nils-herrmann/clean_scirepeval huggingface dataset:
Growth rates of ovarian follicles during natural menstrual cycles, oral contraception cycles, and ovarian stimulation cycles.

To compare growth rates of ovarian follicles during natural menstrual cycles, oral contraception (OC) cycles, and ovarian stimulation cycles using standardized techniques.



Prospective, comparative, observational, longitudinal study.



Healthy volunteers in research trials and infertility patients undergoing treatment at an academic institution.



Women were evaluated during natural cycles (n = 50), OC cycles (n = 71), and ovarian stimulation cycles (n = 131).



Serial transvaginal ultrasonography was performed to measure follicle diameter. Day-to-day growth and regression profiles of individual follicles were determined. Mean growth rates were calculated for ovulatory follicles. Mean growth and regression rates were calculated for anovulatory follicles.



Follicle growth rate (in millimeters per day).


RESULT(S)
Mean follicular growth rate was greater during ovarian stimulation cycles (1.69 +/- 0.03 mm/day) compared to natural (1.42 +/- 0.05 mm/day) and OC cycles (1.36 +/- 0.08 mm/day). The interval from dominant follicle selection to ovulation was shorter during stimulation cycles (5.08 +/- 0.07 days) compared to natural cycles (7.16 +/- 0.23 days).


CONCLUSION(S)
Follicles grew faster during ovarian stimulation therapy compared to natural cycles or OC cycles. Greater follicular growth rates in stimulation cycles were associated with shorter intervals from selection to ovulation. The biologic effects of increased follicular growth rates and shorter intervals to ovulation on oocyte competence in women undergoing assisted reproduction remain to be determined.
Timely Rerandomization for Mitigating Memory Disclosures

Address Space Layout Randomization (ASLR) can increase the cost of exploiting memory corruption vulnerabilities. One major weakness of ASLR is that it assumes the secrecy of memory addresses and is thus ineffective in the face of memory disclosure vulnerabilities. Even fine-grained variants of ASLR are shown to be ineffective against memory disclosures. In this paper we present an approach that synchronizes randomization with potential runtime disclosure. By applying rerandomization to the memory layout of a process every time it generates an output, our approach renders disclosures stale by the time they can be used by attackers to hijack control flow. We have developed a fully functioning prototype for x86_64 C programs by extending the Linux kernel, GCC, and the libc dynamic linker. The prototype operates on C source code and recompiles programs with a set of augmented information required to track pointer locations and support runtime rerandomization. Using this augmented information we dynamically relocate code segments and update code pointer values during runtime. Our evaluation on the SPEC CPU2006 benchmark, along with other applications, show that our technique incurs a very low performance overhead (2.1% on average).
Abu-MaTran at WMT 2016 Translation Task: Deep Learning, Morphological Segmentation and Tuning on Character Sequences

This paper presents the systems submitted by the Abu-MaTran project to the Englishto-Finnish language pair at the WMT 2016 news translation task. We applied morphological segmentation and deep learning in order to address (i) the data scarcity problem caused by the lack of in-domain parallel data in the constrained task and (ii) the complex morphology of Finnish. We submitted a neural machine translation system, a statistical machine translation system reranked with a neural language model and the combination of their outputs tuned on character sequences. The combination and the neural system were ranked first and second respectively according to automatic evaluation metrics and tied for the first place in the human evaluation.
Clinical care of patients with amyotrophic lateral sclerosis

Although amyotrophic lateral sclerosis and its variants are readily recognised by neurologists, about 10% of patients are misdiagnosed, and delays in diagnosis are common. Prompt diagnosis, sensitive communication of the diagnosis, the involvement of the patient and their family, and a positive care plan are prerequisites for good clinical management. A multidisciplinary, palliative approach can prolong survival and maintain quality of life. Treatment with riluzole improves survival but has a marginal effect on the rate of functional deterioration, whereas non-invasive ventilation prolongs survival and improves or maintains quality of life. In this Review, we discuss the diagnosis, management, and how to cope with impaired function and end of life on the basis of our experience, the opinions of experts, existing guidelines, and clinical trials. We highlight the need for research on the effectiveness of gastrostomy, access to non-invasive ventilation and palliative care, communication between the care team, the patient and his or her family, and recognition of the clinical and social effects of cognitive impairment. We recommend that the plethora of evidence-based guidelines should be compiled into an internationally agreed guideline of best practice.
LPM: lightweight progressive meshes towards smooth transmission of Web3D media over internet

