Miracle-dz/UniFAR-FTUs huggingface dataset:
Blastocyst formation--good indicator of clinical results after ICSI with testicular spermatozoa.

BACKGROUND The aim of this study was to evaluate the role of blastocyst culture in patients with azoospermia. METHODS In 98 cycles embryos were cultured for 2 days and in 128 cycles for 5 days to reach the blastocyst stage; a maximum of two of the most developed embryos were transferred in each group. RESULTS There was a negative correlation between a high (>/=20 IU/l) male serum FSH and embryo development, manifested as embryos not reaching the morula stage on day 5 (r = 0.387; P < 0.05). After prolonged culture, 23% of embryos reached the blastocyst stage. The pregnancy rates per transfer, and the abortion rates were approximately the same in the day 2 group and the day 5 group (20 versus 20% and 19 versus 18% respectively). After blastocyst transfer, a high clinical pregnancy rate (55%) and a low abortion rate (6%) were achieved, whereas the transfer of arrested embryos provided a low pregnancy rate (2%) and a high abortion rate (100%). If only blastocysts had been transferred on day 5, the clinical pregnancy rate per started cycle would have been approximately the same in both groups (13 versus 16%). CONCLUSIONS Blastocyst formation is a good indicator of clinical results after ICSI with testicular sperm.
Spitzer Space Telescope Observations of the Aftermath of Microlensing Event MACHO-LMC-5

We have carried out photometry of the microlensing event MACHO-LMC-5 with Spitzer Infrared Array Camera (IRAC) 10 years after the magnification of the LMC source star was recorded. This event is unique in the annals of gravitational microlensing: the lensing star itself has been observed using the Hubble Space Telescope (once with WFPC2 and twice with ACS/HRC). Since the separation between the source and lens at the epoch of the Spitzer observations was ∼0. ″24, the two stars cannot be resolved in the Spitzer images. However, the IRAC photometry clearly establishes that the lens is an M5 dwarf star from its infrared excess, which in turn yields a mass of ∼0.2 M⊙. This demonstrates the potential of Spitzer to detect the lenses in other gravitational microlensing events.
A maintenance of immunological homeostasis regulated by suppressor and helper T cell function is considered to be one of the regulatory mechanisms to prevent autoimmune responses

NONOMURA, A. , TANINO, M. , KURUMAYA, H. , OHTA, G. , KATO, Y. and KoBAYASHI, K. Studies of Immune Functions of Patients with Chronic Hepatitis. Tohoku J. exp. Med., 1982, 137 (2), 163-177 Peripheral T cells from patients with chronic active hepatitis (CAH) showed a significantly decreased suppressor effect (or increased helperr effect) on allogeneic B cell differentiation into Ig-producing cells (Ig-PC) (p<0.05). After irradiation of T cells to eliminate suppressor influences, mean spontaneous helper activity of CAR was not different from that of healthy subjects, indicating that spontaneous helper activity of CAR was normal. Concanavalin A (Con A)-induced suppressor cell activity was significantly decreased in CAR (p <0.01, 9 defective cases out of 18 patients). Minor defect of Con A-induced suppressor activity was also found in some patients with chronic persistent hepatitis (CPH) (2 defective cases out of 14 patients). Autologous mixed lymphocyte reaction (AMLR) was significantly decreased in patients with CAR (p<0.005). Spontaneous suppressor or Con Ainduced suppressor activity was not different statistically between HBsAgpositive and HBsAg-negative cases. Finally, we demonstrated a presence of a serum factor(s) that can decrease Con A-induced suppressor cell function of healthy subjects in 7 of 21 patients with CAR and 2 of 14 CPH. Our results suggest that defective suppressor cell function likely attributable to serum factor(s) may reflect altered immune responses of CAH. suppressor cell; helper cell; autologous MLR; CAH
Financing Obstacles and Growth: An Analysis for Euro Area Non-Financial Corporations

This paper investigates whether financial obstacles, and, more generally, financial pressure faced by firms, significantly affect firm growth. For this purpose, we use an unbalanced panel of about 1,000,000 observations for around 155,000 non-financial corporations in five euro area countries. In addition to the balance sheet information in this panel, we also rely on firm level survey data. In this way we are able to work out a direct measure of the firms’ probability of facing financing obstacles. Our results indicate that, though based on few variables, this measure appears to be relevant in explaining firm growth in four out of the five countries considered. Other firm-level variables related to the financial pressure faced by firms, such as cash flow (debt burden) are found to exert a positive (negative) impact on firm growth, while the results for leverage are less clear-cut.
An evaluation of feature selection and reduction algorithms for network IDS data

