nils-herrmann/clean_scirepeval huggingface dataset:
Case studies in novel narial anatomy : 2 . The enigmatic nose of moose ( Artiodactyla : Cervidae : Alces alces )

The facial region of moose Alces alces is highly divergent relative to other cervids and other ruminants. In particular, the narial region forms an expanded muzzle or proboscis that overhangs the mouth. The nose of moose provides a case study in the evolution of narial novelty within a phylogenetically well-resolved group (Cervidae). The function of the nasal apparatus of moose remains enigmatic, and new hypotheses are proposed based on our anatomical findings. Head specimens of moose and outgroup taxa were subjected to medical imaging (CT scanning), vascular injection, gross anatomical dissection, gross sectioning, and skeletonization. Moose noses are characterized by highly enlarged nostrils accompanied by specialized musculature, expanded nasal cartilages, and an increase in the connective-tissue pad serving as the termination of the alar fold. The nostrils are widely separated, and the rhinarium that encircles both nostrils in outgroups is reduced to a tiny central patch in moose. The dorsal lateral nasal cartilage is modified to form a pulley mechanism associated with the levator muscle of the upper lip. The lateral accessory nasal cartilage is enlarged and serves as an attachment site for musculature controlling the aperture of the nostril, particularly the lateralis nasi, the apical dilatators, and the rectus nasi. Bony support for narial structures is reduced. Moose show greatly enlarged nasal cartilages, and the entire osseocartilaginous apparatus is relatively much larger than in outgroups. The nasal vestibule of moose is very large and houses a system of three recesses: one rostral and one caudal to the nostrils, and one associated with the enlarged fibrofatty alar fold. As a result of the expanded nasal vestibule, osseous support for the nasal conchae (i.e. turbinates) has retracted caudally along with the bony nasal aperture. The nasoturbinate and its mucosal counterparts (dorsal nasal concha and rectal fold) are reduced. The upturned maxilloturbinate, however, is associated with an enlarged ventral nasal concha and alar fold. Moose are the only species of cervid with these particular characteristics, indicating that this anatomical configuration is indeed novel. Although functional hypotheses await testing, our anatomical findings and published behavioural observations suggest that the novel narial apparatus of moose probably has less to do with respiratory physiology than with functions pertaining specifically to the nostrils. The widely separated and laterally facing nostrils may enhance stereolfaction (i.e. extracting directional cues from gradients of odorant molecules in the environment), but other attributes of narial architecture (enlarged cartilages, specialized musculature, recesses, fibrofatty pads) suggest that this function may not have been the evolutionary driving force. Rather, these attributes suggest a mechanical function, namely, an elaborated nostril-closing system.
Space and Time: The Hippocampus as a Sequence Generator

Neural computations are often compared to instrument-measured distance or duration, and such relationships are interpreted by a human observer. However, neural circuits do not depend on human-made instruments but perform computations relative to an internally defined rate-of-change. While neuronal correlations with external measures, such as distance or duration, can be observed in spike rates or other measures of neuronal activity, what matters for the brain is how such activity patterns are utilized by downstream neural observers. We suggest that hippocampal operations can be described by the sequential activity of neuronal assemblies and their internally defined rate of change without resorting to the concept of space or time.
SPIN with Cluster for Data Centric Wireless Sensor Networks

Routing Algorithms are driving the growth of the data transmission in wireless sensor networks. Contextually, many algorithms considered the data gathering and data aggregation. This paper uses the scenario of clustering and its impact over the SPIN protocol and also finds out the effect over the energy consumption in SPIN after uses of clustering. The proposed scheme is implemented using TCL/C++ programming language and evaluated using Ns2.34 simulator and compare with LEACH. Simulation shows proposed protocol exhibits significant performance gains over the LEACH for lifetime of network and guaranteed data transmission.
How do politicians use Facebook ? An applied Social Observatory

In the age of the digital generation, written public data is ubiquitous and acts as an outlet for today’s society. Platforms like Facebook, Twitter, Googleþ and LinkedIn have profoundly changed how we communicate and interact. They have enabled the establishment of and participation in digital communities as well as the representation, documentation and exploration of social behaviours, and had a disruptive effect on how we use the Internet. Such digital communications present scholars with a novel way to detect, observe, analyse and understand online communities over time. This article presents the formalization of a Social Observatory: a low latency method for the observation and measurement of social indicators within an online community. Our framework facilitates interdisciplinary research methodologies via tools for data acquisition and analysis in inductive and deductive settings. By focusing our Social Observatory on the public Facebook profiles of 187 federal German politicians we illustrate how we can analyse and measure sentiment, public opinion, and information discourse in advance of the federal elections. To this extent, we analysed 54,665 posts and 231,147 comments, creating a composite index of overall public sentiment and the underlying conceptual discussion themes. Our case study demonstrates the observation of communities at various resolutions: ‘‘zooming’’ in on specific subsets or communities as a whole. The results of the case study illustrate the ability to observe published sentiment and public dialogue as well as the difficulties associated with established methods within the field of sentiment analysis within short informal text.
Natural products for type II diabetes treatment.

