nils-herrmann/clean_scirepeval huggingface dataset:
Comparing clinical and social-personality conceptualizations of narcissism.

There is a lack of consensus surrounding the conceptualization of narcissism. The present study compared two measures of narcissism-one used in clinical settings (Personality Diagnostic Questionnaire, PDQ-4+; Hyler, 1994) and one used in social-personality research (Narcissistic Personality Inventory, NPI; Raskin & Terry, 1988)-across two samples. Sample 1 (N=271) was composed of undergraduates, whereas Sample 2 (N=211) was composed of parents of the Sample 1 participants. The scales were significantly interrelated but manifested divergent relations with general personality traits, personality disorders (including expert prototypal ratings of narcissism), recollections of parenting received, and psychological distress and self-esteem. PDQ-4 narcissism captured an emotionally unstable, negative-affect-laden, and introverted variant of narcissism; NPI narcissism captured an emotionally resilient, extraverted form. The clinical and social-personality conceptualizations of narcissism primarily share a tendency to use an antagonistic interpersonal style. Implications for the DSM-V are discussed.
EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones

In order to facilitate the introduction of transit tracking and arrival time prediction in smaller transit agencies, we investigate an automatic, smartphone-based system which we call EasyTracker. To use EasyTracker, a transit agency must obtain smartphones, install an app, and place a phone in each transit vehicle. Our goal is to require no other input.
 This level of automation is possible through a set of algorithms that use GPS traces collected from instrumented transit vehicles to determine routes served, locate stops, and infer schedules. In addition, online algorithms automatically determine the route served by a given vehicle at a given time and predict its arrival time at upcoming stops.
 We evaluate our algorithms on real datasets from two existing transit services. We demonstrate our ability to accurately reconstruct routes and schedules, and compare our system's arrival time prediction performance with the current "state of the art" for smaller transit operators: the official schedule. Finally, we discuss our current prototype implementation and the steps required to take it from a research prototype to a real system.
Supervised Extraction of Diagnosis Codes from EMRs: Role of Feature Selection, Data Selection, and Probabilistic Thresholding

Extracting diagnosis codes from medical records is a complex task carried out by trained coders by reading all the documents associated with a patient's visit. With the popularity of electronic medical records (EMRs), computational approaches to code extraction have been proposed in the recent years. Machine learning approaches to multi-label text classification provide an important methodology in this task given each EMR can be associated with multiple codes. In this paper, we study the the role of feature selection, training data selection, and probabilistic threshold optimization in improving different multi-label classification approaches. We conduct experiments based on two different datasets: a recent gold standard dataset used for this task and a second larger and more complex EMR dataset we curated from the University of Kentucky Medical Center. While conventional approaches achieve results comparable to the state-of-the-art on the gold standard dataset, on our complex in-house dataset, we show that feature selection, training data selection, and probabilistic thresholding provide significant gains in performance.
Single-stage posterior corpectomy and expandable cage placement for treatment of thoracic or lumbar burst fractures.

A prospective study was performed.



To assess an unusual technique for corpectomy and expandable cage placement via single-stage posterior approach in acute thoracic or lumbar burst fractures.



Burst fractures represent 10% to 20% of all spine injuries at or near the thoracolumbar junction, and can cause neurologic complications and kyphotic deformity. The goal of surgical intervention is to decompress the neural elements, restore vertebral body height, correct angular deformity, and stabilize the columns of the spine.



The study comprised 14 patients (8 women and 6 men aged 40.3 years) who had 1 spinal burst fracture between T8 and L4 and who underwent single-stage posterior corpectomy, circumferential reconstruction with expandable-cage placement, and transpedicle screwing between January 2003 and May 2005. Neurologic status was classified using the American Spinal Injury Association (ASIA) impairment scale and functional outcomes were analyzed using a visual analogue scale (VAS) for pain. The kyphotic angle (alpha) and lordotic angle (beta) were measured in the thoracic or thoracolumbar and lumbar regions, respectively.  The mean follow-up time was 24 months (range, 12-48 months). Neurologic status was in 7 patients (preop: ASIA-E, postop: unchanged), 2 patients (preop: ASIA-D, postop: 1 unchanged, 1 improved to ASIA-E), 3 patients (preop: ASIA-C, postop: 2 improved to ASIA-D, 1 improved to ASIA-E), 2 patients (preop: ASIA-B, postop: 1 improved to ASIA-C, 1 unchanged). The mean operative time was 187.8 minutes. The mean blood loss was 596.4 mL. Regarding postoperative complications, 1 patient experienced transient worsening of neurologic deficits and 1 patient developed pseudarthrosis. The mean preoperative VAS score was 8.21 and the mean postoperative VAS score was 2.66 (P < 0.05). The mean preoperative kyphotic angle for the 11 individuals with the thoracic or thoracolumbar burst fractures was 24.6 degrees and the mean preoperative lordotic angle for the 3 individuals with lumbar burst fractures was 10.6 degrees. The corresponding values at 12 months postsurgery were 17.1 degrees and 13.6 degrees.



