Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Dual-Frequency Radiation From a ${G}$ -Band Extended Interaction Oscillator With Double Sheet Beam

The dual-frequency radiation scheme of a <inline-formula> <tex-math notation="LaTeX">${G}$ </tex-math></inline-formula>-band extended interaction oscillator (EIO) has been proposed. The analytic estimates of structure, mode characteristics, and oscillation-starting condition of the overmoded double-sheet-beam oscillator have been presented. The counterstreaming beams and optimized parameters were employed to reduce the fundamental mode competition in a TM<sub>21</sub> mode EIO, allowing the high-order transverse mode (<inline-formula> <tex-math notation="LaTeX">${\text {TM}}_{{21}}^{+-}$ </tex-math></inline-formula>) and the fundamental mode (<inline-formula> <tex-math notation="LaTeX">${\text {TM}}_{21}^{++}$ </tex-math></inline-formula>) to operate simultaneously. The analyses are verified by simulations using 3-D particle-in-cell codes. The newly designed EIO was simulated with copper as a background material, and the dual-frequency radiation power of ~517 W is achieved when a double electron beam with 0.4 A and 21.5 kV is injected.
Distributional Semantics in the Real World: Building Word Vector Representations from a Truth-Theoretic Model

Distributional semantics models (DSMs) are known to produce excellent representations of word meaning, which correlate with a range of behavioural data. As lexical representations, they have been said to be fundamentally different from truth-theoretic models of semantics, where meaning is defined as a correspondence relation to the world. There are two main aspects to this difference: a) DSMs are built over corpus data which may or may not reflect ‘what is in the world’; b) they are built from word co-occurrences, that is, from lexical types rather than entities and sets. In this paper, we inspect the properties of a distributional model built over a set-theoretic approximation of ‘the real world’. To achieve this, we take the annotation a large database of images marked with objects, attributes and relations, convert the data into a representation akin to first-order logic and build several distributional models using various combinations of features. We evaluate those models over both relatedness and similarity datasets, demonstrating their effectiveness in standard evaluations. This allows us to conclude that, despite prior claims, truth-theoretic models are good candidates for building graded lexical representations of meaning.
The cerebellum as a neuronal machine

Summary form only given. The exceedingly regular and intricate neuroanatomy of the vertebrate cerebellar cortex has prompted many speculations about its function. The author reviews and assesses models for the functioning of cerebellar cortex, including those suggesting that it is: a timing device, a device enabling spatial navigation, or an associative memory store which is used in motor control. (1 page)
User Needs for Mobile Communication Devices: Requirements Gathering and Analysis through Contextual

proceedings are retained as one file so that they can be printed and browsed as a single volume. I'll split up the papers as separate HTML files if I get time. The 2nd Workshop will be in Edinburgh as part of the Interact'99 conference. Here are the preliminary details. A major problem in exploring user requirements for mobile communication and personal organisation devices is the versatility of usage patterns and usage contexts in which the usage takes place. The traditional means of user interviews or usability testing in a laboratory environment are not capable of revealing insights of users' activities and needs in their "real life". This paper describes an example user needs study at Nokia and concludes that ethnographic methods such as the Contextual Inquiry method can – despite of numerous practical challenges – be successfully applied in the development of mobile communication devices. Introduction Mobile communication is a relatively new field of HCI activity. Although the possibility for mobile voice calls has existed for over a decade, more versatile wireless communication activities have started to emerge only recently. There is a definite requirement to make mobile communication accepted by everyone [1]; thus users' true needs must be understood by the designers of such systems, devices and services. Various participatory user requirements gathering methods have been exploited in early design phases of mobile communication devices [2]. One of the approaches which has been exploited in concept development is the Contextual Inquiry (CI) method [3]. Contextual Inquiry is based on ethnography and sociological research tradition where the researcher/observer goes into the research object's own environment, rather than vice versa [4]. The observers observe the potential users of the developed product for a period of time, typically some hours. The observer stays on the background for most of the time but also inquiries about events that are not obvious but which may be significant regarding the focus of the research. Work products (data sheets, notes, any visible outputs) can also be collected for later reference about user's specific tasks and work practises. All the evidence is recorded together with the designer's detailed written notes of events and verified conclusions about why the user behaved as he or she did. The raw data is analysed by a group of designers, usability specialists and the CI moderator by structuring the individual observations and design ideas into an affinity diagram. This diagram of observed …
Refactoring-Aware Code Review: A Systematic Mapping Study

