Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Oil Prices in Exchange Rate Movements: The CIS Oil Exporters

Undoubtedly, oil prices play a crucial role in the macroeconomic performances of oil-exporting developing countries. In this regard, the exchange rate is one of the key macroeconomic indicators worthy of investigation. Existing literature shows that world oil prices play an important role in the appreciation of the exchange rates of oil-exporting developing countries. However, only a few studies have examined this issue by considering all three oil-exporting countries of the Commonwealth Independent States, namely Azerbaijan, Kazakhstan and Russia, together. In order to fill this gap and given the increasing importance of these economies in the world’s energy markets, this paper examines the role of oil prices in the movement of real effective exchange rates of the above-mentioned CIS countries. We applied the autoregressive distributed lag bounds testing method with a small sample bias correction to the data of these countries over the 2004Q1–2013Q4 period. The estimation results indicate that oil prices are certainly a main driver behind real effective exchange rate appreciation in the selected economies. Moreover, estimations show that productivity, to some extent, can also lead to the appreciation. The policy implication of this research is that an appreciation of the real exchange rate is harmful for the exports of non-oil goods and services in these countries. Since oil prices lead to the appreciation mainly through higher domestic prices, which is a result of tremendous public spending, decision-makers should reconsider the prevailing fiscal policy to make it much more counter-cyclical.
Estimation of number of people in crowded scenes using perspective transformation

In the past, the estimation of crowd density has become an important topic in the field of automatic surveillance systems. In this paper, the developed system goes one step further to estimate the number of people in crowded scenes in a complex background by using a single image. Therefore, more valuable information than crowd density can be obtained. There are two major steps in this system: recognition of the head-like contour and estimation of crowd size. First, the Haar wavelet transform is used to extract the featured area of the head-like contour, and then the support vector machine is used to classify these featured area as the contour of a head or not. Next, the perspective transforming technique of computer vision is used to estimate crowd size more accurately. Finally, a model world is constructed to test this proposed system and the system is also applied for real-world images.
Development Effort Estimation in HPC

In order to cover the ever increasing demands for computational power, while meeting electrical power and budget constraints, HPC systems are continuing to increase in hardware and software complexity. As a direct consequence, this also leads to increased development efforts to parallelize, tune or port applications. For an informed decision on how to spend available budgets, we therefore need quantitative metrics to estimate the development effort in HPC. While development effort estimation is widely used in software engineering, applying it to HPC, with its strong focus on performance, is not straightforward. In this paper, we first review existing approaches of effort estimation for general computing and then derive a novel methodology to estimate development effort specifically targeted at HPC. Further, we propose a concept to identify factors impacting development effort and encapsulate it in an effort log tool to collect data on development time.
Dealing with Public Ethernet Jacks: Switches, Gateways, and Authentication

This paper describes the tools and techniques developed and deployed to address the problem of blocking unauthorized users on unprotected Ethernet jacks. Our solution is being deployed to control public labs at the University of Alberta during the summer of 1999. In this environment, we have a mix of "walk up" Ethernet connections used for laptop computers, and public Windows 95 and 98 workstations with fixed Ethernet connections. By themselves, none of these provide adequate facilities for preventing unauthorized Internet usage and enabling us to track Internet abuses originating from these networks. Prior to the deployment of our new access control system, these networks were not routed off of our campus due to these problems.Our access control system consists of MAC-locked switches behind a gateway at which an IP filter only allows Internet access when authenticated. Now we allow the authenticated users full access to the Internet, while preventing unauthorized people from plugging in for free Internet access. This also provides a record of Internet activity by authenticated users so that abuses can be easily tracked.We also have several transparent proxies on the gateway machine to assist us in handling particularly troublesome security and configuration issues relating to the internal lab. This allows us to selectively proxy out bound IMAP, SMTP, and HTTP requests, as well as answering IDENT requests coming in to the lab with the real user. The solution is inexpensive and easy to deploy, using off-the-shelf switches and a gateway router running a free operating system and software.
A Reduced MIMO Detector Using Post SNR Ordering

SUMMARY In this letter, a novel adaptive detector that combines DFE and QRD-M is proposed for MIMO-OFDM system. QR decomposition (QRD) is commonly used in many MIMO detection algorithms. In particular, sorted QR decomposition (SQRD) is an advanced algorithm that improves MIMO detection performance. The proposed detector uses SQRD to achieve better performance. To reduce the computational complexity, the received layers of each subcarrier are ordered by using the post SNR and are detected by DFE and QRD-M detector based on the order. Therefore, the proposed detector structure is varied according to the channel state. In other words, the proposed detector achieves a good tradeo ﬀ between complexity and performance. A simulation conﬁrms the substantial performance improvements of the proposed adaptive detector with only slightly greater complexity than the conventional detector.
Upregulation of Kv 1.4 protein and gene expression after chronic spinal cord injury

