Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrast Sensitivity of the Wavelet, Dual Tree Complex Wavelet, Curvelet, and Steerable Pyramid Transforms

Accurate estimation of the contrast sensitivity of the human visual system is crucial for perceptually based image processing in applications such as compression, fusion and denoising. Conventional contrast sensitivity functions (CSFs) have been obtained using fixed-sized Gabor functions. However, the basis functions of multiresolution decompositions such as wavelets often resemble Gabor functions but are of variable size and shape. Therefore to use the conventional CSFs in such cases is not appropriate. We have therefore conducted a set of psychophysical tests in order to obtain the CSF for a range of multiresolution transforms: the discrete wavelet transform, the steerable pyramid, the dual-tree complex wavelet transform, and the curvelet transform. These measures were obtained using contrast variation of each transforms' basis functions in a 2AFC experiment combined with an adapted version of the QUEST psychometric function method. The results enable future image processing applications that exploit these transforms such as signal fusion, superresolution processing, denoising and motion estimation, to be perceptually optimized in a principled fashion. The results are compared with an existing vision model (HDR-VDP2) and are used to show quantitative improvements within a denoising application compared with using conventional CSF values.
Placement of the Structural Proteins in Sindbis Virus

ABSTRACT The structure of the lipid-enveloped Sindbis virus has been determined by fitting atomic resolution crystallographic structures of component proteins into an 11-Å resolution cryoelectron microscopy map. The virus has T=4 quasisymmetry elements that are accurately maintained between the external glycoproteins, the transmembrane helical region, and the internal nucleocapsid core. The crystal structure of the E1 glycoprotein was fitted into the cryoelectron microscopy density, in part by using the known carbohydrate positions as restraints. A difference map showed that the E2 glycoprotein was shaped similarly to E1, suggesting a possible common evolutionary origin for these two glycoproteins. The structure shows that the E2 glycoprotein would have to move away from the center of the trimeric spike in order to expose enough viral membrane surface to permit fusion with the cellular membrane during the initial stages of host infection. The well-resolved E1-E2 transmembrane regions form α-helical coiled coils that were consistent with T=4 symmetry. The known structure of the capsid protein was fitted into the density corresponding to the nucleocapsid, revising the structure published earlier.
Hierarchical planning of dynamic movements without scheduled contact sequences

Most animal and human locomotion behaviors for solving complex tasks involve dynamic motions and rich contact interaction. In fact, complex maneuvers need to consider dynamic movement and contact events at the same time. We present a hierarchical trajectory optimization approach for planning dynamic movements with unscheduled contact sequences. We compute whole-body motions that achieve goals that cannot be reached in a kinematic fashion. First, we find a feasible CoM motion according to the centroidal dynamics of the robot. Then, we refine the solution by applying the robot's full-dynamics model, where the feasible CoM trajectory is used as a warm-start point. To accomplish the unscheduled contact behavior, we use complementarity constraints to describe the contact model, i.e. environment geometry and non-sliding active contacts. Both optimization phases are posed as Mathematical Program with Complementarity Constraints (MPCC). Experimental trials demonstrate the performance of our planning approach in a set of challenging tasks.
Multistage Op-Amp Design Method for High Bandwidth , Low Supply Voltage Applications

— A simplistic design technique for a three-stage operational amplifier involves utilizing embedded frequency compensation along with a single, external RC compensation network. This design accommodates low supply voltage applications as well as those requiring low power. Implemented in 0. 25µm CMOS technology, the op-amp has a load capacitance of 2pF and operates under a 1.5V supply voltage (+/-5%), 27ºC to 85ºC temperature range, and 1.0 to 1.45V common-mode input voltage range (VCM). Under nominal conditions, this three-stage amplifier has approximately 77 dB of gain, 140MHz unity gain frequency, 65º phase margin, and dissipates 670uW of power.
Unsupervised learning of visual invariance with temporal coherence

