nils-herrmann/clean_scirepeval huggingface dataset:
Normalization in Training Deep Convolutional Neural Networks for 2D Bio-medical Semantic Segmentation

2D bio-medical semantic segmentation is important for surgical robotic vision. Segmentation methods based on Deep Convolutional Neural Network (DCNN) out-perform conventional methods in terms of both the accuracy and automation. One common issue in training DCNN is the internal covariate shift, where the convolutional kernels are trained to fit the distribution change of input feature, hence both the training speed and performance are decreased. Batch Normalization (BN) is the first proposed method for addressing internal covariate shift and is widely used. Later Instance Normalization (IN) and Layer Normalization (LN) were proposed and are used much less than BN. Group Normalization (GN) was proposed very recently and has not been applied into 2D biomedical semantic segmentation yet. Most DCNN-based biomedical semantic segmentation adopts BN as the normalization method by default, without reviewing its performance. In this paper, four normalization methods BN, IN, LN and GN are compared and reviewed in details specifically for 2D biomedical semantic segmentation. The result proved that GN outperformed the other three normalization methods BN, IN and LN in 2D bio-medical semantic segmentation regarding both the accuracy and robustness. Unet is adopted as the basic DCNN structure. 37 RVs from both asymptomatic and Hypertrophic Cardiomyopathy (HCM) subjects and 20 aortas from asymptomatic subjects were used for the validation. The code and trained models will be available online.
Biomedical signal processing (in four parts). Part 3. The power spectrum and coherence function.

This is the third in a series of four tutorial papers on biomedical signal processing and concerns the estimation of the power spectrum (PS) and coherence function (CF) od biomedical data. The PS is introduced and its estimation by means of the discrete Fourier transform is considered in terms of the problem of resolution in the frequency domain. The periodogram is introduced and its variance, bias and the effects of windowing and smoothing are considered. The use of the autocovariance function as a stage in power spectral estimation is described and the effects of windows in the autocorrelation domain are compared with the related effects of windows in the original time domain. The concept of coherence is introduced and the many ways in which coherence functions might be estimated are considered.
Short Interactive Oracle Proofs with Constant Query Complexity, via Composition and Sumcheck

We study interactive oracle proofs (IOPs) [BCS16, RRR16], which combine aspects of probabilistically checkable proofs (PCPs) and interactive proofs (IPs). We present IOP constructions and techniques that let us achieve tradeoffs in proof length versus query complexity that are not known to be achievable via PCPs or IPs alone. Our main results are: 1. Circuit satisfiability has 3-round IOPs with linear proof length (counted in bits) and constant query complexity. 2. Reed–Solomon codes have 2-round IOPs of proximity with linear proof length and constant query complexity. 3. Tensor product codes have 1-round IOPs of proximity with sublinear proof length and constant query complexity. (A familiar example of a tensor product code is the Reed–Muller code with a bound on individual degrees.) For all the above, known PCP constructions give quasilinear proof length and constant query complexity [BS08, Din07]. Also, for circuit satisfiability, [BKK13] obtain PCPs with linear proof length but sublinear (and super-constant) query complexity. As in [BKK13], we rely on algebraic-geometry codes to obtain our first result; but, unlike that work, our use of such codes is much “lighter” because we do not rely on any automorphisms of the code. We obtain our results by proving and combining “IOP-analogues” of tools underlying numerous IPs and PCPs: • Interactive proof composition. Proof composition [AS98] is used to reduce the query complexity of PCP verifiers, at the cost of increasing proof length by an additive factor that is exponential in the verifier’s randomness complexity. We prove a composition theorem for IOPs where this additive factor is linear. • Sublinear sumcheck. The sumcheck protocol [LFKN92, Sha92] is an IP that enables the verifier to check the sum of values of a low-degree multi-variate polynomial on an exponentially-large hypercube, but the verifier’s running time depends linearly on the bound on individual degrees. We prove a sumcheck protocol for IOPs where this dependence is sublinear (e.g., polylogarithmic). Our work demonstrates that even constant-round IOPs are more efficient than known PCPs and IPs.
Global Economic Impact of Dental Diseases.

