nils-herrmann/clean_scirepeval huggingface dataset:
An Evolutionary Transfer Reinforcement Learning Framework for Multiagent Systems

In this paper, we present an evolutionary transfer reinforcement learning framework (eTL) for developing intelligent agents capable of adapting to the dynamic environment of multiagent systems (MASs). Specifically, we take inspiration from Darwin’s theory of natural selection and Universal Darwinism as the principal driving forces that govern the evolutionary knowledge transfer process. The essential backbone of our proposed eTL comprises several meme-inspired evolutionary mechanisms, namely meme representation, meme expression, meme assimilation, meme internal evolution, and meme external evolution. Our proposed approach constructs social selection mechanisms that are modeled after the principles of human learning to identify appropriate interacting partners. eTL also models the intrinsic parallelism of natural evolution and errors that are introduced due to the physiological limits of the agents’ ability to perceive differences, so as to generate “growth” and “variation” of knowledge that agents have of the world, thus exhibiting higher adaptivity capabilities on solving complex problems. To verify the efficacy of the proposed paradigm, comprehensive investigations of the proposed eTL against existing state-of-the-art TL methods in MAS, are conducted on the “minefield navigation tasks” platform and the “Unreal Tournament 2004” first person shooter computer game, in which homogeneous and heterogeneous learning machines are considered.
Ormond's disease or secondary retroperitoneal fibrosis? An overview of retroperitoneal fibrosis.

Retroperitoneal fibrosis represents a rare inflammatory disease. About two thirds of all cases seem to be idiopathic (= Ormond's disease). The remaining one third is secondary and may be ascribed to infections, trauma, radiation therapy, malignant diseases, and the use of certain drugs. Up to 15 % of patients have additional fibrotic processes outside the retroperitoneum. The clinical symptoms of retroperitoneal fibrosis are non-specific. In sonography retroperitoneal fibrosis appears as a retroperitoneal hypoechoic mass which can involve the ureters and thus cause hydronephrosis. Intravenous urography and MR urography can demonstrate the typical triad of medial deviation and extrinsic compression of the ureters and hydronephrosis. CT and MRI are the modalities of choice for the diagnosis and follow-up of this disease. The lesion typically begins at the level of the fourth or fifth lumbar vertebra and appears as a plaque, encasing the aorta and the inferior vena cava and often enveloping and medially displacing the ureters. In unenhanced CT, retroperitoneal fibrosis appears as a mass that is isodense with muscle. When using MRI, the mass is hypointense in T 1-weighted images and of variable intensity in T 2-weighted images according to its stage: it may be hyperintense in early stages, while the tissue may have a low signal in late stages. After the administration of contrast media, enhancement is greatest in the early inflammatory phase and minimal in the late fibrotic phase. Dynamic gadolinium enhancement can be useful for assessing disease activity, monitoring response to treatment, and detecting relapse. To differentiate retroperitoneal masses, diffusion-weighted MRI may provide useful information.
Prasugrel versus clopidogrel in patients with acute coronary syndromes.

Dual-antiplatelet therapy with aspirin and a thienopyridine is a cornerstone of treatment to prevent thrombotic complications of acute coronary syndromes and percutaneous coronary intervention.



To compare prasugrel, a new thienopyridine, with clopidogrel, we randomly assigned 13,608 patients with moderate-to-high-risk acute coronary syndromes with scheduled percutaneous coronary intervention to receive prasugrel (a 60-mg loading dose and a 10-mg daily maintenance dose) or clopidogrel (a 300-mg loading dose and a 75-mg daily maintenance dose), for 6 to 15 months. The primary efficacy end point was death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke. The key safety end point was major bleeding.



The primary efficacy end point occurred in 12.1% of patients receiving clopidogrel and 9.9% of patients receiving prasugrel (hazard ratio for prasugrel vs. clopidogrel, 0.81; 95% confidence interval [CI], 0.73 to 0.90; P<0.001). We also found significant reductions in the prasugrel group in the rates of myocardial infarction (9.7% for clopidogrel vs. 7.4% for prasugrel; P<0.001), urgent target-vessel revascularization (3.7% vs. 2.5%; P<0.001), and stent thrombosis (2.4% vs. 1.1%; P<0.001). Major bleeding was observed in 2.4% of patients receiving prasugrel and in 1.8% of patients receiving clopidogrel (hazard ratio, 1.32; 95% CI, 1.03 to 1.68; P=0.03). Also greater in the prasugrel group was the rate of life-threatening bleeding (1.4% vs. 0.9%; P=0.01), including nonfatal bleeding (1.1% vs. 0.9%; hazard ratio, 1.25; P=0.23) and fatal bleeding (0.4% vs. 0.1%; P=0.002).



