nils-herrmann/clean_scirepeval huggingface dataset:
Evidence-based management of traumatic thoracolumbar burst fractures: a systematic review of nonoperative management.

OBJECT
The overall evidence for nonoperative management of patients with traumatic thoracolumbar burst fractures is unknown. There is no agreement on the optimal method of conservative treatment. Recent randomized controlled trials that have compared nonoperative to operative treatment of thoracolumbar burst fractures without neurological deficits yielded conflicting results. By assessing the level of evidence on conservative management through validated methodologies, clinicians can assess the availability of critically appraised literature. The purpose of this study was to examine the level of evidence for the use of conservative management in traumatic thoracolumbar burst fractures.



A comprehensive search of the English literature over the past 20 years was conducted using PubMed (MEDLINE). The inclusion criteria consisted of burst fractures resulting from a traumatic mechanism, and fractures of the thoracic or lumbar spine. The exclusion criteria consisted of osteoporotic burst fractures, pathological burst fractures, and fractures located in the cervical spine. Of the studies meeting the inclusion/exclusion criteria, any study in which nonoperative treatment was used was included in this review.



One thousand ninety-eight abstracts were reviewed and 447 papers met inclusion/exclusion criteria, of which 45 were included in this review. In total, there were 2 Level-I, 7 Level-II, 9 Level-III, 25 Level-IV, and 2 Level-V studies. Of the 45 studies, 16 investigated conservative management techniques, 20 studies compared operative to nonoperative treatments, and 9 papers investigated the prognosis of conservative management.



There are 9 high-level studies (Levels I-II) that have investigated the conservative management of traumatic thoracolumbar burst fractures. In neurologically intact patients, there is no superior conservative management technique over another as supported by a high level of evidence. The conservative technique can be based on patient and surgeon preference, comfort, and access to resources. A high level of evidence demonstrated similar functional outcomes with conservative management when compared with open surgical operative management in patients who were neurologically intact. The presence of a neurological deficit is not an absolute contraindication for conservative treatment as supported by a high level of evidence. However, the majority of the literature excluded patients with neurological deficits. More evidence is needed to further classify the appropriate burst fractures for conservative management to decrease variables that may impact the prognosis.
VascuSynth: Simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis

Automated segmentation and analysis of tree-like structures from 3D medical images are important for many medical applications, such as those dealing with blood vasculature or lung airways. However, there is an absence of large databases of expert segmentations and analyses of such 3D medical images, which impedes the validation and training of proposed image analysis algorithms. In this work, we simulate volumetric images of vascular trees and generate the corresponding ground-truth segmentations, bifurcation locations, branch properties, and tree hierarchy. The tree generation is performed by iteratively growing a vascular structure based on a user-defined (possibly spatially varying) oxygen demand map. We describe the details of the algorithm and provide a variety of example results.
Completions, Coordination, and Alignment in Dialogue

Collaborative completions are among the strongest e vidence that dialogue requires coordination even at the sub-sentential level; the study of sen tence completions may thus shed light on a number of central issues both at the `macro’ level of dialogue management and at the `micro’ level of the semantic interpretation of utterances . We propose a treatment of collaborative completions in PTT, a theory of interpretation in dialogue that provi des some of the necessary ingredients for a formal account of completions at the ‘micro’ level, such a theory of incremental utterance interpretation and an account of groundin g. We argue that an account of semantic interpretation in completions can be provided throu gh relatively straightforward generalizations of existing theories of syntax such as Lexical Tree Ad joining Grammar ( LTAG) and of semantics such as (Compositional) DRT and Situation Semantics. At the macro level, we pr ovide an intentional account of completions, as well as a preliminary ac count within Pickering and Garrod’s alignment theory.
Virtual Team Training Model Transactive Memory Systems Virtual Team Training Model

The use of teams, especially virtual teams, is growing significantly in corporations, branches of the government and nonprofit organizations. However, despite this prevalence, little is understood in terms of how to best train these teams for optimal performance. Team training is commonly cited as a factor for increasing team performance, yet, team training is often applied in a haphazard and brash manner, if it is even applied at all. Therefore, this paper attempts to identify the flow of a training model for virtual teams. Rooted in transactive memory systems, this theoretical model combines the science of encoding, storing and retrieving information with the science of team training.
THE GENERALIZED CORRELATION METHODS FOR ESTIMATION OF TIME DELAY WITH APPLICATION TO ELECTROMYOGRAPHY

