nils-herrmann/clean_scirepeval huggingface dataset:
RNA versatility, flexibility, and thermostability for practice in RNA nanotechnology and biomedical applications.

In recent years, RNA has attracted widespread attention as a unique biomaterial with distinct biophysical properties for designing sophisticated architectures in the nanometer scale. RNA is much more versatile in structure and function with higher thermodynamic stability compared to its nucleic acid counterpart DNA. Larger RNA molecules can be viewed as a modular structure built from a combination of many 'Lego' building blocks connected via different linker sequences. By exploiting the diversity of RNA motifs and flexibility of structure, varieties of RNA architectures can be fabricated with precise control of shape, size, and stoichiometry. Many structural motifs have been discovered and characterized over the years and the crystal structures of many of these motifs are available for nanoparticle construction. For example, using the flexibility and versatility of RNA structure, RNA triangles, squares, pentagons, and hexagons can be constructed from phi29 pRNA three-way-junction (3WJ) building block. This review will focus on 2D RNA triangles, squares, and hexamers; 3D and 4D structures built from basic RNA building blocks; and their prospective applications in vivo as imaging or therapeutic agents via specific delivery and targeting. Methods for intracellular cloning and expression of RNA molecules and the in vivo assembly of RNA nanoparticles will also be reviewed. WIREs RNA 2018, 9:e1452. doi: 10.1002/wrna.1452 This article is categorized under: RNA Methods > RNA Nanotechnology RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA in Disease and Development > RNA in Disease Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.
Appearance-and-Relation Networks for Video Classification

Spatiotemporal feature learning in videos is a fundamental problem in computer vision. This paper presents a new architecture, termed as Appearance-and-Relation Network (ARTNet), to learn video representation in an end-to-end manner. ARTNets are constructed by stacking multiple generic building blocks, called as SMART, whose goal is to simultaneously model appearance and relation from RGB input in a separate and explicit manner. Specifically, SMART blocks decouple the spatiotemporal learning module into an appearance branch for spatial modeling and a relation branch for temporal modeling. The appearance branch is implemented based on the linear combination of pixels or filter responses in each frame, while the relation branch is designed based on the multiplicative interactions between pixels or filter responses across multiple frames. We perform experiments on three action recognition benchmarks: Kinetics, UCF101, and HMDB51, demonstrating that SMART blocks obtain an evident improvement over 3D convolutions for spatiotemporal feature learning. Under the same training setting, ARTNets achieve superior performance on these three datasets to the existing state-of-the-art
Should the ketogenic diet be considered for enhancing fertility?

The ketogenic diet was first developed in the 1920s as a treatment for epilepsy in an attempt to create a prolonged physiologic starvation state. Since that time, the diet has been found to have other therapeutic effects, most of which are neurologic. Other diets, mostly based on the principals of caloric restriction, have been shown to improve fertility in certain populations. We explore the data, both clinical and laboratory, for potential fertility enhancing benefits of the ketogenic diet, beyond just caloric restriction or weight loss.
On Effectiveness of Histogram of Oriented Gradient Features for Visible to Near Infrared Face Matching

The advent of near infrared imagery and it's applications in face recognition has instigated research in cross spectral (visible to near infrared) matching. Existing research has focused on extracting textural features including variants of histogram of oriented gradients. This paper focuses on studying the effectiveness of these features for cross spectral face recognition. On NIR-VIS-2.0 cross spectral face database, three HOG variants are analyzed along with dimensionality reduction approaches and linear discriminant analysis. The results demonstrate that DSIFT with subspace LDA outperforms a commercial matcher and other HOG variants by at least 15%. We also observe that histogram of oriented gradient features are able to encode similar facial features across spectrums.
Scaling CNNs for High Resolution Volumetric Reconstruction from a Single Image

One of the long-standing tasks in computer vision is to use a single 2-D view of an object in order to produce its 3-D shape. Recovering the lost dimension in this process has been the goal of classic shape-from-X methods, but often the assumptions made in those works are quite limiting to be useful for general 3-D objects. This problem has been recently addressed with deep learning methods containing a 2-D (convolution) encoder followed by a 3-D (deconvolution) decoder. These methods have been reasonably successful, but memory and run time constraints impose a strong limitation in terms of the resolution of the reconstructed 3-D shapes. In particular, state-of-the-art methods are able to reconstruct 3-D shapes represented by volumes of at most 323 voxels using state-of-the-art desktop computers. In this work, we present a scalable 2-D single view to 3-D volume reconstruction deep learning method, where the 3-D (deconvolution) decoder is replaced by a simple inverse discrete cosine transform (IDCT) decoder. Our simpler architecture has an order of magnitude faster inference when reconstructing 3-D volumes compared to the convolution-deconvolutional model, an exponentially smaller memory complexity while training and testing, and a sub-linear run-time training complexity with respect to the output volume size. We show on benchmark datasets that our method can produce high-resolution reconstructions with state of the art accuracy.
The elusive Minnie G.: revisiting Cushing’s case XLV, and his early attempts at improving quality of life