In patients with acute coronary syndromes with scheduled percutaneous coronary intervention, prasugrel therapy was associated with significantly reduced rates of ischemic events, including stent thrombosis, but with an increased risk of major bleeding, including fatal bleeding. Overall mortality did not differ significantly between treatment groups.
Robust Light Field Depth Estimation for Noisy Scene with Occlusion

Light field depth estimation is an essential part of many light field applications. Numerous algorithms have been developed using various light field characteristics. However, conventional methods fail when handling noisy scene with occlusion. To remedy this problem, we present a light field depth estimation method which is more robust to occlusion and less sensitive to noise. Novel data costs using angular entropy metric and adaptive defocus response are introduced. Integration of both data costs improves the occlusion and noise invariant capability significantly. Cost volume filtering and graph cut optimization are utilized to improve the accuracy of the depth map. Experimental results confirm that the proposed method is robust and achieves high quality depth maps in various scenes. The proposed method outperforms the state-of-the-art light field depth estimation methods in qualitative and quantitative evaluation.
On Upper-Confidence Bound Policies for Switching Bandit Problems

Many problems, such as cognitive radio, parameter control of a scanning tunnelling microscope or internet advertisement, can be modelled as non-stationary bandit problems where the distributions of rewards changes abruptly at unknown time instants. In this paper, we analyze two algorithms designed for solving this issue: discounted UCB (D-UCB) and sliding-window UCB (SW-UCB). We establish an upperbound for the expected regret by upper-bounding the expectation of the number of times suboptimal arms are played. The proof relies on an interesting Hoeffding type inequality for self normalized deviations with a random number of summands. We establish a lower-bound for the regret in presence of abrupt changes in the arms reward distributions. We show that the discounted UCB and the sliding-window UCB both match the lower-bound up to a logarithmic factor. Numerical simulations show that D-UCB and SW-UCB perform significantly better than existing soft-max methods like EXP3.S.
Evaluation of a liquid dressing for minor nonbleeding abrasions and class I and II skin tears in the emergency department.

Minor abrasions and skin tears are usually treated with gauze dressings and topical antibiotics requiring frequent and messy dressing changes.



We describe our experience with a low-cost, cyanoacrylate-based liquid dressing applied only once for minor abrasions and skin tears.



We conducted a single-center, prospective, noncomparative study in adult emergency department (ED) patients with minor nonbleeding skin abrasions and class I and II skin tears. After cleaning the wound and achieving hemostasis, the wounds were covered with a single layer of a cyanoacrylate liquid dressing. Patients were followed every 1-2 days until healing.



We enrolled 40 patients with 50 wounds including 39 abrasions and 11 skin tears. Mean (standard deviation) age was 54.5 (21.9) years and 57.5% were male. Wounds were located on the face (n = 16), hands (n = 14), legs (n = 11), and arms (n = 9). Pain scores (0 to 10 from none to worst) after application of the liquid dressing were 0 in 62% and 1-3 in the remaining patients. Follow-up was available on 36 patients and 46 wounds. No wounds re-bled and there were no wound infections. Only one wound required an additional dressing. Median (interquartile range [IQR]) time to complete sloughing of the adhesive was 7 (5.5-8) days. Median (IQR) time to complete healing and sloughing of the overlying scab was 10 (7.4-14) days.



Our study suggests that a single application of a low-cost cyanoacrylate-based liquid adhesive is a safe and effective treatment for superficial nonbleeding abrasions and class I and II skin tears that eliminates the need for topical antibiotics and dressings.
A Scalable, Hybrid Scheme for Volume Rendering Massive Data Sets

We introduce a parallel, distributed memory algorithm for volume rendering massive data sets. The algorithm's scalability has been demonstrated up to 400 processors, rendering one hundred million unstructured elements in under one second. The heart of the algorithm is a hybrid approach that parallelizes over both the elements of the input data and over the pixels of the output image. At each stage of the algorithm, there are strong limits on how much work each processor performs, ensuring good parallel efficiency. The algorithm is sample-based. We present two techniques for calculating the sample points: a 3D rasterization technique and a kernel-based technique, which trade off between speed and generality. Finally, the algorithm is very flexible. It can be deployed in general purpose visualization tools and can also support diverse mesh types, ranging from structured grids to curvilinear and unstructured meshes to point clouds.
Camera and lidar fusion for pedestrian detection

