Miracle-dz/UniFAR-FTUs huggingface dataset:
A 50% reduction in cyclosporine exposure in stable renal transplant recipients: renal function benefits.

BACKGROUND Although cyclosporine maintenance therapy reduces the risk of acute rejection and increases short-term graft survival in renal transplant recipients, its associated nephrotoxicity increases the risk of chronic graft dysfunction. The dose that allows an optimal risk-to-benefit ratio has not been established. METHODS This multicentre study enrolled stable renal allograft recipients receiving cyclosporine and mycophenolate mofetil without corticosteroids in their second year post-transplant. Patients were randomized to a cyclosporine dose targeted to a standard area under the concentration-time curve (AUC)(0-12 h) (usual exposure, n = 104) or 50% of the study standard AUC(0-12 h) (low exposure, n = 108) using a three-point pharmacokinetic sampling. The primary endpoint was the percentage of patients with treatment failure at 24 months (graft loss/acute rejection/nephrotoxicity/>15% serum creatinine level increase). RESULTS Treatment failure was reported in 37 out of 101 (37%) patients in the usual-exposure and 19 out of 106 (18%) patients in the low-exposure groups (P = 0.003). Mean estimated glomerular filtration rate decreased from baseline to 2 years with usual exposure and increased with low exposure (P < 0.001). Mean systolic and diastolic blood pressures were lower with low exposure (P = 0.03 and P = 0.008, respectively). CONCLUSION In renal transplant recipients receiving maintenance therapy without corticosteroids, a minimization strategy using three-point pharmacokinetic sampling to reduce and maintain cyclosporine exposure to 50% of the usual levels is safe and reduces the risk of graft dysfunction.
Polyakov's string classical mechanics

This paper is almost an exercise in which the Hamiltonian scheme is developed for Polyakov's classical string, by following the usual framework suggested by Dirac and Bergman for the reduction of gauge theories to their essential physical degrees of freedom. The results collected here will be useful in some forthcoming papers, where strings will be studied in the unusual context of Wigner-covariant rest frame theory. After a short introduction outlining the work, the Lagrangean scheme is presented in Section II, where the classical equivalence between Polyakov and Nambu-Goto string is rederived. In Section III the Hamiltonian framework is worked out, primary and secondary constraints are deduced. Then Lagrange multipliers are introduced; finally the Hamiltonian equations of motion are presented. In Section IV gauge symmetries are treated, by constructing their canonical generators; then some gauge degrees of freedom are eliminated by Dirac-Bergmann fixing procedure. In this paper only the gauge-freedom coming from primary constraints is fixed, while secondary constraints will need a deeper analysis. At the end of Section IV the classical version of Virasoro algebra is singled out as that of the secondary constraints, surviving to the first gauge fixing.
On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship

We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that the variance and the mean of interspike intervals are related by a power function characterized by two parameters: the scale factor and exponent. It is shown that this single assumption allows the variability of spike trains to have an arbitrary scale and various dependencies on the firing rate in the spike count statistics, as well as in the interval statistics, depending on the two parameters of the power function. We also propose a statistical model for spike trains that exhibits the variance-to-mean power relationship. Based on this, a maximum likelihood method is developed for inferring the parameters from rate-modulated spike trains. The proposed method is illustrated on simulated and experimental spike trains.
Weak Keys in RSA over The Work of Blomer & May

In this paper we generalize the idea given by Weger and Maitra & Sarkar. This generalization is coming from the concept of x9.31-1997 standard for public key cryptography, Section 4:1:2, i.e., there are a number of recommendations for the generalization of the primes of an RSA modulus. Among them, the ratio of the primes shall not be close to the ratio of small integers. Also we try to improve the range of weak keys of RSA cryptosystem for the Generalized Wiener's attack given by Blomer & May. We have shown that the range of weak keys can be extended by more than 8 times than the range given by Blomer & May. Further we have shown that for |ap - bq| ≤ N(superscript (α/2)) where 0 ＜α≤ 1, if e satisfies an equation ex + y = m∅(N), for m ＞ 0. Then N can be factored in( O poly(logN)) times when 0 ＜ x ≤ 1/6√∅ (N)/e N(superscript (1/2 -α/4)) and |y|≤ |ap - bq|/∅(N)N(superscript (1/4) ex.
Recent improvements on Microsoft's trainable text-to-speech system-Whistler

The Whistler text-to-speech engine was designed so that we can automatically construct the model parameters from training data. This paper focuses on the improvements on prosody and acoustic modeling, which are all derived through the use of probabilistic learning methods. Whistler can produce synthetic speech that sounds very natural and resembles the acoustic and prosodic characteristics of the original speaker. The underlying technologies used in Whistler can significantly facilitate the process of creating generic TTS systems for a new language, a new voice, or a new speech style. Whisper TTS engine supports Microsoft Speech API and requires less than 3 MB of working memory.
Power-Efficient Breadth-First Search with DRAM Row Buffer Locality-Aware Address Mapping