Natural scenes in a video stream contain rich collections of visual transformations. In this paper, a generic neural network is built to lea rn visual invariance from videos in an unsupervised manner. We use temporal coher enc to learn both visual transformations and features with complex invarian ces. Without fine-tuning with labels, our invariant features are superior for classi fying objects in still images. The learned features out-perform features learned wi th sparsity in vision benchmarks Caltech-101, STL-10 and COIL-100.
ORGANIZATIONAL KNOWLEDGE WHEN NEEDED : BETWEEN THRESHOLD AND INERTIA , THE PERSISTENCE & RENEWAL CHALLENGE

This paper suggests to revisit the concept of organizational slack and its relation to performance. Three case studies are first presented, each dealing with a specific situation regarding the availability of organizational knowledge. Based on the case studies, the paper then introduces a map of organizational knowledge, discussing how firms can surf in between two polar extremes, namely lack and slack, i.e. beyond threshold (not enough adequate knowledge) and below inertia (too much inadequate knowledge), to maintain a persistent knowledge base while renewing it in part to keep it fit to evolving needs. Finally the paper discusses whether and to what extent specific aggregates of organizational knowledge (management processes or routines, structure, strategic vision and culture) may contribute to organizational lack or slack and in turn to persistence or inertia.
Chronos: Failure-aware scheduling in shared Hadoop clusters

Hadoop emerged as the de facto state-of-the-art system for MapReduce-based data analytics. The reliability of Hadoop systems depends in part on how well they handle failures. Currently, Hadoop handles machine failures by re-executing all the tasks of the failed machines (i.e., executing recovery tasks). Unfortunately, this elegant solution is entirely entrusted to the core of Hadoop and hidden from Hadoop schedulers. The unawareness of failures therefore may prevent Hadoop schedulers from operating correctly towards meeting their objectives (e.g., fairness, job priority) and can significantly impact the performance of MapReduce applications. This paper presents Chronos, a failure-aware scheduling strategy that enables an early yet smart action for fast failure recovery while still operating within a specific scheduler objective. Upon failure detection, rather than waiting an uncertain amount of time to get resources for recovery tasks, Chronos leverages a lightweight preemption technique to carefully allocate these resources. In addition, Chronos considers data locality when scheduling recovery tasks to further improve the performance. We demonstrate the utility of Chronos by combining it with Fifo and Fair schedulers. The experimental results show that Chronos recovers to a correct scheduling behavior within a couple of seconds only and reduces the job completion times by up to 55% compared to state-of-the-art schedulers.
Water, Sanitation, and Hygiene (WASH): A Critical Component for Sustainable Soil-Transmitted Helminth and Schistosomiasis Control

SJC is funded by an Australian Postgraduate Award and a University of Queensland Advantage Scholarship, ACAC is an Australian National Health and Medical Research Council (NHMRC) Career Development Fellow (631619), RJSM is funded by a Post-doctoral Research Fellowship from the University of Queensland (41795457), JSM is an Australian National Health and Medical Research Council Practitioner Fellow, and DJG is an Australian Research Council (DECRA) Fellow. This work is funded by an NHMRC Partnership project in collaboration with WaterAid Australia.
Refractory Anemia with Ring Sideroblasts Associated with Marked Thrombocytosis Complicated by Massive Splenomegaly Treated with Lenalidomide Resulting in Resolution of Splenomegaly but Severe and Prolonged Pancytopenia

Refractory anemia with ring sideroblasts associated with marked thrombocytosis (RARS-T) is a hematological malignancy that combines features of both a myeloproliferative and myelodysplastic disorder. There have been recent reports of the successful treatment of anemia in 2 patients with RARS-T with lenalidomide. Here we report the successful treatment of massive splenomegaly in a patient with a long history of RARS-T resulting in complete resolution of splenomegaly, but with prolonged severe cytopenias. We also report the acquisition of the t(3;12)(q26;p13) translocation previously described in cases of myelodysplasia and the potential for transformation to myelofibrosis.
Face Detection using Skin Boosting Algorithm

