nils-herrmann/clean_scirepeval huggingface dataset:
Evaluation of the Potential of Dipole Field Navigation for the Targeted Delivery of Therapeutic Agents in a Human Vascular Network

Magnetically guided agents in the vascular network are expected to enable the targeted delivery of therapeutics to localized regions while avoiding their systemic circulation. Due to the small size of the medically applicable superparamagnetic microscale agents required to reach the smaller arteries, high magnetic fields and gradients are required to reach saturation magnetization and generate sufficient directional forces, respectively, for their effective navigation in the vascular environment. Currently, the only method that provides both a high field and high magnetic gradient strengths in deep tissues at the human scale is known as dipole field navigation (DFN). This method relies on the controlled distortion of the field inside a magnetic resonance imaging scanner by precisely positioning ferromagnetic cores around the patient. This paper builds on previous works that have experimentally demonstrated the feasibility of the method and proposed optimization algorithms for placing the cores. The maximum gradient strengths that can be generated for single and multibifurcation vascular routes are investigated while considering the major constraints on core positions (limited space in the scanner, magnetic interactions). Using disc cores, which were previously shown particularly effective for the DFN, results show that gradient strengths exceeding 400 mT/m (a tenfold increase with respect to typical gradients generated by clinical MRI scanners) can be achieved at 10 cm inside the patient, but decrease as the complexity of the vascular route increases. The potential of the method is evaluated for targeting regions of a vascular model of a human liver, segmented from clinical data, with encouraging results showing strengths up to 150 mT/m for generating gradients at three consecutive bifurcations within 20° of average gradient direction error.
CAST: Collaborative Agents for Simulating Teamwork

Psychological studies on teamwork have shown that an effective team often can anticipate information needs of teammates based on a shared mental model. Existing multi-agent models for teamwork are limited in their ability to support proactive information exchange among teammates. To address this issue, we have developed and implemented a multi-agent architecture called CAST that simulates teamwork and supports proactive information exchange in a dynamic environment. We present a formal model for proactive information exchange. Knowledge regarding the structure and process of a team is described in a language called MALLET. Beliefs about shared team processes and their states are represented using Petri Nets. Based on this model, CAST agents offer information proactively to those who might need it using an algorithm called DIARG. Empirical evaluations using a multi-agent synthetic testbed application indicate that CAST enhances the effectiveness of teamwork among agents without sacrificing a high cost for communications.
Towards multi-cue urban curb recognition

This paper presents a multi-cue approach to curb recognition in urban traffic. We propose a novel texture-based curb classifier using local receptive field (LRF) features in conjunction with a multi-layer neural network. This classification module operates on both intensity images and on three-dimensional height profile data derived from stereo vision. We integrate the proposed multi-cue curb classifier as an additional measurement module into a state-of-the-art Kaiman filter-based urban lane recognition system. Our experiments involve a challenging real-world dataset captured in urban traffic with manually labeled ground-truth. We quantify the benefit of the proposed multi-cue curb classifier in terms of the improvement in curb localization accuracy of the integrated system. Our results indicate a 25% reduction of the average curb localization error at real-time processing speeds.
Functional outcome in patients with thoracolumbar burst fractures treated with dorsal instrumentation and transpedicular cancellous bone grafting