After spinal cord injury (SCI), white matter tracts are characterized by demyelination and increased sensitivity to the K+ channel blocker 4‐aminopyridine (4‐AP). These effects appear to contribute to neurological impairment after SCI, although the molecular changes in K+ channel subunit expression remain poorly understood. We examined changes in gene expression of the 4‐AP‐sensitive voltage‐gated K+ channel Kv 1.4 after chronic SCI in the rat. Quantitative immunoblotting showed that Kv 1.4 protein was significantly increased at 6 weeks, but not at 1 week, after SCI in spinal cord white matter. Kv 1.4 was localized to astrocytes, oligodendrocytes, and oligodendrocyte progenitor cells but not to axons in both the normal and the injured spinal cord white matter. Because glial cells proliferate after SCI, we used immunogold electron microscopy to quantify Kv 1.4 protein in individual glial cells and found a sixfold increase of Kv 1.4 in cells of the oligodendrocyte lineage after chronic injury. Finally, quantitative in situ hybridization showed that Kv 1.4 mRNA was significantly upregulated in spinal cord white matter, but not gray matter, after SCI. In summary, we show that Kv 1.4 is expressed in glial cells and not in axons in the rat spinal cord white matter and that its expression is markedly increased in cells of the oligodendrocyte lineage after chronic SCI. Given that K+ channels play a role in glial cell proliferation, cells exhibiting changes in Kv 1.4 expression may represent proliferating oligodendroglia in the chronically injured spinal cord. J. Comp. Neurol. 443:154–167, 2002. © 2002 Wiley‐Liss, Inc.
A fast and effective EA for service restoration of compensated distribution networks

An effective service restoration (SR) strategy for distribution network plays a key role in improving service reliability and enhancing customers' satisfaction. Several evolutionary algorithms (EAs) have been developed in order to solve this problem. However, most of them do not include compensators in the plan. Others use some simplifying assumptions in order to limit the search space, which degrade the quality of obtained results. In addition, EAs suffer from high computational burden especially when improper modeling and operators are used. This paper proposes an integrated framework for EA in order to solve the SR problem which employs an efficient modeling of distribution network and simultaneously optimizes the network's configuration, the amount of load shedding and compensators' settings using special operators. Furthermore, a new technique has been proposed which limits the search space to only the area affected by the fault(s). Simulations prove efficiency of the proposed method by providing fast and high quality results.
MONITORING AEROSOL OPTICAL DEPTH AT BARROW , ALASKA AND SOUTH POLE ; HISTORICAL OVERVIEW , RECENT RESULTS , AND FUTURE GOALS

Atmospheric aerosols affect the Earth's radiation budget through interactions with solar and terrestrial radiation. Various committees involved with assessing global climate change recognize that aerosols can significantly impact the earth’s radiation balance. In particular, the Scientific Committee on Antarctic Research has recommended the establishment of an international network of solar spectrophotometers to monitor aerosol optical depth (AOD) at high latitudes. Although such a network now exists, better coordination is needed in order to provide research quality data to the scientific community. The U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Monitoring and Diagnostics Laboratory (CMDL) is collaborating with other institutes to assimilate AOD data from all polar observatories into a central archive for analysis. Historically, in situ aerosol data have been collected at CMDL baseline observatories located near Barrow, Alaska (BRW) and at South Pole, Antarctica (SPO), and since January 2000 continuous photometric measurements (during sunlit periods) have been made at these sites. An overview of past and current CMDL efforts to monitor AOD is given and some recent results are presented. Significant differences between the magnitudes and spectral signatures of AOD measured at BRW and SPO highlight the importance of assimilating similar data sets from other locations to better characterize polar aerosols spatially and temporally. Initial efforts should focus on defining natural cycles of AOD at a number of high latitude sites. Once these cycles are understood, more accurate assessments of climate forcing due to anthropogenic aerosol perturbations are possible. Through international cooperation this work can be expedited.
Median Absolute Deviation to Improve Hit Selection for Genome-Scale RNAi Screens

