nils-herrmann/clean_scirepeval huggingface dataset:
Neural networks for seismic principal components analysis

The neural network, using an unsupervised generalized Hebbian algorithm (GHA), is adopted to find the principal eigenvectors of a covariance matrix in different kinds of seismograms. We have shown that the extensive computer results of the principal components analysis (PCA) using the neural net of GHA can extract the information of seismic reflection layers and uniform neighboring traces. The analyzed seismic data are the seismic traces with 20-, 25-, and 30-Hz Ricker wavelets, the fault, the reflection and diffraction patterns after normal moveout (NMO) correction, the bright spot pattern, and the real seismogram at Mississippi Canyon. The properties of high amplitude, low frequency, and polarity reversal can be shown from the projections on the principal eigenvectors. For PCA, a theorem is proposed, which states that adding an extra point along the direction of the existing eigenvector can enhance that eigenvector. The theorem is applied to the interpretation of a fault seismogram and the uniform property of other seismograms. The PCA also provides a significant seismic data compression.
Linear logic and permutation stacks - the Forth shall be first

Girard's linear logic can be used to model programming languages in which each bound variable name has exactly one "occurrence"---i.e., no variable can have implicit "fan-out"; multiple uses require explicit duplication. Among other nice properties, "linear" languages need no garbage collector, yet have no dangling reference problems. We show a natural equivalence between a "linear" programming language and a stack machine in which the top items can undergo arbitrary permutations. Such permutation stack machines can be considered combinator abstractions of Moore's Forth programming language.
Recommender System for Literature Review and Writing

In this paper, we introduce Rec4LRW, a recommender system (RS) for assisting researchers in finding research papers for their literature review and writing purposes. This system focuses on three researcher tasks – (1) Building a reading list of research papers, (2) Finding similar papers based on a set of papers, and (3) Shortlisting papers from the final reading list for inclusion in manuscript based on article type. A set of intermediate criteria are proposed to capture the relations between a research paper and its bibliography. The recommendation techniques for the three tasks in Rec4LRW are specifically devised on top of the intermediate criteria. The Rec4LRW workflow along with the screen designs for the three tasks is provided in this paper. The recommendation techniques in the system will be evaluated with state-of-the-art approaches along with user-based evaluation in subsequent studies.
Contain-ed: An NFV Micro-Service System for Containing e2e Latency

Network Functions Virtualization (NFV) has enabled operators to dynamically place and allocate resources for network services to match workload requirements. However, unbounded end-to-end (e2e) latency of Service Function Chains (SFCs) resulting from distributed Virtualized Network Function (VNF) deployments can severely degrade performance. In particular, SFC instantiations with inter-data center links can incur high e2e latencies and Service Level Agreement (SLA) violations. These latencies can trigger timeouts and protocol errors with latency-sensitive operations.
 Traditional solutions to reduce e2e latency involve physical deployment of service elements in close proximity. These solutions are, however, no longer viable in the NFV era. In this paper, we present our solution that bounds the e2e latency in SFCs and inter-VNF control message exchanges by creating micro-service aggregates based on the affinity between VNFs. Our system, Contain-ed, dynamically creates and manages affinity aggregates using light-weight virtualization technologies like containers, allowing them to be placed in close proximity and hence bounding the e2e latency. We have applied Contain-ed to the Clearwater IP Multimedia System and built a proof-of-concept. Our results demonstrate that, by utilizing application and protocol specific knowledge, affinity aggregates can effectively bound SFC delays and significantly reduce protocol errors and service disruptions.
THE ECLECTIC PARADIGM OF INTERNATIONAL PRODUCTION : A RESTATEMENT AND SOME POSSIBLE EXTENSIONS

This article reviews some of the criticisms directed towards the eclectic paradigm of international production over the past decade, and restates its main tenets. The second part of the article considers a number of possible extensions of the paradigm and concludes by asserting that it remains "a robust general framework for explaining and analysing not only the economic rationale of economic production but many organisational nd impact issues in relation to MNE activity as well."
Vault: Fast Bootstrapping for Cryptocurrencies

