nils-herrmann/clean_scirepeval huggingface dataset:
Matrix factorization techniques for context aware recommendation

Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. In this paper we present a novel context-aware recommendation algorithm that extends Matrix Factorization. We model the interaction of the contextual factors with item ratings introducing additional model parameters. The performed experiments show that the proposed solution provides comparable results to the best, state of the art, and more complex approaches. The proposed solution has the advantage of smaller computational cost and provides the possibility to represent at different granularities the interaction between context and items. We have exploited the proposed model in two recommendation applications: places of interest and music.
Typology of Ergativity

Ergativity refers to patterning in a language whereby the subject of a transitive clause behaves differently to the subject of an intransitive clause, which behaves like the object of a transitive clause. Ergativity can be manifested in morphology, lexicon, syntax, and discourse organisation. This article overviews what is known about ergativity in the world’s languages, with a particular focus on one type of morphological ergativity, namely in case-marking. While languages are rarely entirely consistent in ergative case-marking, and the inconsistencies vary considerably across languages, they are nevertheless not random. Thus splits in casemarking, in which ergative patterning is restricted to certain domains, follow (with few exceptions) universal tendencies. So also are there striking cross-linguistic commonalities among systems in which ergative case-marking is optional, although systematic investigation of this domain is quite recent. Recent work on the diachrony of ergative systems and case-markers is overviewed, and issues for further research are identified.
OIL: Observational Imitation Learning

Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-toend policy, which directly predicts controls from raw images. However, these approaches tend to be sensitive to mistakes by the teacher and do not scale well to other environments or vehicles. To this end, we propose Observational Imitation Learning (OIL), a novel imitation learning variant that supports online training and automatic selection of optimal behavior by observing multiple imperfect teachers. We apply our proposed methodology to the challenging problems of autonomous driving and UAV racing. For both tasks, we utilize the Sim4CV simulator [18] that enables the generation of large amounts of synthetic training data and also allows for online learning and evaluation. We train a perception network to predict waypoints from raw image data and use OIL to train another network to predict controls from these waypoints. Extensive experiments demonstrate that our trained network outperforms its teachers, conventional imitation learning (IL) and reinforcement learning (RL) baselines and even humans in simulation.
Virtual Reality-Enhanced Extinction of Phobias and Post-Traumatic Stress

Virtual reality (VR) refers to an advanced technological communication interface in which the user is actively participating in a computer-generated 3-dimensional virtual world that includes computer sensory input devices used to simulate real-world interactive experiences. VR has been used within psychiatric treatment for anxiety disorders, particularly specific phobias and post-traumatic stress disorder, given several advantages that VR provides for use within treatment for these disorders. Exposure therapy for anxiety disorder is grounded in fear-conditioning models, in which extinction learning involves the process through which conditioned fear responses decrease or are inhibited. The present review will provide an overview of extinction training and anxiety disorder treatment, advantages for using VR within extinction training, a review of the literature regarding the effectiveness of VR within exposure therapy for specific phobias and post-traumatic stress disorder, and limitations and future directions of the extant empirical literature.
Sternal Precautions: Is It Time for Change? Precautions versus Restrictions - A Review of Literature and Recommendations for Revision.

The processes that occur with normal sternal healing and potential complications related to median sternotomy are of particular interest to physical therapists. The premise of patients following sternal precautions (SP) or specific activity restrictions is the belief that avoiding certain movements will reduce risk of sternal complications. However, current research has identified that many patients remain functionally impaired long after cardiothoracic surgery. It is possible that some SP may contribute to such functional impairments. Currently, SP have several limitations including that they: (1) have no universally accepted definition, (2) are often based on anecdotal/expert opinion or at best supported by indirect evidence, (3) are mostly applied uniformly for all patients without regard to individual differences, and (4) may be overly restrictive and therefore impede ideal recovery. The purpose of this article is to present an overview of current research and commentary on median sternotomy procedures and activity restrictions. We propose that the optimal degree and duration of SP should be based on an individual patient's characteristics (eg, risk factors, comorbidities, previous activity level) that would enable physical activity to be targeted to particular limitations rather than restricting specific functional tasks and physical activity. Such patient-specific SP focusing on function may be more likely to facilitate recovery after median sternotomy and less likely to impede it.
Active Capacitive-Sensing Circuit Technique for Image Quality Improvement on Fingerprint Sensor

This paper proposes an active capacitive sensing circuit for fingerprint sensors, which includes a pixel level charge-sharing and charge pump to replace an ADC. This paper also proposes the operating algorithm for 16-level gray scale image. The active capacitive technology is more flexible and can be adjusted to adapt to a wide range of different skin types and environments. The proposed novel circuit is composed with unit gain buffer, 6-stage charge pump and analog comparator. The proper operation is validated by the HSPICE simulation of one pixel with condition of 0.35μm typical CMOS parameter and 3.3V power.
Enhancing parenteral nutrition therapy for the neonate.