Transmission of Web3D media over the Internet can be slow, especially when downloading huge 3D models through relatively limited bandwidth. Currently, 3D compression and progressive meshes are used to alleviate the problem, but these schemes do not consider similarity among the 3D components, leaving rooms for improvement in terms of efficiency. This paper proposes a similarity-aware 3D model reduction method, called Lightweight Progressive Meshes (LPM). The key idea of LPM is to search similar components in a 3D model, and reuse them through the construction of a Lightweight Scene Graph (LSG). The proposed LPM offers three significant benefits. First, the size of 3D models can be reduced for transmission without almost any precision loss of the original models. Second, when rendering, decompression is not needed to restore the original model, and instanced rendering can be fully exploited. Third, it is extremely efficient under very limited bandwidth, especially when transmitting large 3D scenes. Performance on real data justifies the effectiveness of our LPM, which improves the state-of-the-art in Web3D media transmission.
Design of a CMOS cross-coupled voltage doubler

This paper describes a design procedure for a CMOS voltage doubler. Test-bench circuit are used to verify the performance of the design. Several equations that relate performance parameters with design variables are presented. This set of equations considers both transient and steady state behavior. Various known energy losses such as switching and conduction losses were taken into account for transistors sizing. The effects of the characteristics of the pump capacitors are analyzed and evaluated through electrical simulations. A design example based on AMS 0.35μm process is presented.
A hierarchical Bayesian model of invariant pattern recognition in the visual cortex

We describe a hierarchical model of invariant visual pattern recognition in the visual cortex. In this model, the knowledge of how patterns change when objects move is learned and encapsulated in terms of high probability sequences at each level of the hierarchy. Configuration of object parts is captured by the patterns of coincident high probability sequences. This knowledge is then encoded in a highly efficient Bayesian network structure. The learning algorithm uses a temporal stability criterion to discover object concepts and movement patterns. We show that the architecture and algorithms are biologically plausible. The large scale architecture of the system matches the large scale organization of the cortex and the micro-circuits derived from the local computations match the anatomical data on cortical circuits. The system exhibits invariance across a wide variety of transformations and is robust in the presence of noise. Moreover, the model also offers alternative explanations for various known cortical phenomena.
Systematic Characterizations of Text Similarity in Full Text Biomedical Publications

Computational methods have been used to find duplicate biomedical publications in MEDLINE. Full text articles are becoming increasingly available, yet the similarities among them have not been systematically studied. Here, we quantitatively investigated the full text similarity of biomedical publications in PubMed Central.



72,011 full text articles from PubMed Central (PMC) were parsed to generate three different datasets: full texts, sections, and paragraphs. Text similarity comparisons were performed on these datasets using the text similarity algorithm eTBLAST. We measured the frequency of similar text pairs and compared it among different datasets. We found that high abstract similarity can be used to predict high full text similarity with a specificity of 20.1% (95% CI [17.3%, 23.1%]) and sensitivity of 99.999%. Abstract similarity and full text similarity have a moderate correlation (Pearson correlation coefficient: -0.423) when the similarity ratio is above 0.4. Among pairs of articles in PMC, method sections are found to be the most repetitive (frequency of similar pairs, methods: 0.029, introduction: 0.0076, results: 0.0043). In contrast, among a set of manually verified duplicate articles, results are the most repetitive sections (frequency of similar pairs, results: 0.94, methods: 0.89, introduction: 0.82). Repetition of introduction and methods sections is more likely to be committed by the same authors (odds of a highly similar pair having at least one shared author,  2.31, methods: 1.83, results: 1.03). There is also significantly more similarity in pairs of review articles than in pairs containing one review and one nonreview paper (frequency of similar pairs: 0.0167 and 0.0023, respectively).



While quantifying abstract similarity is an effective approach for finding duplicate citations, a comprehensive full text analysis is necessary to uncover all potential duplicate citations in the scientific literature and is helpful when establishing ethical guidelines for scientific publications.
The Automated Learning of Deep Features for Breast Mass Classification from Mammograms

The classification of breast masses from mammograms into benign or malignant has been commonly addressed with machine learning classifiers that use as input a large set of hand-crafted features, usually based on general geometrical and texture information. In this paper, we propose a novel deep learning method that automatically learns features based directly on the optmisation of breast mass classification from mammograms, where we target an improved classification performance compared to the approach described above. The novelty of our approach lies in the two-step training process that involves a pre-training based on the learning of a regressor that estimates the values of a large set of handcrafted features, followed by a fine-tuning stage that learns the breast mass classifier. Using the publicly available INbreast dataset, we show that the proposed method produces better classification results, compared with the machine learning model using hand-crafted features and with deep learning method trained directly for the classification stage without the pre-training stage. We also show that the proposed method produces the current state-of-the-art breast mass classification results for the INbreast dataset. Finally, we integrate the proposed classifier into a fully automated breast mass detection and segmentation, which shows promising results.
Network Service Chaining in Fog and Cloud Computing for the 5G Environment: Data Management and Security Challenges