Muscle fiber conduction velocity is based on the ti me delay estimation between electromyography recording channels. The aims of this study is to id entify the best estimator of generalized correlati on methods in the case where time delay is constant in order to extent these estimator to the time-varyin g delay case . The fractional part of time delay was c lculated by using parabolic interpolation. The re sults indicate that Eckart filter and Hannan Thomson (HT ) give the best results in the case where the signa l to noise ratio (SNR) is 0 dB.
Data-Driven Online Speed Optimization in Autonomous Sailboats

This paper addresses the issue of data-driven online velocity optimization of an autonomous sailboat. Autonomous sailboats represent an ideal for long range and duration reconnaissance missions. Sailboat control is a challenging control task; sailboats are characterized by a number of control variables, all of which affect the ship trajectory and state in a highly nonlinear fashion. In this paper, a path-following automatic sailboat controller is presented. The control system has two main components: a heading control, acting on the rudder, and a velocity optimizer, acting on the sails. The optimizer is based on a modified extremum seeking approach. This paper also derives a first-principle-based 4 DoF sailboat model that is experimentally validated and used to guide the design and tuning of the control system. In fact, the control system is first tuned and validated in simulation. The simulation environment enables the comparison of the proposed model against a theoretical benchmark and a state-of-the-art controller. The analysis reveals that the proposed control system achieves near-optimal performance and considerably outperforms the state-of-the-art solution. Finally, the controller is tested and validated on an instrumented scale model.
Emotions Mediated Through Mid-Air Haptics

Touch is a powerful vehicle for communication between humans. The way we touch (how) embraces and mediates certain emotions such as anger, joy, fear, or love. While this phenomenon is well explored for human interaction, HCI research is only starting to uncover the fine granularity of sensory stimulation and responses in relation to certain emotions. Within this paper we present the findings from a study exploring the communication of emotions through a haptic system that uses tactile stimulation in mid-air. Here, haptic descriptions for specific emotions (e.g., happy, sad, excited, afraid) were created by one group of users to then be reviewed and validated by two other groups of users. We demonstrate the non-arbitrary mapping between emotions and haptic descriptions across three groups. This points to the huge potential for mediating emotions through mid-air haptics. We discuss specific design implications based on the spatial, directional, and haptic parameters of the created haptic descriptions and illustrate their design potential for HCI based on two design ideas.
HPC Containers in Use

Linux containers in the commercial world are changing the landscape for application development and deployments. Container technologies are also making inroads into HPC environments, as exemplified by NERSC’s Shifter and LBL’s Singularity. While the first generation of HPC containers offers some of the same benefits as the existing open container frameworks, like CoreOS or Docker, they do not address the cloud/commercial feature sets such as virtualized networks, full isolation, and orchestration. This paper will explore the use of containers in the HPC environment and summarize our study to determine how best to use these technologies in the HPC environment at scale. KeywordsShifter; HPC; Cray; container; virtualization; Docker;
Learning an appearance-based gaze estimator from one million synthesised images

Learning-based methods for appearance-based gaze estimation achieve state-of-the-art performance in challenging real-world settings but require large amounts of labelled training data. Learningby-synthesis was proposed as a promising solution to this problem but current methods are limited with respect to speed, the appearance variability as well as the head pose and gaze angle distribution they can synthesize. We present UnityEyes, a novel method to rapidly synthesize large amounts of variable eye region images as training data. Our method combines a novel generative 3D model of the human eye region with a real-time rendering framework. The model is based on high-resolution 3D face scans and uses realtime approximations for complex eyeball materials and structures as well as novel anatomically inspired procedural geometry methods for eyelid animation. We show that these synthesized images can be used to estimate gaze in difficult in-the-wild scenarios, even for extreme gaze angles or in cases in which the pupil is fully occluded. We also demonstrate competitive gaze estimation results on a benchmark in-the-wild dataset, despite only using a light-weight nearest-neighbor algorithm. We are making our UnityEyes synthesis framework freely available online for the benefit of the research community.
Optimizing R language execution via aggressive speculation

