Miracle-dz/UniFAR-FTUs huggingface dataset:
Wrapping interactions at strong coupling: The giant magnon

We derive generalized Luescher formulas for finite size corrections in a theory with a general dispersion relation. For the AdS{sub 5}xS{sup 5} superstring these formulas encode leading wrapping interaction effects. We apply the generalized {mu}-term formula to calculate finite size corrections to the dispersion relation of the giant magnon at strong coupling. The result exactly agrees with the classical string computation of Arutyunov, Frolov, and Zamaklar. The agreement involved a Borel resummation of all even loop orders of the BES/BHL dressing factor thus providing a strong consistency check for the choice of the dressing factor.
Identification of genes associated with renal cell carcinoma using gene expression profiling analysis

Renal cell carcinoma (RCC) is the most common type of kidney cancer in adults and accounts for ~80% of all kidney cancer cases. However, the pathogenesis of RCC has not yet been fully elucidated. To interpret the pathogenesis of RCC at the molecular level, gene expression data and bio-informatics methods were used to identify RCC associated genes. Gene expression data was downloaded from Gene Expression Omnibus (GEO) database and identified differentially coexpressed genes (DCGs) and dysfunctional pathways in RCC patients compared with controls. In addition, a regulatory network was constructed using the known regulatory data between transcription factors (TFs) and target genes in the University of California Santa Cruz (UCSC) Genome Browser (http://genome.ucsc.edu) and the regulatory impact factor of each TF was calculated. A total of 258,0427 pairs of DCGs were identified. The regulatory network contained 1,525 pairs of regulatory associations between 126 TFs and 1,259 target genes and these genes were mainly enriched in cancer pathways, ErbB and MAPK. In the regulatory network, the 10 most strongly associated TFs were FOXC1, GATA3, ESR1, FOXL1, PATZ1, MYB, STAT5A, EGR2, EGR3 and PELP1. GATA3, ERG and MYB serve important roles in RCC while FOXC1, ESR1, FOXL1, PATZ1, STAT5A and PELP1 may be potential genes associated with RCC. In conclusion, the present study constructed a regulatory network and screened out several TFs that may be used as molecular biomarkers of RCC. However, future studies are needed to confirm the findings of the present study.
Nonstructural Protein 11 of Porcine Reproductive and Respiratory Syndrome Virus Suppresses Both MAVS and RIG-I Expression as One of the Mechanisms to Antagonize Type I Interferon Production

Type I interferons (IFN-α/β) play a key role in antiviral defense, and porcine reproductive and respiratory syndrome virus (PRRSV) is known to down-regulate the IFN response in virus-infected cells and pigs. In this study, we showed that the overexpression of nsp11 of PRRSV induced a strong suppression of IFN production. Nsp11 suppressed both IRF3 and NF-κB activities when stimulated with a dsRNA analogue and TNF-α, respectively. This suppression was RLR dependent, since the transcripts and proteins of MAVS and RIG-I, two critical factors in RLR-mediated pathway, were both found to be reduced in the presence of overexpressed nsp11. Since nsp11 is an endoribonuclease (EndoU), the structure function relationship was examined using a series of nsp11 EndoU mutant plasmids. The mutants that impaired the EndoU activity failed to suppress IFN and led to the normal expression of MAVS. Seven single amino acid substitutions (4 in subdomain A and 3 in subdomain B) plus one insertion (frame-shift in nsp11) were then introduced into PRRSV infectious cDNA clones to generate nsp11 mutant viruses. Unfortunately, all EndoU knock-out nsp11 mutant viruses appeared replication-defective and no progenies were produced. Three mutations in EndoU subdomain A expressed the N and nsp2/3 proteins but their infectivity diminished after 2 passages. Taken together, our data show that PRRSV nsp11 endoribonuclease activity is critical for both viral replication and IFN antagonism. More importantly, the endoribonuclease activity of nsp11 demonstrates the substrate specificity towards MAVS and RIG-I (transcripts and proteins) over p65 and IRF3 in the context of gene transfection and overexpression. This is likely a mechanism of nsp11 suppression of type I IFN production.
Altered Microbiomes in Bovine Digital Dermatitis Lesions, and the Gut as a Pathogen Reservoir