Reporting the economic burden of oral diseases is important to evaluate the societal relevance of preventing and addressing oral diseases. In addition to treatment costs, there are indirect costs to consider, mainly in terms of productivity losses due to absenteeism from work. The purpose of the present study was to estimate the direct and indirect costs of dental diseases worldwide to approximate the global economic impact. Estimation of direct treatment costs was based on a systematic approach. For estimation of indirect costs, an approach suggested by the World Health Organization's Commission on Macroeconomics and Health was employed, which factored in 2010 values of gross domestic product per capita as provided by the International Monetary Fund and oral burden of disease estimates from the 2010 Global Burden of Disease Study. Direct treatment costs due to dental diseases worldwide were estimated at US$298 billion yearly, corresponding to an average of 4.6% of global health expenditure. Indirect costs due to dental diseases worldwide amounted to US$144 billion yearly, corresponding to economic losses within the range of the 10 most frequent global causes of death. Within the limitations of currently available data sources and methodologies, these findings suggest that the global economic impact of dental diseases amounted to US$442 billion in 2010. Improvements in population oral health may imply substantial economic benefits not only in terms of reduced treatment costs but also because of fewer productivity losses in the labor market.
A Survey on Data-driven Dictionary-based Methods for 3D Modeling

Dictionaries are very useful objects for data analysis, as they enable a compact representation of large sets of objects through the combination of atoms. Dictionary-based techniques have also particularly benefited from the recent advances in machine learning, which has allowed for data-driven algorithms to take advantage of the redundancy in the input dataset and discover relations between objects without human supervision or hard-coded rules. Despite the success of dictionary-based techniques on a wide range of tasks in geometric modeling and geometry processing, the literature is missing a principled state-of-the-art of the current knowledge in this field. To fill this gap, we provide in this survey an overview of data-driven dictionary-based methods in geometric modeling. We structure our discussion by application domain: surface reconstruction, compression, and synthesis. Contrary to previous surveys, we place special emphasis on dictionary-based methods suitable for 3D data synthesis, with applications in geometric modeling and design. Our ultimate goal is to enlight the fact that these techniques can be used to combine the data-driven paradigm with design intent to synthesize new plausible objects with minimal human intervention. This is the main motivation to restrict the scope of the present survey to techniques handling point clouds and meshes, making use of dictionaries whose definition depends on the input data, and enabling shape reconstruction or synthesis through the combination of atoms. CCS Concepts •Computing methodologies → Shape modeling; Mesh models; Mesh geometry models; Point-based models; Shape analysis;
Immediate effect of specific nostril manipulating yoga breathing practices on autonomic and respiratory variables.

The effect of right, left, and alternate nostril yoga breathing (i.e., RNYB, LNYB, and ANYB, respectively) were compared with breath awareness (BAW) and normal breathing (CTL). Autonomic and respiratory variables were studied in 21 male volunteers with ages between 18 and 45 years and experience in the yoga breathing practices between 3 and 48 months. Subjects were assessed in five experimental sessions on five separate days. The sessions were in fixed possible sequences and subjects were assigned to a sequence randomly. Each session was for 40 min; 30 min for the breathing practice, preceded and followed by 5 min of quiet sitting. Assessments included heart rate variability, skin conductance, finger plethysmogram amplitude, breath rate, and blood pressure. Following RNYB there was a significant increase in systolic, diastolic and mean pressure. In contrast, the systolic and diastolic pressure decreased after ANYB and the systolic and mean pressure were lower after LNYB. Hence, unilateral nostril yoga breathing practices appear to influence the blood pressure in different ways. These effects suggest possible therapeutic applications.
Optimizing Semi-Honest Secure Multiparty Computation for the Internet

In the setting of secure multiparty computation, a set of parties with private inputs wish to compute some function of their inputs without revealing anything but their output. Over the last decade, the efficiency of secure two-party computation has advanced in leaps and bounds, with speedups of some orders of magnitude, making it fast enough to be of use in practice. In contrast, progress on the case of multiparty computation (with more than two parties) has been much slower, with very little work being done. Currently, the only implemented efficient multiparty protocol has many rounds of communication (linear in the depth of the circuit being computed) and thus is not suited for Internet-like settings where latency is not very low. In this paper, we construct highly efficient constant-round protocols for the setting of multiparty computation for semi-honest adversaries. Our protocols work by constructing a multiparty garbled circuit, as proposed in BMR (Beaver et al., STOC 1990). Our first protocol uses oblivious transfer and constitutes the first concretely-efficient constant-round multiparty protocol for the case of no honest majority. Our second protocol uses BGW, and is significantly more efficient than the FairplayMP protocol (Ben-David et al., CCS 2008) that also uses BGW.
 We ran extensive experimentation comparing our different protocols with each other and with a highly-optimized implementation of semi-honest GMW. Due to our protocol being constant round, it significantly outperforms GMW in Internet-like settings. For example, with 13 parties situated in the Virginia and Ireland Amazon regions and the SHA256 circuit with 90,000 gates and of depth 4000, the overall running time of our protocol is 25 seconds compared to 335 seconds for GMW. Furthermore, our online time is under half a second compared to 330 seconds for GMW.
MicroRNAs in metabolism

