medium the properties of which display a wild fluctuating behaviour across a wide range of scales. The main questions emerging in this project are at best paraphrased by: Is it possible to come up with a dynamic wave theory explaining for the apparent dispersion and reflection induced by scaling/n o differentiable medium heterogeneities? Is it possible to c ome up with a “dynamic wave theory” allowing for a better integration of data acquired at different scale ranges? Before trying to answer these eloquent questions it is of primary importance to first find an adequate, (mathematically) proper, representation for the the medium’s erratic heterogeneity. As I will make clear the con-
Is the basin‐wide warming in the North Atlantic Ocean related to atmospheric carbon dioxide and global warming?

A basin‐wide warming in the North Atlantic Ocean has occurred since the mid‐1990s; however, the cause of this basin‐wide warming is controversial. Some studies argued that the warming is due to global warming in association with the secular increase of the atmospheric greenhouse gas of carbon dioxide (CO2), while others suggested that it is caused by the Atlantic multidecadal oscillation (AMO) – an oscillatory mode occurring in North Atlantic sea surface temperature. Here we show that both global warming and AMO variability make a contribution to the recent basin‐wide warming in the North Atlantic and their relative contribution is approximately equal. It is further shown that after removing a linear trend and the seasonal cycle, atmospheric CO2 measured from 1958–2008 varies approximately with the AMO. On the assumption that a linear trend can be removed from the CO2 time series, then there are suggestive similarities between CO2 and AMO temperature anomalies. That is, atmospheric CO2 increases (decreases) when the AMO is in the warm (cold) phase. This would suggest that the recent basin‐wide warming of the North Atlantic might contribute to global ocean warming via its associated increase of atmospheric CO2.
Spectral Raman unmixing from CASSI system compressive measurements

Raman spectroscopy is a non-invasive technique that characterizes the biochemical components of a sample using the Raman scattering effect. A Raman data cube is a three-dimensional set comprising two spatial and one spectral dimensions. Typically, thousand bands per pixel are captured which considerably increases acquisition times. Furthermore, many pixels capture information from a mixture of several components, thus decreasing the accuracy of substances classification. For this reason, unmixing methods emerge as a solution to this problem. Traditionally, the SUnSAL algorithm is used to solve the spectral mixing problem on linear mixing assumption [1]. Additionally, the recent theory of compressive sensing (CS) provides a solution to the excessive size of the data cube and large acquisition times. An efficient CS optical implementation is the coded aperture snapshot spectral imaging (CASSI) system which capture spatial and spectral information of scene. This paper presents the spectral unmixing process on CASSI measurements of Raman spectral images. The SUnSAL algorithm is modified to be used with CASSI images and a signature re-scaling step is added to take in account the variability of signatures by different capture set-up. Extensive simulations with real and synthetic Raman images show the accuracy of this algorithm.
Selection for Intermediate Genotypes Enables a Key Innovation in Phage Lambda

The evolution of qualitatively new functions is fundamental for shaping the diversity of life. Such innovations are rare because they require multiple coordinated changes. We sought to understand the evolutionary processes involved in a particular key innovation, whereby phage λ evolved the ability to exploit a novel receptor, OmpF, on the surface of Escherichia coli cells. Previous work has shown that this transition repeatedly evolves in the laboratory, despite requiring four mutations in specific regions of a single gene. Here we examine how this innovation evolved by studying six intermediate genotypes that arose during independent transitions to use OmpF. In particular, we tested whether these genotypes were favored by selection, and how a coevolved change in the hosts influenced the fitness of the phage genotypes. To do so, we measured the fitness of the intermediate types relative to the ancestral λ when competing for either ancestral or coevolved host cells. All six intermediates had improved fitness on at least one host, and four had higher fitness on the coevolved host than on the ancestral host. These results show that the evolution of the phage’s new ability to use OmpF was repeatable because the intermediate genotypes were adaptive and, in many cases, because coevolution of the host favored their emergence.
XGrind: a query-friendly XML compressor

XML documents are extremely verbose since the "schema" is repeated for every "record" in the document. While a variety of compressors are available to address this problem, they are not designed to support direct querying of the compressed document, a useful feature from a database perspective. In this paper, we propose a new compression tool, called XGrind, that directly supports queries in the compressed domain. A special feature of XGrind is that the compressed document retains the structure of the original document, permitting reuse of the standard XML techniques for processing the compressed document. Performance evaluations over a variety of XML documents and user queries indicate that XGrind simultaneously delivers improved query processing times and reasonable compression ratios.
Nonrigid Surface Modelling and Fast Recovery