Bovine digital dermatitis (DD) is the most important infectious disease associated with lameness in cattle worldwide. Since the disease was first described in 1974, a series of Treponema species concurrent with other microbes have been identified in DD lesions, suggesting a polymicrobial etiology. However, the pathogenesis of DD and the source of the causative microbes remain unclear. Here we characterized the microbiomes of healthy skin and skin lesions in dairy cows affected with different stages of DD and investigated the gut microbiome as a potential reservoir for microbes associated with this disease. Discriminant analysis revealed that the microbiomes of healthy skin, active DD lesions (ulcerative and chronic ulcerative) and inactive DD lesions (healing and chronic proliferative) are completely distinct. Treponema denticola, Treponema maltophilum, Treponema medium, Treponema putidum, Treponema phagedenis and Treponema paraluiscuniculi were all found to be present in greater relative abundance in active DD lesions when compared with healthy skin and inactive DD lesions, and these same Treponema species were nearly ubiquitously present in rumen and fecal microbiomes. The relative abundance of Candidatus Amoebophilus asiaticus, a bacterium not previously reported in DD lesions, was increased in both active and inactive lesions when compared with healthy skin. In conclusion, our data support the concept that DD is a polymicrobial disease, with active DD lesions having a markedly distinct microbiome dominated by T. denticola, T. maltophilum, T. medium, T. putidum, T. phagedenis and T. paraluiscuniculi. Furthermore, these Treponema species are nearly ubiquitously found in rumen and fecal microbiomes, suggesting that the gut is an important reservoir of microbes involved in DD pathogenesis. Additionally, the bacterium Candidatus Amoebophilus asiaticus was highly abundant in active and inactive DD lesions.
An assessment of support vector machines for land cover classi(cid:142) cation

The support vector machine (SVM) is a group of theoreticallysuperior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensionaldata sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classi(cid:142) cations from satellite images. The SVM was compared to three other popular classi(cid:142) ers, including the maximum likelihood classi(cid:142) er (MLC), neural network classi(cid:142) ers (NNC) and decision tree classi(cid:142) ers (DTC). The impacts of kernel con(cid:142)guration on the performance of the SVM and of the selection of training data and input variables on the four classi(cid:142) ers were also evaluated in this experiment.
Development of Low Power Technologies for Health Monitoring System Using Wireless Sensor Nodes

We have developed low power technologies for animal or human health monitoring system with wireless sensor node. A wireless sensor network system is composed of sensors, signal processing units, receiver and other components. Although there are several reports of low power technologies for these components separately, the technology which has consideration of a total system has not been researched. For example, if we use the combination of a wireless sensor node and a receiver, the number of receiving per a time can be utilized as measured data indirectly. In this case, since the transmitted data can be decreased, the power consumption of the transmission can also be decreased. In this paper, we report low power technologies of wireless sensor network system which was developed for chicken health monitoring system. The calculated average power consumption of the wireless sensor node is less than 1 μW.
Incremental Hashing with Kernels

We propose an incremental strategy for learning hash functions with kernels for large-scale image search. Our method is based on a two-stage classification framework that treats binary codes as intermediate variables between the feature space and the semantic space. In the first stage of classification, binary codes are considered as class labels by a set of binary SVMs; each corresponds to one bit. In the second stage, binary codes become the input space of a multi-class SVM. Hash functions are learned by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we describe an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three largescale image datasets demonstrate the effectiveness of the proposed hashing method, SVM-based Hashing (SVM-Hash), over the state-of-the-art supervised hashing methods.
Construct Validity of the Montreal Cognitive Assessment (MoCA)

Abstract The Montreal Cognitive Assessment (MoCA) is a brief instrument developed for the screening of milder forms of cognitive impairment. The present study aims to assess the construct related validity of the MoCA through the establishment of the factorial, convergent, and discriminant related validities, and the reliability of data. In a Portuguese sample of 830 participants, several models were tested using Confirmatory Factor Analysis. Although all tested models showed a good fit, the six-factor model based on the conceptual model proposed by the MoCA's authors showed a significantly better fit. The results allowed us to establish the factorial, convergent, and discriminant validity of this six-dimensional structure. An overall psychometric adequacy of the items, and a good reliability were also found. This study contributes to overcome an important gap in the construct related validity of this instrument. The present findings corroborate the six-dimensional structure of the MoCA and provide good evidence of the construct related validity. The MoCA has proved to be an appropriate measure for cognitive screening taking into account different cognitive domains, which will enable clinicians and researchers to use this test and its six latent dimensions to achieve a better understanding of the individuals’ cognitive profile. (JINS, 2012, 18, 242–250)
Dimensionality reduction for active learning with nearest neighbour classifier in text categorisation problems