MicroRNAs (miRNAs) have within the past decade emerged as key regulators of metabolic homoeostasis. Major tissues in intermediary metabolism important during development of the metabolic syndrome, such as β-cells, liver, skeletal and heart muscle as well as adipose tissue, have all been shown to be affected by miRNAs. In the pancreatic β-cell, a number of miRNAs are important in maintaining the balance between differentiation and proliferation (miR-200 and miR-29 families) and insulin exocytosis in the differentiated state is controlled by miR-7, miR-375 and miR-335. MiR-33a and MiR-33b play crucial roles in cholesterol and lipid metabolism, whereas miR-103 and miR-107 regulates hepatic insulin sensitivity. In muscle tissue, a defined number of miRNAs (miR-1, miR-133, miR-206) control myofibre type switch and induce myogenic differentiation programmes. Similarly, in adipose tissue, a defined number of miRNAs control white to brown adipocyte conversion or differentiation (miR-365, miR-133, miR-455). The discovery of circulating miRNAs in exosomes emphasizes their importance as both endocrine signalling molecules and potentially disease markers. Their dysregulation in metabolic diseases, such as obesity, type 2 diabetes and atherosclerosis stresses their potential as therapeutic targets. This review emphasizes current ideas and controversies within miRNA research in metabolism.
Facial animation by optimized blendshapes from motion capture data

This paper presents an efficient method to construct optimal facial animation blendshapes from given blendshape sketches and facial motion capture data. At first, a mapping function is established between “Marker Face” of target and performer by RBF interpolating selected feature points. Sketched blendshapes are transferred to performer’s “Marker Face” by using motion vector adjustment technique. Then, the blendshapes of performer’s “Marker Face” are optimized according to the facial motion capture data. At last, the optimized blendshapes are inversely transferred to target facial model. Apart from that, this paper also proposes a method of computing blendshape weights from facial motion capture data more precisely. Experiments show that expressive facial animation can be acquired.
The Role Mining Process Model - Underlining the Need for a Comprehensive Research Perspective

Organizations that migrate from identity-centric to role-based Identity Management face the initial task of defining a valid set of roles for their employees. Due to its capabilities of automated and fast role detection, role mining as a solution for dealing with this challenge has gathered a rapid increase of interest in the academic community. Research activities throughout the last years resulted in a large number of different approaches, each covering specific aspects of the challenge. In this paper, firstly, a survey of the research area provides insight into the development of the field, underlining the need for a comprehensive perspective on role mining. Consecutively, a generic process model for role mining including preand post-processing activities is introduced and existing research activities are classified according to this model. The goal is to provide a basis for evaluating potentially valuable combinations of those approaches in the future.
Ontologies for Describing the Context of Scientific Experiment Processes

The re-usability and repeatability of e-Science experiments is widely understood as a requirement of validating and reusing previous work in data-intensive domains. Experiments are, however, often complex chains of processing, involving a number of data sources, computing infrastructure, software tools, or external and third-party services, rendering repeatability a challenging task. Another important aspect of many experiments is in the social and organisational dimension - very often, knowledge on how experiments are performed is tacit and remains with the researcher, and the collaborative and distributed aspects especially of larger collaborative experiments adds to this challenge. Therefore, a number of approaches have tackled this issue from various angles -- initiatives for data sharing, code versioning and publishing as open source, the use of workflow engines to formalise the steps taken in an experiment, to ways to describe the complex environment an experiment is executed in, e.g. via Research Objects. In this paper, we present a model that has a specific focus on the technical infrastructure that is the basis of the research experiment. We demonstrate how this model can be applied to describe e-Science experiments, and align and compare it to Research Objects.
Role of interconnections in porous bioceramics on bone recolonization in vitro and in vivo.

