nils-herrmann/clean_scirepeval huggingface dataset:
Femur and humerus length in trisomy 21 fetuses at 11-14 weeks of gestation.

To determine the value of measuring fetal femur and humerus length at 11-14 weeks of gestation in screening for chromosomal defects.



Femur and humerus lengths were measured using transabdominal ultrasound in 1018 fetuses immediately before chorionic villus sampling for karyotyping at 11-14 weeks of gestation. In the group of chromosomally normal fetuses, regression analysis was used to determine the association between long bone length and crown-rump length (CRL). Femur and humerus lengths in fetuses with trisomy 21 were compared with those of normal fetuses.



The median gestation was 12 (range, 11-14) weeks. The karyotype was normal in 920 fetuses and abnormal in 98, including 65 cases of trisomy 21. In the chromosomally normal group the fetal femur and humerus lengths increased significantly with CRL (femur length = - 6.330 + 0.215 x CRL in mm, r = 0.874, P < 0.0001; humerus length = - 6.240 + 0.220 x CRL in mm, r = 0.871, P < 0.0001). In the Bland-Altman plot the mean difference between paired measurements of femur length was 0.21 mm (95% limits of agreement - 0.52 to 0.48 mm) and of humerus length was 0.23 mm (95% limits of agreement - 0.57 to 0.55 mm). In the trisomy 21 fetuses the median femur and humerus lengths were significantly below the appropriate normal mean for CRL by 0.4 and 0.3 mm, respectively (P = 0.002), but they were below the respective 5th centile of the normal range in only six (9.2%) and three (4.6%) of the cases, respectively.



At 11-14 weeks of gestation the femur and humerus lengths in trisomy 21 fetuses are significantly reduced but the degree of deviation from normal is too small for these measurements to be useful in screening for trisomy 21.
Why is the snowflake schema a good data warehouse design?

Database design for data warehouses is based on the notion of the snowflake schema and its important special case, the star schema. The snowflake schema represents a dimensional model which is composed of a central fact table and a set of constituent dimension tables which can be further broken up into subdimension tables. We formalise the concept of a snowflake schema in terms of an acyclic database schema whose join tree satisfies certain structural properties. We then define a normal form for snowflake schemas which captures its intuitive meaning with respect to a set of functional and inclusion dependencies. We show that snowflake schemas in this normal form are independent as well as separable when the relation schemas are pairwise incomparable. This implies that relations in the data warehouse can be updated independently of each other as long as referential integrity is maintained. In addition, we show that a data warehouse in snowflake normal form can be queried by joining the relation over the fact table with the relations over its dimension and subdimension tables. We also examine an information-theoretic interpretation of the snowflake schema and show that the redundancy of the primary key of the fact table is zero. r 2002 Elsevier
Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition

Modern machine learning models are beginning to rival human performance on some realistic object recognition tasks, but we still lack a full understanding of how the human brain solves this same problem. This thesis combines knowledge from machine learning and computational neuroscience to create models of human object recognition that are increasingly realistic both in their treatment of low-level neural mechanisms and in their reproduction of high-level human behaviour. First, I present extensions to the Neural Engineering Framework to make its preferred type of model—the “fixed-encoding” network—more accurate for object recognition tasks. These extensions include better distributions—such as Gabor filters—for the encoding weights, and better loss functions—namely weighted squared loss, softmax loss, and hinge loss—to solve for decoding weights. Second, I introduce increased biological realism into deep convolutional neural networks trained with backpropagation, by training them to run using spiking leaky integrate-andfire (LIF) neurons. Convolutional neural networks have been successful in machine learning, and I am able to convert them to spiking networks while retaining similar levels of performance. I present a novel method to smooth the LIF rate response function in order to avoid the common problems associated with differentiating spiking neurons in general and LIF neurons in particular. I also derive a number of novel characterizations of spiking variability, and use these to train spiking networks to be more robust to this variability. Finally, to address the problems with implementing backpropagation in a biological system, I train spiking deep neural networks using the more biological Feedback Alignment algorithm. I examine this algorithm in depth, including many variations on the core algorithm, methods to train using non-differentiable spiking neurons, and some of the limitations of the algorithm. Using these findings, I construct a spiking model that learns online in a biologically realistic manner. The models developed in this thesis help to explain both how spiking neurons in the brain work together to allow us to recognize complex objects, and how the brain may learn this behaviour. Their spiking nature allows them to be implemented on highly efficient neuromorphic hardware, opening the door to object recognition on energy-limited devices such as cell phones and mobile robots.
Design , Modelling and Simulation of a PID Controller for Buck Boostand Cuk Converter

