Miracle-dz/UniFAR-FTUs huggingface dataset:
Poor ovarian response to gonadotrophin stimulation — The role of adjuvant treatments

Poor ovarian response to gonadotrophin stimulation represents a clinical problem in in vitro fertilization practice. Women showing poor ovarian response are a heterogeneous group, many of whom have a reduced ovarian reserve and consequently a lower pregnancy potential. Various management strategies have been proposed to improve ovarian response to gonadotrophins, but these have met with limited success. Adjuvant treatments aim to potentiate the effect of exogenous follicle-stimulating hormone. In separate, randomized, placebo-controlled trials low-dose dexamethasone and aspirin have been shown to reduce the incidence of poor response in an initial stimulation cycle. Preliminary studies using pyridostigmine and l-arginine in established poor responders are encouraging but require confirmation in adequately powered studies. Evidence from randomized controlled trials does not support the use of adjuvant growth hormone or growth hormone-releasing hormone in poor responders without overt growth hormone deficiency. The mechanisms of action of adjuvant treatments require further investigation.
Enhanced perceptrons using contrastive biclusters

Perceptrons are neuronal devices capable of fully discriminating linearly separable classes. Although straightforward to implement and train, their applicability is usually hindered by non-trivial requirements imposed by real-world classification problems. Therefore, several approaches, such as kernel perceptrons, have been conceived to counteract such difficulties. In this paper, we investigate an enhanced perceptron model based on the notion of contrastive biclusters. From this perspective, a good discriminative bicluster comprises a subset of data instances belonging to one class that show high coherence across a subset of features and high differentiation from nearest instances of the other class under the same features (referred to as its contrastive bicluster). Upon each local subspace associated with a pair of contrastive biclusters a perceptron is trained and the model with highest area under the receiver operating characteristic curve (AUC) value is selected as the final classifier. Experiments conducted on a range of data sets, including those related to a difficult biosignal classification problem, show that the proposed variant can be indeed very useful, prevailing in most of the cases upon standard and kernel perceptrons in terms of accuracy and AUC measures.
RFA-cut: Semi-automatic segmentation of radiofrequency ablation zones with and without needles via optimal s-t-cuts

In this contribution, we present a semi-automatic segmentation algorithm for radiofrequency ablation (RFA) zones via optimal s-t-cuts. Our interactive graph-based approach builds upon a polyhedron to construct the graph and was specifically designed for computed tomography (CT) acquisitions from patients that had RFA treatments of Hepatocellular Carcinomas (HCC). For evaluation, we used twelve post-interventional CT datasets from the clinical routine and as evaluation metric we utilized the Dice Similarity Coefficient (DSC), which is commonly accepted for judging computer aided medical segmentation tasks. Compared with pure manual slice-by-slice expert segmentations from interventional radiologists, we were able to achieve a DSC of about eighty percent, which is sufficient for our clinical needs. Moreover, our approach was able to handle images containing (DSC=75.9%) and not containing (78.1%) the RFA needles still in place. Additionally, we found no statistically significant difference (p<;0.423) between the segmentation results of the subgroups for a Mann-Whitney test. Finally, to the best of our knowledge, this is the first time a segmentation approach for CT scans including the RFA needles is reported and we show why another state-of-the-art segmentation method fails for these cases. Intraoperative scans including an RFA probe are very critical in the clinical practice and need a very careful segmentation and inspection to avoid under-treatment, which may result in tumor recurrence (up to 40%). If the decision can be made during the intervention, an additional ablation can be performed without removing the entire needle. This decreases the patient stress and associated risks and costs of a separate intervention at a later date. Ultimately, the segmented ablation zone containing the RFA needle can be used for a precise ablation simulation as the real needle position is known.
High-performance bidiagonal reduction using tile algorithms on homogeneous multicore architectures

This article presents a new high-performance bidiagonal reduction (BRD) for homogeneous multicore architectures. This article is an extension of the high-performance tridiagonal reduction implemented by the same authors [Luszczek et al., IPDPS 2011] to the BRD case. The BRD is the first step toward computing the singular value decomposition of a matrix, which is one of the most important algorithms in numerical linear algebra due to its broad impact in computational science. The high performance of the BRD described in this article comes from the combination of four important features: (1) tile algorithms with tile data layout, which provide an efficient data representation in main memory; (2) a two-stage reduction approach that allows to cast most of the computation during the first stage (reduction to band form) into calls to Level 3 BLAS and reduces the memory traffic during the second stage (reduction from band to bidiagonal form) by using high-performance kernels optimized for cache reuse; (3) a data dependence translation layer that maps the general algorithm with column-major data layout into the tile data layout; and (4) a dynamic runtime system that efficiently schedules the newly implemented kernels across the processing units and ensures that the data dependencies are not violated. A detailed analysis is provided to understand the critical impact of the tile size on the total execution time, which also corresponds to the matrix bandwidth size after the reduction of the first stage. The performance results show a significant improvement over currently established alternatives. The new high-performance BRD achieves up to a 30-fold speedup on a 16-core Intel Xeon machine with a 12000× 12000 matrix size against the state-of-the-art open source and commercial numerical software packages, namely LAPACK, compiled with optimized and multithreaded BLAS from MKL as well as Intel MKL version 10.2.
Serpentine locomotion of a snake-like robot controlled by musical theory