The problem of face detection is Vast. Many techniques have been used for detection of faces from the given set of images. In this paper, we present use human skin color to detect faces as main components. Detection results shows the components having skin color. Adaboost is used to classify the skin and non-skin areas. We proposed skin boosting as variant of Adaboost for detecting faces. The skin boosting algorithm gives us a clear boundary or specifically a hyper plane that separate skin and non-skin areas for detecting face and non-face features of any individuals.
Structure of the YajR transporter suggests a transport mechanism based on the conserved motif A

The major facilitator superfamily (MFS) is the largest family of secondary active transporters and is present in all life kingdoms. Detailed structural basis of the substrate transport and energy-coupling mechanisms of these proteins remain to be elucidated. YajR is a putative proton-driven MFS transporter found in many Gram-negative bacteria. Here we report the crystal structure of Escherichia coli YajR at 3.15 Å resolution in an outward-facing conformation. In addition to having the 12 canonical transmembrane helices, the YajR structure includes a unique 65-residue C-terminal domain which is independently stable. The structure is unique in illustrating the functional role of “sequence motif A. ” This highly conserved element is seen to stabilize the outward conformation of YajR and suggests a general mechanism for the conformational change between the inward and outward states of the MFS transporters.
Accurate and Energy-Efficient Range-Free Localization for Mobile Sensor Networks

Existing localization algorithms for mobile sensor networks are usually based on the Sequential Monte Carlo (SMC) method. They either suffer from low sampling efficiency or require high beacon density to achieve high localization accuracy. Although papers can be found for solving the above problems separately, there is no solution which addresses both issues. In this paper, we propose an energy efficient algorithm, called WMCL, which can achieve both high sampling efficiency and high localization accuracy in various scenarios. In existing algorithms, a technique called bounding-box is used to improve the sampling efficiency by reducing the scope from which the candidate samples are selected. WMCL can further reduce the size of a sensor node's bounding-box by a factor of up to 87 percent and, consequently, improve the sampling efficiency by a factor of up to 95 percent. The improvement in sampling efficiency dramatically reduces the computational cost. Our algorithm uses the estimated position information of sensor nodes to improve localization accuracy. Compared with algorithms adopting similar methods, WMCL can achieve similar localization accuracy with less communication cost and computational cost. Our work has additional advantages. First, most existing SMC-based localization algorithms cannot be used in static sensor networks but WMCL can work well, even without the need of experimentally tuning parameters as required in existing algorithms like MSL*. Second, existing algorithms have low localization accuracy when nodes move very fast. We propose a new algorithm in which WMCL is iteratively executed with different assumptions on nodes' speed. The new algorithm dramatically improves localization accuracy when nodes move very fast. We have evaluated the performance of our algorithm both theoretically and through extensive simulations. We have also validated the performance results of our algorithm by implementing it in real deployed static sensor networks. To the best of our knowledge, we are the first to implement SMC-based localization algorithms for wireless sensor networks in real environment.
Inoculation of tomato plants with rhizobacteria enhances the performance of the phloem-feeding insect Bemisia tabaci

In their natural environment, plants experience multiple biotic interactions and respond to this complexity in an integrated manner. Therefore, plant responses to herbivory are flexible and depend on the context and complexity in which they occur. For example, plant growth promoting rhizobacteria (PGPR) can enhance plant growth and induce resistance against microbial pathogens and herbivorous insects by a phenomenon termed induced systemic resistance (ISR). In the present study, we investigated the effect of tomato (Solanum lycopersicum) pre-inoculation with the PGPR Pseudomonas fluorescens WCS417r, on the performance of the generalist phloem-feeding insect Bemisia tabaci. Based on the ability of P. fluorescens WCS417r to prime for ISR against generalists chewing insects and necrotrophic pathogens, we hypothesized that pre-inoculated plants will strongly resist B. tabaci infestation. In contrast, we discovered that the pre-inoculation treatment increased the tomato plant suitability for B. tabaci which was emphasized both by faster developmental rate and higher survivability of nymph stages on pre-inoculated plants. Our molecular and chemical analyses suggested that the phenomenon is likely to be related to: (I) the ability of the bacteria to reduce the activity of the plant induced defense systems; (II) a possible manipulation by P. fluorescens of the plant quality (in terms of suitability for B. tabaci) through an indirect effect on the rhizosphere bacterial community. The contribution of our study to the pattern proposed for other belowground rhizobacteria and mycorrhizal fungi and aboveground generalist phloem-feeders is discussed.
Digraph Girth via Chromatic Number