The R language, from the point of view of language design and implementation, is a unique combination of various programming language concepts. It has functional characteristics like lazy evaluation of arguments, but also allows expressions to have arbitrary side effects. Many runtime data structures, for example variable scopes and functions, are accessible and can be modified while a program executes. Several different object models allow for structured programming, but the object models can interact in surprising ways with each other and with the base operations of R. 
 R works well in practice, but it is complex, and it is a challenge for language developers trying to improve on the current state-of-the-art, which is the reference implementation -- GNU R. The goal of this work is to demonstrate that, given the right approach and the right set of tools, it is possible to create an implementation of the R language that provides significantly better performance while keeping compatibility with the original implementation. 
 In this paper we describe novel optimizations backed up by aggressive speculation techniques and implemented within FastR, an alternative R language implementation, utilizing Truffle -- a JVM-based language development framework developed at Oracle Labs. We also provide experimental evidence demonstrating effectiveness of these optimizations in comparison with GNU R, as well as Renjin and TERR implementations of the R language.
Growth characteristics of infantile hemangiomas: implications for management.

Infantile hemangiomas often are inapparent at birth and have a period of rapid growth during early infancy followed by gradual involution. More precise information on growth could help predict short-term outcomes and make decisions about when referral or intervention, if needed, should be initiated. The objective of this study was to describe growth characteristics of infantile hemangioma and compare growth with infantile hemangioma referral patterns.



A prospective cohort study involving 7 tertiary care pediatric dermatology practices was conducted. Growth data were available for a subset of 526 infantile hemangiomas in 433 patients from a cohort study of 1096 children. Inclusion criteria were age younger than 18 months at time of enrollment and presence of at least 1 infantile hemangioma. Growth stage and rate were compared with clinical characteristics and timing of referrals.



Eighty percent of hemangioma size was reached during the early proliferative stage at a mean age of 3 months. Differences in growth between hemangioma subtypes included that deep hemangiomas tend to grow later and longer than superficial hemangiomas and that segmental hemangiomas tended to exhibit more continued growth after 3 months of age. The mean age of first visit was 5 months. Factors that predicted need for follow-up included ongoing proliferation, larger size, deep component, and segmental and indeterminate morphologic subtypes.



Most infantile hemangioma growth occurs before 5 months, yet 5 months was also the mean age at first visit to a specialist. Recognition of growth characteristics and factors that predict the need for follow-up could help aid in clinical decision-making. The first few weeks to months of life are a critical time in hemangioma growth. Infants with hemangiomas need close observation during this period, and those who need specialty care should be referred and seen as early as possible within this critical growth period.
Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration

Tracking of multiple objects is an important application in AI City geared towards solving salient problems related to safety and congestion in an urban environment. Frequent occlusion in traffic surveillance has been a major problem in this research field. In this challenge, we propose a model-based vehicle localization method, which builds a kernel at each patch of the 3D deformable vehicle model and associates them with constraints in 3D space. The proposed method utilizes shape fitness evaluation besides color information to track vehicle objects robustly and efficiently. To build 3D car models in a fully unsupervised manner, we also implement evolutionary camera self-calibration from tracking of walking humans to automatically compute camera parameters. Additionally, the segmented foreground masks which are crucial to 3D modeling and camera self-calibration are adaptively refined by multiple-kernel feedback from tracking. For object detection/ classification, the state-of-theart single shot multibox detector (SSD) is adopted to train and test on the NVIDIA AI City Dataset. To improve the accuracy on categories with only few objects, like bus, bicycle and motorcycle, we also employ the pretrained model from YOLO9000 with multiscale testing. We combine the results from SSD and YOLO9000 based on ensemble learning. Experiments show that our proposed tracking system outperforms both state-of-the-art of tracking by segmentation and tracking by detection. multiple object tracking, constrained multiple kernels, 3D deformable model, camera self-calibration, adaptive segmentation, object detection, object classification
Time-Saving First: Coflow Scheduling for Datacenter Networks

Coflow is a collection of parallel flows, while a job consists of a set of coflows. A job is completed if all of the flows completes in the coflows. Therefore, the completion time of a job is affected by the latest flows in the coflows. To guarantee the job completion time and service performance, the job deadline and the dependency of coflows needs to be considered in the scheduling process. However, most existing methods ignore the dependency of coflows which is important to guarantee the job completion. In this paper, we take the dependency of coflows into consideration. To guarantee job completion for performance, we formulate a deadline and dependency-based model called MTF scheduler model. The purpose of MTF model is to minimize the overall completion time with the constraints of deadline and network capacity. Accordingly, we propose our method to schedule dependent coflows. Especially, we consider the dependent coflows as an entirety and propose a valuable coflow scheduling first MTF algorithm. We conduct extensive simulations to evaluate MTF method which outperforms the conventional short job first method as well as guarantees the job deadline.
Brightness of the glare illusion