The aim of this work is to propose a fusion procedure based on lidar and camera to solve the pedestrian detection problem in autonomous driving. Current pedestrian detection algorithms have focused on improving the discriminability of 2D features that capture the pedestrian appearance, and on using various classifier architectures. However, less focus on exploiting the 3D structure of object has limited the pedestrian detection performance and practicality. To tackle these issues, a lidar subsystem is applied here in order to extract object structure features and train a SVM classifier, reducing the number of candidate windows that are tested by a state-of-the-art pedestrian appearance classifier. Additionally, we propose a probabilistic framework to fuse pedestrian detection given by both subsystems. With the proposed framework, we have achieved state-of-the-art performance at 20 fps on our own pedestrian dataset gathered in a challenging urban scenario.
Drivable Road Detection Based on Dilated FPN with Feature Aggregation

In the field of ADAS and self-driving car, lane and drivable road detection play an essential role in reliably accomplishing other tasks, such as objects detection. For monocular vision based semantic segmentation of lane and road, we propose a dilated feature pyramid network (FPN) with feature aggregation, called DFFA, where feature aggregation is employed to combine multi-level features enhanced with dilated convolution operations and FPN under the framework of ResNet. Experimental results validate effectiveness and efficiency of the proposed deep learning model for semantic segmentation of lane and drivable road. Our DFFA achieves the best performance both on Lane Estimation Evaluation and Behavior Evaluation tasks in KITTI-ROAD and take the second place on UU ROAD task.
Convex Factorization Machines

Factorization machines are a generic framework which allows to mimic many factorization models simply by feature engineering. In this way, they combine the high predictive accuracy of factorization models with the flexibility of feature engineering. Unfortunately, factorization machines involve a non-convex optimization problem and are thus subject to bad local minima. In this paper, we propose a convex formulation of factorization machines based on the nuclear norm. Our formulation imposes fewer restrictions on the learned model and is thus more general than the original formulation. To solve the corresponding optimization problem, we present an efficient globally-convergent twoblock coordinate descent algorithm. Empirically, we demonstrate that our approach achieves comparable or better predictive accuracy than the original factorization machines on 4 recommendation tasks and scales to datasets with 10 million samples.
Osteonecrosis Secondary to Paget's Disease: Radiologic and Pathologic Features

Paget's disease (PD) is a chronic progressive disease of the bone characterized by abnormal bone metabolism affecting either a single bone (monostotic) or many bones (polyostotic) with uncertain etiology. We report a case of PD in a 70-year-old male, which was initially identified as osteonecrosis of the maxilla. Non-drug induced osteonecrosis in PD is rare and very few cases have been reported in the literature.
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models

Along with the interpretability algorithms cited in the main document, we briefly point out some of the relevant stateof-the-art algorithms. One of the most notable interpretability frameworks at the moment is an extension of generative adversarial networks (GANs) (Goodfellow et al., 2014) referred to as InfoGAN (Chen et al., 2016). In Chen et al. (2016), a recognition network covering a subset of the variables is established in a GAN such that the mutual information between the recognition network and a set of prespecified variables representing salient attributes is maximized. The claim that InfoGAN is unsupervised is not fully precise due to the required knowledge of such variables. Performance of InfoGAN is also affected by the instability of GANs. Another issue directly stemming from the reliance on a GAN framework is the lack of a comprehensive inference network like those guaranteed in VAE-based frameworks.
Residual Dense Network for Image Super-Resolution

A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN based SR models do not make full use of the hierarchical features from the original low-resolution (LR) images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via dense connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory (CM) mechanism. Local feature fusion in RDB is then used to adaptively learn more effective features from preceding and current local features and stabilizes the training of wider network. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. Experiments on benchmark datasets with different degradation models show that our RDN achieves favorable performance against state-of-the-art methods.
GVDB: raytracing sparse voxel database structures on the GPU

Simulation and rendering of sparse volumetric data have different constraints and solutions depending on the application area. Generating precise simulations and understanding very large data are problems in scientific visualization, whereas convincing simulations and realistic visuals are challenges in motion pictures. Both require volumes with dynamic topology, very large domains, and efficient high quality rendering. We present the GPU voxel database structure, GVDB, based on the voxel database topology of Museth [Mus13], as a method for efficient GPU-based compute and raytracing on a sparse hierarchy of grids. GVDB introduces an indexed memory pooling design for dynamic topology, and a novel hierarchical traversal for efficient raytracing on the GPU. Examples are provided for ray sampling of volumetric data, rendering of isosurfaces with multiple scattering, and raytracing of level sets. We demonstrate that GVDB can give large performance improvements over CPU methods with identical quality.
CUDA vs OpenACC: Performance Case Studies with Kernel Benchmarks and a Memory-Bound CFD Application