Natural products such as plant extracts and complex microbial secondary metabolites have recently attracted the attention of scientific world for their potential use as drugs for treating chronic diseases such as Type II diabetes. Non-Insulin-Dependent Diabetes Mellitus (NIDDM) or Type II diabetes has complicated basis and has various treatment options, each targeting different mechanism of action. One such option relies on digestive enzyme inhibition. Almost all of the currently used clinically digestive enzyme inhibitors are bacterial secondary metabolites. However in most cases understanding of their complete biosynthetic pathways remains a challenge. The currently used digestive enzyme inhibitors have significant side effects that have restricted their usage. Hence, many active plant metabolites are being investigated as more effective treatment with fewer side effects. Flavonoids, terpenoids, glycosides are few to name in that class. Many of these are proven inhibitors of digestive enzymes but their large scale production remains a technical conundrum. Their successful heterologous production in simple host bacteria in scalable quantities gives a new dimension to the continuously active research for better treatment for type II diabetes. Looking at existing and new methods of mass level production of digestive inhibitors and latest efforts to effectively discover new potential drugs is the subject of this book chapter.
Galvanic Coupling Transmission in Intrabody Communication: A Finite Element Approach

Galvanic coupling in intrabody communication (IBC) is a technique that couples low-power and low-frequency voltages and currents into the human body, which acts as a transmission medium, and thus constitutes a promising approach in the design of personal health devices. Despite important advances being made during recent years, the investigation of relevant galvanic IBC parameters, including the influence of human tissues and different electrode configurations, still requires further research efforts. The objective of this work is to disclose knowledge into IBC galvanic coupling transmission mechanisms by using a realistic 3-D finite element model of the human arm. Unlike other computational models for IBC, we have modeled the differential configuration of the galvanic coupling as a four-port network in order to analyze the electric field distribution and current density through different tissues. This has allowed us to provide an insight into signal transmission paths through the human body, showing them to be considerably dependent on variables such as frequency and inter-electrode distance. In addition, other important variables, for example bioimpedance and pathloss, have also been analyzed. Finally, experimental measurements were also carried out for the sake of validation, demonstrating the reliability of the model to emulate in general forms some of the behaviors observed in practice.
High Capacity Colored Two Dimensional codes

Barcodes enable automated work processes without human intervention, and are widely deployed because they are fast and accurate, eliminate many errors and often save time and money. In order to increase the data capacity of barcodes, two dimensional (2D) code were developed; the main challenges of 2D codes lie in their need to store more information and more character types without compromising their practical efficiency. This paper proposes the High Capacity Colored Two Dimensional (HCC2D) code, a new 2D code which aims at increasing the space available for data, while preserving the strong reliability and robustness properties of QR. The use of colored modules in HCC2D poses some new and non-trivial computer vision challenges. We developed a prototype of HCC2D, which realizes the entire Print&Scan process. The performance of HCC2D was evaluated considering different operating scenarios and data densities. HCC2D was compared to other barcodes, such as QR and Microsoft's HCCB; the experiment results showed that HCC2D codes obtain data densities close to HCCB and strong robustness similar to QR.
Modeling ionizing radiation effects in solid state materials and CMOS devices

A comprehensive model is presented which enables the effects of ionizing radiation on bulk CMOS devices and integrated circuits to be simulated with closed form functions. The model adapts general equations for defect formation in uniform SiO2 films to facilitate analytical calculations of trapped charge and interface trap buildup in structurally irregular and radiation sensitive shallow trench isolation (STI) oxides. A new approach whereby non-uniform defect distributions along the STI sidewall are calculated, integrated into implicit surface potential equations, and ultimately used to model radiation-induced ldquoedgerdquo leakage currents in n-channel MOSFETs is described. The results of the modeling approach are compared to experimental data obtained on 130 nm and 90 nm devices. The features having the greatest impact on the increased radiation tolerance of advanced deep-submicron bulk CMOS technologies are also discussed. These features include increased doping levels along the STI sidewall.
Identification and application of Extract Class refactorings in object-oriented systems