Intrusion detection is concerned with monitoring and analysing events occurring in a computer system in order to discover potential malicious activity. Data mining, which is part of the procedure of knowledge discovery in databases, is the process of analysing the collected data to find patterns or correlations. As the amount of data collected, store and processed only increases, so does the significance and importance of intrusion detection and data mining. A dataset that has been particularly exposed to research is the dataset used for the Third International Knowledge Discovery and Data Mining Tools competition, KDD99. The KDD99 dataset has been used to identify what data mining techniques relate to certain attack and employed to demonstrate that decision trees are more efficient than the Naïve Bayes model when it comes to detecting new attacks. When it comes to detecting network intrusions, the C4.5 algorithm performs better than SVM. The aim of our research is to evaluate and compare the usage of various feature selection and reduction algorithms against publicly available datasets. In this contribution, the focus is on feature selection and reduction algorithms. Three feature selection algorithms, consisting of an attribute evaluator and a test method, have been used. Initial results indicate that the performance of the classifier is unaffected by reducing the number of attributes.
Default Bayesian analysis for multi-way tables: a data-augmentation approach

This paper proposes a strategy for regularized estimation in multi-way contingency tables, which are common in meta-analyses and multi-center clinical trials. Our approach is based on data augmentation, and appeals heavily to a novel class of Polya{Gamma distributions. Our main contributions are to build up the relevant distributional theory and to demonstrate three useful features of this data-augmentation scheme. First, it leads to simple EM and Gibbs-sampling algorithms for posterior inference, circumventing the need for analytic approximations, numerical integration, Metropolis{Hastings, or variational methods. Second, it allows modelers much more exibility when choosing priors, which have traditionally come from the Dirichlet or logistic-normal family. For example, our approach allows users to incorporate Bayesian analogues of classical penalized-likelihood techniques (e.g. the lasso or bridge) in computing regularized estimates for log-odds ratios. Finally, our dataaugmentation scheme naturally suggests a default strategy for prior selection based on the logistic-Z model, which is strongly related to Jereys’ prior for a
A Hybrid Approach for Selecting a Course Management System: A Case Study

The Internet is creating exciting opportunities for universities and their stakeholders. Universities are experimenting with different modes of education. E-learning is one such mode, where students can learn from any place at any time, as long as they have an Internet connection. A course management system (CMS) facilitates e-learning, making it critical to select a system that can meet the needs of all its stakeholders. This paper addresses a hybrid approach used to select a course management system for a mid-Atlantic university. The resulting product is the outcome of faculty input, inputs from the program manager of e-Learning, students, and other stakeholders. The authors have experienced four different CMSs over twelve years of teaching online. Instead of re-inventing the wheel, universities planning to venture into e-learning or trying to revamp their current CMS can learn from our experiences. The case study could assist current and future Web course faculty in selecting a CMS to suit their teaching needs. In addition, by increasing their awareness of various capabilities that might be present in their current CMS, the discussion in the paper could also encourage the faculty to revamp their courses.
Visualizing multiphase flow and trapped fluid configurations in a model three‐dimensional porous medium

We report an approach to fully visualize the flow of two immiscible fluids through a model three-dimensional (3-D) porous medium at pore-scale resolution. Using confocal microscopy, we directly image the drainage of the medium by the nonwetting oil and subsequent imbibition by the wetting fluid. During imbibition, the wetting fluid pinches off threads of oil in the narrow crevices of the medium, forming disconnected oil ganglia. Some of these ganglia remain trapped within the medium. By resolving the full 3-D structure of the trapped ganglia, we show that the typical ganglion size, as well as the total amount of residual oil, decreases as the capillary number Ca increases; this behavior reflects the competition between the viscous pressure in the wetting fluid and the capillary pressure required to force oil through the pores of the medium. This work thus shows how pore-scale fluid dynamics influence the trapped fluid configurations in multiphase flow through 3-D porous media. © 2013 American Institute of Chemical Engineers AIChE J, 59:1022-1029, 2013
On the energy (in)efficiency of Hadoop clusters

Distributed processing frameworks, such as Yahoo! 's Hadoop and Google's MapReduce, have been successful at harnessing expansive datacenter resources for large-scale data analysis. However, their effect on datacenter energy efficiency has not been scrutinized. Moreover, the filesystem component of these frameworks effectively precludes scale-down of clusters deploying these frameworks (i.e. operating at reduced capacity). This paper presents our early work on modifying Hadoop to allow scale-down of operational clusters. We find that running Hadoop clusters in fractional configurations can save between 9% and 50% of energy consumption, and that there is a tradeoff between performance energy consumption. We also outline further research into the energy-efficiency of these frameworks.
Page ranking based on number of visits of links of Web page