In the last few years, we have seen an exponential increase in the number of Internet-enabled devices, which has resulted in popularity of fog and cloud computing among end users. End users expect high data rates coupled with secure data access for various applications executed either at the edge (fog computing) or in the core network (cloud computing). However, the bidirectional data flow between the end users and the devices located at either the edge or core may cause congestion at the cloud data centers, which are used mainly for data storage and data analytics. The high mobility of devices (e.g., vehicles) may also pose additional challenges with respect to data availability and processing at the core data centers. Hence, there is a need to have most of the resources available at the edge of the network to ensure the smooth execution of end-user applications. Considering the challenges of future user demands, we present an architecture that integrates cloud and fog computing in the 5G environment that works in collaboration with the advanced technologies such as SDN and NFV with the NSC model. The NSC service model helps to automate the virtual resources by chaining in a series for fast computing in both computing technologies. The proposed architecture also supports data analytics and management with respect to device mobility. Moreover, we also compare the core and edge computing with respect to the type of hypervisors, virtualization, security, and node heterogeneity. By focusing on nodes' heterogeneity at the edge or core in the 5G environment, we also present security challenges and possible types of attacks on the data shared between different devices in the 5G environment.
Gaussian Mixture Model-Based Ensemble Kalman Filtering for State and Parameter Estimation for a PMMA Process

Polymer processes often contain state variables whose distributions are multimodal; in addition, the models for these processes are often complex and nonlinear with uncertain parameters. This presents a challenge for Kalman-based state estimators such as the ensemble Kalman filter. We develop an estimator based on a Gaussian mixture model (GMM) coupled with the ensemble Kalman filter (EnKF) specifically for estimation with multimodal state distributions. The expectation maximization algorithm is used for clustering in the Gaussian mixture model. The performance of the GMM-based EnKF is compared to that of the EnKF and the particle filter (PF) through simulations of a polymethyl methacrylate process, and it is seen that it clearly outperforms the other estimators both in state and parameter estimation. While the PF is also able to handle nonlinearity and multimodality, its lack of robustness to model-plant mismatch affects its performance significantly.
Nasal valves--importance and surgical procedures.

One of the most difficult aspects in rhinoplasty is resolving and preventing functional compromise of the nasal valve area reliably. The nasal valves are crucial for the individual breathing competence of the nose. Structural and functional elements contribute to this complex system: the nasolabial angle, the configuration and stability of the alae, the function of the internal nasal valve, the anterior septum symmetrically separating the bilateral airways and giving structural and functional support to the alar cartilage complex and to their junction with the upper lateral cartilages, the scroll area. Subsequently, the open angle between septum and sidewalls is important for sufficient airflow as well as the position and function of the head of the turbinates. The clinical examination of these elements is described. Surgical techniques are more or less well known and demonstrated with patient examples and drawings: anterior septoplasty, reconstruction of tip and dorsum support by septal extension grafts and septal replacement, tip suspension and lateral crural sliding technique, spreader grafts and suture techniques, splay grafts, alar batten grafts, lateral crural extension grafts, and lateral alar suspension. The numerous literature is reviewed.
HAL- The Missing Piece of the Puzzle for Hardware Reverse Engineering, Trojan Detection and Insertion

Hardware manipulations pose a serious threat to numerous systems, ranging from a myriad of smart-X devices to military systems. In many attack scenarios an adversary merely has access to the low-level, potentially obfuscated gate-level netlist. In general, the attacker possesses minimal information and faces the costly and time-consuming task of reverse engineering the design to identify security-critical circuitry, followed by the insertion of a meaningful hardware Trojan. These challenges have been considered only in passing by the research community. The contribution of this work is threefold: First, we present HAL, a comprehensive reverse engineering and manipulation framework for gate-level netlists. HAL allows automating defensive design analysis (e.g., including arbitrary Trojan detection algorithms with minimal effort) as well as offensive reverse engineering and targeted logic insertion. Second, we present a novel static analysis Trojan detection technique ANGEL which considerably reduces the false-positive detection rate of the detection technique FANCI. Furthermore, we demonstrate that ANGEL is capable of automatically detecting Trojans obfuscated with DeTrust. Third, we demonstrate how a malicious party can semi-automatically inject hardware Trojans into third-party designs. We present reverse engineering algorithms to disarm and trick cryptographic self-tests, and subtly leak cryptographic keys without any a priori knowledge of the design’s internal workings.
The Assessment of SpondyloArthritis international Society (ASAS) handbook: a guide to assess spondyloarthritis.