Dimensionality reduction techniques are commonly used in text categorisation problems to improve training and classification efficiency as well as to avoid overfitting. The best performing dimensionality reduction techniques for text categorisation are supervised, hence utilise the label information of the training data. Active learning is used to reduce the number of labelled training examples for problems where obtaining label information is expensive. Since the vast majority of data supplied to active learning are unlabelled, supervised dimensionality reduction techniques cannot be readily employed. For this reason, active learning in text categorisation problems do not perform dimensionality reduction thereby restricting the choice of classifier. In this paper we investigate unsupervised dimensionality reduction techniques in active learning for text categorisation problems. Two unsupervised techniques are investigated, namely document frequency and principal components analysis. We empirically show increased performance of active learning, using a k-nearest neighbour classifier, when dimensionality reduction is applied using the unsupervised techniques.
Directional Wind-Wave Coupling in Fully Coupled Atmosphere-Wave-Ocean Models: Results from CBLAST-Hurricane

AbstractThe extreme high winds, intense rainfall, large ocean waves, and copious sea spray in hurricanes push the surface-exchange parameters for temperature, water vapor, and momentum into untested regimes. The Coupled Boundary Layer Air–Sea Transfer (CBLAST)-Hurricane program is aimed at developing improved coupling parameterizations (using the observations collected during the CBLAST-Hurricane field program) for the next-generation hurricane research prediction models. Hurricane-induced surface waves that determine the surface stress are highly asymmetric, which can affect storm structure and intensity significantly. Much of the stress is supported by waves in the wavelength range of 0.1–10 m, which is the unresolved “spectral tail” in present wave models. A directional wind–wave coupling method is developed to include effects of directionality of the wind and waves in hurricanes. The surface stress vector is calculated using the two-dimensional wave spectra from a wave model with an added short-wave s.
Concepts for Modelling Enterprise Architectures

A coherent description of an enterprise architecture provides insight, enables communication among stakeholders and guides complicated change processes. Unfortunately, so far no enterprise architecture description language exists that fully enables integrated enterprise modelling, because for each architectural domain, architects use their own modelling techniques and concepts, tool support, visualisation techniques, etc. In this paper we outline such an integrated language and we identify and study concepts that relate architectural domains. In our language concepts for describing the relationships between architecture descriptions at the business, application, and technology levels play a central role, related to the ubiquitous problem of businessIT alignment, whereas for each architectural domain we conform to existing languages or standards such as UML. In particular, usage of services offered by one layer to another plays an important role in relating the behaviour aspects of the layers. The structural aspects of the layers are linked through the interface concept, and the information aspects through realisation relations. Concepts for Modelling Enterprise Architectures
Circuit design of intelligent frequency tracking for ultrasonic cell disruption system

An intelligent ultrasonic cell disruption system with the function of frequency tracking is presented in this paper. The system is mainly composed of power supply module, frequency generator module, power amplification module and phase difference detection module. The power supply module transforms AC220V from grid into the desired power supply of the system. The frequency generator module generates the driving signal for power amplification module. The power amplification module amplifies the output power for ultrasonic transducer. The phase difference detection module detects the phase error by sampling the current and voltage of ultrasonic transducer. To verify the performance of the designed frequency tracking system, contrast experiments between system with frequency tracking and system without frequency tracking were conducted. The experimental results indicate that the system could meet the requirements of cell disruption system with high efficiency in various application conditions.
The Influence of Exercise Environment and Gender on Mood and Exertion

This study examined the influence of exercise environment and gender on post-exercise mood and exertion. College student participants (55 females, 49 males) were instructed to pedal a stationary bike at a moderate pace for 20 minutes. Participants were randomly assigned to one of three laboratory conditions: (1) exercising in front of a mirror and posters showing ideal fit body types (i.e., celebrity male and female personal trainers), (2) exercising in front of a mirror only, or (3) a control condition in which participants exercised without a mirror or posters. The Activation-Deactivation Adjective Check List (AD-ACL), measuring exercise-induced mood states, was administered both before and after exercise. Average bike speed throughout the exercise session measured exertion. Mirrors and posters of ideally fit celebrities did interact with gender on post-exercise tension in that women felt most tense after exercising in front of the mirror and posters while men were most tense after exercising in front of the mirror only. Exercise exertion was also impacted by experimental condition such that participants rode significantly faster in the mirror and posters condition. There was no significant interaction of gender and condition on exercise exertion, but women pedaled fastest in the mirror and poster condition relative to the other conditions. Results suggest that exercise exertion and tension reduction are partially a by-product of gender and exercise environment.
Equilibrium Growth, Inflation, and Bond Yields