Although researchers have discovered that Minnie G. had nearly 50 years of progression-free survival, the absence of her original surgical records have precluded anything more than speculation as to the etiology of her symptoms or the details of her admission. Following IRB approval, and through the courtesy of the Alan Mason Chesney Archives, the microfilm surgical records from the Johns Hopkins Hospital, 1896–1912 were reviewed. Using the surgical number provided in Cushing’s publications, the record for Minnie G. was recovered for further review. Cushing’s diagnosis relied largely on history and physical findings. Minnie G. presented with stigmata associated with classic Cushings Syndrome: abdominal stria, supraclavicular fat pads, and a rounded face. However, she also presented with unusual physical findings: exophthalmos, and irregular pigmentation of the extremities, face, and eyelids. A note in the chart indicates Minnie G. spoke very little English, implying the history-taking was fraught with opportunities for error. Although there remains no definitive etiology for Minnie G.’s symptoms, this report contributes additional information about her diagnosis and treatment.
The Climate CoLab: Large scale model-based collaborative planning

The Climate CoLab is a system to help thousands of people around the world collectively develop plans for what humans should do about global climate change. This paper shows how the system combines three design elements (model-based planning, on-line debates, and electronic voting) in a synergistic way. The paper also reports early usage experience showing that: (a) the system is attracting a continuing stream of new and returning visitors from all over the world, and (b) the nascent community can use the platform to generate interesting and high quality plans to address climate change. These initial results indicate significant progress towards an important goal in developing a collective intelligence system—the formation of a large and diverse community collectively engaged in solving a single problem.
A Secure, Usable, and Transparent Middleware for Permission Managers on Android

Android’s permission system offers an all-or-nothing choice when installing an app. To make it more flexible and fine-grained, users may choose a popular app tool, called permission manager, to selectively grant or revoke an app’s permissions at runtime. A fundamental requirement for such permission manager is that the granted or revoked permissions should be enforced faithfully. However, we discover that none of existing permission managers meet this requirement due to permission leaks, in which an unprivileged app can exercise certain permissions which are revoked or not-granted through communicating with a privileged app.To address this problem, we propose a secure, usable, and transparent OS-level middleware for any permission manager to defend against the permission leaks. The middleware is provably secure in a sense that it can effectively block all possible permission leaks.The middleware is designed to have a minimal impact on the usability of running apps. In addition, the middleware is transparent to users and app developers and it requires minor modifications on permission managers and Android OS. Finally, our evaluation shows that the middleware incurs relatively low performance overhead and power consumption.
Annotation of the Corymbia terpene synthase gene family shows broad conservation but dynamic evolution of physical clusters relative to Eucalyptus

Terpenes are economically and ecologically important phytochemicals. Their synthesis is controlled by the terpene synthase (TPS) gene family, which is highly diversified throughout the plant kingdom. The plant family Myrtaceae are characterised by especially high terpene concentrations, and considerable variation in terpene profiles. Many Myrtaceae are grown commercially for terpene products including the eucalypts Corymbia and Eucalyptus. Eucalyptus grandis has the largest TPS gene family of plants currently sequenced, which is largely conserved in the closely related E. globulus. However, the TPS gene family has been well studied only in these two eucalypt species. The recent assembly of two Corymbia citriodora subsp. variegata genomes presents an opportunity to examine the conservation of this important gene family across more divergent eucalypt lineages. Manual annotation of the TPS gene family in C. citriodora subsp. variegata revealed a similar overall number, and relative subfamily representation, to that previously reported in E. grandis and E. globulus. Many of the TPS genes were in physical clusters that varied considerably between Eucalyptus and Corymbia, with several instances of translocation, expansion/contraction and loss. Notably, there was greater conservation in the subfamilies involved in primary metabolism than those involved in secondary metabolism, likely reflecting different selective constraints. The variation in cluster size within subfamilies and the broad conservation between the eucalypts in the face of this variation are discussed, highlighting the potential contribution of selection, concerted evolution and stochastic processes. These findings provide the foundation to better understand terpene evolution within the ecologically and economically important Myrtaceae.
Play Ms. Pac-Man Using an Advanced Reinforcement Learning Agent