Based on the structure of both biological snakes and snake-like robots and their rhythmic locomotion, the musical theory is adopted as a control method to study on a snake-like robot. The relation between the rules and symbols of the musical theory and the control process of the snake-like robot is defined in this paper. Thus the data sequence of the relative angles in the serpentine locomotion is melodized. The gait score of the serpentine locomotion is utilized to control a snake-like robot named "Perambulator-I". Finally, the aspects of the future researches are discussed.
Reinforcement of Polypropylene Composites Based on Recycled Wool or Cotton Powders

ABSTRACT This article addresses the preparation and characterization of polymer composites of polypropylene (PP) containing recycled wool or cotton micro-powder. The PP composites were produced in a lab-scale twin-screw extruder. The recycled wool or cotton powder was obtained from wool and cotton waste fibers (from textile industry) by freeze milling technique. The amount of powder (wool or cotton) was varied from 1% to 10 %. Cotton and wool powders were homogenously distributed in the polymer matrix, as observed from scanning electron microscopic (SEM) images. Thermal, physical, and mechanical properties of the obtained composites were investigated by differential scanning calorimetry (DSC), Thermogravimetric analysis (TGA), tensile strength, dyeability as well as the moisture uptake behavior is also explored. The overall results clarify that wool or cotton powders could be used as effective reinforcing materials for the production of PP composites with enhanced properties.
Mechanical and physico - chemical properties of biodegradable protein-based films: A comparative study

Biodegradable protein-based films were prepared using the commercial gelatin (bovine hide) (CG), giant catfish skin gelatin (GG), soy protein isolates (SPI) in comparison with commercial wrap film (polyvinyl chloride; PVC). The physical, chemical and mechanical properties of the resulting films were determined and compared. The molecular weight distributions of the GG had high quantities of α-chains and β-components. The GG film had higher mechanical properties [tensile strength: TS 26.42 MPa and elongation at break: EAB 128.25 (%)] but lower thickness (0.0454 mm) than those protein-based films. The PVC film had higher mechanical properties [TS: 44.93 MPa and EAB: 253.71 (%)] but lower water vapor permeability [3.37x10-6 g·mm·h-1·cm-2·Pa-1], film thickness [0.0103 mm] and film insolubility than protein-based films. The film appearance of protein-based films was similar to commercial wrap film with uniform transparency.
Learning User Keystroke Patterns for Authentication

— Keystroke authentication is a new access control system to identify legitimate users via their typing behavior. In this paper, machine learning techniques are adapted for keystroke authentication. Seven learning methods are used to build models to differentiate user keystroke patterns. The selected classiﬁcation methods are Decision Tree, Naive Bayesian, Instance Based Learning, Decision Table, One Rule, Random Tree and K-star. Among these methods, three of them are studied in more details. The results show that machine learning is a feasible alternative for keystroke authentication. Compared to the conventional Nearest Neighbour method in the recent research, learning methods especially Decision Tree can be more accurate. In addition, the experiment results reveal that 3-Grams is more accurate than 2-Grams and 4-Grams for feature extraction. Also, combination of attributes tend to result higher accuracy.
Multitarget Hierarchical Negotiation for Distributed Design in Collaborative Product Development

Distributed design in collaborative product development involves interorganizational teams of different disciplines, often geographically separated, that work jointly for the interest of an overall goal. The teams must agree on the product design without fully disclosing individual design information. Price-based negotiation is an effective design methodology under such circumstances. This work improves the negotiation mechanism based on the price schedules decomposition algorithm (PSDA), which only allows bi-level negotiation with a single design target, in two regards. The augmented price schedules decomposition algorithm (APSDA) is proposed to simultaneously consider multiple design targets in negotiation. Decision models and negotiation protocols are developed for complex design tasks decomposed in a hierarchy. The resultant APSDA-based negotiation mechanism enables multi-target hierarchical distributed design. A test scenario implemented with multiagent technologies validates the effectiveness of this mechanism. This study solves automating engineering collaborations with secure information sharing.
A case study on designing business processes based on collaborative and mining approaches

Companies invest a significant amount of time and resources to discover and represent how they work into business processes models. However, traditional process mapping has been done in an ad hoc manner and tends to be resource-intensive and time-consuming due to the informal and ambiguous collection of process information. The Story Mining Method aims to address those problems by the union of free-form narratives about processes and the usage of Text Mining and Natural Language techniques for text translation into process models. This paper presents a case study of the method, detailing its implementation as well as major issues found on a practical scenario within an organization.
Vaccines and immunizations.