Decentralized cryptocurrencies rely on participants to keep track of the state of the system in order to verify new transactions. As the number of users and transactions grows, this requirement places a significant burden on the users, as they need to download, verify, and store a large amount of data in order to participate. Vault is a new cryptocurrency designed to minimize these storage and bootstrapping costs for participants. Vault builds on Algorand’s proof-of-stake consensus protocol and uses several techniques to achieve its goals. First, Vault decouples the storage of recent transactions from the storage of account balances, which enables Vault to delete old account state. Second, Vault allows sharding state across participants in a way that preserves strong security guarantees. Finally, Vault introduces the notion of stamping certificates that allow a new client to catch up securely and efficiently in a proof-of-stake system without having to verify every single block. Experiments with a prototype implementation of Vault’s data structures shows that Vault reduces the bandwidth cost of joining the network as a full client by 99.7% compared to Bitcoin and 90.5% compared to Ethereum when downloading a ledger containing 500 million transactions.
Fast on-the-fly composition for weighted finite-state transducers in 1.8 million-word vocabulary continuous speech recognition

This paper proposes a new on-the-fly composition algorithm for Weighted Finite-State Transducers (WFSTs) in large-vocabulary continuous-speech recognition. In general on-the-fly composition, two transducers are composed during decoding, and a Viterbi search is performed based on the composed search space. In this new method, a Viterbi search is performed based on the first of two transducers. The second transducer is only used to rescore the hypotheses generated during the search. Since this rescoring is very efficient, the total amount of computation in the new method is almost the same as when using only the first transducer. In a 30kword vocabulary spontaneous lecture speech transcription task, our proposed method significantly outperformed the general on-the-fly composition method. Furthermore the speed of our method was slightly faster than that of decoding with a single fully composed and optimized WFST, where our method consumed only 20% of the memory usage required for decoding with the single WFST. Finally, we have achieved one-pass real-time speech recognition in an extremely large vocabulary of 1.8 million words.
A Multihop Peer-Communication Protocol With Fairness Guarantee for IEEE 802.16-Based Vehicular Networks

Wireless-communication technology can be used to improve road safety and to provide Internet access inside vehicles. This paper proposes a cross-layer protocol called coordinated external peer communication (CEPEC) for Internet-access services and peer communications for vehicular networks. We assume that IEEE 802.16 base stations (BS) are installed along highways and that the same air interface is equipped in vehicles. Certain vehicles locating outside of the limited coverage of their nearest BSs can still get access to the Internet via a multihop route to their BSs. For Internet-access services, the objective of CEPEC is to increase the end-to-end throughput while providing a fairness guarantee in bandwidth usage among road segments. To achieve this goal, the road is logically partitioned into segments of equal length. A relaying head is selected in each segment that performs both local-packet collecting and aggregated packets relaying. The simulation results have shown that the proposed CEPEC protocol provides higher throughput with guaranteed fairness in multihop data delivery in vehicular networks when compared with the purely IEEE 802.16-based protocol.
Multi-client/Multi-server split architecture

This paper proposes a novel client/server architecture known as Multi-client/Multi-server (MC/MS). MC/MS is an alternative to BitTorrent-like peer-to-peer systems, single source multiple mirrored techniques, and n-tier client and server protocol. The use of a peer-to-peer resource protocol increases network traffic and impacts all users on the network. In the MC/MS architecture, one client establishes a connection with one connection server, and concurrent data are dispatched from multiple data servers located on different subnets from each other. The data can be dispatched to the multiple clients anywhere on the network, which then reassemble or otherwise process the data. MC/MS distributes the data of a large file across multiple servers without any redundancy. The separation of data transfer from a connection establishment is entirely transparent to the client. It reduces network impact, avoids redundancy of data, and improves reliability of the system by avoiding a bottlenecks at both the server and client sides. MC/MS reduces transmission time up to 82% as compared to the conventional techniques. It also offers much better data transmission time than comparable BitTorrent systems in a LAN environment. This paper proposes a `mission-critical' application as the motivation for this work. The paper describes a design and an initial implementation of the MC/MS architecture, and serves as a foundation for prospective application implementers.
Spinal epidural abscess in two calves.