High-throughput screening (HTS) of large-scale RNA interference (RNAi) libraries has become an increasingly popular method of functional genomics in recent years. Cell-based assays used for RNAi screening often produce small dynamic ranges and significant variability because of the combination of cellular heterogeneity, transfection efficiency, and the intrinsic nature of the genes being targeted. These properties make reliable hit selection in the RNAi screen a difficult task. The use of robust methods based on median and median absolute deviation (MAD) has been suggested to improve hit selection in such cases, but mean and standard deviation (SD)—based methods are still predominantly used in many RNAi HTS. In an experimental approach to compare these 2 methods, a genome-scale small interfering RNA (siRNA) screen was performed, in which the identification of novel targets increasing the therapeutic index of the chemotherapeutic agent mitomycin C (MMC) was sought. MAD values were resistant to the presence of outliers, and the hits selected by the MAD-based method included all the hits that would be selected by SD-based method as well as a significant number of additional hits. When retested in triplicate, a similar percentage of these siRNAs were shown to genuinely sensitize cells to MMC compared with the hits shared between SD- and MAD-based methods. Confirmed hits were enriched with the genes involved in the DNA damage response and cell cycle regulation, validating the overall hit selection strategy. Finally, computer simulations showed the superiority and generality of the MAD-based method in various RNAi HTS data models. In conclusion, the authors demonstrate that the MAD-based hit selection method rescued physiologically relevant false negatives that would have been missed in the SD-based method, and they believe it to be the desirable 1st-choice hit selection method for RNAi screen results. ( Journal of Biomolecular Screening 2008:149-158)
Constant resistance control of solar array regulator using average current mode control

In this paper, a single current control loop to perform the MPPT and battery charging control as well as the current sharing control for the parallel connected SAR is presented. It transforms the effective load characteristic of the SAR seen by the SA from the constant power load into the constant resistance load. Thus, the system becomes large-signally stable for the entire operation region of the SA. The proposed method is simple and flexible since it is not affected by the characteristic of the SAR. Although it uses the nonlinear control to transform the effective load characteristic of SAR, it can be easily implemented using digital controller, which may be already available for the MPPT controller without additional circuits. For the design and the stability analysis of the proposed method, the small signal analysis is performed and the Z.O. H effect is analyzed. The experimental results from a 180W prototype hardware show that the proposed control scheme has a simple implementation structure and can stabilize the system in the entire region of SA.
Economic Growth and Convergence in India

Economic Growth and Convergence in India Kshamanidhi Adabar Abstract In the recent years there has been considerable emphasis on understanding regional dimensions of economic growth in India within the convergence implications of neoclassical growth paradigm. The most important issue emerges from this literature is that how to control for differences in the steady state. This paper attempts to re-examine the issue of convergence and economic growth by focusing on the differences in the steady state of 14 major states of India from 1976-77 to 2000-01 by employing dynamic fixed effects panel growth regression. Once per capita investment, population growth rate and human capital along with state-specific effects are controlled for, then there has been evidence of conditional convergence at the rate around 12 % per five-year span. These variables alone could explain around 93 per cent variation in the growth rate of per capita real income across 14 major states from 1976-2000. This highlights the importance of policy activism to achieve balanced growth and regional convergence.
Thermal stress analysis of PBGA using a fluid-solid coupling method under steady convective heat transfer

As the power density enhances while package size decreases, thermal management becomes a primary concern of the reliability and performance of the electronic packaging. When the natural convective heat transfer can't meet the thermal budget, a forced convective heat transfer must be employed in order to prevent the inappropriate temperature and thermal induced stress which would cause many problems to the electronic packaging.
Inference of string mappings for language technology

Mappings between formal languages play an important role in speech and language processing. This thesis explores issues related to inductive inference or learning of string-to-string mappings. The kinds of mappings considered fall within the larger class of rational transductions realized by finite state machines. Such mappings have applications in speech synthesis, speech recognition, and information retrieval and extraction. The present work takes its examples from speech synthesis, and is in particular concerned with the task of predicting the pronunciation of words from their spelling. When applied to this task, deterministic mappings are also known as letter-to-sound rules. The three most commonly used metrics for evaluating letter-to-sound rules are prediction error, which is not generally applicable; string error, which can only distinguish between perfect and flawed pronunciations and is therefore too coarse; and symbol error, which is based on string edit distance and subsumes string error. These three performance measures are independent in the sense that they may prefer different models for the same data set. The use of an evaluation measure based on some version of string edit distance is recommended. Existing proposals for learning deterministic letter-to-sound rules are systematized and formalized. Most formal problems underlying the learning task are shown to be intractable, even when they are severely restricted. The traditional approaches based on aligned data and prediction error are tractable, but have other undesirable properties. Approximate and heuristic methods are recommended. The formalization of learning problems also reveals a number of new open problems. Recent probabilistic approaches based on stochastic transducers are discussed and extended. A simple proposal due to Ristad and Yianilos is reviewed and recast in an algebraic framework for weighted transducers. Simple models based on memoryless transducers are generalized to stochastic finite transducers without any restrictions on their state graphs. Four fundamental problems for stochastic transducers (evaluation, parameter estimation, derivation of marginal and conditional models, and decoding) are identified and discussed for memoryless and unrestricted machines. An empirical evaluation demonstrates that stochastic transducers perform better on a letter-to-sound conversion task than deterministic mappings.
Diagnostic Value of p16 Methylation for Malignant Pleural Effusions: A Meta-analysis.