Records from antiquity mention crippling diseases compatible with poliomyelitis. Michael Underwood first described a debility of the lower extremities in children that was recognizable as poliomyelitis in England in 1789. The first outbreaks in Europe were reported in the early 19th century, and outbreaks were first reported in the United States in 1843. For the next hundred years, epidemics of polio were reported from developed countries in the Northern Hemisphere each summer and fall. These epidemics became increasingly severe, and the average age of persons affected rose. The increasingly older age of persons with primary infection increased both the disease severity and number of deaths from polio. Polio reached a peak in the United States in 1952, with more than 21,000 paralytic cases. However, following introduction of effective vaccines, polio incidence declined rapidly. The last case of wild-virus polio acquired in the United States was in 1979, and global polio eradication may be achieved within the next decade.
Interpersonal variation in understanding robots as social actors

In this paper, I investigate interpersonal variation in verbal HRI with respect to the computers-as-social-actors hypothesis. The analysis of a corpus of verbal human-robot interactions shows that only a subgroup of the users treat the robot as a social actor. Thus, taking interpersonal variation into account reveals that not all users transfer social behaviors from human interactions into HRI. This casts doubts on the suggestion that the social responses to computers and robots reported on previously are due to mindlessness. At the same time, participants' understanding of robots as social or non-social actors can be shown to have a considerable influence on their linguistic behavior throughout the dialogs.
A post-routing stage IR drop reduction technique with less routing resources

IR Drop turned into a significant problem due to faster switching frequencies and device density through power distribution network. In a backend design flow, Power Delivery Networks are drawn at initial stages. The Power delivery improvement techniques available are applicable at PNR stages and less at ECO i.e. Post routing stages. Also, Power delivery improvement techniques generally widen the stripes or thicken the stripes using the available space, which may lead to reducing the routing resources for other design techniques. To deal with these problems, paper contains the techniques to be implemented at sign off stage. The technique2 is utilized to choose the effective locales to upgrade the power rail connectivity. They improve the IR drop with less usage of routing resources. These techniques are implemented and checked using IC Compiler and Red Hawk Explorer tools. In the Red Hawk Explorer tool reports, the experimental results demonstrate that the proposed approach can enhance the most noticeably awful IR-drop from 23.57mv (2.36%) to 10.02mv(1%) (i.e. 57.8% improvement) at sign-off stage, where the ECO stripe width is 7.5 times the default width.
Restriction access

We introduce a notion of non-black-box access to computational devices (such as circuits, formulas, decision trees, and so forth) that we call restriction access. Restrictions are partial assignments to input variables. Each restriction simplifies the device, and yields a new device for the restricted function on the unassigned variables. On one extreme, full restrictions (assigning all variables) correspond to evaluating the device on a complete input, yielding the result of the computation on that input, which is the same as standard black-box access. On the other extreme, empty restrictions (assigning no variables) yield a full description of the original device. We explore the grey-scale of possibilities in the middle. Focusing on learning theory, we show that restriction access provides a setting in which one can obtain positive results for problems that have resisted attack in the black-box access model. We introduce a PAC-learning version of restriction access, and show that one can efficiently learn both decision trees and DNF formulas in this model. These two classes are not known to be learnable in the PAC model with black-box access. Our DNF learning algorithm is obtained by a reduction to a general learning problem we call population recovery, in which random samples from an unknown distribution become available only after a random part of each is obliterated. Specifically, assume that every member of an unknown population is described by a vector of values. The algorithm has access to random samples, each of which is a random member of the population, whose values are given only on a random subset of the attributes. Analyzing our efficient algorithm to fully recover the unknown population calls for understanding another basic problem of independent interest: "robust local inversion" of matrices. The population recovery algorithm and construction of robust local inverses for some families of matrices are the main technical contributions of the paper. We also discuss other possible variants of restriction access, in which the values to restricted variables, as well as the subset of free (unassigned) variables, are generated deterministically or randomly, in friendly or adversarial fashions. We discuss how these models may naturally suit situations in computational learning, computational biology, automated proofs, cryptography and complexity theory.
Metastable Bose-Einstein condensation in a strongly correlated optical lattice