To report clinical signs, diagnostic and surgical or necropsy findings, and outcome in 2 calves with spinal epidural abscess (SEA).



Clinical report.



Calves (n=2).



Calves had neurologic examination, analysis and antimicrobial culture of cerebrospinal fluid (CSF), vertebral column radiographs, myelography, and in 1 calf, magnetic resonance imaging (MRI). A definitive diagnosis of SEA was confirmed by necropsy in 1 calf and during surgery and histologic examination of vertebral canal tissue in 1 calf.



Clinical signs were difficulty in rising, ataxia, fever, apparent spinal pain, hypoesthesia, and paresis/plegia which appeared 15 days before admission. Calf 1 had pelvic limb weakness and difficulty standing and calf 2 had severe ataxia involving both thoracic and pelvic limbs. Extradural spinal cord compression was identified by myelography. SEA suspected in calf 1 with discospondylitis was confirmed at necropsy whereas calf 2 had MRI identification of the lesion and was successfully decompressed by laminectomy and SEA excision. Both calves had peripheral neutrophilia and calf 2 had neutrophilic pleocytosis in CSF. Bacteria were not isolated from CSF, from the surgical site or during necropsy. Calf 2 improved neurologically and had a good long-term outcome.



Good outcome in a calf with SEA was obtained after adequate surgical decompression and antibiotic administration.



SEA should be included in the list of possible causes of fever, apparent spinal pain, and signs of myelopathy in calves.
Bayesian Multi-Task Reinforcement Learning

We consider the problem of multi-task reinforcement learni ng where the learner is provided with a set of tasks, for which only a small number o f samples can be generated for any given policy. As the number of samples may n ot be enough to learn an accurate evaluation of the policy, it would be neces sary to identify classes of tasks with similar structure and to learn them jointly. We consider the case where the tasks share structure in their value functions, an d model this by assuming that the value functions are all sampled from a common pri or. We adopt the Gaussian process temporal-difference value function mode l and use a hierarchical Bayesian approach to model the distribution over the value f unctions. We study two cases, where all the value functions belong to the same cl ass and where they belong to an undefined number of classes. For each case, we pre sent a hierarchical Bayesian model, and derive inference algorithms for (i) joint learning of the value functions, and (ii) efficient transfer of the informat ion gained in (i) to assist learning the value function of a newly observed task.
CrocoBLAST: Running BLAST efficiently in the age of next-generation sequencing

CrocoBLAST is a tool for dramatically speeding up BLAST+ execution on any computer. Alignments that would take days or weeks with NCBI BLAST+ can be run overnight with CrocoBLAST. Additionally, CrocoBLAST provides features critical for NGS data analysis, including: results identical to those of BLAST+; compatibility with any BLAST+ version; real-time information regarding calculation progress and remaining run time; access to partial alignment results; queueing, pausing, and resuming BLAST+ calculations without information loss.


Availability and implementation
CrocoBLAST is freely available online, with ample documentation (webchem.ncbr.muni.cz/Platform/App/CrocoBLAST). No installation or user registration is required. CrocoBLAST is implemented in C, while the graphical user interface is implemented in Java. CrocoBLAST is supported under Linux and Windows, and can be run under Mac OS X in a Linux virtual machine.



jkoca@ceitec.cz.


