Miracle-dz/UniFAR-FTUs huggingface dataset:
Weak-Lensing Mass Reconstruction of the Interacting Cluster 1E 0657–558: Direct Evidence for the Existence of Dark Matter

We present a weak-lensing mass reconstruction of the interacting cluster 1E 0657-558, in which we detect both the main cluster and a subcluster. The subcluster is identified as a smaller cluster that has just undergone initial infall and pass-through of the primary cluster and has been previously identified in both optical surveys and X-ray studies. The X-ray gas has been separated from the galaxies by ram pressure-stripping during the pass-through. The detected mass peak is located between the X-ray peak and galaxy concentration, although the position is consistent with the galaxy centroid within the errors of the mass reconstruction. We find that the mass peak for the main cluster is in good spatial agreement with the cluster galaxies and is offset from the X-ray halo at 3.4 σ significance, and we determine that the mass-to-light ratios of the two components are consistent with those of relaxed clusters. The observed offsets of the lensing mass peaks from the peaks of the dominant visible mass component (the X-ray gas) directly demonstrate the presence, and dominance, of dark matter in this cluster. This proof of dark matter existence holds true even under the assumption of modified Newtonian dynamics (MOND); based on the observed gravitational shear-optical light ratios and the mass peak-X-ray gas offsets, the dark matter component in a MOND regime would have a total mass that is at least equal to the baryonic mass of the system.
An Empirical study of clock skew behavior in modern mobile and hand-held devices

Host identification, today, can be done at many layers of the network protocol stack depending on the identifiable parameter used for classification. But, these generally include fields from TCP/IP/MAC packets; that can be spoofed or manipulated very easily to misguide the identification process or include intolerable error into. Identification on wireless networks can be done with better precision by using physical layer, machine-dependent characteristics. We provide an empirical study of another such parameter, the host's clock information, that may lead to accurate identification of a host, among other applications. It is resistant to the earlier mentioned methods of spoofing, as clock information is very specific to the oscillator that generates it. We provide a simplification into the measurement technique of an already investigated approach of remote identification, to achieve lower error rates. We also provide a detailed study of clock skew behavior on a LAN, consisting of wired, wireless nodes and modern mobile and hand-held devices. To our knowledge, this work is the first in the mobile and hand-held device domain to identify such devices definitively. Clock skew based host identification can be put to many applications, that may be specific to each Enterprise network. For instance, aiding the network administrator in monitoring the network, malicious activity flagging mechanism for IDS's/IPS's, isolating unknown or new machines, keeping count of the number of active machines at any time for the purpose of say IP address allocation, associating virtual machines to their corresponding physical machines and so on.
Creating heterogeneity at run time by dynamic cache and bandwidth partitioning schemes

A heterogeneous chip multiprocessor (CMP) architecture consists of processor cores and caches of varying size and complexity. In a multi-programmed computing environment, threads of execution exhibit different run time characteristics and hardware resource requirements. So heterogeneous multiprocessor significantly out perform homogeneous multiprocessor system. Issues in designing and managing heterogeneity in multiprocessor have significant impact on overall system cost and performance. These issues are (a) replicating standard cores is an efficient strategy in homogeneous CMP design but in heterogeneous CMP architecture, particularly a fully custom heterogeneous processor not necessarily composed of pre-existing cores, incurs additional costs in design, verification, and testing, (b) in order to take advantage of a heterogeneous architecture, an appropriate policy to map running tasks to processor cores must be determined to maximize the performance of the whole system by accurately exploiting its resources, so a very good "software scheduler" require to take advantage of heterogeneity, and (c) processor speeds are improving at a much faster than the memories speed, as a result the data access time dominates the execution times of many programs. And in multiprocessor environment this gap increasing, as core count in chip multiprocessor increase, on-chip cache and also the off-chip memory bandwidth get scarcer to the cores. In this paper, we propose a method of creating heterogeneity at run time by partitioning cache and memory bandwidth. In this case, we can take advantage of using pre-existing standard core in designing multiprocessor and a use of basic scheduler with out considering heterogeneity as heterogeneity is created at run time by partitioning cache and bandwidth. Also we have described a method of creating heterogeneity of system by coordinated partitioning of shared last level cache and off-chip memory bandwidth. We have proposed an efficient low overhead approach to partition the cache based on set wise partitioning by separating addressing part and data part, and along with graceful space acquirement policy. This approach quickly re-partitions the cache with minimum overhead and with smaller granularity. Also we have extended the bandwidth partition model which is based on CPI model to handle read/write access behavior of applications. Finally we have analyzed and experimentally evaluated six different cache partitioning schemes and concluded that partition based on available bandwidth partitioning and access frequency of L2 out perform others.
SAR: SSD Assisted Restore Optimization for Deduplication-Based Storage Systems in the Cloud