Search engines generally return a large number of pages in response to user queries. To assist the users to navigate in the result list, ranking methods are applied on the search results. Most of the ranking algorithms proposed in the literature are either link or content oriented, which do not consider user usage trends. In this paper, a page ranking mechanism called Page Ranking based on Visits of Links(VOL) is being devised for search engines, which works on the basic ranking algorithm of Google i.e. PageRank and takes number of visits of inbound links of Web pages into account. This concept is very useful to display most valuable pages on the top of the result list on the basis of user browsing behavior, which reduces the search space to a large scale. The paper also presents a method to find link-visit counts of Web pages and a comparison between VOL with the PageRank algorithm.
Modeling current and future potential wintering distributions of eastern North American monarch butterflies

Monarch butterflies overwinter in restricted areas in montane oyamel fir forests in central Mexico with specific microclimates that allow the butterflies to survive for up to 5 months. We use ecological niche modeling (ENM) to identify areas adequate for overwintering monarch colonies under both current and future climate scenarios. The ENM approach permits testing and validation of model predictivity, and yields quantitative, testable predictions regarding likely future climate change effects. Our models predicted monarch presence with a high degree of accuracy, and indicated that precipitation and diurnal temperature range were key environmental factors in making locations suitable for monarchs. When we projected monarch distribution onto future climate scenarios (Hadley Centre climate models), we found that conditions were likely to be inadequate across the entire current winter range, particularly owing to increased cool-weather precipitation that could cause increased mortality. This study applies ENM to understanding the seasonal dynamics of a migratory species under climate change, and uses ENM to identify key limiting environmental parameters in species' responses to climate change.
Tight Lower Bounds for Homology Inference

The homology groups of a manifold are important topological invariants that provide an algebraic summary of the manifold. These groups contain rich topological information, for instance, about the connected components, holes, tunnels and sometimes the dimension of the manifold. In earlier work, we have considered the statistical problem of estimating the homology of a manifold from noiseless samples and from noisy samples under several different noise models. We derived upper and lower bounds on the minimax risk for this problem. In this note we revisit the noiseless case. In previous work we used Le Cam's lemma to establish a lower bound that differed from the upper bound of Niyogi, Smale and Weinberger by a polynomial factor in the condition number. In this note we use a different construction based on the direct analysis of the likelihood ratio test to show that the upper bound of Niyogi, Smale and Weinberger is in fact tight, thus establishing rate optimal asymptotic minimax bounds for the problem. The techniques we use here extend in a straightforward way to the noisy settings considered in our earlier work.
Refinement of Operator-valued Reproducing Kernels

This paper studies the construction of a refinement kernel for a given operator-valued reproducing kernel such that the vector-valued reproducing kernel Hilbert space of the refinement kernel contains that of the given kernel as a subspace. The study is motivated from the need of updating the current operator-valued reproducing kernel in multi-task learning when underfitting or overfitting occurs. Numerical simulations confirm that the established refinement kernel method is able to meet this need. Various characterizations are provided based on feature maps and vector-valued integral representations of operator-valued reproducing kernels. Concrete examples of refining translation invariant and finite Hilbert-Schmidt operator-valued reproducing kernels are provided. Other examples include refinement of Hessian of scalar-valued translation-invariant kernels and transformation kernels. Existence and properties of operator-valued reproducing kernels preserved during the refinement process are also investigated.
Adaptive Cryptographically Synchronized Authentication (ACSA) Final Report

The information in this document may change at any time. No part of this document can be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose, without the express written permission of Network Associates, Inc. The U.S. Department of Commerce controls export of cryptographic items such as the ACSA prototype toolkit mentioned herein under the Export Administration Regulations. Exporters of cryptographic items such as the ACSA prototype toolkit mentioned herein must obtain any export authorizations and licenses required under the Export Administration Regulations. LINUX is a trademark of Linus Torvalds. RED HAT is a registered trademark of Red Hat, Inc. All other names and trademarks are the property of their respective owners.Executive Summary Conventional authentication mechanisms do not operate at speeds fast enough to meet the demands of ultra-fast networks [1][23][29]. This disparity presents a great challenge for high-speed applications that demand network authentication. Much work has focused on devising fast authentication algorithms. The Adaptive Cryptographically Synchronized Authentication (ACSA) Project [10] provides a new solution to this challenge by trading off authentication strength and performance to achieve data origin authentication and connectionless integrity. Some of the applications that motivate our work include real-time high-speed video, high-performance distributed computing, high-speed distributed storage, and 3-D virtual reality. Such applications might run on conventional platforms with high-speed network devices (e.g. Gigabit Ethernet or ATM networks on Pentium class machines without cryptographic hardware), on special platforms with conventional cryptographic hardware, or on computationally limited single-processor devices. The ACSA Project offers a practical and flexible solution to high-speed network authentication that can be implemented in software or hardware. Figure 1 illustrates hypothetical strength-performance tradeoffs that ACSA might achieve. For example, the user can select a lower-speed, high-strength authentication mechanism, a higher-speed, lower-strength mechanism, or a high-speed Partial Message Authentication Code (PMAC) that authenticates only a portion of the message. Throughout, we use the term " network authentication " to mean data origin authentication and connectionless integrity, as defined in Internet standards [17]. This type of authentication is without regard to the ordering of the message in a stream of messages. By " authentication strength " we mean the computational difficulty of producing a forgery. By " performance " we mean computational complexity (CPU time, memory space). The ACSA project aims to lighten the processing loads of the sender and receiver by requiring them to spend less processor resources …
Cells with Stem Cell Characteristics in Somatic Compartments of the Ovary