OBJECTIVE To evaluate the overall diagnostic performance of the p16 methylation for diagnosing malignant pleural effusion (MPE). METHODS All published literature in English and Chinese were reviewed. Sensitivity, specificity, likelihood ratio and diagnostic odds ratio (DOR) were pooled by using random-effects model or fixed-effects model. Summary receiver operating characteristic (SROC) curve was used to evaluate the overall diagnostic value. RESULTS Six studies were included with a total of 378 cases. The sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and DOR of p16 methylation in the diagnosis of MPE were 0.41 [95% confidence interval (CI) 0.35, 0.48], 0.97 [95% CI 0.93, 0.99], 9.57 [95% CI 4.53, 20.20], 0.61 [95% CI 0.45, 0.82] and 19.82 [95% CI 8.35, 47.04], respectively. The area under the curve (AUC) was 0.864. CONCLUSION Pleural p16 methylation test plays a useful role in the diagnosis of MPE.
Bandwidth Improvement in Large Reflectarrays by Using True-Time Delay

A significant improvement in the bandwidth of large reflectarrays is demonstrated using elements which allow true-time delay. Two identical, large reflectarrays have been designed using different phase distributions to generate a collimated beam. In the former, the phase distribution is truncated to 360deg as is usual in reflectarray antennas, while in the second, the true phase delay is maintained (three cycles of 360deg). The chosen phase-shifter elements are based on previously measured and validated patches aperture-coupled to delay lines. The radiation patterns for both reflectarrays have been computed at several frequencies and the gain is represented as a function of frequency for both cases. Bandwidth curves are presented as a function of the reflectarray size.
Un nouveau système indépendant de rejet multi-seuils pour la reconnaissance de mots manuscrits

En reconnaissance de mots manuscrits, la capacite de rejeter les mots qui n'appartiennent pas au lexique ou presentent une ambiguite est indispensable pour fiabiliser un systeme de reconnaissance utilise en condition reelle. Dans cet article, un nouveau systeme de rejet reposant sur des classifieurs de type separateur a vaste marge (SVM) et sur un algorithme original d'apprentissage de seuils multiples est presente. En substance, nous proposons d'utiliser un ensemble de SVM pour reevaluer les hypotheses produites par un sys-teme de reconnaissance de mots classique a base de modeles de Markov caches. Le mecanisme de verification rejette ou accepte ces hypotheses en comparant leur mesure de confiance aux seuils de rejet. Un algorithme optimal de programmation dynamique qui vise a apprendre ces seuils de rejet en maximisant le taux de reconnaissance pour un taux d'erreur desire est egalement detaille. Les experimentations ont ete menees sur la base nationale RIMES du programme Techno-Vision et les resultats presentes montrent la superiorite de cette approche sur les autres methodes existantes en fiabilite et en performance.
Optimal Power Allocation for Spectrum Sharing in Frequency-Selective Unlicensed Bands

Power allocation is an important issue for spectrum sharing of unlicensed bands, in which multiple unlicensed systems may coexist and operate. Recently some works have been reported on spectrum sharing in frequency-flat unlicensed bands. However, there has not been much work on power allocation for spectrum sharing in frequency-selective unlicensed bands. In this letter, we propose an optimal power allocation strategy on frequency-selective interference channels (IC). This strategy, also referred as general power allocation (GPA), allows the transmission power density to vary within one subcarrier. By showing the duality of frequency-selective and parallel frequency- flat channels, we can therefore compute the achievable rate region of GPA. Our method can be regarded as an extension of previous results for frequency-flat scenarios. It will also be shown that over frequency-selective Gaussian IC, the proposed GPA can achieve larger rate region than conventional power allocation strategies, where the transmission power density on each subcarrier is set equal.
Recruitment of human muscleblind proteins to (CUG)n expansions associated with myotonic dystrophy

Myotonic dystrophy (DM1) is an autosomal dominant neuromuscular disorder associated with a (CTG)n expansion in the 3′‐untranslated region of the DM1 protein kinase (DMPK) gene. To explain disease pathogenesis, the RNA dominance model proposes that the DM1 mutation produces a gain‐of‐function at the RNA level in which CUG repeats form RNA hairpins that sequester nuclear factors required for proper muscle development and maintenance. Here, we identify the triplet repeat expansion (EXP) RNA‐binding proteins as candidate sequestered factors. As predicted by the RNA dominance model, binding of the EXP proteins is specific for dsCUG RNAs and proportional to the size of the triplet repeat expansion. Remarkably, the EXP proteins are homologous to the Drosophila muscleblind proteins required for terminal differentiation of muscle and photoreceptor cells. EXP expression is also activated during mammalian myoblast differentiation, but the EXP proteins accumulate in nuclear foci in DM1 cells. We propose that DM1 disease is caused by aberrant recruitment of the EXP proteins to the DMPK transcript (CUG)n expansion.