This paper first presents open loop behavior of buck boost and cuk dc-dc converter operated under Continuous conduction mode (CCM). This is achieved withthe help of state equations and MATLAB/SIMULINK tool for simulation of those state equations. Using the knowledge of open loop converter behavior, a closed loop converter is designed. DC-DC converter will be designed for specific line and load conditions. But in practice there is deviation of the circuit operation from the desired nominal behavior due to changes in the source, load and circuit parameters. So we need to design a proper controller or compensator to overcome this situation of the circuit operation. This paper presents PID controller designed such that any input variations produces a constant output voltage.
LSTM Benchmarks for Deep Learning Frameworks

This study provides benchmarks for different implementations of long short-term memory (LSTM) units between the deep learning frameworks PyTorch, TensorFlow, Lasagne and Keras. The comparison includes cuDNN LSTMs, fused LSTM variants and less optimized, but more flexible LSTM implementations. The benchmarks reflect two typical scenarios for automatic speech recognition, notably continuous speech recognition and isolated digit recognition. These scenarios cover input sequences of fixed and variable length as well as the loss functions Connectionist Temporal Classification (CTC) and cross entropy. Additionally, a comparison between four different PyTorch versions is included. The code is available online https://github.com/stefbraun/rnn_benchmarks.
Applications of self-interference cancellation in 5G and beyond

Self-interference cancellation invalidates a long-held fundamental assumption in wireless network design that radios can only operate in half duplex mode on the same channel. Beyond enabling true in-band full duplex, which effectively doubles spectral efficiency, self-interference cancellation tremendously simplifies spectrum management. Not only does it render entire ecosystems like TD-LTE obsolete, it enables future networks to leverage fragmented spectrum, a pressing global issue that will continue to worsen in 5G networks. Self-interference cancellation offers the potential to complement and sustain the evolution of 5G technologies toward denser heterogeneous networks and can be utilized in wireless communication systems in multiple ways, including increased link capacity, spectrum virtualization, any-division duplexing (ADD), novel relay solutions, and enhanced interference coordination. By virtue of its fundamental nature, self-interference cancellation will have a tremendous impact on 5G networks and beyond.
Shoulder, hip, and knee arthrography needle placement using fluoroscopic guidance: practice patterns of musculoskeletal radiologists in North America

The aim of this study was to evaluate the range of techniques used by radiologists performing shoulder, hip, and knee arthrography using fluoroscopic guidance. Questionnaires on shoulder, hip, and knee arthrography were distributed to radiologists at a national radiology meeting. We enquired regarding years of experience, preferred approaches, needle gauge, gadolinium dilution, and volume injected. For each approach, the radiologist was asked their starting and end needle position based on a numbered and lettered grid superimposed on a radiograph. Sixty-eight questionnaires were returned. Sixty-eight radiologists performed shoulder and hip arthrography, and 65 performed knee arthrograms. Mean experience was 13.5 and 12.8 years, respectively. For magnetic resonance arthrography, a gadolinium dilution of 1/200 was used by 69–71%. For shoulder arthrography, an anterior approach was preferred by 65/68 (96%). The most common site of needle end position, for anterior and posterior approaches, was immediately lateral to the humeral cortex. A 22-gauge needle was used by 46/66 (70%). Mean injected volume was 12.7 ml (5–30). For hip arthrography, an anterior approach was preferred by 51/68 (75%). The most common site of needle end position, for anterior and lateral approaches, was along the lateral femoral head/neck junction. A 22-gauge needle was used by 53/68 (78%). Mean injected volume was 11.5 ml (5–20). For knee arthrography, a lateral approach was preferred by 41/64 (64%). The most common site of needle end position, for lateral and medial approaches, was mid-patellofemoral joint level. A 22-gauge needle was used by 36/65 (56%). Mean injected volume was 28.2 ml (5–60). Arthrographic approaches for the shoulder, hip, and knee vary among radiologists over a wide range of experience levels.
Spatio-Temporal Pricing for Ridesharing Platforms