The explosive growth of digital content results in enormous strains on the storage systems in the cloud environment. The data deduplication technology has been demonstrated to be very effective in shortening the backup window and saving the network bandwidth and storage space in cloud backup, archiving and primary storage systems such as VM platforms. However, the delay and power consumption of the restore operations from a deduplicated storage can be significantly higher than those without deduplication. The main reason lies in the fact that a file or block is split into multiple small data chunks that are often located in non-sequential locations on HDDs after deduplication, which can cause a subsequent read operation to invoke many HDD I/O requests involving multiple disk seeks. To address this problem, in this paper we propose SAR, an SSD Assisted Restore scheme, that effectively exploits the high random-read performance and low power-consumption properties of SSDs and the unique data sharing characteristic of deduplication-based storage system by storing in SSDs the unique data chunks with high reference count, small size and non-sequential characteristics. In this way, many critical random-read requests to HDDs are replaced by read requests to SSDs, thus significantly improving the system performance and energy efficiency. The extensive trace-driven and VM restore evaluations on the prototype implementation of SAR show that SAR outperforms the traditional deduplication-based schemes significantly, in terms of both restore performance and energy efficiency.
Performance of computational tools in evaluating the functional impact of laboratory-induced amino acid mutations

Summary: Site-directed mutagenesis is frequently used by scientists to investigate the functional impact of amino acid mutations in the laboratory. Over 10 000 such laboratory-induced mutations have been reported in the UniProt database along with the outcomes of functional assays. Here, we explore the performance of state-of-the-art computational tools (Condel, PolyPhen-2 and SIFT) in correctly annotating the function-altering potential of 10 913 laboratory-induced mutations from 2372 proteins. We find that computational tools are very successful in diagnosing laboratory-induced mutations that elicit significant functional change in the laboratory (up to 92% accuracy). But, these tools consistently fail in correctly annotating laboratory-induced mutations that show no functional impact in the laboratory assays. Therefore, the overall accuracy of computational tools for laboratory-induced mutations is much lower than that observed for the naturally occurring human variants. We tested and rejected the possibilities that the preponderance of changes to alanine and the presence of multiple base-pair mutations in the laboratory were the reasons for the observed discordance between the performance of computational tools for natural and laboratory mutations. Instead, we discover that the laboratory-induced mutations occur predominately at the highly conserved positions in proteins, where the computational tools have the lowest accuracy of correct prediction for variants that do not impact function (neutral). Therefore, the comparisons of experimental-profiling results with those from computational predictions need to be sensitive to the evolutionary conservation of the positions harboring the amino acid change. Contact: s.kumar@asu.edu
Who Cares About Stock Market Booms and Busts? Evidence from Data on Mental Health

This paper investigates the relationship between share prices and mental health, exploiting the availability of interview dates in the British Household Panel Survey to match the level and changes in the FTSE All Share price index to respondents over the period 1991-2008. We present evidence that the level, 6 month and yearly changes in the share price index are associated with better mental health while greater uncertainty, as measured by index volatility, is associated with poorer mental well-being. Finally, using several proxies of investor status, we find little evidence that this relationship is confined to holders of equity based assets, suggesting that the observed relationship does not arise via wealth effects. Instead, it appears as though share prices matter to mental health because they perform the role of economic barometer.
Glucagon infusion, plasma FFA and triglycerides, blood sugar, and liver lipids in birds.

GRANDE, FRANCISCO, AND WILLIAM F. PRIGGE. Glucagon infusion, plasma FFA and triglycerides, blood sugar, and liver lipids in birds. Am. J. Physiol. 218(5) : 1406-1411. 1970. -Glucagon infusion produced elevations of plasma free fatty acids (FFA), triglycerides (TGL), and blood sugar in geeese and ducks. Free fatty acids rose promptly after the beginning of the infusion, reaching a level which was maintained constant until the end of infusion, Blood sugar rose continuously throughout the infusion. The effect of doses between 0.05 and 0.5 pg/kg per min on the plasma FFA of geese showed significant correlation (r = +0.74, P < 0.01) with the logarithm of the glucagon-infusion rate. In ducks, glucagon infusion (0.5 pg/kg per min, for 2 hr) caused significant elevation of the TGL content of the liver, but epinephrine and norepinephrine, infused at the same rate as glucagon, failed to elevate plasma FFA and caused no significant change of liver TGL. The results demonstrate that glucagon has a marked adipokinetic effect in birds, which is associated with elevations of plasma ‘I’GL and deposition of TGL in the liver. By contrast, catecholamines, which elevate plasma FFA and produce elevations of liver TGL in mammals, have no such effects in ducks.
The herpes simplex virus type 1 Fc receptor discriminates between IgG1 allotypes

