Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency-domain space-time block coded single-carrier distributed antenna network

In this paper, space-time block coding (STBC) is applied to single-carrier distributed antenna network (SC-DAN). By using the frequency-domain STBC coded joint transmit/receive diversity (FDSTBC-JTRD) for the downlink while using the well-known frequencydomain space-time transmit diversity (FD-STTD) for the uplink, the diversity order is given by the product of the number of distributed antennas and that of mobile terminal (MT) antennas. It is shown by computer simulation that the downlink and uplink can achieve almost the same bit error rate (BER) performance and that by increasing the number of distributed antennas, the BER performance can be significantly improved while keeping the MT complexity low.
An improved harmony search algorithm for multi-dimensional function optimization problem

An Improved Harmony search algorithm (IHS) is proposed for solving multi-dimensional and multi-extremal function optimization problems. The IHS algorithm uses a common cross-operation to generate new solutions instead of the traditional improvisation mechanism presented in the basic Harmony search. The number of the new solutions varies from 1 to HMS. In each iteration, IGHS uses a one-on-one selection scheme to compare the old solutions with the new ones, and the winner will enter the next generation. Simulation results show that the proposed algorithm is more quickly and accurately to solve function optimization problems than the compared algorithms.
Learning efficiency of redundant neural networks in Bayesian estimation

This paper proves that the Bayesian stochastic complexity of a layered neural network is asymptotically smaller than that of a regular statistical model if it contains the true distribution. We consider a case when a three-layer perceptron with M input units, H hidden units and N output units is trained to estimate the true distribution represented by the model with H(0) hidden units and prove that the stochastic complexity is asymptotically smaller than (1/2) {H(0) (M+N)+R} log n where n is the number of training samples and R is a function of H-H(0), M, and N that is far smaller than the number of redundant parameters. Since the generalization error of Bayesian estimation is equal to the increase of stochastic complexity, it is smaller than (1/2n) {H(0) (M+N)+R} if it has an asymptotic expansion. Based on the results, the difference between layered neural networks and regular statistical models is discussed from the statistical point of view.
A LSA Based Image Classification Framework Utilizing Relative Spatial Arrangement

This paper focuses on the problem of automatic image classification (AIC) by proposing a framework based on latent semantic analysis (LSA) and image region pairs. The novel framework employs relative spatial arrangements for region pairs as the primary feature to capture semantics. The significance of this paper is twofold. Firstly, to the best our knowledge, this is the first study of the influence of region pairs as well as their relative spatial information in latent semantic analysis as applied to automatic image classification. Secondly, our proposed method for using the relative spatial information of region pairs show great promise in improving image semantic classi- fication compared with the classical latent semantic analysis method and 2D string representation algorithm.
Model-free inference in statistics : how and why

Parametric models served as the cornerstone for the foundation of Statistical Science in the beginning of the 20th century by R.A. Fisher, K. Pearson, J. Neyman, E.S. Pearson, W.S. Gosset (also known as “Student”), etc.; their seminal developments resulted into a complete theory of statistics that could be practically implemented using the technology of the time, i.e., pen and paper (and slide-rule! ). While some models are inescapable, e.g. modeling a polling dataset as a sequence of independent Bernoulli random variables, others appear contrived, often invoked for the sole reason to make the mathematics work. As a prime example, the ubiquitous—and typically unjustified—assumption of Gaussian data permeates statistics textbooks to the day. Model criticism and diagnostics were subsequently developed as a practical way out. With the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as resampling and cross-validation created a revolution in modern statistics. Using computers, statisticians became able to analyze big datasets for the first time, paving the way towards the ‘big data’ era of the 21st century. But perhaps more important was the realization that the way we do the analysis could/should be changed as well, as practitioners were gradually freed from the limitations of parametric models. For instance, the great success of Efron’s (1979) bootstrap was in providing a complete theory for statistical inference under a nonparametric setting much like Maximum Likelihood Estimation had done half a century earlier under the restrictive parametric setup. Nevertheless, there is a further step one may take, i.e., going beyond even nonparametric models. To explain this, let us first focus on regression, i.e., data that are pairs: (Y1, X1), (Y2, X2), . , (Yn, Xn) where Yi is the measured response associated with a regressor value of Xi. The standard homoscedastic additive model in this situation reads: Yi = μ(Xi) + i (1)
Phantom Tooth Pain