The interconnections in a porous biomaterial are the pathways between the pores. They conduct cells and vessels between pores. Thus they favour bone ingrowth inside ceramics. The aim of our study was to determine the effect on bone ingrowth of interconnections in two ceramics: hydroxyapatite (HA) and beta-tricalcium phosphate (beta-TCP) with the same porosity of about 50% and a mean pores size of 100-300 microm and a mean interconnection size of 30-100 microm. In vitro, four discs for osteoblast culture were studied after 14 and 28 days of incubation. The results show that human osteoblasts can penetrate interconnections over 20 microm in size, and colonize and proliferate inside macropores, but the most favourable size is over 40 microm. In vivo, eight cylinders were implanted in the middle shaft of both rabbit femurs for 12 or 24 weeks. The histomorphometric results show that interconnections in porous ceramics favour bone ingrowth inside the macropores. In the HA group the rate of calcification and bone ingrowth do not differ, and chondroid tissue is observed inside pores. But in beta-TCP, the calcification rate and the bone ingrowth increased significantly. At week 12 significant correlation between new bone ingrowth and the size of the interconnections is observed between new bone ingrowth and the density of pores. In conclusion we notice that in vivo a 20 microm interconnection size only allows cell penetration and chondroid tissue formation; however the size of the interconnections must be over 50 microm to favour new bone ingrowth inside the pores. We propose the concept of "interconnection density" which expresses the quantity of links between pores of porous materials. It assures cell proliferation and differentiation with blood circulation and extracellular liquid exchange. In resorbable materials, pore density and interconnection density are more important than their size, contrary to unresorbable materials in which the sizes and the densities are equally important.
Generating Query Suggestions to Support Task-Based Search

We address the problem of generating query suggestions to support users in completing their underlying tasks (which motivated them to search in the first place). Given an initial query, these query suggestions should provide a coverage of possible subtasks the user might be looking for. We propose a probabilistic modeling framework that obtains keyphrases from multiple sources and generates query suggestions from these keyphrases. Using the test suites of the TREC Tasks track, we evaluate and analyze each component of our model.
A New Metal Control Gate Last process (MCGL process) for high performance DC-SF (Dual Control gate with Surrounding Floating gate) 3D NAND flash memory

A new Metal Control Gate Last process (MCGL process) has been successfully developed for the DC-SF (Dual Control gate with Surrounding Floating gate cell)[1] three-dimensional (3D) NAND flash memory. The MCGL process can realize a low resistive tungsten (W) metal word-line with high-k IPD, a low damage on tunnel oxide/IPD, and a preferable FG shape. And also, a conventional bulk erase can be used, replaced GIDL erase in BiCS[3][4], due to direct connection between channel poly and p-well by the channel contact holes. Therefore, by using MCGL process, high performance and high reliability of DC-SF cell can be achieved for MLC/TLC 256Gb/512Gb 3D NAND flash memories.
Inverse Synthetic Aperture Secondary Radar Concept for Precise Wireless Positioning

In this paper, the novel inverse synthetic aperture secondary radar wireless positioning technique is introduced. The proposed concept allows for a precise spatial localization of a backscatter transponder even in dense multipath environments. A novel secondary radar signal evaluation concept compensates for the unknown modulation phase of the returned signal and thus leads to radar signals comparable to common primary radar. With use of this concept, inverse synthetic aperture radar algorithms can be applied to the signals of backscatter transponder systems. In simulations and first experiments, we used a broadband holographic reconstruction principle to realize the inverse synthetic aperture approach. The movement of the transponder along a short arbitrary aperture path is determined with assisting relative sensors (dead reckoning or inertia sensors). A set of signals measured along the aperture is adaptively focused to the transponder position. By this focusing technique, multipath reflections can be suppressed impressively and a precise indoor positioning becomes feasible. With our technique, completely new and powerful options for integrated navigation and sensor fusion in RF identification systems and wireless local positioning systems are now possible.
Visual Question Answering with Memory-Augmented Networks

In this paper, we exploit memory-augmented neural networks to predict accurate answers to visual questions, even when those answers rarely occur in the training set. The memory network incorporates both internal and external memory blocks and selectively pays attention to each training exemplar. We show that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting. Experimental results in two large-scale benchmark datasets show the favorable performance of the proposed algorithm with the comparison to state of the art.
Identification of the cellular targets of bioactive small organic molecules using affinity reagents.

The elucidation of molecular targets of bioactive small organic molecules remains a significant challenge in modern biomedical research and drug discovery. This tutorial review summarizes strategies for the derivatization of bioactive small molecules and their use as affinity probes to identify cellular binding partners. Special emphasis is placed on logistical concerns as well as common problems encountered during such target identification experiments. The roadmap provided is a guide through the process of affinity probe selection, target identification, and downstream target validation.
PET Image Reconstruction Using Kernel Method

Image reconstruction from low-count positron emission tomography (PET) projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4-D dynamic PET patient dataset showed promising results.