We experimentally and theoretically study the peak fraction of a Bose-Einstein condensate loaded into a cubic optical lattice as the lattice potential depth and entropy per particle are varied. This system is well-described by the superuid regime of the Bose-Hubbard model, which allows for comparison with mean-eld theories and exact quantum Monte Carlo (QMC) simulations. Despite correcting for systematic discrepancies between condensate fraction and peak fraction, we discover that the experiment consistently shows the presence of a condensate at temperatures higher than the critical temperature predicted by QMC simulations. This metastability suggests that turning on the lattice potential is non-adiabatic. To conrm this behavior, we compute the timescales for relaxation in this system, and nd that equilibration times are comparable with the known heating rates. The similarity of these timescales implies that turning on the lattice potential adiabatically may be impossible. Our results point to the urgent need for a better theoretical and experimental understanding of the timescales for relaxation and adiabaticity in strongly interacting quantum gases, and the importance of model-independent probes of thermometry in optical lattices.
Voltage Designability: An enabler for selector technology

In this paper, first we evaluate array power performance using both Voltage Designable (VD) and Non-Voltage Designable (NVD) selection device (1S) technologies for (a) compatibility with range of memory (1M) operating voltages and currents and (b) selector variability. Firstly, cross-point (1S1M) on-off ratio degrades exponentially with NVD selectors for higher memory voltages while VD selectors provide immunity from such degradation by designing (increasing) the selector voltage. Consequently, array power increases exponentially for NVD selectors for higher memory voltages while it can be controlled effectively using VD selectors. Secondly, selector variability causes a necessary increase in cross-point voltage to ensure that majority (>99%) of the cross-points can be programmed. We show that selector variability also increases total array leakage (i.e Ioff). Designability of selector voltage available in VD selector enables the immunity from array power increase due to selector variability. Thus, VD selector is attractive due to its broad memory technology compatibility and selector variability immunity.
Lipid-Based Self-Assembled NPs: The Effects of Morphology and Targeting Molecules on the Cellular Uptake Using In Vitro Tumor Model

The morphology of nanoparticles (NPs) has been presumed to play a major role in cellular uptake and in vivo stability. This report experimentally demonstrates such dependence by using two types of uniform-sized self-assembled lipid-based NPs, namely nanodiscs and nanovesicles, composed of identical lipid composition. The morphology is characterized by small angle neutron scattering, dynamic light scattering, and transmission electron microscopy. Both NPs have similar biostability in serum and cellular cytotoxicity. However, cellular uptake of the nanodiscs at 37°C is consistently and significantly higher than that of the vesicles according to the uptake results of several human cancer cell lines, i.e., CCRF-CEM, KB, and OVCAR-8, indicating a strong morphological dependence of cellular internalization. Further studies on such morphological dependence using CCRF-CEM reveals that vesicles only use Clathrinand caveolae-mediated endocytic pathways, while nanodiscs also take the additional routes of macropinocytosis and microtubule-mediated endocytosis.
Two sexes, one genome: the evolutionary dynamics of intralocus sexual conflict

As the evolutionary interests of males and females are frequently divergent, a trait value that is optimal for the fitness of one sex is often not optimal for the other. A shared genome also means that the same genes may underlie the same trait in both sexes. This can give rise to a form of sexual antagonism, known as intralocus sexual conflict (IASC). Here, a tug-of-war over allelic expression can occur, preventing the sexes from reaching optimal trait values, thereby causing sex-specific reductions in fitness. For some traits, it appears that IASC can be resolved via sex-specific regulation of genes that subsequently permits sexual dimorphism; however, it seems that whole-genome resolution may be impossible, due to the genetic architecture of certain traits, and possibly due to the changing dynamics of selection. In this review, we explore the evolutionary mechanisms of, and barriers to, IASC resolution. We also address the broader consequences of this evolutionary feud, the possible interactions between intra- and interlocus sexual conflict (IRSC: a form of sexual antagonism involving different loci in each sex), and draw attention to issues that arise from using proxies as measurements of conflict. In particular, it is clear that the sex-specific fitness consequences of sexual dimorphism require characterization before making assumptions concerning how this relates to IASC. Although empirical data have shown consistent evidence of the fitness effects of IASC, it is essential that we identify the alleles mediating these effects in order to show IASC in its true sense, which is a “conflict over shared genes. ”
Risk factors for endometrial hyperplasia concomitant endometrial polyps in pre- and post-menopausal women.

PURPOSE To evaluate the risk factors for endometrial hyperplasia concomitant endometrial polyps in pre- and post-menopausal women. MATERIALS AND METHODS A total of 203 patients undergoing endometrial sampling before hysterectomy were evaluated in this retrospective study. Data recorded were age, gravidity, parity, body mass index (BMI: weight(kg)/height(m)2), endometrial thickness (ET), menopausal status, presence of adenomyosis and diabetes mellitus. RESULTS Endometrial hyperplasia and polyps were detected in 13 patients. There were statistically significant differences in terms of age, menopausal status, morbid obesity and diabetes mellitus (p<0.005). Logistic regression demonstrated that menopausal status and presence of diabetes mellitus were independent risk factors. CONCLUSIONS According to the current study; menopause and diabetes mellitus are strong risk factors for the presence of concomitant endometrial polyps and endometrial hyperplasia.