Modern Code Review (MCR) demands enhancements in the way change logs are presented to reviewers. Their task benefits from higher-level descriptions about the intention behind commits; for instance, evolution tasks, such as refactorings, could be more effectively reviewed in the presence of the intended transformation – better if automatically detected by MCR tool support. This paper presents a systematic literature mapping (combining results from search strings, snowballing and a two-phase classification scheme) on refactoring-aware solutions to support MCR. We could observe that, since 2015, interest in tools and techniques for automatic detection of refactorings has been steadily growing. Most publications refer to new development methods or carry out characterisation studies. As a consequence of this overview, we point out a few potential research topics for the next years. In special, detection of multiple refactoring types in a mixed change log (in which refactorings are combined with other kinds of changes), or the need for case studies or experiments in applying refactoring detection in MCR, on distinct application domains and development environments.
The Scientization of Literary Study

Scholarly practices within the humanities have historically been perceived as distinct from the natural sciences. We look at literary studies, a discipline strongly anchored in the humanities, and hypothesize that over the past half-century literary studies has instead undergone a process of “scientization”, adopting linguistic behavior similar to the sciences. We test this using methods based on information theory, comparing a corpus of literary studies articles (around 63,400) with a corpus of standard English and scientific English respectively. We show evidence for “scientization” effects in literary studies, though at a more muted level than scientific English, suggesting that literary studies occupies a middle ground with respect to standard English in the larger space of academic disciplines. More generally, our methodology can be applied to investigate the social positioning and development of language use across different domains (e.g. scientific disciplines, language varieties, registers).
Macrophage Cytokine Production and Regeneration of Pancreas at Experimental Diabetes Mellitus Type 2 and at Modulation Activity of Macrophages

It was shown, that modulation of macrophage activity by sodium phthalhydrazide (APH) increases concentration of anti-inflammatory cytokine TGF-$\boldsymbol{\beta}$ in the pancreas and decreases content of the proinflammatory cytokine complex in the pancreas and blood plasma of rats with experimental diabetes mellitus type 2 (DM2). It is possible, that shifts in macrophage production underlie the activation of regenerative processes (increasing of proliferation and decreasing in intensity of apoptosis of pancreatic $\boldsymbol{\beta}$-cells) at administration of APH.
Effective Parsing for Human Aided NLP Systems

In this paper, we present our efforts towards identifying probable incorrect edges and then suggesting k -best alternates for the same in a typed-dependency framework. Such a setup is beneﬁcial in human aided NLP systems where the decisions are largely automated with minimal human intervention. Minimizing the human intervention calls for automatic identiﬁcation of ambiguous cases. We have employed an entropy based confusion measure to capture uncertainty exerted by the parser oracle and later ﬂag the highly uncertain predictions. To further assist human decisions, k -best alternatives are supplied in the order of their likelihood. Our experiments, conducted for Hindi, establish the effectiveness of the proposed approach towards increasing the label accuracy with economically viable manual intervention. This work leads to new directions for parser development and also in the human-aided NLP systems.
Long non-coding RNA NEAT1 can predict various malignant tumour lympha node metastasis: a meta-analysis

Abstract It has been found in several studies and research that the long non-coding RNA (Inc RNA) NEAT1 plays an important role in the development and succession in various malignant tumour. The development and metastasis of tumour mainly happen through lymph node. The purpose of this research is to explore the value of lymph node metastasis (LNM) in cancer. We have collected all the concerned studies about NEAT1 and researched the relationship between NEAT1 and lymph node metastasis. We have searched the studies by seeking database PubMed, Web of Science, and China National Knowledge Infrastructure (up to 10 January 2019), as well as a total of 821 patients from eight studies topics and made accordingly meta-analysis. By analyzing the data, we have found that the result is the high expression of NEAT1 associate with the metastasis of lymph node in different malignant tumours. The high level of NEAT1 expression can predict the metastasis of lymph node (OR = 3.36, 95% CI = 1.66–6.77, p = .000) and it is a molecular marker of positive lymph node for treatment.
Linguistic Information in Neural Semantic Parsing with Multiple Encoders

Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks. However, they often do not exploit available linguistic resources, while these, when employed correctly, are likely to increase performance even further. Research in neural machine translation has shown that employing this information has a lot of potential, especially when using a multi-encoder setup. We employ a range of semantic and syntactic resources to improve performance for the task of Discourse Representation Structure Parsing. We show that (i) linguistic features can be beneficial for neural semantic parsing and (ii) the best method of adding these features is by using multiple encoders.
Domain Adaptation with Source Selection for Motor-Imagery based BCI