Phantom tooth pain is a form of neuropathic pain of the oral cavity and further information can be found under that section. The information on this page is aimed at dental practitioners. The information is in two parts and is based on the following publications: Glossary of terms after discharge continued firing of dorsal horn neurons due to repetitive peripheral stimulation algesic pain producing algogen pain producing substance algogenic pain producing allodynia pain from stimulus that does not normally cause pain analgesic pain relieving atypical facial pain a term no longer used but was characterised by vague signs and symptoms with significant psychological factors atypical odontalgia severe, throbbing pain without major pathology, phantom tooth pain central sensitisation a phenomenon occurring in the dorsal horn and other central structures causing allodynia and secondary hyperalgesia in uninjured tissue surrounding a site of injury complex regional pain syndrome (CRPS) type I a syndrome that usually develops after an initiating noxious event, is not limited to the distribution of a single peripheral nerve, and is apparently disproportionate to the inciting event CRPS type II burning pain, allodynia and hyperpathia usually in the hand or foot, after partial injury to a nerve or one of its major branches hyperalgesia increased response to a painful stimuli neuronal plasticity nerve sprouting and neuroma formation causing neuropathic pain neuropathic pain pain initiated or caused by a primary lesion or dysfunction in the nervous system neuropraxis crush injury to a nerve Pain Management Research Institute/Orofacial/Phantom Tooth Pain 2 neurotmesis sectioning or cutting of a nerve nociception activation of peripheral nociceptor which is recognised centrally as pain peripheral sensitisation a phenomenon where inflammatory mediators sensitise high threshold nociceptors pharmacodynamics effect of drug on body pharmacokinetics metabolic action of body on drug physiological pain pain that serves a protective function (as a warning for tissue damage), is transient and well localised sympathetic hyperfunction characterised by changes in skin temperature, blood flow, resting sweat output and presence of oedema sympathetically maintained pain defined as pain that is maintained by sympathetic efferent innervation or circulating catecholamines windup progressive increase in response of dorsal horn neurons due to repetitive peripheral stimulation Summary Neuropathic pain is defined as " pain initiated or caused by a primary lesion or dysfunction in the nervous system ". Neuropathic orofacial pain has previously been known as " atypical odontalgia " (AO) and "phantom tooth pain". The patient afflicted with …
Collaborative beamforming for distributed wireless ad hoc sensor networks

The performance of collaborative beamforming is analyzed using the theory of random arrays. The statistical average and distribution of the beampattern of randomly generated phased arrays is derived in the framework of wireless ad hoc sensor networks. Each sensor node is assumed to have a single isotropic antenna and nodes in the cluster collaboratively transmit the signal such that the signal in the target direction is coherently added in the far-field region. It is shown that with N sensor nodes uniformly distributed over a disk, the directivity can approach N, provided that the nodes are located sparsely enough. The distribution of the maximum sidelobe peak is also studied. With the application to ad hoc networks in mind, two scenarios (closed-loop and open-loop) are considered. Associated with these scenarios, the effects of phase jitter and location estimation errors on the average beampattern are also analyzed.
Inter-voice Audio Morphing

The Problem: This project seeks to develop a framework, which we have called “inter-voice morphing”, for morphing between samples of speech that are identical in content, but produced by different speakers. Given two spoken phrases, we wish to smoothly morph between the characteristics that define the speakers in order to produce intermediate sequences that lie along the perceptual continuum connecting the two individuals.
Ray-Triangle Intersection Algorithm for Modern CPU Architectures

We present an algorithm for determining if a ray intersects a triangle interior; and computing intersection point parameters as well as distance of intersection in response to the ray intersecting a triangle interior. Particularly a variation of a hybrid test having all benefits of Plucker and projected barycentric tests is proposed. The test is also vectorized using SIMD instructions for efficient handling ray packets. It is essential for achieving high ray tracing performance on modern CPUs. Our implementation also detects axis-orthogonal triangles and processing them separately.
Predicting Attendance at Major League Soccer Matches: A Comparison of Four Techniques

Sport team managers need to predict attendance levels at sporting events to plan staffing levels, plan inventories, and decide upon possible promotions. This paper discusses predicting attendance at Major League Soccer events using data from the 2014 and 2015 seasons. Panel data is obtained for each team, season, and weather category. A traditional least squared dummy variable linear regression technique is used along with three machine learning algorithms – random forest, M5 prime, and extreme gradient boosting. Extreme gradient boosting provides superior results with respect to out-of-sample root mean square error statistics. Well-founded technique for working with different methods is presented and the efficacy of contemporary algorithms is offered.
Desmoplastic small round cell tumor: extra abdominal and abdominal presentations and the results of treatment.

