nils-herrmann/clean_scirepeval huggingface dataset:
Primary High-Grade Osteosarcoma of the Clivus: A Case Report and Literature Review.

Osteosarcoma is the second most common primary tumor of the skeletal system and the most common primary bone tumor. Usually occurring at the metaphysis of long bones, osteosarcomas are highly aggressive lesions that comprise osteoid-producing spindle cells. Craniofacial osteosarcomas comprise <8% and are believed to be less aggressive and lower grade. Primary osteosarcomas of the skull and skull base comprise <2% of all skull tumors. Osteosarcomas originating from the clivus are rare. We present a case of a primar, high-grade clival osteosarcoma.



A 29-year-old man presented to our institution with a progressively worsening right frontal headache for 3 weeks. There were no sensory or cranial nerve deficits. Computed tomography revealed a destructive mass involving the clivus with extension into the left sphenoid sinus. Magnetic resonance imaging revealed a homogenously enhancing lesion measuring 2.7 × 2.5 × 3.2 cm. The patient underwent endonasal transphenoidal surgery for gross total resection. The histopathologic analysis revealed proliferation of malignant-appearing spindled and epithelioid cells with associated osteoclast-like giant cells and a small area of osteoid production. The analysis was consistent with high-grade osteosarcoma. The patient did well and was discharged on postoperative day 2. He was referred for adjuvant radiation therapy and chemotherapy. Two-year follow-up showed postoperative changes and clival expansion caused by packing material.



Osteosarcoma is a highly malignant neoplasm. These lesions are usually found in the extremities; however, they may rarely present in the craniofacial region. Clival osteosarcomas are relatively infrequent. We present a case of a primary clival osteosarcoma with high-grade pathology.
Using Examples for Teaching Software Design: An Experiment Using a Repository of UML Class Diagrams

This research is positioned in the field of methods for creating software design and the teaching thereof.  The goal of this research is to study the effects of using a collection of examples for creating a software design.  We ran a controlled experiment for evaluating the use of a broad collection of examples for creating software designs by software engineering students. In this study, we focus on software designs as represented through UML class diagrams. The treatment is the use of the collection of examples. These examples are offered via a searchable repository. The outcome variable we study is the quality of the design (as assessed by a group of experts). After this, all students were offered the opportunity to improve their design using the collection of examples. We ran a post-assignment questionnaire to collect qualitative data about the experience of the participants.  Considering six quality attributes measured by experts, our results show that: 1) the models of the students who used examples are 18% better than those of who did not use examples. 2) the models of the students who did not use examples for constructing became 19% better after updating their models using examples. We complement our statistical analysis with insights from the post assignment questionnaire. Also, we observed that students are more confident about their design when they use examples.  Students deliver better software designs when they use a collection of example software designs.
Mechanisms of Learning and Memory

The nature of the cellular basis of learning and memory remains an often-discussed, but elusive problem in neurobiology. A popular model for the physiological mechanisms underlying learning and memory postulates that memories are stored by alterations in the strength of neuronal connections within the appropriate neural circuitry. Thus, an understanding of the cellular and molecular basis of synaptic plasticity will expand our knowledge of the molecular basis of learning and memory. The view that learning was the result of altered synaptic weights was first proposed by Ramon y Cajal in 1911 and formalized by Donald O. Hebb. In 1949, Hebb proposed his " learning rule, " which suggested that alterations in the strength of synapses would occur between two neurons when those neurons were active simultaneously (1). Hebb's original postulate focused on the need for synaptic activity to lead to the generation of action potentials in the postsynaptic neuron, although more recent work has extended this to include local depolarization at the synapse. One problem with testing this hypothesis is that it has been difficult to record directly the activity of single synapses in a behaving animal. Thus, the challenge in the field has been to relate changes in synaptic efficacy to specific behavioral instances of associative learning. In this chapter, we will review the relationship among synaptic plasticity, learning, and memory. We will examine the extent to which various current models of neuronal plasticity provide potential bases for memory storage and we will explore some of the signal transduction pathways that are critically important for long-term memory storage. We will focus on two systems—the gill and siphon withdrawal reflex of the invertebrate Aplysia californica and the mammalian hippocam-pus—and discuss the abilities of models of synaptic plasticity and learning to account for a range of genetic, pharmacological, and behavioral data.
Outcomes after scarf osteotomy for treatment of adult hallux valgus deformity.

Many procedures have been developed to correct hallux valgus deformity using distal soft tissue realignment, metatarsal osteotomy, and periodically, a proximal phalanx osteotomy (Akin). The ideal metatarsal osteotomy allows for varying degrees of correction with reliable improvement in deformity and patient satisfaction.