Refactoring is recognized as an essential practice in the context of evolutionary and agile software development. Recognizing the importance of the practice, modern IDEs provide some support for low-level refactorings. A notable exception in the list of supported refactorings is the “Extract Class” refactoring, which is conceived to simplify large, complex, unwieldy and less cohesive classes. In this work, we describe a method and a tool, implemented as an Eclipse plugin, designed to fulfill exactly this need. Our method involves three steps: (a) recognition of Extract Class opportunities, (b) ranking of the identified opportunities in terms of the improvement each one is anticipated to bring about to the system design, and (c) fully automated application of the refactoring chosen by the developer. The first step relies on an agglomerative clustering algorithm, which identifies cohesive sets of class members
Authentication and Authorization for the Internet of Things

Authentication and authorization are essential parts of basic security processes and are sorely needed in the Internet of Things (IoT). The emergence of edge and fog computing creates new opportunities for security and trust management in the IoT. In this article, the authors discuss existing solutions to establish and manage trust in networked systems and argue that these solutions face daunting challenges when scaled to the IoT. They give a vision of efficient and scalable trust management for the IoT based on locally centralized, globally distributed trust management using an open source infrastructure with local authentication and authorization entities to be deployed on edge devices.
Chaotic Searchable Encryption for Mobile Cloud Storage

This paper considers the security problem of outsourcing storage from user devices to the cloud. A secure searchable encryption scheme is presented to enable searching of encrypted user data in the cloud. The scheme simultaneously supports fuzzy keyword searching and matched results ranking, which are two important factors in facilitating practical searchable encryption. A chaotic fuzzy transformation method is proposed to support secure fuzzy keyword indexing, storage and query. A secure posting list is also created to rank the matched results while maintaining the privacy and confidentiality of the user data, and saving the resources of the user mobile devices. Comprehensive tests have been performed and the experimental results show that the proposed scheme is efficient and suitable for a secure searchable cloud storage system.
Dynamic feature generation and selection on heterogeneous graph for music recommendation

In the past decade, online music streaming services (MSS), e.g., Pandora and Spotify, revolutionized the way people access, consume and share music. MSS serve users with a huge digital music library, various kinds of music discovery channels, and a number of tools for music sharing and management (e.g. bookmark, playlist, comment, etc.). As a result, metadata and user-generated data hosted on MSS demonstrate great heterogeneity, which provides important potential to enhance music recommendation performance. In this study, we propose a novel music recommendation approach by leveraging heterogeneous graph schema mining and ranking feature selection. Unlike existing heterogeneous graph-based recommendation techniques, the new method can automatically generate and select the optimized meta-path-based features for the learning to rank model. To make feature selection more efficient, we propose the Dynamic Feature Generation Tree algorithm (DFGT), which can activate and eliminate the short sub-meta-paths for feature evolution at a low cost. Experiments show that the proposed algorithm can efficiently generate optimized ranking feature set for meta-path-based music recommendation, which significantly enhances the state-of-the-art collaborative filtering algorithms.
Panel vector autoregressive models: a survey

This paper provides an overview of the panel VAR models used in macroeconomics and nance. It discusses what are their distinctive features, what they are used for, and how they can be derived from economic theory. It also describes how they are estimated and how shock identication is performed, and compares panel VARs to other approaches used in the literature to deal with dynamic models involving heterogeneous units. Finally, it shows how structural time variation can be dealt with and illustrates the challanges that they present to researchers interested in studying cross-unit dynamics interdependences in heterogeneous setups.  Panel VAR, Estimation, Identication, Inference.
A Statistical Learning Approach to Ultra-Reliable Low Latency Communication

Mission-critical applications require Ultra-Reliable Low Latency (URLLC) wireless connections, where the packet error rate (PER) goes down to 10. Fulfillment of the bold reliability figures becomes meaningful only if it can be related to a statistical model in which the URLLC system operates. However, this model is generally not known and needs to be learned by sampling the wireless environment. In this paper we treat this fundamental problem in the simplest possible communicationtheoretic setting: selecting a transmission rate over a dynamic wireless channel in order to guarantee high transmission reliability. We introduce a novel statistical framework for design and assessment of URLLC systems, consisting of three key components: (i) channel model selection; (ii) learning the model using training; (3) selecting the transmission rate to satisfy the required reliability. As it is insufficient to specify the URLLC requirements only through PER, two types of statistical constraints are introduced, Averaged Reliability (AR) and Probably Correct Reliability (PCR). The analysis and the evaluations show that adequate model selection and learning are indispensable for designing consistent physical layer that asymptotically behaves as if the channel was known perfectly, while maintaining the reliability requirements in URLLC systems.
Low Distortion Delaunay Embedding of Trees in Hyperbolic Plane