Herpes simplex virus type 1 (HSV‐1) expresses a complex of two virally encoded glycoproteins, gE and gI, which is capable of binding nonimmune human IgG. The gE‐gI complex has thus become known as an Fc receptor (FcR), which reportedly binds human IgG subclasses in the order IgG4 > IgG1 ≥ IgG2 and does not bind IgG3 from many individuals. There is, however, allelic variation in the genes encoding the human IgG1 heavy chain constant region and this gives rise to allotypes of IgG1. Using recombinant monoclonal IgG molecules of known isotype and mutants thereof we have unexpectedly discovered that the HSV‐1 FcR discriminates between IgG1 allotypes. This is evidence of functional differences between IgG1 allotypes that may account for their distribution in populations. Furthermore, these findings suggest HSV‐1 FcR binding sites on the IgG molecule some distance from the proposed binding site in the CH2‐CH3 domain interface.
Deep Neural Network based system for solving Arithmetic Word problems

This paper presents DILTON a system which solves simple arithmetic word problems. DILTON uses a Deep Neural based model to solve math word problems. DILTON divides the question into two parts - worldstate and query. The worldstate and the query are processed separately in two different networks and finally, the networks are merged to predict the final operation. We report the first deep learning approach for the prediction of operation between two numbers. DILTON learns to predict operations with 88.81% accuracy in a corpus of primary school questions.
WebManager: Transforming a Network Management Application Into a Component-Based Framework

This paper describes a network management tool. Visits to organizations of all sizes reveal that more than 95% of them are empty-handed as far as network management tools are concerned and pursue ad hoc management techniques. We argue that this sad state of affairs is due to the lack of cheap, easy to use tools. Although we don't solve the problem completely by providing such a tool, we show how a black-box component-oriented framework can be the basis for constructing it. We report on results of actual use of the tool to manage a small network and show that the postulated requirements (flexibility, scalability, ease of use and portability) are met.
Robust adaptive vision for robot soccer

Robot soccer requires vision to be fast, accurate and reliable for a team to perform well. To win competitions, the vision system must work every day under all contest conditions. In practice, the field conditions for robot soccer can vary enormously from field to field, and even from day to day throughout a contest. This paper presents a system that can adapt to rapidly varying light and colouring conditions, while maintaining speed and accuracy. Quantitative results of performance are presented, and applications outside the robot soccer domain illustrated.
Robust consistent correspondence between 3D non-rigid shapes based on “Dual Shape-DNA”

In this paper, we propose a novel framework to construct dense and high-quality consistent correspondence between non-rigid surfaces. Our correspondence framework exploits “Dual Shape-DNA” (dual Laplace-Beltrami spectral embedding) to capture global characteristics of objects, and converts two originally different and complex shapes into two similar and simple shapes to facilitate the correspondence. Since our method avoids the computation of geodesic distances, it is robust to local topology changes. By exploiting the excellent properties of the dual domain, our dual spectral framework can robustly construct Laplace-Beltrami embeddings on highly non-regular 3D meshes. After performing initial non-rigid matching in the dual Laplace-Beltrami spectral domain, we return 3D spatial domain and apply a shape-preserving non-rigid deformation to produce the final dense consistent correspondence. We show that our framework is suitable for non-rigid consistent correspondence, and the high-quality correspondence results are achieved.
Combining Thickness Information with Surface Tensor-based Morphometry for the 3D Statistical Analysis of the Corpus Callosum

We propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum (CC) between two groups of subjects. The CCs are segmented from whole brain T1-weighted magnetic resonance images and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace’s equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to compute the Laplacian operator, so our computation can achieve subvoxel accuracy. Thickness is estimated by tracing the streamlines in the harmonic field. We combine areal changes found using surface tensorbased morphometry and thickness information into a vector at each vertex to be used as a metric for the statistical analysis. Group differences are assessed on this combined measure through Hotelling’s T 2 test. The method is applied to statistically compare three groups consisting of: congenitally blind (CB), late blind (LB; onset > 8 years old) and sighted (SC) subjects. Our results reveal significant differences in several regions of the CC between both blind groups and the sighted group, and to a lesser extent between the LB and CB groups. These results demonstrate the crucial role of visual deprivation during the developmental period in reshaping the structural architecture of the CC.
Revitalizing Economic Geography through Teaching Excellence: Some Pedagogic Reflections