Recent successes of deep learning methods in various applications have inspired BCI researchers for their use in EEG classification. However, data insufficiency and high intra- and inter-subject variabilities hinder from taking their advantage of discovering complex patterns inherent in data, which can be potentially useful to enhance EEG classification accuracy. In this paper, we devise a novel framework of training a deep network by adapting samples of other subjects as a means of domain adaptation. Assuming that there are EEG trials of motor-imagery tasks from multiple subjects available, we first select a subject whose EEG signal characteristics are similar to the target subject based on their power spectral density in resting-state EEG signals. We then use EEG signals of both the selected subject (called a source subject) and the target subject jointly in training a deep network. Rather than training a single path network, we adopt a multi-path network architecture, where the shared bottom layers are used to discover common features for both source and target subjects, while the upper layers branch out into (1) source-target subject identification, (2) label prediction optimized for a source subject, and (3) label prediction optimized for a target subject. Based on our experimental results over the BCI Competition IV-IIa dataset, we validated the effectiveness of the proposed framework in various aspects.
Pyridostigmine regulates glucose metabolism and mitochondrial homeostasis to reduce myocardial vulnerability to injury in diabetic mice.

Diabetic patients are more susceptible to myocardial ischemia damage than nondiabetic patients, with worse clinical outcomes and greater mortality. The mechanism may be related to glucose metabolism, mitochondrial homeostasis and oxidative stress. Pyridostigmine may improve vagal activity to protect cardiac function in cardiovascular diseases. Researchers have not determined whether pyridostigmine regulates glucose metabolism and mitochondrial homeostasis to reduce myocardial vulnerability to injury in diabetic mice. In the present study, autonomic imbalance, myocardial damage, mitochondrial dysfunction and oxidative stress were exacerbated in isoproterenol-stimulated diabetic mice, revealing the myocardial vulnerability of diabetic mice to injury compared with mice with diabetes or exposed to isoproterenol alone. Compared with normal mice, the expression of GLUT1/4, PFKFB3 and PKM was decreased in diabetic mice, but increased in isoproterenol-stimulated normal mice. Following exposure to isoproterenol, the expression of GLUT1/4, PFKFB3 and PKM decreased in diabetic mice compared with normal mice. The downregulation of SIRT3/AMPK and IRS-1/Akt in isoproterenol-stimulated diabetic mice was exacerbated compared with that in diabetic mice or isoproterenol-stimulated normal mice. Pyridostigmine improved vagus activity, increased GLUT1/4, PFKFB3 and PKM expression, and ameliorated mitochondrial dysfunction and oxidative stress to reduce myocardial damage in isoproterenol-stimulated diabetic mice. Based on these results, pyridostigmine may reduce myocardial vulnerability to injury via the SIRT3/AMPK and IRS-1/Akt pathways in diabetic mice with isoproterenol-induced myocardial damage. This study may provide a potential therapeutic target for myocardial damage in diabetic patients.
Visual and User-Defined Smart Contract Designing System Based on Automatic Coding

Smart contract applications based on Ethereum blockchain have been widely used in many fields. They are developed by professional developers using specialized programming languages like solidity. It requires high requirements on knowledge of the specialized field and the proficiency in contract programming. Thus, it is hard for normal users to design a usable smart contract based on their own demands. Most current studies about smart contracts focus on the security of coding while lack of friendly tools for users to design the specialized templates of contracts coding. This paper provides a visual and user-defined smart contract designing systems. It makes the development of domain-specific smart contracts simpler and visualization for contract users. The system implements the domain-specific features extraction about the crawled data sets of smart contract programs by TF-IDF and K-means++ clustering algorithm. Then, it achieves the automatic generation of unified basic function codes by Char-RNN (improved by LSTM) based on the domain-specific features. The system adopts Google Blockly and links the generated codes with UI controls. Finally, it provides a set of specialized templates of basic functions for users to design smart contracts by the friendly interface. It reduces the difficulty and costs of contract programming. The paper offers a case study to design contracts by users. The designed contracts were validated on the existing system to implement the food trading and traders’ credit evaluation. The experimental results show that the designed smart contracts achieve good integration with the existing system and they can be deployed and compiled successfully.
Named Entity Recognition From Biomedical Texts Using a Fusion Attention-Based BiLSTM-CRF

Biomedical named entity recognition (BNER) is the basis of biomedical text mining and one of the core sub-tasks of information extraction. Previous BNER models based on conventional machine learning rely on time-consuming feature engineering. Though most neural network methods improve the problems with automatic learning, they cannot pay attention to the significant areas when capturing features. In this paper, we propose an attention-based BiLSTM-CRF model. First, this model adopts a bidirectional long short-term memory network (BiLSTM) to obtain more complete context information. At the same time, the attention mechanism is proposed to improve the vector representations in BiLSTM. We design different attention weight redistribution methods and fuse them. It effectively prevents the significant information loss when extracting features. Finally, combining BiLSTM with conditional random field (CRF) layer effectively solves the problems of the inability to handle the strong dependence of tags in the sequence. With the simple architecture, our model achieves a reasonable performance on the JNLPBA corpus. It obtains an F1-score of 73.50. Our model can enhance the ability of the neural network to extract significant information and does not rely on any feature engineering, with only general pre-training word vectors. It makes our model have high portability and extendibility.
Defining and Classifying Software Bots: A Faceted Taxonomy