We evaluated the results after scarf osteotomy with respect to American Orthopaedic Foot and Ankle Society (AOFAS) scores, patient satisfaction, radiographic results, and complications.



We evaluated 29 patients (34 feet) during an 18-month period who underwent a unilateral scarf osteotomy combined with distal soft tissue realignment. Preoperative and postoperative AOFAS scores, patient satisfaction, and radiographic data were used to evaluate the effectiveness of the procedure. Complications were recorded. Minimal followup was 12 months (average, 26.4 months; range, 12-48 months).



The mean AOFAS scores improved from 61.5 to 90.3. At final followup, 94% of patients were satisfied with the surgery. The hallux valgus angle improved from 34.6° to 14.9° and the intermetatarsal angle improved from 15.8° to 7.2° postoperatively. A combined Akin osteotomy was performed in only four cases. Complications included superficial wound infection (one), recurrence (two), and troughing (three).



Our results suggest the scarf osteotomy produces improved AOFAS scores, high percentage of patient satisfaction, and effective correction of hallux valgus deformities. Using our scarf technique of rotation combined with translation minimizes the need for an Akin osteotomy while still obtaining good correction and avoids associated complications described in the literature.
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory

Binarization is an extreme network compression approach that provides large computational speedups along with energy and memory savings, albeit at significant accuracy costs. We investigate the question of where to binarize inputs at layer-level granularity and show that selectively binarizing the inputs to specific layers in the network could lead to significant improvements in accuracy while preserving most of the advantages of binarization. We analyze the binarization tradeoff using a metric that jointly models the input binarization-error and computational cost and introduce an efficient algorithm to select layers whose inputs are to be binarized. Practical guidelines based on insights obtained from applying the algorithm to a variety of models are discussed. Experiments on Imagenet dataset using AlexNet and ResNet-18 models show 3-4% improvements in accuracy over fully binarized networks with minimal impact on compression and computational speed. The improvements are even more substantial on sketch datasets like TU-Berlin, where we match state-of-the-art accuracy as well, getting over 8% increase in accuracies. We further show that our approach can be applied in tandem with other forms of compression that deal with individual layers or overall model compression (e.g., SqueezeNets). Unlike previous quantization approaches, we are able to binarize the weights in the last layers of a network, which often have a large number of parameters, resulting in significant improvement in accuracy over fully binarized models.
The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs)

The growth of the Internet has enabled the popularity of open online learning platforms to increase over the years. This has led to the inception of Massive Open Online Courses (MOOCs) that globally enrol millions of people. Such courses operate under the concept of open learning, where content does not have to be delivered via standard mechanisms that institutions employ, such as physically attending lectures. Instead learning occurs online via recorded lecture material and online tasks. This shift has allowed more people to gain access to education, regardless of their learning background. However, despite these advancements, completion rates for MOOCs are low. The paper presents our approach to learner predication in MOOCs by exploring the impact that technology has on open learning and identifies how data about student performance can be captured to predict trend so that at risk students can be identified before they drop-out. The study we have undertaken uses the eRegister system, which has been developed to capture and analyze data. The results indicate that high/active engagement, interaction and attendance is reflective of higher marks. Additonally, our approach is able to normalize the data into consistent a series so that the end result can be transformed into a dashboard of statistics that can be used by organizers of the MOOC. Based on this, we conclude that there is a fundamental need for predictive systems within learning communities.
A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

The advancement of visual sensing has introduced better capturing of the discrete information from a complex, crowded scene for assisting in the analysis. However, after reviewing existing system, we find that majority of the work carried out till date is associated with significant problems in modeling event detection as well as reviewing abnormality of the given scene. Therefore, the proposed system introduces a model that is capable of identifying the degree of abnormality for an event captured on the crowded scene using unsupervised training methodology. The proposed system contributes to developing a novel region-wise repository to extract the contextual information about the discrete-event for a given scene. The study outcome shows highly improved the balance between the computational time and overall accuracy as compared to the majority of the standard research work emphasizing on event
O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images

Haze removal or dehazing is a challenging ill-posed problem that has drawn a significant attention in the last few years. Despite this growing interest, the scientific community is still lacking a reference dataset to evaluate objectively and quantitatively the performance of proposed dehazing methods. The few datasets that are currently considered, both for assessment and training of learning-based dehazing techniques, exclusively rely on synthetic hazy images. To address this limitation, we introduce the first outdoor scenes database (named O-HAZE) composed of pairs of real hazy and corresponding haze-free images. In practice, hazy images have been captured in presence of real haze, generated by professional haze machines, and O-HAZE contains 45 different outdoor scenes depicting the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. To illustrate its usefulness, O-HAZE is used to compare a representative set of state-of-the-art dehazing techniques, using traditional image quality metrics such as PSNR, SSIM and CIEDE2000. This reveals the limitations of current techniques, and questions some of their underlying assumptions.
Design and optimization of roof trusses using morphological indicators