Modelling and recovering nonrigid surface are interesting research problem for image analysis and computer vision, which can be applied in a variety of applications. In this report, two different approaches to nonrigid surface recovery have been proposed. One is based on the appearance models, the other employs the local features. As for the appearance-based method, a two-stage scheme method is presented for online nonrigid surface recovery toward Augmented Reality (AR) applications using Active Appearance Models (AAMs). First, 3D shape models are constructed from AAMs offline, which do not involve processing of the 3D scan data. Based on the computed 3D shape models, an efficient online algorithm is proposed to estimate both 3D pose and non-rigid shape parameters via local bundle adjustment for building up point correspondences. This approach, without manual intervention, can recover the 3D non-rigid shape effectively from either real-time video sequences or single image. The recovered 3D pose parameters can be used for AR registrations. Furthermore, the facial feature can be tracked simultaneously, which is critical for many face related applications. Promising experimental results demonstrate the proposed scheme is effective for real-time AR applications. For the feature-based approach, a progressive finite Newton optimization scheme is proposed for the nonrigid surface detection problem, which is reduced to only solving a set of linear equations. The key of this method is to formulate the nonrigid surface detection as an unconstrained quadratic optimization problem which has a closedform solution for a given set of observations. Moreover, a progressive active-set selection scheme is employed, which takes advantage of the rank information of detected correspondences. Extensive experiments have been conducted for performance evaluation on various environments, whose promising results show that the proposed algorithm is more efficient and effective than the existing iterative methods.
Bilinear models of natural images

Previous work on unsupervised learning has shown that it is possible to learn Gabor-like feature representations, similar to those employed in the primary visual cortex, from the statistics of natural images. However, such representations are still not readily suited for object recognition or other high-level visual tasks because they can change drastically as the image changes to due object motion, variations in viewpoint, lighting, and other factors. In this paper, we describe how bilinear image models can be used to learn independent representations of the invariances, and their transformations, in natural image sequences. These models provide the foundation for learning higher-order feature representations that could serve as models of higher stages of processing in the cortex, in addition to having practical merit for computer vision tasks.
Silencing of the Aflatoxin Gene Cluster in a Diploid Strain of Aspergillus flavus Is Suppressed by Ectopic aflR Expression

Aflatoxins are toxic secondary metabolites produced by a 70-kb cluster of genes in Aspergillus flavus. The cluster genes are coordinately regulated and reside as a single copy within the genome. Diploids between a wild-type strain and a mutant (649) lacking the aflatoxin gene cluster fail to produce aflatoxin or transcripts of the aflatoxin pathway genes. This dominant phenotype is rescued in diploids between a wild-type strain and a transformant of the mutant containing an ectopic copy of aflR, the transcriptional regulator of the aflatoxin biosynthetic gene cluster. Further characterization of the mutant showed that it is missing 317 kb of chromosome III, including the known genes for aflatoxin biosynthesis. In addition, 939 kb of chromosome II is present as a duplication on chromosome III in the region previously containing the aflatoxin gene cluster. The lack of aflatoxin production in the diploid was not due to a unique or a mis-expressed repressor of aflR. Instead a form of reversible silencing based on the position of aflR is likely preventing the aflatoxin genes from being expressed in 649 × wild-type diploids. Gene expression analysis revealed the silencing effect is specific to the aflatoxin gene cluster.
Change of the unbinding mechanism upon a mutation: A molecular dynamics study of an antibody–hapten complex

We study forced unbinding of fluorescein from the wild type (WT) and a mutant [H(H58)A] of the single‐chain variable‐fragment (scFv) anti‐fluorescein antibody FITC‐E2 by molecular dynamics simulations using various pulling techniques. A large number of long simulations were needed to obtain statistically meaningful results as both the wild type and the H(H58)A mutant unbinding occurs through multiple pathways, often with metastable intermediates. For the wild type, the rate‐limiting step in the unbinding process corresponds to the breaking of the non‐native interactions characteristic of a specific intermediate. The H(H58)A mutation disfavors the occurrence of this intermediate. Two events where the hapten partially unbinds in the absence of pulling force are observed in extensive equilibrium simulations of the wild type, and their analysis indicates that forced unbinding and spontaneous unbinding proceed along similar pathways. The different unbinding mechanisms observed in the simulations suggest a possible reason for the difference in the experimental off‐rate between the two antibodies. We predict mutations that are expected to modulate the occurrence of the unbinding intermediate. For two such new mutants [H(H58)A and S(H52)A], our predictions are validated in silico by additional simulations. The accompanying paper in this issue by Honegger et al. reports the X‐ray structure of FITC‐E2 with a derivative of fluorescein, which was used as the starting conformation for the work presented here.
Quantifying Interference between Measurements on the RIPE Atlas Platform