Graph analysis applications have been widely used in real services such as road-traffic analysis and social network services. Breadth-first search (BFS) is one of the most representative algorithms for such applications; therefore, many researchers have tuned it to maximize performance. On the other hand, owing to the strict power constraints of modern HPC systems, it is necessary to improve power efficiency (i.e., performance per watt) when executing BFS. In this work, we focus on the power efficiency of DRAM and investigate the memory access pattern of a state-of-the-art BFS implementation using a cycle-accurate processor simulator. The results reveal that the conventional address mapping schemes of modern memory controllers do not efficiently exploit row buffers in DRAM. Thus, we propose a new scheme called per-row channel interleaving and improve the DRAM power efficiency by 30.3% compared to a conventional scheme for a certain simulator setting. Moreover, we demonstrate that this proposed scheme is effective for various configurations of memory controllers.
Finding locations of flickr resources using language models and similarity search

We present a two-step approach to estimate where a given photo or video was taken, using only the tags that a user has assigned to it. In the first step, a language modeling approach is adopted to find the area which most likely contains the geographic location of the resource. In the subsequent second step, a precise location is determined within the area that was found to be most plausible. The main idea of this step is to compare the multimedia object under consideration with resources from the training set, for which the exact coordinates are known, and which were taken in that area. Our final estimation is then determined as a function of the coordinates of the most similar among these resources. Experimental results show this two-step approach to improve substantially over either language models or similarity search alone.
Study of Bayes Theorem for Classification of Synthetic Aperture Data

In this paper we describe a probabilistic approach of image classification based on Bayes classifier for SAR (Synthetic Aperture Radar) data. It classifies the images of different polarizations as well as the combination of these polarizations. The classification is carried out on the HH, HV and VV polarized image, their combination HH-HV, HH-VV and HV-VV and considering all the three polarized images, i.e., HH-HV-VV. It is an supervised classification approach which obtains the training data set from the region under observation. The results obtained are compared on the basis of better distinguishing ability of different classes by the classifier. The proposed classifier incorporates the use of Bayesian function for the evaluation of PDF (Probability Density Function) and with the consideration of PDF the decision function allocates a particular class to a pixel which better suits to that pixel.
Transcriptome Analysis of Encystation in Entamoeba invadens

Encystation is an essential differentiation process for the completion of the life cycle of a group of intestinal protozoa including Entamoeba histolytica, the causative agent of intestinal and extraintestinal amebiasis. However, regulation of gene expression during encystation is poorly understood. To comprehensively understand the process at the molecular level, the transcriptomic profiles of E . invadens , which is a related reptilian species that causes an invasive disease similar to that of E. histolytica, was investigated during encystation. Using a custom-generated Affymetrix platform microarray, we performed time course (0.5, 2, 8, 24, 48, and 120 h) gene expression analysis of encysting E . invadens . ANOVA analysis revealed that a total of 1,528 genes showed ≥3 fold up-regulation at one or more time points, relative to the trophozoite stage. Of these modulated genes, 8% (116 genes) were up-regulated at the early time points (0.5, 2 and 8h), while 63% (962 genes) were up-regulated at the later time points (24, 48, and 120 h). Twenty nine percent (450 genes) are either up-regulated at 2 to 5 time points or constitutively up-regulated in both early and late stages. Among the up-regulated genes are the genes encoding transporters, cytoskeletal proteins, proteins involved in vesicular trafficking (small GTPases), Myb transcription factors, cysteine proteases, components of the proteasome, and enzymes for chitin biosynthesis. This study represents the first kinetic analysis of gene expression during differentiation from the invasive trophozoite to the dormant, infective cyst stage in Entamoeba . Functional analysis on individual genes and their encoded products that are modulated during encystation may lead to the discovery of targets for the development of new chemotherapeutics that interfere with stage conversion of the parasite.
Centroids of Type-1 and Type-2 Fuzzy Sets When Membership Functions Have Spikes

Centroids are practically important in type-1 and type-2 fuzzy logic systems as a method of defuzzification and type reduction. However, computational problems arise when membership functions (MF) have singleton spikes. The novel thresholding aggregation operators that were described in our companion paper “ New Classes of Threshold Aggregation Functions Based Upon the Tsallis q-Exponential with Applications to Perceptual Computing” produce such MFs with spikes. Such spikes may occur when modeling concepts defined on a real-valued domain, and they are also formed in unions of fuzzy sets in which some have MFs with discrete support and others have support defined on an interval. This paper presents a modified definition of the centroid of a fuzzy set that avoids the computational problems associated with the usual definition and reduces to this definition when MFs are continuous and normal (i.e., of unit height) on some interval. We also present an enhanced Karnik-Mendel-type algorithm to compute the modified centroid of interval type-2 fuzzy sets whose MFs have spikes.
Analyzing the impact of churn and malicious behavior on the quality of peer-to-peer web search