While bots have been around for many decades, recent technological advancements and the increasing adoption of language-based communication platforms have led to a surge of new software bots, which have become increasingly pervasive in our everyday lives. Although many novel bots are being designed and deployed, the terms used to describe them and their properties are vast, diverse, and often inconsistent. Even the concept of what is or is not a bot is unclear. This hinders our ability to study, understand, design, and classify bots. In this paper, we present a taxonomy of software bots, which focuses on the observable properties and behaviours of software bots, as well as the environments where bots are deployed and designed. We see this taxonomy as a focal point for a discussion in our community so that together we can deeply consider how to evaluate and understand existing bots, as well as how we may design more innovative and productive bots.
Statistical Properties of the Interaction Between Linear Mode Coupling and Kerr-Nonlinearities in Few-Mode Fibers

In this paper we consider intrachannel interaction of Kerr-nonlinearities and linear mode coupling in mode division multiplexed fiber communications. We present statistics of the influence of linear coupling on the nonlinear waveform distortions. Simulations show that modulation format sufficiently influence on the dispersion of probability distribution of nonlinear impairments enhancement caused by linear coupling. QAM-modulated signals are more robust to stochastic nonlinear impairments enhancement than OOK-modulated signals. Signals with higher order of QAM-modulation demonstrate higher robustness. In the case of OOK modulation RZ-coding is preferable. Obtained statistical results are useful for estimation of system outages and for proper choice of modulation type in mode division multiplexed communications.
Becoming a word learner : a debate on lexical acquisition

Contents 1. Word Learning: Icon, Index, or Symbol? 2. The Intentionality Model of Word Learning: How to Learn a Word, Any Word 3. Learning How to Learn Words: An Associative Crane 4. Contraining the Problem Space in Early Word Learning 5. The Social Nature of Words and Word Learning 6. An Emergentist, Coalition Model for Word Learning: Mapping Words to Objects is Product of the Internation of Multiple Cues 7. Commentary
Enhanced Mobility in InAlN/AlN/GaN HEMTs Using a GaN Interlayer

An enhancement of the electron mobility (<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>) in InAlN/AlN/GaN heterostructures is demonstrated by the incorporation of a thin GaN interlayer (IL) between the InAlN and AlN. The introduction of a GaN IL increases <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> at room temperature (RT) from 1600 to 1930 cm<sup>2</sup>/Vs. The effect is further enhanced at cryogenic temperature (5 K), where the GaN IL sample exhibits a <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> of 16000 cm<sup>2</sup>/Vs, compared to 6900 cm<sup>2</sup>/Vs without IL. The results indicate the reduction of one or more scattering mechanisms normally present in InAlN/AlN/GaN heterostructures. We propose that the improvement in <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> is either due to the suppression of fluctuations in the quantum well subband energies or to reduced Coulomb scattering, both related to compositional variations in the InAlN. HEMTs fabricated on the GaN IL sample demonstrate larger improvement in dc- and high-frequency performance at 5 K; <inline-formula> <tex-math notation="LaTeX">${f}_{\text {max}}$ </tex-math></inline-formula> increases by 25 GHz to 153 GHz, compared to an increase of 6 GHz to 133 GHz without IL. The difference in improvement was associated mainly with the drop in the access resistances.
On the Capacity of Cloud Radio Access Networks With Oblivious Relaying

We study the transmission over a network in which users send information to a remote destination through relay nodes that are connected to the destination via finite-capacity error-free links, i.e., a cloud radio access network. The relays are constrained to operate without knowledge of the users’ codebooks, i.e., they perform oblivious processing. The destination, or central processor, however, is informed about the users’ codebooks. We establish a single-letter characterization of the capacity region of this model for a class of discrete memoryless channels in which the outputs at the relay nodes are independent given the users’ inputs. We show that both relaying à-la Cover–El Gamal, i.e., compress-and-forward with joint decompression and decoding, and “noisy network coding” are optimal. The proof of the converse part establishes, and utilizes, connections with the Chief Executive Officer source coding problem under logarithmic loss distortion measure. Extensions to general discrete memoryless channels are also investigated. In this case, we establish the inner and outer bounds on the capacity region. For memoryless Gaussian channels within the studied class of channels, we characterize the capacity region when the users are constrained to time-share among Gaussian codebooks. Furthermore, we also discuss the suboptimality of separate decompression and decoding and the role of time sharing.