This paper explores bond pricing implications of a stochastic endogenous growth model with imperfect price adjustment. In this setting, the production and price-setting decisions of firms drive low-frequency movements in macro growth and inflation rates that are negatively related, as in the data. With recursive preferences, these endogenous long-run growth and inflation dynamics are crucial for explaining a number of stylized facts in bond markets. Notably, when calibrated to a wide range of macroeconomic data, the model quantitatively explains the means and volatilities of nominal bond yields. The model also generates a sizeable equity premium and high investment volatility.
The Tradition of Categoricity and Prospects for Stochasticity

“Everyone knows that language is variable. ” This is the bald sentence with which Sapir (1921:147) begins his chapter on language as an historical product. He goes on to emphasize how two speakers’ usage is bound to differ “in choice of words, in sentence structure, in the relative frequency with which particular forms or combinations of words are used”. I should add that much sociolinguistic and historical linguistic research has shown that the same speaker’s usage is also variable (Labov 1966, Kroch 2001:722). However, the tradition of most syntacticians has been to ignore this thing that everyone knows.1 Human languages are the prototypical example of a symbolic system. From very early on, logics and logical reasoning were invented for handling natural language understanding. Logics and formal languages have a language-like form that draws from and meshes well with natural languages. It is not immediately obvious where the continuous and quantitative aspects of syntax are. The dominant answer in syntactic theory has been “nowhere” (Chomsky 1969:57; also 1956, 1957, etc.): “It must be recognized that the notion ‘probability of a sentence’ is an entirely useless one, under any known interpretation of this term. ” In the 1950s there were prospects for probabilistic methods taking hold in linguistics, in part due to the influence of the new field of Information Theory (Shannon 1948). 2 Chomsky’s influential remarks had the effect of killing off interest in probabilistic methods for syntax, just as for a long time McCarthy and Hayes (1969) discouraged exploration of probabilistic methods in Artificial Intelligence. Among his arguments were that: (i) Probabilistic models wrongly mix in world knowledge (New York occurs more in text than Dayton, Ohio, but for no linguistic reason), (ii) Probabilistic models don’t model grammaticality (neither Colorless green ideas sleep furiously nor Furiously sleep ideas green colorless have previously been uttered – and hence must be estimated to have probability zero, Chomsky wrongly assumes – but the former is grammatical while the latter is not, and (iii) Use of probabilities does not meet the goal of describing the mind-internal I-language as opposed to the observed-in-the-world E-language. This chapter is not meant to be a detailed critique of Chomsky’s arguments – Abney (1996) provides a survey and a rebuttal, and Pereira (2000) has further useful discussion – but some of these concerns are still important to discuss. I
Systematic review of the effectiveness of mirror therapy in upper extremity function

Purpose. This review gives an overview of the current state of research regarding the effectiveness of mirror therapy in upper extremity function. Method. A systematic literature search was performed to identify studies concerning mirror therapy in upper extremity. The included journal articles were reviewed according to a structured diagram and the methodological quality was assessed. Results. Fifteen studies were identified and reviewed. Five different patient categories were studied: two studies focussed on mirror therapy after an amputation of the upper limb, five studies focussed on mirror therapy after stroke, five studies focussed on mirror therapy with complex regional pain syndrome type 1 (CRPS1) patients, one study on mirror therapy with complex regional pain syndrome type 2 (CRPS2) and two studies focussed on mirror therapy after hand surgery other than amputation. Conclusions. Most of the evidence for mirror therapy is from studies with weak methodological quality. The present review showed a trend that mirror therapy is effective in upper limb treatment of stroke patients and patients with CRPS, whereas the effectiveness in other patient groups has yet to be determined.
Analysis of Spoofing Detection using Video Subsection Processing

Imposters gain unauthorized access to biometric recognition systems using fake biometric data of the legitimate user termed as spoofing. Spoofing of face recognition systems is done by photographs, 3D models and videos of the user. Attack video contains noise from the acquisition process. In this work, we use noise residual content of the video in order to detect spoofed videos. We take advantage of wavelet transform for representing the noise video. Samples of the noise video, termed as visual rhythm image is created for each video. Local Binary Pattern (LBP) and uniform Local Binary Pattern (LBPu2) are extracted from the visual rhythm image followed by classification using Support Vector Machine (SVM). Large size of video from which a number of frames are used for analysis results in huge execution timing. In this work the spoof detection algorithm is applied on various levels of subsections of the video frames resulting in reduced execution timing with reasonable detection accuracies.