This paper considers the problem of embedding trees into the hyperbolic plane. We show that any tree can be realized as the Delaunay graph of its embedded vertices. Particularly, a weighted tree can be embedded such that the weight on each edge is realized as the hyperbolic distance between its embedded vertices. Thus the embedding preserves the metric information of the tree along with its topology. The distance distortion between non adjacent vertices can be made arbitrarily small – less than a (1 + ε) factor for any given ε. Existing results on low distortion of embedding discrete metrics into trees carry over to hyperbolic metric through this result. The Delaunay character implies useful properties such as guaranteed greedy routing and realization as minimum spanning trees.
Overcoming the challenges in administering biopharmaceuticals: formulation and delivery strategies

The formulation and delivery of biopharmaceutical drugs, such as monoclonal antibodies and recombinant proteins, poses substantial challenges owing to their large size and susceptibility to degradation. In this Review we highlight recent advances in formulation and delivery strategies — such as the use of microsphere-based controlled-release technologies, protein modification methods that make use of polyethylene glycol and other polymers, and genetic manipulation of biopharmaceutical drugs — and discuss their advantages and limitations. We also highlight current and emerging delivery routes that provide an alternative to injection, including transdermal, oral and pulmonary delivery routes. In addition, the potential of targeted and intracellular protein delivery is discussed.
Modeling of PV panels performance based on datasheet values for solar micro energy harvesting

Typically photovoltaic (PV) panel datasheets provide the electrical parameters at only Standard Test Condition (STC) i.e. at irradiance of 1000 W/m2 and cell temperature of 25°C. However, the PV panels operate at various operating conditions. Therefore the datasheet information is not sufficient to determine its behavior. There is a need of a novel model to predict the behavior of PV panel under all conditions. Single Mechanism Five Parameters (1M5P) model uses datasheet values along with series and parallel resistance of the PV panel. The basic equations of the model and parameters extraction from datasheet are shown. The advantage of this model is that it only requires the minimum parameters from datasheet. Furthermore, it can predict the behavior of PV panel under all operating conditions and can be used for solar micro energy harvesting. At STC, the model is validated with both datasheet and experimental values with difference less than 5%.
Management of the pediatric plunging ranula: results of 15 years' clinical experience.

This study was designed to evaluate the diagnosis and surgical treatment of the plunging ranula in children.



A retrospective study of 129 pediatric patients with a clinical diagnosis of plunging ranula was conducted. All children underwent clinical assessment and fine needle aspiration cytology. Cytologic diagnosis was considered to be definitive, and radiologic investigations were not conducted. Intraoral excision of the ipsilateral sublingual gland and partial pseudocyst was performed for all patients.



The preoperative diagnosis was consistent with postoperative and histopathologic analyses in all cases. There were no recurrences at 36 months' follow-up. There were no long-term complications as of writing this report.



Fine needle aspiration cytology can be used as the routine method to reach a diagnosis for plunging ranula, especially in the absence of floor of mouth involvement. Intraoral excision of the ipsilateral sublingual gland and partial pseudocyst is an effective and safe method for the treatment of the plunging ranula in the pediatric population, with absence of recurrence and low morbidity.
Generating smooth surface meshes from multi-region medical images.

Thanks to advances in medical imaging technologies and numerical methods, patient-specific modelling is more and more used to improve diagnosis and to estimate the outcome of surgical interventions. It requires the extraction of the domain of interest from the medical scans of the patient, as well as the discretisation of this geometry. However, extracting smooth multi-material meshes that conform to the tissue boundaries described in the segmented image is still an active field of research. We propose to solve this issue by combining an implicit surface reconstruction method with a multi-region mesh extraction scheme. The surface reconstruction algorithm is based on multi-level partition of unity implicit surfaces, which we extended to the multi-material case. The mesh generation algorithm consists in a novel multi-domain version of the marching tetrahedra. It generates multi-region meshes as a set of triangular surface patches consistently joining each other at material junctions. This paper presents this original meshing strategy, starting from boundary points extraction from the segmented data to heterogeneous implicit surface definition, multi-region surface triangulation and mesh adaptation. Results indicate that the proposed approach produces smooth and high-quality triangular meshes with a reasonable geometric accuracy. Hence, the proposed method is well suited for subsequent volume mesh generation and finite element simulations.
Convolutional Neural Network for Humor Recognition

For the purpose of automatically evaluating speakers’ humor usage, we build a presentation corpus containing humorous utterances based on TED talks. Compared to previous data resources supporting humor recognition research, ours has several advantages, including (a) both positive and negative instances coming from a homogeneous data set, (b) containing a large number of speakers, and (c) being open. Focusing on using lexical cues for humor recognition, we systematically compare a newly emerging text classification method based on Convolutional Neural Networks (CNNs) with a well-established conventional method using linguistic knowledge. The CNN method shows its advantages on both higher recognition accuracies and being able to learn essential features auto-