This single-stage posterior approach for acute thoracic and lumbar burst fractures offers some advantages over the classic combined anterior-posterior approach. The results from this small series suggest that a single-stage posterior approach should be considered in select cases.
A passivity based Cartesian impedance controller for flexible joint robots - part II: full state feedback, impedance design and experiments

The paper presents a Cartesian impedance controller for flexible joint robots based on the feedback of the complete state of the system, namely the motor position, the joint torque and their derivatives. The approach is applied to a quite general robot model, in which also a damping element is considered in parallel to the joint stiffness. Since passivity and asymptotic stability of the controller hold also for varying damping matrices, some possibilities of designing those gain matrices (depending on the actual inertia matrix) are addressed. The passivity of the controller relies on the usage of only motor side measurements for the position feedback. A method is introduced, which provides the exact desired link side stiffness based on this motor position information. Experimental results are validating the proposed controller.
Improving robot manipulation through fingertip perception

Better sensing is crucial to improve robotic grasping and manipulation. Most robots currently have very limited perception in their manipulators, typically only fingertip position and velocity. Additional sensors make richer interactions with the objects possible. In this paper, we present a versatile, robust and low cost sensor for robot fingertips, that can improve robotic grasping and manipulation in several ways: 3D reconstruction of the shape of objects, material surface classification, and object slip detection. We extended TUM-Rosie, our robot for mobile manipulation, with fingertip sensors on its humanoid robotic hand, and show the advantages of the fingertip sensor integrated in our robot system.
Comparing 2D vector field visualization methods: a user study

We present results from a user study that compared six visualization methods for two-dimensional vector data. Users performed three simple but representative tasks using visualizations from each method: 1) locating all critical points in an image, 2) identifying critical point types, and 3) advecting a particle. Visualization methods included two that used different spatial distributions of short arrow icons, two that used different distributions of integral curves, one that used wedges located to suggest flow lines, and line-integral convolution (LIC). Results show different strengths and weaknesses for each method. We found that users performed these tasks better with methods that: 1) showed the sign of vectors within the vector field, 2) visually represented integral curves, and 3) visually represented the locations of critical points. Expert user performance was not statistically different from nonexpert user performance. We used several methods to analyze the data including omnibus analysis of variance, pairwise t-tests, and graphical analysis using inferential confidence intervals. We concluded that using the inferential confidence intervals for displaying the overall pattern of results for each task measure and for performing subsequent pairwise comparisons of the condition means was the best method for analyzing the data in this study. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand the tradeoffs among the methods. In the future, we also envision extending this work to more ambitious comparisons, such as evaluating two-dimensional vectors on two-dimensional surfaces embedded in three-dimensional space and defining analogous tasks for three-dimensional visualization methods.
Side channels in cloud services , the case of deduplication in cloud storage

Cloud storage services commonly use deduplication, which eliminates redundant data by storing only a single copy of each file or block. Deduplication reduces the space and bandwidth requirements of data storage services, and is most effective when applied across multiple users, a common practice by cloud storage offerings. We study the privacy implications of cross-user deduplication. We demonstrate how deduplication can be used as a side channel which reveals information about the contents of files of other users. In a different scenario, deduplication can be used as a covert channel by which malicious software can communicate with its control center, regardless of any firewall settings at the attacked machine. Due to the high savings offered by cross-user deduplication, cloud storage providers are unlikely to stop using this technology. We therefore propose simple mechanisms that enable cross-user deduplication while greatly reducing the risk of data leakage.
Mutual Information, Fisher Information, and Population Coding