The neonate receiving parenteral nutrition (PN) therapy requires a physiologically appropriate solution in quantity and quality given according to a timely, cost-effective strategy. Maintaining tissue integrity, metabolism, and growth in a neonate is challenging. To support infant growth and influence subsequent development requires critical timing for nutrition assessment and intervention. Providing amino acids to neonates has been shown to improve nitrogen balance, glucose metabolism, and amino acid profiles. In contrast, supplying the lipid emulsions (currently available in the United States) to provide essential fatty acids is not the optimal composition to help attenuate inflammation. Recent investigations with an omega-3 fish oil IV emulsion are promising, but there is need for further research and development. Complications from PN, however, remain problematic and include infection, hepatic dysfunction, and cholestasis. These complications in the neonate can affect morbidity and mortality, thus emphasizing the preference to provide early enteral feedings, as well as medication therapy to improve liver health and outcome. Potential strategies aimed at enhancing PN therapy in the neonate are highlighted in this review, and a summary of guidelines for practical management is included.
How Robust is 3D Human Pose Estimation to Occlusion?

Zhong, Z.; Zheng, L.; Kang, G., Li, S.; Yang, Y.: Random erasing data augmentation.  2017  Pavlakos, G.; Zhou, X.; Derpanis, K. G.; Daniilidis, K.: Coarse-to-fne volumetric prediction for single-image 3D human pose. CVPR 2017  Sun, X.; Xiao, B.; Liang, S.; Wei, Y.: Integral human pose regression.  2018  Sárándi, I.; Linder, T.; Arras, K. O.; Leibe, B.: Synthetic occlusion augmentation with volumetric heatmaps for the 2018 ECCV PoseTrack Challenge on 3D human pose estimation.  (2018) ● Baseline 3D pose estimator is sensitive even to low degrees of occlusion
Root Cause Analysis of Zero-Rate Output Sources in an MEMS Gyroscope

This paper analyzes the root causes of zero-rate output (ZRO) in microelectromechanical system (MEMS) vibratory gyroscopes. ZRO is one of the major challenges for high-performance gyroscopes. The knowledge of its causes is important to minimize ZRO and achieve a robust sensor design. In this paper, a new method to describe an MEMS gyroscope with a parametric state space model is introduced. The model is used to theoretically describe the behavioral influences. A new, more detailed and general gyroscope approximation is used to vary influence parameters, and to verify the method with simulations. The focus is on varying stiffness terms and an extension of the model to other gyroscope approximations is also discussed.
Information in Organisations: Directions for Information Management

The work of managers in small and medium-sized enterprises is very information-intensive and the environment in which it is done is very information rich. But are managers able to exploit the wealth of information which surrounds them? And how can information be managed in organisations so that its potential for improving business performance and enhancing the competitiveness of these enterprises can be realised? Answers to these questions lie in clarifying the context of the practice of information management by exploring aspects of organisations and managerial work and in exploring the nature of information at the level of the organisation and the individual manager. From these answers it is possible to suggest some guidelines for managing the integration of business strategy and information, the adoption of a broadly-based definition of information and the development of information capabilities.
Universal representations: The missing link between faces, text, planktons, and cat breeds

With the advent of large labelled datasets and highcapacity models, the performance of machine vision systems has been improving rapidly. However, the technology has still major limitations, starting from the fact that different vision problems are still solved by different models, trained from scratch or fine-tuned on the target data. The human visual system, in stark contrast, learns a universal representation for vision in the early life of an individual. This representation works well for an enormous variety of vision problems, with little or no change, with the major advantage of requiring little training data to solve any of them. In this paper we investigate whether neural networks may work as universal representations by studying their capacity in relation to the “size” of a large combination of vision problems. We do so by showing that a single neural network can learn simultaneously several very different visual domains (from sketches to planktons and MNIST digits) as well as, or better than, a number of specialized networks. However, we also show that this requires to carefully normalize the information in the network, by using domainspecific scaling factors or, more generically, by using an instance normalization layer.
Structured Labels in Random Forests for Semantic Labelling and Object Detection