Let $D$ be a digraph. The chromatic number $\chi(D)$ of $D$ is the smallest number of colors needed to color the vertices of $D$ such that every color class induces an acyclic subdigraph. The girth of $D$ is the length of a shortest directed cycle, or $\infty$ if $D$ is acyclic. Let $G(k,n)$ be the maximum possible girth of a digraph on $n$ vertices with $\chi(D) > k$. It is shown that $G(k,n) \ge \left\lfloor n^{1/k}\right\rfloor$ and $G(k,n) \le (3\log_2 n \log_2\log_2 n)^{1-1/k} n^{1/k}$ for $n \ge 3$ and $k \ge 2$.
Referential Integrity Revisited: An Object-Oriented Perspective

Referential integrity underlies the relational represen tation of object-oriented structures. The concept of referential integrity in relational databases is hindered by the confusion surrounding both the concept itself and its implementation by relational database manage ment systems (RDBMS). Most of this confusion is caused by the diversity of relational representations for object-oriented structures. We examine the relationship between these representations and the structure of referential integrity constraints, and show that the controversial structures either do not occur or can b; avoided in the relational representations of object oriented structures. Referential integrity is not supported uniformly by RDBMS products. Thus, referential integrity con straints can be specified in some RDBMSs non procedurally (declaratively) , while in other RDBMSs they must be specified procedurally. Moreover, some RDBMSs do not allow the specification of certain referential integrity constraints. We discuss the referential integrity capabilities provided by three representative RDBMSs, DB2, SYBASE, and INGRES.
Mrgprd Enhances Excitability in Specific Populations of Cutaneous Murine Polymodal Nociceptors

The Mas-related G protein-coupled receptor D (Mrgprd) is selectively expressed in nonpeptidergic nociceptors that innervate the outer layers of mammalian skin. The function of Mrgprd in nociceptive neurons and the physiologically relevant somatosensory stimuli that activate Mrgprd-expressing (Mrgprd+) neurons are currently unknown. To address these issues, we studied three Mrgprd knock-in mouse lines using an ex vivo somatosensory preparation to examine the role of the Mrgprd receptor and Mrgprd+ afferents in cutaneous somatosensation. In mouse hairy skin, Mrgprd, as marked by expression of green fluorescent protein reporters, was expressed predominantly in the population of nonpeptidergic, TRPV1-negative, C-polymodal nociceptors. In mice lacking Mrgprd, this population of nociceptors exhibited decreased sensitivity to cold, heat, and mechanical stimuli. Additionally, in vitro patch-clamp studies were performed on cultured dorsal root ganglion neurons from Mrgprd −/− and Mrgprd +/− mice. These studies revealed a higher rheobase in neurons from Mrgprd −/− mice than from Mrgprd +/− mice. Furthermore, the application of the Mrgprd ligand β-alanine significantly reduced the rheobase and increased the firing rate in neurons from Mrgprd +/− mice but was without effect in neurons from Mrgprd −/− mice. Our results demonstrate that Mrgprd influences the excitability of polymodal nonpeptidergic nociceptors to mechanical and thermal stimuli.
Autoregressive Conditional Skewness, Kurtosis and Jarque-bera in Lithuanian Stock Market Measurement