In the context of parameter estimation and model selection, it is only quite recently that a direct link between the Fisher information and information-theoretic quantities has been exhibited. We give an interpretation of this link within the standard framework of information theory. We show that in the context of population coding, the mutual information between the activity of a large array of neurons and a stimulus to which the neurons are tuned is naturally related to the Fisher information. In the light of this result, we consider the optimization of the tuning curves parameters in the case of neurons responding to a stimulus represented by an angular variable.
SmartPort: A Platform for Sensor Data Monitoring in a Seaport Based on FIWARE

Seaport monitoring and management is a significant research area, in which infrastructure automatically collects big data sets that lead the organization in its multiple activities. Thus, this problem is heavily related to the fields of data acquisition, transfer, storage, big data analysis and information visualization. Las Palmas de Gran Canaria port is a good example of how a seaport generates big data volumes through a network of sensors. They are placed on meteorological stations and maritime buoys, registering environmental parameters. Likewise, the Automatic Identification System (AIS) registers several dynamic parameters about the tracked vessels. However, such an amount of data is useless without a system that enables a meaningful visualization and helps make decisions. In this work, we present SmartPort, a platform that offers a distributed architecture for the collection of the port sensors' data and a rich Internet application that allows the user to explore the geolocated data. The presented SmartPort tool is a representative, promising and inspiring approach to manage and develop a smart system. It covers a demanding need for big data analysis and visualization utilities for managing complex infrastructures, such as a seaport.
Effect of Speech Recognition Errors on Text Understandability for People who are Deaf or Hard of Hearing

Recent advancements in the accuracy of Automated Speech Recognition (ASR) technologies have made them a potential candidate for the task of captioning. However, the presence of errors in the output may present challenges in their use in a fully automatic system. In this research, we are looking more closely into the impact of different inaccurate transcriptions from the ASR system on the understandability of captions for Deaf or Hard-of-Hearing (DHH) individuals. Through a user study with 30 DHH users, we studied the effect of the presence of an error in a text on its understandability for DHH users. We also investigated different prediction models to capture this relation accurately. Among other models, our random forest based model provided the best mean accuracy of 62.04% on the task. Further, we plan to improve this model with more data and use it to advance our investigation on ASR technologies to improve ASR based captioning for DHH users.
[Sensitization to acrylates caused by artificial acrylic nails: review of 15 cases].

Allergic contact dermatitis due to acrylates present in the workplace is a disease frequently reported among dentists, printers, and fiberglass workers. Recently, the number of cases of contact allergic dermatitis among beauticians specialized in sculpting artificial nails has increased.



Our objective was to study the clinical characteristics and allergens implicated in allergic contact dermatitis due to acrylates in beauticians and users of sculpted nails.



This was an observational, retrospective study of patients diagnosed with allergic contact dermatitis due to acrylates used in sculpting artificial nails over the last 26 years in the Hospital General Universitario, Valencia, Spain.



In total, 15 patients were diagnosed: 14 beauticians and 1 client. Most cases were diagnosed in the past 2 years. All were women, their mean age was 32.2 years, and 26.7 % had a personal or family history of atopy. The sensitization time varied between 1 month and 15 years. The most frequently affected areas were the fleshy parts of the fingers and hands. Three patients - 2 beauticians and 1 client - presented allergic asthma due to acrylates. All patients underwent patch testing with a standard battery of allergens and a battery of acrylates. The most frequent allergens were ethylene glycol dimethacrylate (13/15, 86.7 %), hydroxyethyl methacrylate (13/15, 86.7 %), triethylene glycol dimethacrylate (7/15, 46.7 %), 2-hydroxypropyl methacrylate (5/15, 33.3 %), and methyl methacrylate (5/15, 33.3 %).