Reinforcement Learning (RL) algorithms have been promising methods for designing intelligent agents in games. Although their capability of learning in real time has been already proved, the high dimensionality of state spaces in most game domains can be seen as a significant barrier. This paper studies the popular arcade video game Ms. Pac-Man and outlines an approach to deal with its large dynamical environment. Our motivation is to demonstrate that an abstract but informative state space description plays a key role in the design of efficient RL agents. Thus, we can speed up the learning process without the necessity of Q-function approximation. Several experiments were made using the multiagent MASON platform where we measured the ability of the approach to reach optimum generic policies which enhances its generalization abilities.
First and second branchial arch syndromes: multimodality approach

First and second branchial arch syndromes (BAS) manifest as combined tissue deficiencies and hypoplasias of the face, external ear, middle ear and maxillary and mandibular arches. They represent the second most common craniofacial malformation after cleft lip and palate. Extended knowledge of the embryology and anatomy of each branchial arch derivative is mandatory for the diagnosis and grading of different BAS lesions and in the follow-up of postoperative patients. In recent years, many new complex surgical approaches and procedures have been designed by maxillofacial surgeons to treat extensive maxillary, mandibular and external and internal ear deformations. The purpose of this review is to evaluate the role of different imaging modalities (orthopantomogram (OPG), lateral and posteroanterior cephalometric radiographs, CT and MRI) in the diagnosis of a wide spectrum of first and second BAS, including hemifacial microsomia, mandibulofacial dysostosis, branchio-oto-renal syndrome, Pierre Robin sequence and Nager acrofacial dysostosis. Additionally, we aim to emphasize the importance of the systematic use of a multimodality imaging approach to facilitate the precise grading of these syndromes, as well as the preoperative planning of different reconstructive surgical procedures and their follow-up during treatment.
Neologisms, Nonces and Word Formation

Neologism dictionaries celebrate the influence of s cience and technology on language – how scientists and technologists add to the stock of words, and oc casionally, add new grammatical structures. There are two claims made in the literature on neologisms : Fir t, that borrowings, from classical languages and ‘prestige’ languages are widespread in the sci ence and technology literature. Second that affixat ion is an ephemeral mechanism for forming new words. B old claims? Let us look at some of the evidence for and against these claims.
Vessel enhancing diffusion: A scale space representation of vessel structures

A method is proposed to enhance vascular structures within the framework of scale space theory. We combine a smooth vessel filter which is based on a geometrical analysis of the Hessian's eigensystem, with a non-linear anisotropic diffusion scheme. The amount and orientation of diffusion depend on the local vessel likeliness. Vessel enhancing diffusion (VED) is applied to patient and phantom data and compared to linear, regularized Perona-Malik, edge and coherence enhancing diffusion. The method performs better than most of the existing techniques in visualizing vessels with varying radii and in enhancing vessel appearance. A diameter study on phantom data shows that VED least affects the accuracy of diameter measurements. It is shown that using VED as a preprocessing step improves level set based segmentation of the cerebral vasculature, in particular segmentation of the smaller vessels of the vasculature.
The Common Lisp Object System: An Overview

1.  The Common Lisp Object System is an object-oriented system that is based on the concepts of generic functions, multiple inheritance, and method combination. All objects in the Object System are instances of classes that form an extension to the Common Lisp type system. The Common Lisp Object System is based on a meta-object protocol that renders it possible to alter the fundamental structure of the Object System itself. The Common Lisp Object System has been proposed as a standard for ANSI Common Lisp and has been tentatively endorsed by X3J13. The Common Lisp Object System is an object-oriented programming paradigm designed for Common Lisp. The lack of a standardized object-oriented extension for Common Lisp has long been regarded as a shortcoming by the Common Lisp community. Two separate and independent groups began work on an object-oriented extension to Common Lisp several years ago. One group is Symbolics, Inc. with New Flavors, and the other is Xerox PARC with CommonLoops. During the summer of 1986, these two groups met to explore combining their designs for submission to X3J13, a technical working group charged with producing an ANSI standard for Common Lisp. At the time of the exploratory meetings between Symbolics and Xerox, the authors of this paper became involved in the technical design work. The major participants in this effort were David Moon and Sonya Keene from Symbolics, Daniel Bobrow and Gregor Kiczales from Xerox, and Richard Gabriel and Linda DeMichiel from Lucid.
Post-Editing Error Correction Algorithm for Speech Recognition using Bing Spelling Suggestion