Ridesharing platforms match drivers and riders to trips, using dynamic prices to balance supply and demand. A challenge is to set prices that are appropriately smooth in space and time, so that drivers will choose to accept their dispatched trips, rather than drive to another area or wait for higher prices or a better trip. We work in a complete information, discrete time, multiperiod, multi-location model, and introduce the Spatio-Temporal Pricing (STP) mechanism. The mechanism is incentive-aligned, in that it is a subgame-perfect equilibrium for drivers to accept their dispatches. The mechanism is also welfare-optimal, envy-free, individually rational, budget balanced and core-selecting from any history onward. The proof of incentive alignment makes use of the M ♮ concavity of min-cost flow objectives. We also give an impossibility result, that there can be no dominant-strategy mechanism with the same economic properties. An empirical analysis conducted in simulation suggests that the STP mechanism can achieve significantly higher social welfare than a myopic pricing mechanism.
Cognitive control and brain resources in major depression: an fMRI study using the n-back task.

Several neuroimaging studies have reported 'hypofrontality' in depressed patients performing a cognitive challenge compared to control subjects. Hypofrontality in depression is likely associated with an impaired behavioral performance. It is unclear whether this impaired performance is the consequence or the cause of hypofrontality. Consequently, we proposed to compare the cerebral activity of depressed patients and healthy subjects while controlling for the level of performance. Ten individuals meeting DSM-IV criteria for Major Depression and 10 healthy controls were tested with a verbal version of the n-back task during fMRI scanning. The working memory load was manipulated across the experiment (1,2,3-back) to increase the cognitive demands. fMRI data were acquired on a 1.5-T GE scanner and analyzed using SPM99 software. We did not find any difference between groups in both performance and reaction times for each level of complexity of the n-back task. Depressed patients and control subjects showed bilateral activation of the lateral prefrontal cortex, anterior cingulate and parietal cortex. Activation of these regions was modulated by the complexity of the task. Within this n-back neural network, depressed patients showed greater activation of the lateral prefrontal cortex and the anterior cingulate compared to healthy subjects. This study provides evidence that depressed patients need greater activation within the same neural network to maintain a similar level of performance as controls during a working memory task. Our findings suggest that depression may impair the cognitive capacity of depressed patients by recruiting more brain resources than controls during cognitive control.
Quantum nonlocality without entanglement

and William K. Wootters IBM Research Division, T. J. Watson Research Center, Yorktown Heights, New York 10598 Norman Bridge Laboratory of Physics 12-33, California Institute of Technology, Pasadena, California 91125 Département d’Informatique et de Recherche Ope ́rationelle, Succursale Centre-Ville, Montre ́al, Canada H3C 3J7 AT&T Shannon Laboratory, 180 Park Avenue, Building 103, Florham Park, New Jersey 07932 Physics Department, Williams College, Williamstown, Massachusetts 01267 ~Received 17 June 1998 !
Initial Orienting Towards Sexually Relevant Stimuli: Preliminary Evidence from Eye Movement Measures