In an attempt to increase the spectrum of searchable information while attenuating scalability issues, Peer-to-Peer (P2P) networks have been viewed as an alternative way to design new Web search engines. However, the effectiveness of P2P Web searching may be severely limited by characteristics commonly observed in real P2P systems such as peer churn and malicious behavioral patterns. This paper analyzes the impact of each such issue on the effectiveness of P2P Web searching. In order to estimate boundaries, we focus our analysis on P2P network models with very high and low levels of peer collaboration. Our findings reveal that such patterns could strongly affect the effectiveness of P2P Web searching. In networks with a high level of peer collaboration, a significant fraction of queries suffer an impact on the quality of search of at least 24% and 26% even in slightly insecure and unstable scenarios. We also confirm that the impact of each such issue in real-world, less collaborative networks can be even more intense (73% and 75%). Thus, together with the high autonomy of peers and the absence of file-sharing benefits in replicating documents into the network, we argue that effectiveness of P2P Web search engines would strongly depend on new, application-specific reputation and incentive mechanisms.
Price Setting Under Uncertainty About Inflation

When setting prices firms use idiosyncratic information about the demand for their products as well as public information about the aggregate macroeconomic state. This paper provides an empirical assessment of the effects of the availability of public information about inflation on price setting. We exploit an event in which economic agents lost access to information about the inflation rate: starting in 2007 the Argentinean government began to misreport the national inflation rate. Our difference-in-difference analysis reveals that this policy led to an increase in the coefficient of variation of prices of 18% with respect to its mean. This effect is analyzed in the context of a general equilibrium model in which agents make use of publicly available information about the inflation rate to set prices. We quantify the model and use it to further explore the effects of higher uncertainty about inflation on the effectiveness of monetary policy and aggregate welfare. We find that monetary policy becomes more effective in a context of higher uncertainty about inflation and that not reporting accurate measures of the CPI entails significant welfare losses.
A Conversant Robotic Guide to Art Collections

We present the dialogue system of a robot that has been develo p d t serve as a museum guide. The robot interacts with human visitors in natural language, receiving instructions and p roviding information about the exhibits. Moreover, being m obile, it physically approaches the exhibits it provides information about. Alt hough the robotic platform contains many modules, includin g navigation, speech recognition and synthesis, our focus in this paper is the dialogue system, which supports the sessions between hu mans and the robot, as well as the natural language generation engine, wh ich generates the text to be spoken. Both modules are closely int rwined and depend on an ontology represented in OWL. The robot supports dialogues in both English and Greek.
Understanding the basic mechanisms underlying seizures in mesial temporal lobe epilepsy and possible therapeutic targets: A review

Despite years of research, epilepsy remains a poorly understood disorder. In the past several years, work has been conducted on a variety of projects with the goal of better understanding the pathogenesis and progression of mesial temporal lobe epilepsy (MTLE), in particular, and how to exploit those properties to generate innovative therapies for treatment of refractory epilepsies. This review seeks to give an overview of common morphological and biochemical changes associated with epilepsy and proposed treatments to address those changes. Furthering the understanding of ictogenesis and epileptogenesis remains an important goal for scientists seeking to find more effective treatments for MTLE. © 2012 Wiley Periodicals, Inc.
Chunk incremental IDR/QR LDA learning

Training data in real world is often presented in random chunks. Yet existing sequential Incremental IDR/QR LDA (s-QR/IncLDA) can only process data one sample after another. This paper proposes a constructive chunk Incremental IDR/QR LDA (c-QR/IncLDA) for multiple data samples incremental learning. Given a chunk of s samples for incremental learning, the proposed c-QR/IncLDA increments current discriminant model Ω, by implementing computation on the compressed the residue matrix Δ ϵ Rd×n, instead of the entire incoming data chunk X ϵ Rd×s, where η ≤ s holds. Meanwhile, we derive a more accurate reduced within-class scatter matrix W to minimize the discriminative information loss at every incremental learning cycle. It is noted that the computational complexity of c-QR/IncLDA can be more expensive than s-QR/IncLDA for single sample processing. However, for multiple samples processing, the computational efficiency of c-QR/IncLDA deterministically surpasses s-QR/IncLDA when the chunk size is large, i.e., s ≫ η holds. Moreover, experiments evaluation shows that the proposed c-QR/IncLDA can achieve an accuracy level that is competitive to batch QR/LDA and is consistently higher than s-QR/IncLDA.
Personality and Motivational Factors Predicting Internet Abuse at Work