The glare illusion is commonly used in CG rendering, especially in game engines, to achieve a higher brightness than that of the maximum luminance of a display. In this work, we measure the perceived luminance of the glare illusion in a psychophysical experiment. To evoke the illusion, an image is convolved with either a point spread function (PSF) of the eye or a Gaussian kernel. It is found that 1) the Gaussian kernel evokes an illusion of the same or higher strength than that produced by the PSF while being computationally much less expensive, 2) the glare illusion can raise the perceived luminance by 20 -- 35%, 3) some convolution kernels can produce undesirable Mach-band effects and thereby reduce the brightness boost of the glare illusion. The reported results have practical implications for glare rendering in computer graphics.
Modeling of layout-dependent stress effect in CMOS design

Strain technology has been successfully integrated into CMOS fabrication to improve carrier transport properties since 90nm node. Due to the non-uniform stress distribution in the channel, the enhancement in carrier mobility, velocity, and threshold voltage shift strongly depend on circuit layout, leading to systematic performance variations among transistors. A compact stress model that physically captures this behavior is essential to bridge the process technology with design optimization. In this paper, starting from the first principle, a new layout-dependent stress model is proposed as a function of layout, temperature, and other device parameters. Furthermore, a method of layout decomposition is developed to partition the layout into a set of simple patterns for efficient model extraction. These solutions significantly reduce the complexity in stress modeling and simulation. They are comprehensively validated by TCAD simulation and published Si-data, including the state-of-the-art strain technologies and the STI stress effect. By embedding them into circuit analysis, the interaction between layout and circuit performance is well benchmarked at 45nm node.
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding

Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventional cross-lingual entity alignment methods rely on machine translation to eliminate the language barriers. These approaches often suffer from the uneven quality of translations between languages. While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment, a significant number of attributes remain largely unexplored. In this paper, we propose a joint attribute-preserving embedding model for cross-lingual entity alignment. It jointly embeds the structures of two KBs into a unified vector space and further refines it by leveraging attribute correlations in the KBs. Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.
Metronomic chemotherapy: new rationale for new directions

Tumor angiogenesis is recognized as a major therapeutic target in the fight against cancer. The key involvement of angiogenesis in tumor growth and metastasis has started to redefine chemotherapy and new protocols have emerged. Metronomic chemotherapy, which is intended to prevent tumor angiogenesis, is based on more frequent and low-dose drug administrations compared with conventional chemotherapy. The potential of metronomic chemotherapy was revealed in animal models a decade ago and the efficacy of this approach has been confirmed in the clinic. In the past 5 years, multiple clinical trials have investigated the safety and efficacy of metronomic chemotherapy in a variety of human cancers. While the results have been variable, clinical studies have shown that these new treatment protocols represent an interesting alternative for either primary systemic therapy or maintenance therapy. We review the latest clinical trials of metronomic chemotherapy in adult and pediatric cancer patients. Accumulating evidence suggests that the efficacy of such treatment may not only rely on anti-angiogenic activity. Potential new mechanisms of action, such as restoration of anticancer immune response and induction of tumor dormancy are discussed. Finally, we highlight the research efforts that need to be made to facilitate the optimal development of metronomic chemotherapy.
Ruthenium interconnect resistivity and reliability at 48 nm pitch

48 nm pitch dual damascene interconnects are patterned and filled with ruthenium. Ru interconnect has comparable high yield for line and via macros. Electrical results show minimal impact for via resistance and around 2 times higher line resistance. Resistivity and cross section area of Ru interconnects are measured by temperature coefficient of resistivity method and the area was verified by TEM. Reliability results show non-failure in electromigration and longer time dependent dielectric breakdown. Based on the data collected, Ru could be a metallization contender at linewidth of 16 nm and below.
BULLWHIP EFFECT AND SUPPLY CHAIN MODELLING AND ANALYSIS USING CPN TOOLS

The paper presents some of the results obtained by studying Petri nets’ capability for modeling and analysis of Supply Chain performances. It is well known that the absence of coordination in Supply Chain management causes the so-called Bullwhip Effect, in which fluctuations in orders increase as they move up the chain. A simple three-stage supply chain with one player at each stage – a retailer, a wholesaler and a manufacturer – is considered. The model of the chain is developed using a timed, hierarchical coloured Petri Net. Simulation and performance analysis have been performed applying software package CPN Tools.