The aim of the study was to develop an insight into the impairments in spinal fracture patients, operatively treated with an internal fixator, and also into their ability to participate in daily living, return to work and quality of life as defined by the World Health Organization. Nineteen patients operated for a type A fracture of the thoracolumbar spine (T9–L4) between 1993 and 1998 in the University Hospital Groningen, the Netherlands, aged between 18 and 60 years, without neurological deficit were included in the study. Operative treatment consisted of fracture reduction and internal fixation using the Universal Spine System, combined with transpedicular cancellous bone grafting and dorsal spondylodesis. No ventral fusion operations, laminectomies or discectomies were done. Restrictions in body function and structure were measured on radiographs and in functional capacity tests, such as lifting tests and ergometry. Restrictions in activities were studied with the Visual Analogue Scale (VAS) Spine Score and the Roland Morris Disability Questionnaire (RMDQ). Restrictions in participation/quality of life were analysed with the Short Form 36 (SF36) and described in the return to work status. The radiological results are comparable to the literature. The reduction of the anterior wedge angle was followed by a gradual partial loss of intervertebral angle and regional angle. The maximum oxygen uptake (VO2-max) was reduced in only 8.3% of the patients. Arm and trunk lift was within the normal range in 87% and 80% of the patients respectively, but only 53% of the patients were able to perform a leg lift within the normal range. A mean RMDQ score of 4.0 positive items (SD 6.0) was found, and the mean VAS Spinal Score was 79.4 (SD 25.0), both better than in other series. No significant differences compared to the values of a comparable (healthy) age group could be identified in any variable of the SF36. A high correlation was seen between RMDQ, VAS Spine Score and the SF36 categories. No correlation was found between the anterior wedge angle and the regional angle on the one hand, and functional capacity tests or questionnaire scores on the other. Of the patients in paid employment before the trauma, 87% had returned to work at follow-up. About 50% of the patients had been obliged to change the intensity of their work or the kind of work they performed after the injury and treatment. In this matter, leg (muscle) performance seems a more important factor than overall condition (VO2-max). The results of the study indicate that patients with thoracolumbar spinal fractures without neurological deficit, treated with dorsal instrumentation, perform like healthy people 3–8 years after injury, according to the RMDQ, VAS Spine Score and SF36 results. Physical capacity tests reveal that leg (muscle) performance seems a more important factor in impairment than arm lift or overall condition.
Design & comparison of a conventional and permanent magnet based claw-pole machine for automotive application

This paper presents the design and performance comparison of a conventional claw-pole machine with a permanent magnet based claw-pole machine for automotive application. The magnets are placed in the inter-claw region to decrease the leakage flux and to supply increased magnetic flux in the machine. It has been observed that with the addition of permanent magnets weighing only a few grams, there is a significant increment of more than 22% in output power of the machine. The geometric dimensions of magnets were also varied to verify their effects on performance and it has been observed that with the increase in magnet weight there is a non-linear increment in torque of the machine.
[Botulinum toxin injection techniques in the lower third and middle of the face, the neck and the décolleté: the "Nefertiti lift"].

Although correction of the dynamic wrinkles of the upper part of the face is the major indication for botulinum toxin, there are also many possibilities for the middle and lower thirds of the face and neck. However, these injections are more delicate and require an experienced operator who has excellent knowledge of the muscles of these regions, their functions, the antagonist actions exercised on other muscles, particularly in terms of the complex equilibrium of the mouth. An excessive dose, an inappropriate injection point, or a centering mistake can all easily be responsible for undesirable side effects. However, the results obtained, often with lower doses than in the superior part of the face, can be highly satisfactory, notably in erasing bunny lines, improvement of marionette lines, peau d'orange chin, attenuation of peribuccal lines, melomental folds, correction of a gummy smile, and facial asymmetries. In the neck it is possible to reduce platysmal bands, horizontal lines, and diagonal lines of the neck and décolleté. The face contours can also be improved by the Nefertiti lift. In the mid and lower regions of the face, botulinum toxin is often a complement to other esthetic techniques, particularly filling procedures.
Analysis of intercrossed open-source software repositories data in GitHub

The use of public repositories of software is becoming increasingly common as a strategy for reducing costs and streamlining software development processes. However, given the large amount of software available on such deposits, the task of selecting the components and applications of higher quality or that best meet the quality requirements of a product, has become a demanding task in time and effort. In this paper we present a tool to support the analysis and selection of components and software applications in public repositories, particularly GitHub.
Image super-resolution using gradient profile prior

In this paper, we propose an image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can provide a constraint on image gradients when we estimate a hi-resolution image from a low-resolution image. With this simple but very effective prior, we are able to produce state-of-the-art results. The reconstructed hi-resolution image is sharp while has rare ringing or jaggy artifacts.
A Survey on Approaches for Interoperability and Portability of Cloud Computing Services

Over the recent years, the rapid development of Cloud Computing has driven to a large market of cloud services that offer infrastructure, platforms and software to everyday users. Yet, due to the lack of common accepted standards, cloud service providers use different technologies and offer their clients services that are operated by a variety of proprietary APIs. The lack of standardization results in numerous heterogeneities (e.g., heterogeneous service descriptions, message level naming conflicts, data representation conflicts etc.) making the interoperation, collaboration and portability of services a very complex task. In this work we focus on the problems of interoperability and portability in Cloud Computing, we address their differences and we discuss some of the latest research work in this area. Finally we evaluate and point out relationships between the identified solutions.
Unsupervised Simultaneous Orthogonal basis Clustering Feature Selection