The present study investigates whether a personality variable (Locus of Control) and motivational factors rooted in Uses & Gratifications theory (the desire for virtual community, beautiful websites, maintaining interpersonal relations, and the desire for diversion & entertainment) predict Internet abuse at work in the form of shopping, searching for information unrelated to work, and communicating with friends. Data were analyzed for 320 participants, all of whom were employed and had access to computers at work. Results indicate that external Locus of Control predicts all three forms of Internet abuse. The desire to use the Internet for diversion and entertainment purposes predicts only online shopping and searching for non-work-related information. Results suggest that factors predicting some forms of Internet abuse may not predict other forms of Internet abuse. Implications for research and practice are discussed.
Sigma-delta modulation technique for low noise fractional-N frequency synthesizer

The fractional-N frequency synthesis technique provides agile switching in narrow channel spacing systems and alleviates phase-locked loop (PLL) design constraints for phase noise and reference spur. A sigma-delta fractional-N frequency synthesizer (FS) achieves the phase noise performance using a spur supression method. A sigma-delta modulator is represented in this work which reduces spur in output frequency spectrum of the fractional-N frequency synthesizers. This technique also improves the performance of the frequency synthesizer. A fractional-N frequency synthesizer is designed by employing a third-order MASH modulator. A fourth order type II PLL with two out of band poles are used to suppress quantization noise of the modulator. The in-band phase noise of−95 dBc/Hz at 10-kHz offset with a spur of less than −96 dBc is achieved with a reference frequency of 8 MHz and a loop bandwidth of 40 kHz.
The Eect of Skills on Employment Growth: Adjusting Bias and Weak IVs with New Evidence from German Metropolitan Areas

German metropolitan areas have been diverging in terms of local human capital concentration between 1975 and 2003, and this has strong implications for urban employment growth. Conventional estimates show that the share of college graduates aects growth by the same magnitude as it does in American MSAs. The eect is robust to various controls across two data sets. However, we show that this figure is biased upwards because of endogeneity of initial employment. Corrected estimates using lagged values as instruments are closer to 0.5% employment growth for a 10% increase in the concentration of graduates. We question that aggregate productivity growth is solely caused by college graduates. After distinguishing between 6 dierent skill levels we find positive growth eects
Productivity Differences Between and Within Countries

We document substantial within-country (cross-municipality) differences in incomes for a large number of countries in the Americas. A significant fraction of the within-country differences cannot be explained by observed human capital. We conjecture that the sources of within-country and between-country differences are related. As a first step towards a united framework, we propose a simple model incorporating both differences in technological know-how across countries and differences in productive efficiency within countries.
The Design of RBF Neural Networks for Solving Overfitting Problem

One of the biggest problems in designing or training RBF neural networks are the overfitting problem. The traditional design of RBF neural networks may be pursued in a variety of ways. In this paper, we present a method for the design of RBF networks to solve overfitting problem. For a practical application, frequency information is usually available for designing RBF networks by frequency domain analysis, which has a sound mathematical basis. We try to include the frequency information into the design of RBF networks, which achieve the task of approximated a function in certain frequency range and have the property of structural risk minimization. After the structure of designed network is determined, the linear weights of the output layer are the only set of adjustable parameters. The approach of design is verified by approximation cases
Robot Space Exploration by Trial and Error

This paper argues that evolutionary robotics (ER) techniques can act as useful and potentially wide ranging tools in the scientific investigation of adaptive behaviour. After discussing the kinds of investigations ER can play a central role in, a concrete example is presented. We conclude that these kinds of studies are not only scientifically useful, but are necessary for the field to develop as an engineering methodology for autonomous robotics.
Modelling Syntactic and Semantic Tasks with Linguistically Enriched Recursive Neural Networks

In this thesis a compositional distributional semantic approach, the Recursive Neural Network, is used to syntactically-semantically compose non-symbolic representations of words. Unlike previous Recursive Neural Network models which use either no linguistic enrichment or significant symbolic syntactic enrichment, I propose minimal linguistic enrichments which are both semantic and syntactic. I achieve this by enriching the Recursive Neural Networks’ models with core syntactic/semantic linguistic types: head, argument and adjunct. This approach brings together formal linguistics and computational linguistics, as such I give a broad account of these theories. The syntactic understanding of the model is tested by a parsing task and the semantic understanding is tested by a paraphrase detection task. The results of these tasks not only show the benefits of linguistic enrichment but also raise further questions of study.
Selection of the objective function of reduction parameter optimization algorithm, and its impact on the properties of reduced MIMO models

This article presents a range of approximation error measures for reduced mathematical MIMO models of controlled objects. These measures can be of use in evaluations of reduction quality. The impact of selection of a particular approximation error measure as the objective function of the reduction parameter optimization algorithm on the frequency characteristics of the reduced model was analyzed. Particular emphasis was given to those cases where the individual channels of a MIMO model differ significantly from one another. The results are presented on the example of a one-phase zone evaporating tubes model of a BP-1150 steam boiler.