Most of the statistical tools are designed to model theconditional mean of a random variable. The tools describedin this article have a rather different purpose – tomodel the conditional variance, or volatility of a variable.There are several reasons to model and forecast volatility.First, we need to analyze the risk of holding an assetor the value of an option. Second, forecast confidence intervalsmay be time-varying, so that more accurate intervalscan be obtained by modeling the variance of the errors.Third, more efficient estimators can be obtained ifheteroskedasticity in the errors is handled properly.Autoregressive Conditional Heteroskedasticity (ARCH)models are specifically designed to model and forecastconditional variances. The variance of the dependentvariable is modeled as a function of past values of thedependent variable and independent, or exogenous variables.These models are widely used in various branches ofeconometrics, especially in financial time series analysis.This article analyses GARCH and ARCH models’ twomain characteristics: skewness and kurtosis.
The optimal incentive contract for collaborative knowledge creation

In knowledge alliances, a typical form is leader-subordinate alliance, where leader dominates knowledge creation, followed by subordinates. In the alliance, subordinates tend to become opportunistic free-riders who make fewer efforts while harvest more than their fair share of benefit. This behavior will harm the performance of the whole alliance, especially that of the leader. In order to encourage the subordinates to act in line with the expectation of the leader, we models collaborative knowledge creation as the principal-agent game. Using the game theoretic framework, we attempt to find the optimal incentive contract that induces the subordinates to act in the best interests of the leader. Under the assumption of one-time collaborative knowledge creation, we use a static principal-agent model, specifically called as state-space formulation. To make the model more practical, we propose particular functional forms based on the researches of Samaddar and Gian-Italo. By solving the maximization problem of the model, we are able to get the optimal incentive terms. In addition, from the equations derived from the model, we make observations about some factors affecting the contract, including degree of uncertainty of the leader's gain, risk preference and marginal cost of the subordinates.
A density based method for multivariate time series clustering in kernel feature space

Time series clustering finds applications in diverse fields of science and technology. Kernel based clustering methods like kernel K-means method need number of clusters as input and cannot handle outliers or noise. In this paper, we propose a density based clustering method in kernel feature space for clustering multivariate time series data of varying length. This method can also be used for clustering any type of structured data, provided a kernel which can handle that kind of data is used. We present heuristic methods to find the initial values of the parameters used in our proposed algorithm. To show the effectiveness of this method, this method is applied to two different online handwritten character data sets which are multivariate time series data of varying length, as a real world application. The performance of the proposed method is compared with the spectral clustering and kernel k-means clustering methods. Besides handling outliers, the proposed method performs as well as the spectral clustering method and outperforms the kernel k-means clustering method.
DNA Barcoding for Honey Biodiversity

Honey is produced by honeybees from nectar and from secretions of living plants. It reflects the honeybees’ diet and the local plant communities. Honey also shows different plant compositions in different geographical locations. We propose a new method for studying the plant diversity and the geographical origin of honey using a DNA barcoding approach that combines universal primers and massive parallel pyrosequencing. To test this method we use two commercial honeys, one from a regional origin and one composed of a worldwide mix of different honeys. We demonstrate that the method proposed here is fast, simple to implement, more robust than classical methods, and therefore suitable for analyzing plant diversity in honey.
An Imanishism‐based genetic algorithm for sampling various Pareto‐optimal solutions: An application to the multiobjective resource division problem

For sampling various solutions from the entire Pareto front of the multiobjective resource division problem, a new Genetic Algorithm (GA) based on an evolutionary theory advocated by Kinji Imanishi is proposed. First, two types of distance between two individuals, namely, structural and functional distances, are introduced and used to define four types of relation between them, namely, homogeneous, heterogeneous, homologous, and analogous species. Then, for keeping a variety of species within a population as far as possible, a new generation alternation model with variable population size is presented. In order to find Pareto‐optimal solutions effectively, a new genetic operation that combines conventional harmonic crossover with a local optimization algorithm is also proposed. Finally, the advantage of the Imanishism‐based GA is demonstrated through computational experiments conducted on two‐ and three‐objective problem instances. © 2002 Scripta Technica, Electr Eng Jpn, 139(2): 23–35, 2002; DOI 10.1002/eej.10010