Supplementary information
Supplementary data are available at Bioinformatics online.
A wireless smart-shoe system for gait assistance

Methodical studies on Smart-shoe-based gait detection systems have become an influential constituent in decreasing elderly injuries due to fall. This paper proposes smartphone-based system for analyzing characteristics of gait by using a wireless Smart-shoe. The system employs four force sensitive resistors (FSR) to measure the pressure distribution underneath a foot. Data is collected via a Wi-Fi communication network between the Smart-shoe and smartphone for further processing in the phone. Experimentation and verification is conducted on 10 subjects with different gait including free gait. The sensor outputs, with gait analysis acquired from the experiment, are presented in this paper.
A 24-GHz Receiver Frontend With an LO Signal Generator in 0.18-$\mu$m CMOS

Utilizing a standard 0.18-mum CMOS process, a receiver frontend and a local oscillator (LO) module are implemented for RF applications at the 24-GHz industrial, scientific, medical band. The proposed frontend is composed of a three-stage low-noise amplifier, a down-conversion mixer, and IF amplifiers. With an IF frequency of 4.82 GHz, the fabricated circuit demonstrates a conversion gain of 28.4 dB and a noise figure of 6.0 dB while maintaining an input return loss better than 14 dB. The measured P in - 1dB and IIP3 of the receiver frontend are -23.2 and -13.0 dBm, respectively. In addition, a circuit module, which generates the required dual down-conversion LO signals, is also included in this study. The proposed LO generator consists of a 19-GHz low-phase-noise voltage-controlled oscillator (VCO), a 4 : 1 frequency divider, and a quadrature phase-tuning circuit. From the measurement results, the VCO exhibits a tuning range of 850 MHz and a phase noise of -110 dBc/Hz at 1-MHz offset frequency. Operated at a supply voltage of 1.8 V, the current consumptions for the receiver frontend and the LO generator are both 30 mA.
Communication synthesis for distributed embedded systems

Designers of distributed embedded systems face many challenges in determining the tradeoffs when defining a system architecture or retargeting an existing design. Communication synthesis, the automatic generation of the necessary software and hardware for system components to exchange data, is required to more effectively explore the design space and automate very error prone tasks. The paper examines the problem of mapping a high level specification to an arbitrary architecture that uses specific, common bus protocols for interprocessor communication. The communication model presented allows for easy retargeting to different bus topologies, protocols, and illustrates that global considerations are required to achieve a correct implementation. An algorithm is presented that partitions multihop communication timing constraints to effectively utilize the bus bandwidth along a message path. The communication synthesis tool is integrated with a system co-simulator to provide performance data for a given mapping.
Classification of retinal diseases based on OCT Images.

Optical Coherence Topography (OCT) is an emerging biomedical imaging technology that offers non-invasive real-time, high-resolution imaging of highly scattering tissues. It is widely used in ophthalmology to perform diagnostic imaging on the structure of the anterior eye and the retina. Clinical studies are carried out to assess the application of OCT for some retinal diseases. OCT can provide means for early detection for various types of diseases because morphological changes often occur before the physical symptoms of these diseases. In addition, follow-up imaging can assess treatment effectiveness and recurrence of a disease. A review in this area is needed to identify the results and the findings from OCT images in the field of retinal diseases and how to use these findings to help in clinical applications. This paper overviews the current techniques that are developed to determine the ability of OCT images for early detection/diagnosis of retinal diseases. Also, the paper remarks several challenges that face the researchers in the analysis of the OCT retinal images.
Widening siamese architectures for stereo matching

Computational stereo is one of the classical problems in computer vision. Numerous algorithms and solutions have been reported in recent years focusing on developing methods for computing similarity, aggregating it to obtain spatial support and finally optimizing an energy function to find the final disparity. In this paper, we focus on the feature extraction component of stereo matching architecture and we show standard CNNs operation can be used to improve the quality of the features used to find point correspondences. Furthermore, we propose a simple space aggregation that hugely simplifies the correlation learning problem. Our results on benchmark data are compelling and show promising potential even without refining the solution.
BEME Guide No. 1: Best Evidence Medical Education.