Public measurement platforms composed of low-end hardware devices such as RIPE Atlas have gained significant traction in the research community. Such platforms are indeed particularly interesting as they provide Internet-wide measurement capabilities together with an ever growing set of measurement tools. To be scalable though, they allow for concurrent measurements between users. This paper answers a fundamental question for any platform user: Do measurements launched by others impact my results? If so, what can I do about it? We measured the impact of multiple users running experiments in parallel on the RIPE Atlas platform. We found that overlapping measurements do interfere with each other in at least two ways. First, we show that measurements performed from and towards the platform can significantly increase timings reported by the probe. We found that increasing hardware CPU greatly helped in limiting interference on the measured timings. Second, we show that measurement campaigns can end up completely out-of-synch (by up to one hour), due to concurrent loads. In contrast to precision, we found that better hardware does not help.
Ammonia excretion in aquatic and terrestrial crabs

SUMMARY The excretory transport of toxic ammonia across epithelia is not fully understood. This review presents data combined with models of ammonia excretion derived from studies on decapod crabs, with a view to providing new impetus to investigation of this essential issue. The majority of crabs preserve ammonotely regardless of their habitat, which varies from extreme hypersaline to freshwater aquatic environments, and ranges from transient air exposure to obligate air breathing. Important components in the excretory process are the Na+/K+(NH4+)-ATPase and other membrane-bound transport proteins identified in many species, an exocytotic ammonia excretion mechanism thought to function in gills of aquatic crabs such as Carcinus maenas, and gaseous ammonia release found in terrestrial crabs, such as Geograpsus grayi and Ocypode quadrata. In addition, this review presents evidence for a crustacean Rhesus-like protein that shows high homology to the human Rhesus-like ammonia transporter both in its amino acid sequence and in its predicted secondary structure.
Trafficking of the Menkes copper transporter ATP7A is regulated by clathrin-, AP-2–, AP-1–, and Rab22-dependent steps

ATP7A mediates copper absorption and feeds cuproenzymes in the trans-Golgi network. To regulate copper homeostasis, ATP7A cycles between the TGN and plasma membrane. The roles of clathrin, adaptor complexes, lipid rafts, and Rab22a are assessed in an attempt to decipher the regulatory proteins involved in ATP7A cycling.
Autonomously generating a 3D map of unknown environment by using mobile robots equipped with LRF

This paper proposes an exploration method for an automated 3D map generation of an unknown environment by using a group of robots equipped with LRF (laser range finder). The robots are composed of a parent robot measuring its environmental structure and two child robots being used as landmarks for localization of the parent robot. The method decides positions of the robots to generate the accurate map. We conducted experiment of the map generation of a hall whose size is 70[m] × 35[m] × 8[m]. The positional error in the map was less than 10[cm].
Software component independence

Independence is a fundamental requirement for calculating system reliability from component reliabilities, whether in hardware or software systems. Markov analysis is often used in such calculation; however, procedures as conventionally used do not qualify as nodes in a Markov system. We outline the requirements for several classes of component independence and use the CPS (continuation passing style) transformation to convert conventional procedures into fragments that are appropriate to Markov analysis.
Phrase Training Based Adaptation for Statistical Machine Translation

We present a novel approach for translation model (TM) adaptation using phrase training. The proposed adaptation procedure is initialized with a standard general-domain TM, which is then used to perform phrase training on a smaller in-domain set. This way, we bias the probabilities of the general TM towards the in-domain distribution. Experimental results on two different lectures translation tasks show significant improvements of the adapted systems over the general ones. Additionally, we compare our results to mixture modeling, where we report gains when using the suggested phrase training adaptation method.
Real-Time Avatar Construction Using Shape Tape

Motion capture is used in video games and computer animated films to create realistic animations of moving virtual characters. In games and films, an actor’s motion is captured offline, saved, and finally replayed to animate a virtual character. The same principles can be applied to Virtual Reality (VR), but the motion capture must be done in real-time so when a user moves, her corresponding virtual character – her avatar – mimics her movement. Additionally, the user must identify with her avatar, so the shape of the avatar’s limbs should closely match the shape of the human’s in length, radius, and curvature. Finally, the avatar should also be able to interact with other virtual objects in the environment, thus we need a representation that is suitable for use with collision detection algorithms. In this paper, we analyze the problem of tracking a user’s limbs with Shape Tape and constructing a virtual representation of the user in real-time. We will compare and contrast this problem to problems in sampling/reconstruction, geometric modeling and computational geometry and finally introduce possible solutions.