Ensembles of randomized decision trees, known as Random Forests, have become a valuable machine learning tool for addressing many computer vision problems. Despite their popularity, few works have tried to exploit contextual and structural information in random forests in order to improve their performance. In this paper, we propose a simple and effective way to integrate contextual information in random forests, which is typically reflected in the structured output space of complex problems like semantic image labelling. Our paper has several contributions: We show how random forests can be augmented with structured label information and be used to deliver structured low-level predictions. The learning task is carried out by employing a novel split function evaluation criterion that exploits the joint distribution observed in the structured label space. This allows the forest to learn typical label transitions between object classes and avoid locally implausible label configurations. We provide two approaches for integrating the structured output predictions obtained at a local level from the forest into a concise, global, semantic labelling. We integrate our new ideas also in the Hough-forest framework with the view of exploiting contextual information at the classification level to improve the performance on the task of object detection. Finally, we provide experimental evidence for the effectiveness of our approach on different tasks: Semantic image labelling on the challenging MSRCv2 and CamVid databases, reconstruction of occluded handwritten Chinese characters on the Kaist database and pedestrian detection on the TU Darmstadt databases.
A Mechanism for Turing Pattern Formation with Active and Passive Transport

We propose a novel mechanism for Turing pattern formation that provides a possible explanation for the regular spacing of synaptic puncta along the ventral cord of C. elegans during development. The model consists of two interacting chemical species, where one is passively diffusing and the other is actively trafficked by molecular motors. We identify the former as the kinase CaMKII and the latter as the glutamate receptor GLR-1. We focus on a one-dimensional model in which the motor-driven chemical switches between forward and backward moving states with identical speeds. We use linear stability analysis to derive conditions on the associated nonlinear interaction functions for which a Turing instability can occur. We find that the dimensionless quantity γ = αd/v has to be sufficiently small for patterns to emerge, where α is the switching rate between motor states, v is the motor speed, and d is the passive diffusion coefficient. One consequence is that patterns emerge outside the parameter regime of fast switching where the model effectively reduces to a twocomponent reaction-diffusion system. Numerical simulations of the model using experimentally based parameter values generates patterns with a wavelength consistent with the synaptic spacing found in C. elegans. Finally, in the case of biased transport, we show that the system supports spatially periodic patterns in the presence of boundary forcing, analogous to flow distributed structures in reaction-diffusion-advection systems. Such forcing could represent the insertion of new motor-bound GLR-1 from the soma of ventral cord neurons.
Fabrication of Low Cost Surface Acoustic Wave Sensors Using Direct Printing by Aerosol Inkjet

Advancements in additive manufacturing techniques, printed electronics, and nanomaterials have made it possible for the cost-effective fabrication of sensors and systems. Low-cost sensors for continuous and real time monitoring of physical and chemical parameters will directly impact the energy-efficiency, safety, and manufacturing challenges of diverse technology sectors. In this paper, we present the design, printing, and characterization of a two-port surface acoustic wave (SAW) integrated on LiNbO3 substrate. The aerosol jet printer was used for direct-writing of interdigitated transducers for SAW devices with center frequency in the range of 40–87 MHz. The linear response of a temperature sensor based on the SAW design shows promise for direct-writing of environmental sensors on low-temperature substrates.
Automatic Code Mapping on an Intelligent Memory Architecture

This paper presents an algorithm to automatically map code on a generic intelligent memory system that consists of a high-end host processor and a simpler memory processor. To achieve high performance with this type of architecture, the code needs to be partitioned and scheduled such that each section is assigned to the processor on which it runs most efficiently. In addition, the two processors should overlap their execution as much as possible. With our algorithm, applications are mapped fully automatically using both static and dynamic information. Using a set of standard applications and a simulated architecture, we obtain average speedups of 1.7 for numerical applications and 1.2 for non-numerical applications over a single host with plain memory. The speedups are very close and often higher than ideal speedups on a more expensive multiprocessor system composed of two identical host processors. Our work shows that heterogeneity can be cost-effectively exploited and represents one step toward effectively mapping code on heterogeneous intelligent memory systems.
A Scalable Priority Queue Architecture for High Speed Network Processing

Priority queues are essential for implementing various types of service disciplines on network devices. However, state-of-the-art priority queues can barely catch up with the OC192 line speed and the size of active priorities is limited to a few hundred KB which is far from the worst-case requirement. Our hybrid design stores most priorities sorted in the FIFO queue and they are used for dequeue only. Since the dequeue operation always takes the highest priorities from the head of the FIFO queue, it can be efficiently handled using caching in the SRAM and wide word accesses to the DRAM. Meanwhile, incoming priorities are stored temporarily in a small input heap. Once the input heap is full, it becomes creation heap and the priorities are then quickly dequeued from the creation heap and transformed into a sorted array to be stored in the FIFO queues. When these operations are in progress, previously emptied creation heap is connected to the input and continues to get incoming priorities. Swapping between input and creation heap sustains continuous operation of the system. Our results show that this hybrid design can meet the requirements of OC-3072 line speed and provide enough capacity queuing a large number of priorities in the worst case. Also, the required DRAM bandwidth and SRAM size (which is precious) are reasonable. Network Processor; Priority Queue; FIFO Queue.
Auditory brainstem response morphology and analysis in very preterm neonatal intensive care unit infants.