There is a need to move from opinion-based education to evidence-based education. Best evidence medical education (BEME) is the implementation, by teachers in their practice, of methods and approaches to education based on the best evidence available. It involves a professional judgement by the teacher about his/her teaching taking into account a number of factors-the QUESTS dimensions. The Quality of the research evidence available-how reliable is the evidence? the Utility of the evidence-can the methods be transferred and adopted without modification, the Extent of the evidence, the Strength of the evidence, the Target or outcomes measured-how valid is the evidence? and the Setting or context-how relevant is the evidence? The evidence available can be graded on each of the six dimensions. In the ideal situation the evidence is high on all six dimensions, but this is rarely found. Usually the evidence may be good in some respects, but poor in others.The teacher has to balance the different dimensions and come to a decision on a course of action based on his or her professional judgement.The QUESTS dimensions highlight a number of tensions with regard to the evidence in medical education: quality vs. relevance; quality vs. validity; and utility vs. the setting or context. The different dimensions reflect the nature of research and innovation. Best Evidence Medical Education encourages a culture or ethos in which decision making takes place in this context.
Measuring e-Excise Tax Success Factors: Applying the DeLone & McLean Information Systems Success Model

The rapid growth of e-Commerce amongst private sectors and Internet usage amongst citizens has vastly stimulated e-Government initiatives from many countries. The Thailand e-Government initiative is based on the government's long-term strategic policy that aims to reform and overhaul the Thai bureaucracy. This study attempted to identify the e-Excise success factors by employing the IS success model. The study focused on finding the factors that may contribute to the success of the e-Excise initiative. The Delphi Technique was used to investigate the determinant factors for the success of the e-Excise initiative. Three-rounds of data collection were conducted with 77 active users from various industries. The results suggest that by increasing Trust in the e-Government website, Perceptions of Information Quality, Perceptions of System Quality, and Perceptions of Service Quality will influence System Usage and User Satisfaction, and will ultimately have consequences for the Perceived Net Benefits.
Eruption of an impacted canine in an adenomatid odontogenic tumor treated with combined orthodontic and surgical therapy.

An adenomatoid odontogenic tumor is an uncommon asymptomatic lesion that is often misdiagnosed as a dentigerous cyst. It originates from the odontogenic epithelium. Enucleation and curettage is the usual treatment of choice. Marsupialization may be attempted instead of extraction of the impacted tooth, since it provides an opportunity for tooth eruption. This case report is the first to report on the eruption of an impacted canine in an adenomatoid odontogenic tumor treated with combined orthodontics and marsupialization. The impacted canine erupted uneventfully, with no evidence of recurrence 3 years after the treatment.
Enumeration versus multiple object tracking: the case of action video game players

Here, we demonstrate that action video game play enhances subjects' ability in two tasks thought to indicate the number of items that can be apprehended. Using an enumeration task, in which participants have to determine the number of quickly flashed squares, accuracy measures showed a near ceiling performance for low numerosities and a sharp drop in performance once a critical number of squares was reached. Importantly, this critical number was higher by about two items in video game players (VGPs) than in non-video game players (NVGPs). A following control study indicated that this improvement was not due to an enhanced ability to instantly apprehend the numerosity of the display, a process known as subitizing, but rather due to an enhancement in the slower more serial process of counting. To confirm that video game play facilitates the processing of multiple objects at once, we compared VGPs and NVGPs on the multiple object tracking task (MOT), which requires the allocation of attention to several items over time. VGPs were able to successfully track approximately two more items than NVGPs. Furthermore, NVGPs trained on an action video game established the causal effect of game playing in the enhanced performance on the two tasks. Together, these studies confirm the view that playing action video games enhances the number of objects that can be apprehended and suggest that this enhancement is mediated by changes in visual short-term memory skills.