BACKGROUND Desmoplastic small round cell tumor (DSRCT) is a rare malignant neoplasm of adolescent males. Current multimodality treatment prolongs life and rarely achieves cure. AIM To review the presenting features, histopathology and outcome of 18 patients with DSRCT treated at a single institution. SETTING AND DESIGN This is a retrospective observational study of patients with DSRCT who presented at the Tata Memorial Hospital between January 1994 to January 2005. MATERIALS AND METHODS Eighteen patients of DSRCT seen during this period were evaluated for their clinical presentation, response to chemotherapy and other multimodality treatment and overall survival. The cohort of 18 patients included 11 males (61%) and 7 females (39%) with a mean age of 16 years (Range 1(1/2)--30 years). Majority (83%) presented with abdomino-pelvic disease. The others, involving chest wall and extremities. There were 6 patients (33%) with metastatic disease at presentation. RESULTS The treatment primarily included a multimodality approach using a combination of multiagent chemotherapy with adjuvant surgery and radiotherapy as applicable. A response rate of 39% (CR-1, PR-6), with chemotherapy was observed. The overall response rate after multimodality treatment was 39% (CR-5, PR-2). The overall survival was poor except in patients who had complete excision of the tumor. CONCLUSION 0 Abdomino-pelvic site was the commonest presentation, the disease can occur at other non-serosal surfaces also. Despite aggressive treatment the outcome was poor. However, complete surgical excision seems to provide a better survival.
Modeling Lipase Production From Co-cultures of Lactic Acid Bacteria Using Neural Networks and Support Vector Machine with Genetic Algorithm Optimization

I. Introduction Lipases are versatile enzymes, which are used in food, dairy, detergent, pharmaceutical, leather, cosmetics, biosensor, pulp and paper industries[1]. They are important biocatalysts that perform various chemical transformations synthesizing a variety of stereo-specific esters, sugar esters, thioesters and amides[2]. Usually acid/base catalysts will catalyze organic chemical reactions, but due to their disadvantages like difficulty in recovery of byproduct, requirement of intensive energy for undergoing reactions and difficulty in removing catalyst, the reactions are accomplished by enzyme catalyst like lipases. The interest in microbial lipase production increased because of its huge industrial applications but the availability of genetically distinct lipases with specific characteristic is still limited and thus there is an immense need to develop stable lipases which can replace chemical catalysts. The primary importance in any fermentation process is an optimization of medium components[3]. Even a small increase in performance can have a significant impact on its production. Therefore, process optimization is one of the most frequently used operation in biotechnology. The classical method of optimization process was carried out by one factor at a time (OFAT) method by varying only a single factor and keeping the remaining factors constant[4]. This approach is not only time consuming, but also ignores the combined interactions between physico-chemical parameters[5]. Hence these classical techniques of optimization form a basis for developing advanced techniques more suitable to today's practical problems. Though microbial consortium of various microorganisms has been applied in many fields of biotechnology[6] its application for the production of lipase is yet to be explored more. It is well known that extracellular lipase production in microorganism is greatly influenced by physical factors like pH, temperature, incubation period, substrate volume and inoculum volume[7]. Therefore, considering the many industrial applications of lipase, we report here for the first time the optimization of extracellular lipase production using co-culture of Lactococcus lactis and Lactobacillus plantarum, Lactococcus lactis and Lactobacillus brevis.
Application of Statistical Testing to the Data Processing and Control System for the Dungeness B Nuclear Power Plant (Practical Experience Report)

This experience report describes the successful application of statistical software testing to a large-scale realworld equipment replacement project. The Data Processing System at Dungeness B nuclear power station is obsolescent and needs to be replaced by a new system involving different technology. The new Data Processing and Control System (DPCS) involves a large amount of software code, with the potential for software failures, and therefore confidence needs to be established in the dependability of the new software-based system. Statistical software testing (ST) was used as one technique to do this. The aim was to support the claim that with high confidence the higher integrity parts of the replacement software have a failure probability of better than 10-2 failures per demand (fpd). This paper describes how this has been achieved by careful design of the statistical tests and by the development of a large number of supporting tools and procedures. A total of 395 statistical tests with no failure was achieved providing at least 98% confidence that the safety claim has been met.
“ Data-Driven ” Ontologies for an Information Extraction System from Polish Mammography Reports