Antral follicular growth in the ovary is characterized by rapid expansion of granulosa cells accompanied by a rising complexity of their functionality. Within two weeks the number of human granulosa cells increases from less than 500,000 to more than 50 millions cells per follicle and differentiates into groups of cells with a variety of specialized functions involved in steroidogenesis, nursing the oocyte, and forming a functional syncitium. Both the rapid proliferation and different specialized functions of the granulosa cells can only be explained through the involvement of stem cells. However, luteinizing granulosa cells were believed to be terminally differentiated cells. Only recently, stem and progenitor cells with FSH-receptor activity were identified in populations of luteinizing granulosa cells obtained during oocyte collected for assisted reproduction. In the presence of the leukaemia-inhibiting factor (LIF), it was possible to culture a subpopulation of the luteinizing granulosa cells over prolonged time periods. Furthermore, when embedded in a matrix consisting of collagen type I, these cells continued to express the FSH receptor over prolonged time periods, developed globular formations that surrogated as follicle-like structures, providing a promising tool for reproductive biology.
Business experience with computer integrated manufacturing. A survey of current strategy and practice

The paper describes the results of a recent field study of CIM adoption strategies in US manufacturing firms. The purpose of the study was to identify the extent to which CIM technologies are in use in US firms, the impact of a facility's process characteristics on the CIM development process, and the adoption policy being followed implicitly or explicitly. The survey focused on the following aspects:(a) manufacturing process characteristics, (b) the CIM development process, (c) the CIM architecture, and (d) perceived value and benefits. Our results indicate that CIM implementations follow a definite temporal pattern with respect to the adoption of certain information technologies. In addition, the initiative for CIM programs is usually generated from the bottom-up. This gradual bottom-up approach appears to restrain, rather than enable, plant-wide integration for critical business processes such as order fulfilment or product development. While most CIM users find that their CIM projects successfully meet their initial operational goals, the technology seems to be poorly integrated still. More crucially, it appears that CIM is not being adopted as a strategic information system for competitive missions. <<ETX>>
Design and Preliminary Evaluation of a Two DOFs Cable-Driven Ankle–Foot Prosthesis with Active Dorsiflexion–Plantarflexion and Inversion–Eversion

This paper describes the design of an ankle–foot robotic prosthesis controllable in the sagittal and frontal planes. The prosthesis was designed to meet the mechanical characteristics of the human ankle including power, range of motion, and weight. To transfer the power from the motors and gearboxes to the ankle–foot mechanism, a Bowden cable system was used. The Bowden cable allows for optimal placement of the motors and gearboxes in order to improve gait biomechanics such as the metabolic energy cost and gait asymmetry during locomotion. Additionally, it allows flexibility in the customization of the device to amputees with different residual limb sizes. To control the prosthesis, impedance controllers in both sagittal and frontal planes were developed. The impedance controllers used torque feedback from strain gages installed on the foot. Preliminary evaluation was performed to verify the capability of the prosthesis to track the kinematics of the human ankle in two degrees of freedom (DOFs), the mechanical efficiency of the Bowden cable transmission, and the ability of the prosthesis to modulate the impedance of the ankle. Moreover, the system was characterized by describing the relationship between the stiffness of the impedance controllers to the actual stiffness of the ankle. Efficiency estimation showed 85.4% efficiency in the Bowden cable transmission. The prosthesis was capable of properly mimicking human ankle kinematics and changing its mechanical impedance in two DOFs in real time with a range of stiffness sufficient for normal human walking. In dorsiflexion–plantarflexion (DP), the stiffness ranged from 0 to 236 Nm/rad and in inversion–eversion (IE), the stiffness ranged from 1 to 33 Nm/rad.
Track Extraction with Hidden Reciprocal Chain Models

This paper develops Bayesian track extraction algorithms for targets modelled as hidden reciprocal chains (HRC). HRC are a class of finite-state random process models that generalise the familiar hidden Markov chains (HMC). HRC are able to model the "intention" of a target to proceed from a given origin to a destination, behaviour which cannot be properly captured by a HMC. While Bayesian estimation problems for HRC have previously been studied, this paper focusses principally on the problem of track extraction, of which the primary task is confirming target existence in a set of detections obtained from thresholding sensor measurements. Simulation examples are presented which show that the additional model information contained in a HRC improves detection performance when compared to HMC models.