The field of spondyloarthritis (SpA) has experienced major progress in the last decade, especially with regard to new treatments, earlier diagnosis, imaging technology and a better definition of outcome parameters for clinical trials. In the present work, the Assessment in SpondyloArthritis international Society (ASAS) provides a comprehensive handbook on the most relevant aspects for the assessments of spondyloarthritis, covering classification criteria, MRI and x rays for sacroiliac joints and the spine, a complete set of all measurements relevant for clinical trials and international recommendations for the management of SpA. The handbook focuses at this time on axial SpA, with ankylosing spondylitis (AS) being the prototype disease, for which recent progress has been faster than in peripheral SpA. The target audience includes rheumatologists, trial methodologists and any doctor and/or medical student interested in SpA. The focus of this handbook is on practicality, with many examples of MRI and x ray images, which will help to standardise not only patient care but also the design of clinical studies.
Development of a management system with RFID and QR code for matching and breeding in Taiwan pig farm

The outbreak of swine diseases in Taiwan during the past years has caused a large number of meat from sick pigs to reach the market. This greatly affected the willingness of consumers to purchase pork. In order to enhance food safety and sustain the income of pig farmers, the Council of Agriculture recommended that the pig's production history be presented to the public so that product information could be made transparent. At present, most of Taiwan's pig farmers still use handwritten cards for administrative work. New record cards are filled out for each batch of swine and collected as a booklet for management. For some pig farm, this management approach requires very high labor costs with regard to vaccination and production management and lacks management efficiency as well. Therefore, this study established a management system for (1) pig matching, (2) vaccination, (3) production date, and (4) production capacity that combines radiofrequency identification (RFID) and Quick Response (QR) code technology with electronic database management system for stock people to keep track of the farm situation at all times. Results of actual test in pig farms showed that aside from effectively improving the efficiency of breeding and matching, this system can also significantly improve and upgrade administration of vaccines and efficiency of breeding.
Predictive role of prenasal thickness and nasal bone for Down syndrome in the second trimester.

To assess the efficacy of prenasal thickness (PNT) and nasal bone (NB) for prediction of Down syndrome (DS) fetuses in the second trimester ultrasound examination.



PNT was measured from stored two-dimensional fetal profile images taken at 15-23 weeks in 242 fetuses with normal karyotype (Group 1) and 24 fetuses with DS (Group 2). It was measured as the shortest distance from the anterior edge of the lowest part of the frontal bone to the skin. The efficacy of PNT, NB, PNT/NB and biparietal diameter (BPD)/NB was evaluated for prediction of DS.



PNT values increased with gestational age in normal fetuses. PNT measurement was ≥95th percentile in 54.2% (13/24) of the DS cases and 2.9% of the normal cases. Receiver operator curve analysis showed that PNT/NB ratio had the best area under the curve with a detection rate of 80% for a false positive rate of 5% at a cut-off value of 0.76.



PNT is increased in fetuses with DS as compared to normal fetuses. PNT/NB≥0.76 in the second trimester is a better predictor of DS than the use of PNT or NB alone.
Logic Synthesis of Ternary Quantum Circuits with Minimal Qutrits

This paper investigates a universal approach of synthesizing arbitrary ternary logic circuits in quantum computation based on the truth table technology. It takes into account of the relationship of classical logic and quantum logic circuits. By adding inputs with constant value and garbage outputs, the classical non-reversible logic can be transformed into reversible logic. Combined with group theory, it provides an algorithm using the ternary Swap gate, ternary NOT gate and ternary Toffoli gate library. Simultaneously, the main result shows that the numbers of qutrits we use are minimal compared to other methods. We also illustrate with two examples to test our approach.
LTE/LTE-A jamming, spoofing, and sniffing: threat assessment and mitigation

LTE is currently being proposed for use in a nationwide wireless broadband public safety network in the United States as well as for other critical applications where reliable communication is essential for safety. Unfortunately, like any wireless technology, disruption of these networks is possible through radio jamming. This article investigates the extent to which LTE is vulnerable to RF jamming, spoofing, and sniffing, and assesses different physical layer threats that could affect next-generation critical communication networks. In addition, we examine how sniffing the LTE broadcast messages can aid an adversary in an attack. The weakest links of LTE are identified and used to establish an overall threat assessment. Lastly, we provide a survey of LTE jamming and spoofing mitigation techniques that have been proposed in the open literature.