The paper describes the ontology development for an IE (Information Extraction) application for Polish mammography reports, experiences and lessons learned, and the evaluation of the system. Information extraction requires prior knowledge on data structures we would like to identify. When information being searched for is as complicated as this contained in mammography reports, a theoretical approach of using the predefined domain knowledge is required. For our research goal, extraction of possibly all precise information from mammography reports, ontology has been chosen as platform of knowledge representation. During the work two ontologies have been developed, the first based mainly on BI-RADS [5], the second adjusted to the task of information extraction. The paper is structured as follows: section 2 relates our experiences concerning the reuse of existing ontologies, sections 3 and 4 present respectively initial mammographic ontology and modified model adapted for the task of information extraction, section 5 presents the IE system and results of information extractions, section 6 concludes the paper.
Tractable POMDP Planning Algorithms for Optimal Teaching in “ SPAIS ”

In this paper, we develop a system for teaching the task of sorting a set of virtual coins. Teaching is a challenging domain for AI systems because three problems must be solved at once: a teacher must simultaneously infer both social variables (attention, boredom, confusion, expertise, aptitude), as well as physical ones (task progress, objects being used, current activity), and finally she must combine this knowledge to plan effective moment-to-moment interaction strategies. We develop a framework called SPAIS (Socially and Physically Aware Interaction Systems), in which Social Variables define the transition probabilities of a POMDP whose states are Physical Variables. Optimal Teaching with SPAIS corresponds to solving an optimal policy in a very large factored POMDP that combines both types of variables, a difficult computational problem. To make the POMDP approach more tractable we devised a policy-switching methodology among simpler POMDP solutions, each one representing the best way to teach a different type of student (set of Social Variables). Our algorithms switch between prototypical states of pupils either based on Social Variables likelihood, or simply using the reward signal in algorithms for online learning with expert advice. In our results we demonstrate a system for teaching the task by prompting in an optimal way. Second, we show that our policy switching algorithms can produce POMDP policies with equivalent teaching performance to the complete, single model approach in a fraction of the time.
TaMYB13-1, a R2R3 MYB transcription factor, regulates the fructan synthetic pathway and contributes to enhanced fructan accumulation in bread wheat

Fructans are the major component of temporary carbon reserve in the stem of temperate cereals, which is used for grain filling. Three families of fructosyltransferases are directly involved in fructan synthesis in the vacuole of Triticum aestivum. The regulatory network of the fructan synthetic pathway is largely unknown. Recently, a sucrose-upregulated wheat MYB transcription factor (TaMYB13-1) was shown to be capable of activating the promoter activities of sucrose:sucrose 1-fructosyltransferase (1-SST) and sucrose:fructan 6-fructosyltransferase (6-SFT) in transient transactivation assays. This work investigated TaMYB13-1 target genes and their influence on fructan synthesis in transgenic wheat. TaMYB13-1 overexpression resulted in upregulation of all three families of fructosyltransferases including fructan:fructan 1-fructosyltransferase (1-FFT). A γ-vacuolar processing enzyme (γ-VPE1), potentially involved in processing the maturation of fructosyltransferases in the vacuole, was also upregulated by TaMYB13-1 overexpression. Multiple TaMYB13 DNA-binding motifs were identified in the Ta1-FFT1 and Taγ-VPE1 promoters and were bound strongly by TaMYB13-1. The expression profiles of these target genes and TaMYB13-1 were highly correlated in recombinant inbred lines and during stem development as well as the transgenic and non-transgenic wheat dataset, further supporting a direct regulation of these genes by TaMYB13-1. TaMYB13-1 overexpression in wheat led to enhanced fructan accumulation in the leaves and stems and also increased spike weight and grain weight per spike in transgenic plants under water-limited conditions. These data suggest that TaMYB13-1 plays an important role in coordinated upregulation of genes necessary for fructan synthesis and can be used as a molecular tool to improve the high fructan trait.
Sebeprezentace na sociálních sítích

This paper focuses on social network sites, as a space in which people present themselves to their surroundings. This space will be viewed as a new interaction environment, which, unlike face-to-face interactions, is not sociologically thoroughly mapped. Through presentation of selected literature, I will describe social network sites as a structure, that has specific characteristics, that shape the present interactions. The characteristics I formulate as Durability and traceability of information and Invisibility of the audience and overlapping of social contexts result from the nonphysical and timeless nature of the digital environment of social network sites. These characteristics significantly shape the context in which users engage in self-presentation and create specific interaction conditions for these users. The set environment will be perceived as a structure that users perceive and adequately respond to in their self-presentation processes. In addition to introducing the context of self-presentation, I will also focus on the actions of the users themselves who adapt the self-presentation strategies to the present context. I particularly emphasize the use of available technology tools, that are part of the social networking structure and which users use to overcome the specific challenges generated by this digital structure. The work should therefore, through the introduction of selected literature, respond to how self-presentation of social network sites users is shaped by the digital structure present and how this structure is reflected in the self-presentation by the users themselves.