In this paper, we propose a novel unsupervised feature selection method: Simultaneous Orthogonal basis Clustering Feature Selection (SOCFS). To perform feature selection on unlabeled data effectively, a regularized regression-based formulation with a new type of target matrix is designed. The target matrix captures latent cluster centers of the projected data points by performing orthogonal basis clustering, and then guides the projection matrix to select discriminative features. Unlike the recent unsupervised feature selection methods, SOCFS does not explicitly use the pre-computed local structure information for data points represented as additional terms of their objective functions, but directly computes latent cluster information by the target matrix conducting orthogonal basis clustering in a single unified term of the proposed objective function. It turns out that the proposed objective function can be minimized by a simple optimization algorithm. Experimental results demonstrate the effectiveness of SOCFS achieving the state-of-the-art results with diverse real world datasets.
Effects of color on emotions.

Emotional reactions to color hue, saturation, and brightness (Munsell color system and color chips) were investigated using the Pleasure-Arousal-Dominance emotion model. Saturation (S) and brightness (B) evidenced strong and consistent effects on emotions. Regression equations for standardized variables were; Pleasure = .69B + .22S, Arousal = -.31B + .60S, Dominance = -.76B + .32S. Brightness effects were nearly the same for chromatic and achromatic colors. Blue, blue-green, green, red-purple, purple, and purple-blue were the most pleasant hues, whereas yellow and green-yellow were the least pleasant. Green-yellow, blue-green, and green were the most arousing, whereas purple-blue and yellow-red were the least arousing. Green-yellow induced greater dominance than red-purple.
Semi-Markov model for simulating MOOC students

Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this study, we suggest a methodology for simulating MOOC students, which allow estimation of distributions, before implementing a large-scale experiment. To this end, we employ generative models to draw independent samples of artificial students in Monte Carlo simulations. We use SemiMarkov Chains for modeling student’s activities and ExpectationMaximization algorithm for fitting the model. From the fitted model, we generate simulated students whose processes of weekly activities are similar to these of the real students.
Tampering with the Delivery of Blocks and Transactions in Bitcoin

Given the increasing adoption of Bitcoin, the number of transactions and the block sizes within the system are only expected to increase. To sustain its correct operation in spite of its ever-increasing use, Bitcoin implements a number of necessary optimizations and scalability measures. These measures limit the amount of information broadcast in the system to the minimum necessary. In this paper, we show that current scalability measures adopted by Bitcoin come at odds with the security of the system. More specifically, we show that an adversary can exploit these measures in order to effectively delay the propagation of transactions and blocks to specific nodes for a considerable amount of time---without causing a network partitioning in the system. Notice that this attack alters the information received by Bitcoin nodes, and modifies their views of the ledger state. Namely, we show that this allows the adversary to considerably increase its mining advantage in the network, and to double-spend transactions in spite of the current countermeasures adopted by Bitcoin. Based on our results, we propose a number of countermeasures in order to enhance the security of Bitcoin without deteriorating its scalability.
Prediction and Simulation of Human Mobility Following Natural Disasters

In recent decades, the frequency and intensity of natural disasters has increased significantly, and this trend is expected to continue. Therefore, understanding and predicting human behavior and mobility during a disaster will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is very difficult to perform owing to the uniqueness of various disasters and the unavailability of reliable and large-scale human mobility data. In this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users1 over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data) to study human mobility following natural disasters. An empirical analysis is conducted to explore the basic laws governing human mobility following disasters, and an effective human mobility model is developed to predict and simulate population movements. The experimental results demonstrate the efficiency of our model, and they suggest that human mobility following disasters can be significantly more predictable and be more easily simulated than previously thought.
Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation

With the explosive growth of web data, effective and efficient technologies are in urgent need for retrieving semantically relevant contents of heterogeneous modalities. Previous studies devote efforts to modeling simple cross-modal statistical dependencies, and globally projecting the heterogeneous modalities into a measurable subspace. However, global projections cannot appropriately adapt to diverse contents, and the naturally existing multilevel semantic relation in web data is ignored. We study the problem of semantic coherent retrieval, where documents from different modalities should be ranked by the semantic relevance to the query. Accordingly, we propose TINA, a correlation learning method by adaptive hierarchical semantic aggregation. First, by joint modeling of content and ontology similarities, we build a semantic hierarchy to measure multilevel semantic relevance. Second, with a set of local linear projections and probabilistic membership functions, we propose two paradigms for local expert aggregation, i.e., local projection aggregation and local distance aggregation. To learn the cross-modal projections, we optimize the structure risk objective function that involves semantic coherence measurement, local projection consistency, and the complexity penalty of local projections. Compared to existing approaches, a better bias-variance tradeoff is achieved by TINA in real-world cross-modal correlation learning tasks. Extensive experiments on widely used NUS-WIDE and ICML-Challenge for image-text retrieval demonstrate that TINA better adapts to the multilevel semantic relation and content divergence, and, thus, outperforms state of the art with better semantic coherence.
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step

Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion. Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost. GANs are designed to reach a Nash equilibrium at which each player cannot reduce their cost without changing the other players’ parameters. One useful approach for the theory of GANs is to show that a divergence between the training distribution and the model distribution obtains its minimum value at equilibrium. Several recent research directions have been motivated by the idea that this divergence is the primary guide for the learning process and that every step of learning should decrease the divergence. We show that this view is overly restrictive. During GAN training, the discriminator provides learning signal in situations where the gradients of the divergences between distributions would not be useful. We provide empirical counterexamples to the view of GAN training as divergence minimization. Specifically, we demonstrate that GANs are able to learn distributions in situations where the divergence minimization point of view predicts they would fail. We also show that gradient penalties motivated from the divergence minimization perspective are equally helpful when applied in other contexts in which the divergence minimization perspective does not predict they would be helpful. This contributes to a growing body of evidence that GAN training may be more usefully viewed as approaching Nash equilibria via trajectories that do not necessarily minimize a specific divergence at each step.
A regression tree approach to identifying subgroups with differential treatment effects.

In the fight against hard-to-treat diseases such as cancer, it is often difficult to discover new treatments that benefit all subjects. For regulatory agency approval, it is more practical to identify subgroups of subjects for whom the treatment has an enhanced effect. Regression trees are natural for this task because they partition the data space. We briefly review existing regression tree algorithms. Then, we introduce three new ones that are practically free of selection bias and are applicable to data from randomized trials with two or more treatments, censored response variables, and missing values in the predictor variables. The algorithms extend the generalized unbiased interaction detection and estimation (GUIDE) approach by using three key ideas: (i) treatment as a linear predictor, (ii) chi-squared tests to detect residual patterns and lack of fit, and (iii) proportional hazards modeling via Poisson regression. Importance scores with thresholds for identifying influential variables are obtained as by-products. A bootstrap technique is used to construct confidence intervals for the treatment effects in each node. The methods are compared using real and simulated data.
A New Target-specific Object Proposal Generation Method for Visual Tracking

Object proposal generation methods have been widely applied to many computer vision tasks. However, existing object proposal generation methods often suffer from the problems of motion blur, low contrast, deformation, etc., when they are applied to video related tasks. In this paper, we propose an effective and highly accurate target-specific object proposal generation (TOPG) method, which takes full advantage of the context information of a video to alleviate these problems. Specifically, we propose to generate target-specific object proposals by integrating the information of two important objectness cues: colors and edges, which are complementary to each other for different challenging environments in the process of generating object proposals. As a result, the recall of the proposed TOPG method is significantly increased. Furthermore, we propose an object proposal ranking strategy to increase the rank accuracy of the generated object proposals. The proposed TOPG method has yielded significant recall gain (about 20%-60% higher) compared with several state-of-the-art object proposal methods on several challenging visual tracking datasets. Then, we apply the proposed TOPG method to the task of visual tracking and propose a TOPG-based tracker (called as TOPGT), where TOPG is used as a sample selection strategy to select a small number of highquality target candidates from the generated object proposals. Since the object proposals generated by the proposed TOPG cover many hard negative samples and positive samples, these object proposals can not only be used for training an effective classifier, but also be used as target candidates for visual tracking. Experimental results show the superior performance of TOPGT for visual tracking compared with several other state-of-the-art visual trackers (about 3%-11% higher than the winner of the VOT2015 challenge in term of distance precision).