It has been proposed that sexual stimuli will be processed in a comparable manner to other evolutionarily meaningful stimuli (such as spiders or snakes) and therefore elicit an attentional bias and more attentional engagement (Spiering and Everaerd, In E. Janssen (Ed.), The psychophysiology of sex (pp. 166-183).  2007). To investigate early and late attentional processes while looking at sexual stimuli, heterosexual men (n = 12) viewed pairs of sexually preferred (images of women) and sexually non-preferred images (images of girls, boys or men), while eye movements were measured. Early attentional processing (initial orienting) was assessed by the number of first fixations and late attentional processing (maintenance of attention) was assessed by relative fixation time. Results showed that relative fixation time was significantly longer for sexually preferred stimuli than for sexually non-preferred stimuli. Furthermore, the first fixation was more often directed towards the preferred sexual stimulus, when simultaneously presented with a non-sexually preferred stimulus. Thus, the current study showed for the first time an attentional bias to sexually relevant stimuli when presented simultaneously with sexually irrelevant pictures. This finding, along with the discovery that heterosexual men maintained their attention to sexually relevant stimuli, highlights the importance of investigating early and late attentional processes while viewing sexual stimuli. Furthermore, the current study showed that sexually relevant stimuli are favored by the human attentional system.
ROBUST ADAPTIVE BEAMFORMER USING INTERPO- LATION TECHNIQUE FOR CONFORMAL ANTENNA ARRAY

A novel robust adaptive beamforming method for conformal array is proposed. By using interpolation technique, the cylindrical conformal array with directional antenna elements is transformed to a virtual uniform linear array with omni-directional elements. This method can compensate the amplitude and mutual coupling errors as well as desired signal point errors of the conformal array efficiently. It is a universal method and can be applied to other curved conformal arrays. After the transformation, most of the existing adaptive beamforming algorithms can be applied to conformal array directly. The efficiency of the proposed scheme is assessed through numerical simulations.
BubbleRank: Safe Online Learning to Rerank

We study the problem of online learning to re-rank, where users provide feedback to improve the quality of displayed lists. Learning to rank has been traditionally studied in two settings. In the offline setting, rankers are typically learned from relevance labels of judges. These approaches have become the industry standard. However, they lack exploration, and thus are limited by the information content of offline data. In the online setting, an algorithm can propose a list and learn from the feedback on it in a sequential fashion. Bandit algorithms developed for this setting actively experiment, and in this way overcome the biases of offline data. But they also tend to ignore offline data, which results in a high initial cost of exploration. We propose BubbleRank, a bandit algorithm for re-ranking that combines the strengths of both settings. The algorithm starts with an initial base list and improves it gradually by swapping higher-ranked less attractive items for lower-ranked more attractive items. We prove an upper bound on the n-step regret of BubbleRank that degrades gracefully with the quality of the initial base list. Our theoretical findings are supported by extensive numerical experiments on a large real-world click dataset.
Myomectomy during pregnancy: a feasible option.

Myomectomy is rarely performed during an ongoing pregnancy because of fear of pregnancy loss and the risk of uncontrolled hemorrhage necessitating hysterectomy. Even now, few cases are performed during pregnancy and most cases have been performed at the time of Cesarean section. A case of successful myomectomy performed in the second trimester for a large subserous fibroid, weighing 3900 g and causing severe pressure symptoms and subacute intestinal obstruction, is described. The pregnancy continued uneventfully until term.
Low-Profile Planar Filtering Dipole Antenna With Omnidirectional Radiation Pattern

This paper presents a low-profile planar dipole antenna with omnidirectional radiation pattern and filtering response. The proposed antenna consists of a microstrip-to-slotline transition structure as the feeding network and a planar dipole as the radiator. Filtering response is obtained by adding nonradiative elements, including a coupled U-shaped microstrip line and two I-shaped slots, to the feeding network. Within the operating passband, the added nonradiative elements do not work, and thus the in-band radiation performance of the dipole antenna is nearly not affected. However, at the side stopbands, the added elements resonate and prevent the signal passing through the feeding network to the dipole antenna, suppressing the out-of-band radiation significantly. As a result, both satisfactory filtering and radiation performances are obtained. For demonstration, an omnidirectional filtering dipole antenna is implemented. Single-band bandpass filtering responses in both the reflection coefficient and realized gain are obtained. The measured in-band gain is ~2.5 dBi, whereas the out-of-band radiation suppression is more than 15 dB.
2D Barcode Image Decoding