0965-9978/$ see front matter 2013  In honour of my friend Zdenek Bittnar, for his 70th birthday. W. Patrick De Wilde. ⇑ Corresponding author.  +32 (0)2 629 29 20; fax: +32 (0)2 629 29 28. E-mail addresses: Lincy.Pyl@vub.ac.be (L. Pyl), csitters@gmail.com (C.W.M. Sitters), de.wilde.willy@gmail.com (W.P. De Wilde). L. Pyl a,⇑, C.W.M. Sitters , W.P. De Wilde a
Defective podocyte insulin signalling through p85-XBP1 promotes ATF6-dependent maladaptive ER-stress response in diabetic nephropathy

Endoplasmic reticulum (ER) stress is associated with diabetic nephropathy (DN), but its pathophysiological relevance and the mechanisms that compromise adaptive ER signalling in podocytes remain unknown. Here we show that nuclear translocation of the transcription factor spliced X-box binding protein-1 (sXBP1) is selectively impaired in DN, inducing activating transcription factor-6 (ATF6) and C/EBP homology protein (CHOP). Podocyte-specific genetic ablation of XBP1 or inducible expression of ATF6 in mice aggravates DN. sXBP1 lies downstream of insulin signalling and attenuating podocyte insulin signalling by genetic ablation of the insulin receptor or the regulatory subunits phosphatidylinositol 3-kinase (PI3K) p85α or p85β impairs sXBP1 nuclear translocation and exacerbates DN. Corroborating our findings from murine DN, the interaction of sXBP1 with p85α and p85β is markedly impaired in the glomerular compartment of human DN. Thus, signalling via the insulin receptor, p85, and XBP1 maintains podocyte homeostasis, while disruption of this pathway impairs podocyte function in DN.
Security in Industrial Ethernet

Ethernet has become increasingly popular in automation, due to its easy integration with other networks but also for its low costs and commercial availability. Characteristics like real-time capabilities required in automation have also been added and now form Industrial Ethernet allowing its deployment in application areas formerly reserved for dedicated fieldbus systems. Due to the vertical integration of office and automation level and the transparency for common Internet protocols security is a major issue although it seems that this fact again has not adequately taken into account in the context of specification of Industrial Ethernet. This article give an overview of security measures and how to protect systems build upon Industrial Ethernet.
A machine learning approach for authorship attribution for Bengali blogs

In this paper we described an authorship attribution system for Bengali blog texts. We have presented a new Bengali blog corpus of 3000 passages written by three authors. Our study proposes a text classification system, based on lexical features such as character bigrams and trigrams, word n-grams (n = 1, 2, 3) and stop words, using four classifiers. We achieve best results (more than 99%) on the held-out dataset using Multi layered Perceptrons (MLP) amongst the four classifiers, which indicates MLP can produce very good results for big data sets and lexical n-gram based features can be the best features for any authorship attribution system.
Impact of Frequent Interruption on Nurses' Patient-Controlled Analgesia Programming Performance

The purpose was to add to the body of knowledge regarding the impact of interruption on acute care nurses' cognitive workload, total task completion times, nurse frustration, and medication administration error while programming a patient-controlled analgesia (PCA) pump.



Data support that the severity of medication administration error increases with the number of interruptions, which is especially critical during the administration of high-risk medications. Bar code technology, interruption-free zones, and medication safety vests have been shown to decrease administration-related errors. However, there are few published data regarding the impact of number of interruptions on nurses' clinical performance during PCA programming.



Nine acute care nurses completed three PCA pump programming tasks in a simulation laboratory. Programming tasks were completed under three conditions where the number of interruptions varied between two, four, and six. Outcome measures included cognitive workload (six NASA Task Load Index [NASA-TLX] subscales), total task completion time (seconds), nurse frustration (NASA-TLX Subscale 6), and PCA medication administration error (incorrect final programming).



Increases in the number of interruptions were associated with significant increases in total task completion time ( p = .003). We also found increases in nurses' cognitive workload, nurse frustration, and PCA pump programming errors, but these increases were not statistically significant.