OpenACC is a new accelerator programming interface that provides a set of OpenMP-like loop directives for the programming of accelerators in an implicit and portable way. It allows the programmer to express the offloading of data and computations to accelerators, such that the porting process for legacy CPU-based applications can be significantly simplified. This paper focuses on the performance aspects of OpenACC using two micro benchmarks and one real-world computational fluid dynamics application. Both evaluations show that in general OpenACC performance is approximately 50\% lower than CUDA. However, for some applications it can reach up to 98\% with careful manual optimizations. The results also indicate several limitations of the OpenACC specification that hamper full use of the GPU hardware resources, resulting in a significant performance gap when compared to a fully tuned CUDA code. The lack of a programming interface for the shared memory in particular results in as much as three times lower performance.
Full Quantification of Left Ventricle via Deep Multitask Learning Network Respecting Intra- and Inter-Task Relatedness

Cardiac left ventricle (LV) quantification is among the most clinically important tasks for identification and diagnosis of cardiac diseases, yet still a challenge due to the high variability of cardiac structure and the complexity of temporal dynamics. Full quantification, i.e., to simultaneously quantify all LV indices including two areas (cavity and myocardium), six regional wall thicknesses (RWT), three LV dimensions, and one cardiac phase, is even more challenging since the uncertain relatedness intra and inter each type of indices may hinder the learning procedure from better convergence and generalization. In this paper, we propose a newly-designed multitask learning network (FullLVNet), which is constituted by a deep convolution neural network (CNN) for expressive feature embedding of cardiac structure; two followed parallel recurrent neural network (RNN) modules for temporal dynamic modeling; and four linear models for the final estimation. During the final estimation, both intraand inter-task relatedness are modeled to enforce improvement of generalization: (1) respecting intra-task relatedness, group lasso is applied to each of the regression tasks for sparse and common feature selection and consistent prediction; (2) respecting inter-task relatedness, three phase-guided constraints are proposed to penalize violation of the temporal behavior of the obtained LV indices. Experiments on MR sequences of 145 subjects show that FullLVNet achieves high accurate prediction with our intraand inter-task relatedness, leading to MAE of 190 mm, 1.41 mm, 2.68 mm for average areas, RWT, dimensions and error rate of 10.4% for the phase classification. This endows our method a great potential in comprehensive clinical assessment of global, regional and dynamic cardiac function.
To better stand on the shoulder of giants

Usually scientists breed research ideas inspired by previous publications, but they are unlikely to follow all publications in the unbounded literature collection. The volume of literature keeps on expanding extremely fast, whilst not all papers contribute equal impact to the academic society. Being aware of potentially influential literature would put one in an advanced position in choosing important research references. Hence, estimation of potential influence is of great significance. We study a challenging problem of identifying potentially influential literature. We examine a set of hypotheses on what are the fundamental characteristics for highly cited papers and find some interesting patterns. Based on these observations, we learn to identify potentially influential literature via Future Influence Prediction (FIP), which aims to estimate the future influence of literature. The system takes a series of features of a particular publication as input and produces as output the estimated citation counts of that article after a given time period. We consider several regression models to formulate the learning process and evaluate their performance based on the coefficient of determination (R2). Experimental results on a real-large data set show a mean average predictive performance of 83.6% measured in R^2. We apply the learned model to the application of bibliography recommendation and obtain prominent performance improvement in terms of Mean Average Precision (MAP).
Designing pen-and-paper user interfaces for interaction with documents

Despite numerous predictions of the paperless office, knowledge work is still characterized by the combined use of paper and digital documents. Digital pen-and-paper user interfaces bridge the gap between both worlds by electronically capturing the interactions of a user with a pen on real paper. The contribution of this paper is two-fold: First, we introduce an interaction framework for pen-and-paper user interfaces consisting of six core interactions. This helps both in analyzing existing work practices and interfaces and in guiding the design of interfaces which offer complex functionality and nevertheless remain simple to use. Second, we apply this framework and contribute three novel pen-and-paper interaction strategies for creating hyperlinks between printed and digital documents and for tagging both types of documents.
Adaptive Droop Control Strategy for Load Sharing and Circulating Current Minimization in Low-Voltage Standalone DC Microgrid

This paper addresses load current sharing and circulating current issues of parallel-connected DC-DC converters in low-voltage DC microgrid. Droop control is the popular technique for load current sharing in DC microgrid. The main drawbacks of the conventional droop method are poor current sharing and drop in dc grid voltage due to the droop action. Circulating current issue will also arise due to mismatch in the converters output voltages. In this work, a figure of merit called droop index (DI) is introduced in order to improve the performance of dc microgrid, which is a function of normalized current sharing difference and losses in the output side of the converters. This proposed adaptive droop control method minimizes the circulating current and current sharing difference between the converters based on instantaneous virtual resistance Rdroop. Using Rdroop shifting, the proposed method also eliminates the tradeoff between current sharing difference and voltage regulation. The detailed analysis and design procedure are explained for two DC-DC boost converters connected in parallel. The effectiveness of the proposed method is verified by detailed simulation and experimental studies.