ASR short for Automatic Speech Recognition is the process of converting a spoken speech into text that can be manipulated by a computer. Although ASR has several applications, it is still erroneous and imprecise especially if used in a harsh surrounding wherein the input speech is of low quality. This paper proposes a post-editing ASR error correction method and algorithm based on Bing’s online spelling suggestion. In this approach, the ASR recognized output text is spell-checked using Bing’s spelling suggestion technology to detect and correct misrecognized words. More specifically, the proposed algorithm breaks down the ASR output text into several word-tokens that are submitted as search queries to Bing search engine. A returned spelling suggestion implies that a query is misspelled; and thus it is replaced by the suggested correction; otherwise, no correction is performed and the algorithm continues with the next token until all tokens get validated. Experiments carried out on various speeches in different languages indicated a successful decrease in the number of ASR errors and an improvement in the overall error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized to take advantage of multiprocessor computers. KeywordsSpeech Recognition; Error Correction;
ECG biometric authentication based on non-fiducial approach using kernel methods

Identity recognition faces several challenges especially in extracting an individual's unique features from biometric modalities and pattern classifications. Electrocardiogram (ECG) waveforms, for instance, have unique identity properties for human recognition, and their signals are not periodic. At present, in order to generate a significant ECG feature set, nonfiducial methodologies based on an autocorrelation (AC) in conjunction with linear dimension reduction methods are used. This paper proposes a new non-fiducial framework for ECG biometric verification using kernel methods to reduce both high autocorrelation vectors' dimensionality and recognition system after denoising signals of 52 subjects with Discrete Wavelet Transform (DWT). The effects of different dimensionality reduction techniques for use in feature extraction were investigated to evaluate verification performance rates of a multi-class Support Vector Machine (SVM) with the One-Against-All (OAA) approach. The experimental results demonstrated higher test recognition rates of Gaussian OAA SVMs on random unknown ECG data sets with the use of the Kernel Principal Component Analysis (KPCA) as compared to the use of the Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). Keyword: ECG biometric recognition; Non-fiducial feature extraction; Kernel methods; Dimensionality reduction; Gaussian OAA SVM
BioFed: federated query processing over life sciences linked open data

Biomedical data, e.g. from knowledge bases and ontologies, is increasingly made available following open linked data principles, at best as RDF triple data. This is a necessary step towards unified access to biological data sets, but this still requires solutions to query multiple endpoints for their heterogeneous data to eventually retrieve all the meaningful information. Suggested solutions are based on query federation approaches, which require the submission of SPARQL queries to endpoints. Due to the size and complexity of available data, these solutions have to be optimised for efficient retrieval times and for users in life sciences research. Last but not least, over time, the reliability of data resources in terms of access and quality have to be monitored. Our solution (BioFed) federates data over 130 SPARQL endpoints in life sciences and tailors query submission according to the provenance information. BioFed has been evaluated against the state of the art solution FedX and forms an important benchmark for the life science domain.



The efficient cataloguing approach of the federated query processing system 'BioFed', the triple pattern wise source selection and the semantic source normalisation forms the core to our solution. It gathers and integrates data from newly identified public endpoints for federated access. Basic provenance information is linked to the retrieved data. Last but not least, BioFed makes use of the latest SPARQL standard (i.e., 1.1) to leverage the full benefits for query federation. The evaluation is based on 10 simple and 10 complex queries, which address data in 10 major and very popular data sources (e.g., Dugbank, Sider).



BioFed is a solution for a single-point-of-access for a large number of SPARQL endpoints providing life science data. It facilitates efficient query generation for data access and provides basic provenance information in combination with the retrieved data. BioFed fully supports SPARQL 1.1 and gives access to the endpoint's availability based on the EndpointData graph. Our evaluation of BioFed against FedX is based on 20 heterogeneous federated SPARQL queries and shows competitive execution performance in comparison to FedX, which can be attributed to the provision of provenance information for the source selection.



Developing and testing federated query engines for life sciences data is still a challenging task. According to our findings, it is advantageous to optimise the source selection. The cataloguing of SPARQL endpoints, including type and property indexing, leads to efficient querying of data resources over the Web of Data. This could even be further improved through the use of ontologies, e.g., for abstract normalisation of query terms.
A branch-and-bound algorithm for hardware/software partitioning

This paper investigates hardware/software (Hw/Sw) partitioning, a key problem in embedded co-design system. An efficient algorithm are proposed to optimally solve the problem in which the communication overhead is taken into account. The proposed algorithm constructs an efficient branch-and-bound approach to partition the hot path selected by path profiling techniques. The techniques for generation of good initial solution and the efficient lower bound for the feasible solution are customized in branch and bound search. Experimental results show that the partition result proposed by the new algorithm produces 10% increase in speedup as compared with the traditional approximate algorithm in most of the cases.