Complex technology use permeates the acute care nursing practice environment. These results add new knowledge on nurses' clinical performance during PCA pump programming and high-risk medication administration.
A Comparison of Scheduling Latency in Linux, PREEMPT-RT, and LITMUS RT

Scheduling latency under Linux and its principal real-time variant, the PREEMPT RT patch, are typically measured using cyclictest, a tracing tool that treats the kernel as a black box and directly reports scheduling latency. LITMUS, a real-time extension of Linux focused on algorithmic improvements, is typically evaluated using Feather-Trace, a finedgrained tracing mechanism that produces a comprehensive overhead profile suitable for overhead-aware schedulability analysis. This difference in tracing tools and output has to date prevented a direct comparison. This paper reports on a port of cyclictest to LITMUS and a case study comparing scheduling latency on a 16-core Intel platform. The main conclusions are: (i) LITMUS introduces only minor overhead itself, but (ii) it also inherits mainline Linux’s severe limitations in the presence of I/O-bound background tasks.
Lipoatrophia Semicircularis

The medical literature describes lipoatrophia semicircularis as a rare, idiopathic condition, that consists clinically of a semicircular zone of atrophy of the subcutaneous fatty tissue located mostly on the front of the thighs. The disorder is mainly afflicting office workers. Since 1995, we have diagnosed more than 900 cases in our company. Also in other companies (national and international) lipoatrophia semicircularis is diagnosed.
Computation Offloading for Machine Learning Web Apps in the Edge Server Environment

Machine leaning apps require heavy computations, especially with the use of the deep neural network (DNN), so an embedded device with limited hardware cannot run the apps by itself. One solution for this problem is to offload DNN computations from the client to a nearby edge server. Existing approaches to DNN offloading with edge servers either specialize the edge server for fixed, specific apps, or customize the edge server for diverse apps, yet after migrating a large VM image that contains the client's back-end software system. In this paper, we propose a new and simple approach to offload DNN computations in the context of web apps. We migrate the current execution state of a web app from the client to the edge server just before executing a DNN computation, so that the edge server can execute the DNN computation with its powerful hardware. Then, we migrate the new execution state from the edge server to the client so that the client can continue to execute the app. We can save the execution state of the web app in the form of another web app called the snapshot, which immensely simplifies saving and restoring the execution state with a small overhead. We can offload any DNN app to any generic edge server, equipped with a browser and our offloading system. We address some issues related to offloading DNN apps such as how to send the DNN model and how to improve the privacy of user data. We also discuss how to install our offloading system on the edge server on demand. Our experiment with real DNN-based web apps shows that snapshot-based offloading achieves a promising performance result, comparable to running the app entirely on the server.
Androtrace: framework for tracing and analyzing IOs on Android

In this work, we develop IO trace and analysis framework, Androtrace, which is specifically tailored for Android platform. Unlike earlier works that required prolonged post processing procedures, Androtrace not only traces with low overhead, but also provides efficient solution for storage with--in mobile devices. Captured IO trace is temporarily stored in main memory and storage device, and they are transferred to Androtrace server when the device is connected to WiFi. We use server and client model to support and analyze multiple Android users. Using the framework, we find that write IOs are dominant in mobile workload.
Waveform design and imaging method of MIMO ISAR based on orthogonal LFM signal

Orthogonal frequency division multiplexing linear frequency modulation (OFDM-LFM) is widely applied in multiple-input multiple-output (MIMO) radar systems to obtain better orthogonality and range resolution. However, when it is used in MIMO inverse synthetic aperture radar (ISAR) imaging, the frequency interval between each sub-signal will cause different Doppler effects in the target scattering centre echo. Azimuth deviation will occur after azimuth imaging. To eliminate the deviation, orthogonal LFM with different chirp rates are adopted and designed in this paper. The frequency interval is avoided, and the better performance of ISAR images is ensured. With the flow of MIMO ISAR imaging based on orthogonal LFM, simulations and comparisons are conducted to verify the validity of the designed waveform and imaging method.
Harvesting Low-Frequency (<5 Hz) Irregular Mechanical Energy: A Possible Killer Application of Triboelectric Nanogenerator.

Electromagnetic generators (EMGs) and triboelectric nanogenerators (TENGs) are the two most powerful approaches for harvesting ambient mechanical energy, but the effectiveness of each depends on the triggering frequency. Here, after systematically comparing the performances of EMGs and TENGs under low-frequency motion (<5 Hz), we demonstrated that the output performance of EMGs is proportional to the square of the frequency, while that of TENGs is approximately in proportion to the frequency. Therefore, the TENG has a much better performance than that of the EMG at low frequency (typically 0.1-3 Hz). Importantly, the extremely small output voltage of the EMG at low frequency makes it almost inapplicable to drive any electronic unit that requires a certain threshold voltage (∼0.2-4 V), so that most of the harvested energy is wasted. In contrast, a TENG has an output voltage that is usually high enough (>10-100 V) and independent of frequency so that most of the generated power can be effectively used to power the devices. Furthermore, a TENG also has advantages of light weight, low cost, and easy scale up through advanced structure designs. All these merits verify the possible killer application of a TENG for harvesting energy at low frequency from motions such as human motions for powering small electronics and possibly ocean waves for large-scale blue energy.