In this position paper, the authors outline some of the pressing trends in the recent development of economic geography as a sub-discipline in human geography. In particular, they note the lack of critical discussion of important pedagogical issues in teaching what might be termed ‘new economic geographies’, and particularly those associated with the ‘cultural turn’. In doing so, the most challenging politics and practices of teaching economic geography are introduced. Drawing on the various contributions to this symposium, five areas for pedagogical developments and cross-fertilization are outlined.
Impact of manufacturing proces on optimal shape of induction machine slots

This article deals with optimization of slots of an induction machine with a genetic algorithm. The considered machine is a small induction machine with output power of 600 W. Properties of sheets of the considered machine could be strongly influenced by cutting because of their small size. The main aim of this article is to show how optimal shape of slots (in terms of efficiency) is influenced by manufacturing process. To study this, three finite elements models of the machine are used for computing within genetic algorithm run. First one is simple model without any consideration of manufacturing process. The second model is shaped exactly according to drawings from manufacturer (the real shape is not circular) and there are also rivets in stator modeled in this model. The third model additionally contains areas with different material properties along cutting edges which simulate impact of cutting on materials properties.
Learning Spatial Con(cid:12)guration Models Using Modi(cid:12)ed Dirichlet Priors

Semantic scene classi(cid:12)cation is a challenging problem in computer vision. Special-purpose semantic object and material (e.g., sky and grass) detectors help, but are faulty in practice. In this paper, we propose a generative model of outdoor scenes based on spatial con(cid:12)gurations of objects in the scene. Because the number of semantically-meaningful regions (for classi(cid:12)cation purposes) in the image is expected to be small, we infer exact probabilities by utilizing a brute-force approach. However, it is impractical to obtain enough training data to learn the joint distribution of the con(cid:12)guration space. To help overcome this problem, we propose a smoothing technique that modi(cid:12)es the naive uniform (Dirichlet) prior by using model-based graph-matching techniques to populate the con(cid:12)guration space. The proposed technique is inspired by the backo(cid:11) technique from statistical language models. We compare scene classi(cid:12)cation performance using our method with two baselines: no smoothing and smoothing with a uniform prior. Initial results on a small set of natural images show the potential of the method. Detailed exploration of the behavior of the method on this set may lead to future improvements.
Support knowledge transfer in process of ERP system

In recent years, information in an enterprise break out, managers cannot deal with millions of information, they need to organize the information in an efficient way, they want to get the information of sales, financial, support chain immediately, so comes the ERP system. The ERP system contains the most useful model of business, however, the model must meet the requirement of a certain company, or the disaster will come. There were several companies which became bankrupt because they implemented the wrong idea of ERP system such as FoxMeyer Drug. To avoid that to happen, the company should consider the knowledge management more than just implementing the ERP system. The key point to success is how to plan, implement and use the ERP system with knowledge management.
The impact of peer tutoring strategies on student learning in social studies.

This study investigated how Class Wide Peer tutoring (CWPT) impacted student knowledge and students’ ability to better relate course material to historical themes in an 8 th grade social studies classroom. Located in an urban school in Western New York, the classroom was composed of 18 students, eight males and ten females. The mixed method study sought to answer the following central research questions: What effect does CWPT have on students’ academic performance in social studies measured by weekly vocabulary quizzes? What effect does CWPT have on students’ ability to link content to one another using a common theme? Is there a difference in students’ test scores when CWPT is combined with a group oriented motivator compared to a team oriented motivator? What are student’s perceptions of the effectiveness of CWPT and how do these perceptions relate to the finding of the study? Using a mixed method single baseline design, weekly quizzes were given at the end of each intervention for seven weeks. Findings demonstrated that CWPT had a positive impact on student’s content knowledge and on a student’s ability to link course content to historical themes. CWPT was also found to be positively correlated with the academic performance of the 8 th grade student’s social studies measured by the weekly quizzes. Findings concluded that there was no statistically significant difference between the group oriented motivators and the team oriented motivators. The student’s response on a Likert scale-based survey showed that the majority of students believed they were learning more because of CWPT, which is consistent with the data collected during the interventions. Table of