Acrylate monomers used for sculpting artificial nails are important sensitizers for contact and occupational dermatitis. The most important consideration is primary and secondary prevention.
Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units

We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map, combining the intuitions of dropout and zoneout while respecting neuron values. This connection suggests a new probabilistic understanding of nonlinearities. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks.
A Minimalist ’ s Implementation of the 3-d Divide-and-Conquer Convex Hull Algorithm

We give a simple interpretation and a simple implementation of the classical divide-andconquer algorithm for computing 3-d convex hulls (and in particular, 2-d Delaunay triangulations and Voronoi diagrams). The entire C++ code is under 100 lines long, requires no special data structures, and uses only 6n pointers for space.
Accelerating real-time shading with reverse reprojection caching

Evaluating pixel shaders consumes a growing share of the computational budget for real-time applications. However, the significant temporal coherence in visible surface regions, lighting conditions, and camera location allows reusing computationally-intensive shading calculations between frames to achieve significant performance improvements at little degradation in visual quality. This paper investigates a caching scheme based on reverse reprojection which allows pixel shaders to store and reuse calculations performed at visible surface points. We provide guidelines to help programmers select appropriate values to cache and present several policies for keeping cached entries up-to-date. Our results confirm this approach offers substantial performance gains for many common real-time effects, including precomputed global lighting effects, stereoscopic rendering, motion blur, depth of field, and shadow mapping.
Progressive Neural Networks for Image Classification

The inference structures and computational complexity of existing deep neural networks, once trained, are fixed and remain the same for all test images. However, in practice, it is highly desirable to establish a progressive structure for deep neural networks which is able to adapt its inference process and complexity for images with different visual recognition complexity. In this work, we develop a multi-stage progressive structure with integrated confidence analysis and decision policy learning for deep neural networks. This new framework consists of a set of network units to be activated in a sequential manner with progressively increased complexity and visual recognition power. Our extensive experimental results on the CIFAR-10 and ImageNet datasets demonstrate that the proposed progressive deep neural network is able to obtain more than 10 fold complexity scalability while achieving the stateof-the-art performance using a single network model satisfying different complexity-accuracy requirements.
Drawing plane graphs nicely

This paper presents two efficient algorithms for drawing plane graphs nicely. Both draw all edges of a graph as straight line segments without crossing lines. The first draws a plane graph “convex” if possible, that is, in a way that every inner face and the complement of the outer face are convex polygons. The second, using the first, produces a pleasing drawing of a given plane graph that satisfies the following property as far as possible: the complements of 3-connected components, together with inner faces and the complement of the outer face, are convex polygons. The running time and storage space of both algorithms are linear in the number of vertices of the graph.
"Eigenlips" for robust speech recognition

In this study we improve the performance of a hybrid connectionist speech recognition system by incorporating visual information about the corresponding lip movements. Specifically, we investigate the benefits of adding visual features in the presence of additive noise and crosstalk (cocktail party effect). Our study extends our previous experiments  by using a new visual front end, and an alternative architecture for combining the visual and acoustic information. Furthermore, we have extended our recognizer to a multi-speaker, connected letters recognizer. Our results show a significant improvement for the combined architecture (acoustic and visual information) over just the acoustic system in the presence of additive noise and crosstalk.
The anterolateral ligament of the human knee: an anatomic and histologic study

The functional anatomy of the knee is frequently studied but remains incompletely understood. Numerous authors have described a structure in the lateral knee connecting the lateral femoral condyle with the lateral meniscus and tibial plateau. The goal of this study is to define the incidence, anatomy, and histology of this structure, the anterolateral ligament. The incidence of the ligament was determined in 30 consecutive patients undergoing total knee arthroplasty (TKA) for medial compartment osteoarthritis. The anatomy and histology were evaluated using 10 cadaveric knees. The anterolateral ligament was noted to be present in all 40 knees. In all cases, it was noted to take origin near or on the popliteus tendon insertion and insert into the lateral meniscus and tibial plateau 5 mm distal to the articular surface and posterior to Gerdy’s Tubercle. The average width of the relatively flat structure was 8.2 ± 1.5 mm, and the average length was 34.1 ± 3.4 mm. Histologic analysis revealed a discreet structure with a fibrous core surrounded by synovium. Fibers blended with the popliteus at its origin and with the lateral meniscus as it passed distally. The anterolateral ligament may play a role in preventing anterior tibial translation. The role, if any, of this structure in meniscal stability and the pathology of meniscal tears remain unclear. Not applicable—Descriptive Anatomic Study.
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice, however, there are significant algorithmic and performance challenges. In this work, we address these challenges and finally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. A trainable gating network determines a sparse combination of these experts to use for each example. We apply the MoE to the tasks of language modeling and machine translation, where model capacity is critical for absorbing the vast quantities of knowledge available in the training corpora. We present model architectures in which a MoE with up to 137 billion parameters is applied convolutionally between stacked LSTM layers. On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost.