Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing Monetary Policy

The purpose of the paper is to discuss how monetary policy decisions made by the board or monetary policy committee of a central bank can be implemented. It distinguishes between the polar extremes of direct and indirect methods of implementation, and explains why indirect methods are generally preferred. It describes the circumstances in which various elements of a central bank’s balance sheet can grow very quickly, threatening to cause the growth rate of central bank money to rise to levels inconsistent with the objectives of monetary policy, and discusses what offsetting measures the central bank can take to contain the growth of central bank money. It describes in detail how open market operations can be conducted, and discusses techniques of intervention in foreign exchange markets. Finally, it reviews the usefulness of direct controls of instruments of monetary policy, and discusses the conditions in which such controls might be needed and how they can best be designed.
An Innovative Control Strategy of a Single Converter for Hybrid Fuel Cell/Supercapacitor Power Source

In this paper, an innovative control strategy for hybrid power sources dedicated to automotive applications is detailed. First, an analysis of classical hybrid architectures using fuel cell (FC)/supercapacitors is presented. Then, an analysis of load requirements for automotive applications is proposed. A new control strategy of a single converter, based on a cascaded control loop with a decoupling strategy in the frequency domain, is fully explained. Finally, experimental results on a Ballard proton exchange membrane FC are presented to illustrate the effectiveness of the proposed method.
Roles of the Y chromosome genes in human cancers

Male and female differ genetically by their respective sex chromosome composition, that is, XY as male and XX as female. Although both X and Y chromosomes evolved from the same ancestor pair of autosomes, the Y chromosome harbors male-specific genes, which play pivotal roles in male sex determination, germ cell differentiation, and masculinization of various tissues. Deletions or translocation of the sex-determining gene, SRY, from the Y chromosome causes disorders of sex development (previously termed as an intersex condition) with dysgenic gonads. Failure of gonadal development results not only in infertility, but also in increased risks of germ cell tumor (GCT), such as gonadoblastoma and various types of testicular GCT. Recent studies demonstrate that either loss of Y chromosome or ectopic expression of Y chromosome genes is closely associated with various male-biased diseases, including selected somatic cancers. These observations suggest that the Y-linked genes are involved in male health and diseases in more frequently than expected. Although only a small number of protein-coding genes are present in the male-specific region of Y chromosome, the impacts of Y chromosome genes on human diseases are still largely unknown, due to lack of in vivo models and differences between the Y chromosomes of human and rodents. In this review, we highlight the involvement of selected Y chromosome genes in cancer development in men.
Parametrization and Classifiication of Closed Anatomical Curves

We present a streamlined mathematical framework for modeling and classifying closed curves from medical images. The method of gradient vector flow (GVF) snakes is used to extract object boundaries in the magnetic resonance images of the human brain. The curvature based arc-length parametriz ation is used to parameterize the extracted boundary. A linea r curve registration is then performed to align the orientati on of the curves via the least-squares method. Afterwards, a finit e Fourier series expansion is used to get a smooth functional representation of the parametrization. Various classifica tion and regression tree techniques are applied to determine the best classification techniques in separating the corpus cal losum (CC) boundary curves obtained from 12 autistic and 15 normal control subjects. The goal of this paper is to develop a methodological framework for classifying CC curves into different clinical groups, and compare the performance of d ifferent classification methods.
Minimax estimation of deterministic parameters in linear models with a random model matrix

We consider the problem of estimating an unknown deterministic parameter vector in a linear model with a random model matrix, with known second-order statistics. We first seek the linear estimator that minimizes the worst-case mean-squared error (MSE) across all parameter vectors whose (possibly weighted) norm is bounded above. We show that the minimax MSE estimator can be found by solving a semidefinite programming problem and develop necessary and sufficient optimality conditions on the minimax MSE estimator. Using these conditions, we derive closed-form expressions for the minimax MSE estimator in some special cases. We then demonstrate, through examples, that the minimax MSE estimator can improve the performance over both a Baysian approach and a least-squares method. We then consider the case in which the norm of the parameter vector is also bounded below. Since the minimax MSE approach cannot account for a nonzero lower bound, we consider, in this case, a minimax regret method in which we seek the estimator that minimizes the worst-case difference between the MSE attainable using a linear estimator that does not know the parameter vector, and the optimal MSE attained using a linear estimator that knows the parameter vector. For analytical tractability, we restrict our attention to the scalar case and develop a closed-form expression for the minimax regret estimator.
Transcription profiles of the bacterium Mycoplasma pneumoniae grown at different temperatures.

Applying microarray technology, we have investigated the transcriptome of the small bacterium Mycoplasma pneumoniae grown at three different temperature conditions: 32, 37 and 32 degrees C followed by a heat shock for 15 min at 43 degrees C, before isolating the RNA. From 688 proposed open-reading frames, 676 were investigated and 564 were found to be expressed (P < 0.001; 606 with P < 0.01) and at least 33 (P < 0.001; 77 at P < 0.01) regulated. By quantitative real-time PCR of selected mRNA species, the expression data could be linked to absolute molecule numbers. We found M.pneumoniae to be regulated at the transcriptional level. Forty-seven genes were found to be significantly up-regulated after heat shock (P < 0.01). Among those were the conserved heat shock genes like dnaK, lonA and clpB, but also several genes coding for ribosomal proteins and 10 genes of unassigned functions. In addition, 30 genes were found to be down-regulated under the applied heat shock conditions. Further more, we have compared different methods of cDNA synthesis (random hexamer versus gene-specific primers, different RNA concentrations) and various normalization strategies of the raw microarray data.
Plagiarism in programming assignments

The assessment of programming courses is usually carried out by means of programming assignments. Since it is simple to copy and edit computer programs, however, there will always be a temptation among some students following such courses to copy and modify the work of others. As the number of students in these courses is often high, it can be very difficult to detect this plagiarism. The authors have developed a package which will allow programming assignments to be submitted online, and which includes software to assist in detecting possible instances of plagiarism. In this paper, they discuss the concerns that motivated this work, describe the developed software, tailoring the software to different requirements and finally consider its implications for large group teaching.
A Text Transformation Scheme for Degenerate Strings

The Burrows-Wheeler Transformation computes a permutation of a string of letters over an alphabet, and is well-suited to compression-related applications due to its invertability and data clustering properties. For space e ciency the input to the transform can be preprocessed into Lyndon factors. We consider scenarios with uncertainty regarding the data: a position in an indeterminate or degenerate string is a set of letters. We first define Indeterminate Lyndon Words and establish their associated unique string factorization; we then introduce the novel Degenerate Burrows-Wheeler Transformation which may apply the indeterminate Lyndon factorization. A core computation in Burrows-Wheeler type transforms is the linear sorting of all conjugates of the input string we achieve this in the degenerate case by applying lex-extension ordering. Indeterminate Lyndon factorization, and the degenerate transform and its inverse, can all be computed in linear time and space with respect to total input size of degenerate strings.
The Architecture Design for Dam Safety Monitoring System

It is the core goal to establish reasonable system architecture during the summary design phase of dam safety monitoring system (DSMS) development. At first, measure point comprehensive information model (MPCIM) is built as the key object to DSMS. Then its attributes and operations are analyzed to form inherent class hierarchies and to complete database design. At last, subsystems are divided to establish the whole system architecture. In some degree, this system analysis method can deal with the situation that the maximum design freedom is in contradiction with the minimum professional knowledge, improve the software design efficiency and decrease the development risk.
On robustness properties in Empirical Centroid Fictitious Play