Barcode technology is one of the most important parts of Automatic Identification and Data Capture (AIDC). Quick Response code (QR code) is one of the most popular types of two-dimensional barcodes. How to decode various QR code images efficiently and accurately is a challenge. In this paper, we revise the traditional decoding procedure by proposing a serial of carefully designed preprocessing methods. The decoding procedure consists of image binarization, QR code extraction, perspective transformation and resampling, and error correction. By these steps, we can recognize different types of QR code images. The experiment results show that our method has better accuracy than Google open-source 1D/2D barcode image processing library Zxing-2.1. Moreover, we evaluate the execution time for different-size images. Our method can decode these images in real time.
Noninvasive blood pressure (NIBP) measurement by oscillometric principle

This paper describes the development of a noninvasive blood pressure measurement (NIBP) device based on the oscillometric principle. The device is composed of an arm cuff, an air-pumping motor, a solenoid valve, a pressure transducer, and a 2×16 characters LCD display module and a microcontroller which acts as the central controller and processor for the hardware. In the development stage, an auxiliary instrumentation for signal acquisition and digital signal processing using LabVIEW, which is also known as virtual instrument (VI), is incorporated for learning and experimentation purpose. Since the most problematic part of metrological evaluation of an oscillometric NIBP system is in the proprietary algorithms of determining systolic blood pressure (SBP) and diastolic blood pressure (DBP), the amplitude algorithm is used. The VI is a useful tool for studying data acquisition and signal processing to determine SBP and DBP from the maximum of the oscillations envelope. The knowledge from VI procedures is then adopted into a stand alone NIBP device. SBP and DBP are successfully obtained using the circuit developed for the NIBP device. The work done is a proof of design concept that requires further refinement.
A survey of data mining technology on electronic medical records

Medical institutes use Electronic Medical Record (EMR) to record a series of medical events, including diagnostic information (diagnosis codes), procedures performed (procedure codes) and admission details. Plenty of data mining technologies are applied in the EMR data set for knowledge discovery, which is precious to medical practice. The knowledge found is conducive to develop treatment plans, improve health care and reduce medical expenses, moreover, it could also provide further assistance to predict and control outbreaks of epidemic disease. The growing social value it creates has made it a hot spot for experts and scholars. In this paper, we will summarize the research status of data mining technologies on EMR, and analyze the challenges that EMR research is confronting currently.
Pros and cons of apomorphine and L-dopa continuous infusion in advanced Parkinson's disease.

Motor fluctuations and dyskinesia are common in advanced Parkinson's disease and can be poorly managed by current oral medications. Risk factors include the amount of L-dopa administered, gender and patient age. Continuous duodenal L-dopa or subcutaneous apomorphine infusions are helpful strategies because they can control motor complications by providing continuous dopaminergic drug delivery. Apomorphine subcutaneous infusion provides a motor benefit similar to that of dopamine and is relatively easy to use in advanced PD. However, it commonly requires concomitant administration of oral L-dopa and its long-term use is limited by compliance. Continuous administration of L-dopa/carbidopa by infusion in the duodenum/jejunum is a more complex procedure requiring a gastrostomy for the placement of the infusion tube, but it allows replacement of all oral medications and the achievement of a satisfactory therapeutic response paralleled by a reduction of motor complication severity. It should be noted that although these procedures are effective, most evidence relates to small case series and, particularly in the case of apomorphine, despite its long-term availability, there is a complete lack of randomized blinded studies. In addition, unlike deep brain stimulation, it is unclear which patients are the best candidates for these procedures, making any indirect comparison very complex, given the clinical heterogeneity of reported patients. This has consequences in resource allocation and in estimating cost-benefit ratios for these complex therapies in advanced PD.