Analysis of auditory brainstem response (ABR) in very preterm infants can be difficult owing to the poor detectability of the various components of the ABR. We evaluated the ABR morphology and tried to extend the current assessment system.



Prospective cohort study.



We included 28 preterm very low birth weight infants admitted to the neonatal intensive care unit of Sophia Children's Hospital. ABRs were measured between 26 and 34 weeks postconceptional age. The presence of the following ABR parameters was recorded: the ipsilateral peaks I, III and V, the contralateral peaks III and V, and the response threshold.



In 82% of our population, a typical "bow tie" response pattern was present as a sign of early auditory development. This bow tie pattern is the narrowest part of the response wave and is predominantly characterized by the ipsilateral negative peak III. This effect may be emphasized by the contralateral peak III. The bow tie pattern is seen approximately 0.1 milliseconds before the ipsilateral peak III. From 30 weeks postconceptional age onward, a more extensive morphologic pattern is recorded in 90% of the infants. A flow chart was designed to analyze the ABR morphology of preterm infants in an unambiguous stepwise fashion.



A typical bow tie pattern preceding peak III seems to be the earliest characteristic of the developing ABR morphology in preterm infants. As ABR characteristics will improve with increasing age, neonatal hearing screening should be postponed until after 34 weeks.
Impact of an LPT(II) concept with Thin Wafer Process Technology for IGBT's vertical structure

In this paper, for the first time, 600 ∼ 6500 V IGBTs utilizing a new vertical structure of “Light Punch-Through (LPT) (II)” with Thin Wafer Process Technology demonstrate high total performance with low overall loss and high safety operating area (SOA) capability. This collector structure enables a wide position in the trade-off characteristics between on-state voltage (VCE(sat)) and turn-off loss (EOFF) without utilizing any conventional carrier lifetime technique. In addition, this device concept achieves a wide operating junction temperature (@218 ∼ 423 K) of IGBT without the snap-back phenomena (≤298 K) and thermal destruction (≥398 K). From the viewpoint of the high performance of IGBT, the breaking limitation of any Si wafer size, the proposed LPT(II) concept that utilizes an FZ silicon wafer and Thin Wafer Technology is the most promising candidate as a vertical structure of IGBT for the any voltage class.
Application of extended Kalman filter to parameter estimation of doubly-fed induction generators in variable-speed wind turbine systems

This paper proposes a parameter estimation method for doubly-fed induction generators (DFIGs) in variable-speed wind turbine systems (WTS). The proposed method employs an extended Kalman filter (EKF) for estimation of all electrical parameters of the DFIG, i.e., the stator and rotor resistances, the leakage inductances of stator and rotor, and the mutual inductance. The nonlinear state space model of the DFIG is derived and the design procedure of the EKF is described. The observability matrix of the linearized DFIG model is computed and the observability is checked online for different operation conditions. The estimation performance of the EKF is illustrated by simulation results. The estimated parameters are plotted against their actual values. The estimation performance of the EKF is also tested under variations of the DFIG parameters to investigate the estimation accuracy for changing parameters.
Dysphagia The Ability of the 10-Item Eating Assessment Tool ( EAT-10 ) to Predict Aspiration Risk in Persons With

Dysphagia is common and costly. The ability of patient symptoms to predict objective swallowing dysfunction is uncertain.  This study aimed to evaluate the ability of the Eating Assessment Tool (EAT-10) to screen for aspiration risk in patients with dysphagia.  Data from individuals with dysphagia undergoing a videofluoroscopic swallow study between January 2012 and July 2013 were abstracted from a clinical database. Data included the EAT-10, Penetration Aspiration Scale (PAS), total pharyngeal transit (TPT) time, and underlying diagnoses. Bivariate linear correlation analysis, sensitivity, specificity, and predictive values were calculated.  The mean age of the entire cohort (N = 360) was 64.40 (± 14.75) years. Forty-six percent were female. The mean EAT-10 was 16.08 (± 10.25) for nonaspirators and 23.16 (± 10.88) for aspirators (P < .0001). There was a linear correlation between the total EAT-10 score and the PAS (r = 0.273, P < .001). Sensitivity and specificity of an EAT-10 > 15 in predicting aspiration were 71% and 53%, respectively.  Subjective dysphagia symptoms as documented with the EAT-10 can predict aspiration risk. A linear correlation exists between the EAT-10 and aspiration events (PAS) and aspiration risk (TPT time). Persons with an EAT10 > 15 are 2.2 times more likely to aspirate (95% confidence interval, 1.3907-3.6245). The sensitivity of an EAT-10 > 15 is 71%.