Empirical Centroid Fictitious Play (ECFP) is a generalization of the well-known Fictitious Play (FP) algorithm designed for implementation in large-scale games. In ECFP, the set of players is subdivided into equivalence classes with players in the same class possessing similar properties. Players choose a next-stage action by tracking and responding to aggregate statistics related to each equivalence class. This setup alleviates the difficult task of tracking and responding to the statistical behavior of every individual player, as is the case in traditional FP. Aside from ECFP, many useful modifications have been proposed to classical FP, e.g., rules allowing for network-based implementation, increased computational efficiency, and stronger forms of learning. Such modifications tend to be of great practical value; however, their effectiveness relies heavily on two fundamental properties of FP: robustness to alterations in the empirical distribution step size process, and robustness to best-response perturbations. The main contribution of the paper is to show that similar robustness properties also hold for the ECFP algorithm. This result serves as a first step in enabling practical modifications to ECFP, similar to those already developed for FP.
Sectional domain specific languages

Nowadays, many problems are solved by using a domain specific language (DSL), i.e., a programming language tailored to work on a particular application domain. Normally, a new DSL is designed and implemented from scratch requiring a long time-to-market due to implementation and testing issues. Whereas when the DSL simply extends another language it is realized as a source-to-source transformation or as an external library with limited flexibility. The Hive framework is developed with the intent of overcoming these issues by providing a mechanism to compose different programming features together forming a new DSL, what we call a sectional DSL. The support (both at compiler and interpreter level) of each feature is separately described and easily composed with the others. This approach is quite flexible and permits to build up a new DSL from scratch or simplifying an existing language without penalties. Moreover, it has the desirable side-effect that each DSL can be extended at any time potentially also at run-time.
Design of Intelligent Parking Management System Based on License Plate Recognition

License Plate Recognition (LPR) which has been extensively studied recently is becoming one important part of Intelligent Transportation Systems (ITS). It is not only applied to public security monitoring, but also applied to a red light shot, speeding violation paparazzi, parking system and so on. The present paper introduces an intelligent parking management system based on LPR, recognizing license plate automatically at the parking lot access and realizing vehicle information integrated management conveniently. The software part is developed with VC++, QT and Microsoft Office Access Database. In the system, LPR is carried out through video streaming frame by frame to choose the best result with a high speed. After accurate LPR, the vehicle information, such as LP number, LP color, car type and access time, will be stored in system database. In addition, the system interface is designed very friendly and simple for administrators to operate, which can largely improve the whole performance of LPR system. Experimental results show that this parking management system can achieve high correct rate at 95%, and can be applied to real-time implementation
Improving k-nearest neighbors algorithms: practical application of dataset analysis

In the last years, recommender systems have achieved a great popularity. Many different techniques have been developed and applied to this field. However, in many cases the algorithms do not obtain the expected results. In particular, when the applied model does not fit the real data the results are especially bad. This happens because many times models are directly applied to a domain without a previous analysis of the data. In this work we study the most popular datasets in the movie recommendation domain, in order to understand how the users behave in this particular context. We have found some remarkable facts that question the utility of the similarity measures traditionally used in k-Nearest Neighbors (kNN) algorithms. These findings can be useful in order to develop new algorithms. In particular, we modify traditional kNN algorithms by introducing a new similarity measure specially suited for sparse contexts, where users have rated very few items. Our experiments show slight improvements in prediction accuracy, which proves the importance of a thorough dataset analysis as a previous step to any algorithm development.
Simulation of a backward compatible IEEE 802.11g network: Access delay and throughput performance degradation

Performance degradation of an IEEE 802.11g network is studied in case legacy stations are associated with the AP. Custom event-driven simulator was developed for the purpose. We demonstrate its consistency by comparing the outputs with a widely accepted network simulator and with results of an analytical study. Performance degradation is analyzed in terms of access delay and throughput. We study the effect of an associated legacy node on access delay as a function of number of nodes in the network. Additionally, we focus on a fixed size network and examine the effect of the bit rate used for transmission of protection mechanisms' control frames. Significant performance drop is observed both in terms of access delay and throughput.
Riparian and in-stream controls on nutrient concentrations and fluxes in a headwater forested stream

Abstract. Headwater streams are recipients of water sources draining through terrestrial ecosystems. At the same time, stream biota can transform and retain nutrients dissolved in stream water. Yet studies considering simultaneously these two sources of variation in stream nutrient chemistry are rare. To fill this gap of knowledge, we analyzed stream water and riparian groundwater concentrations and fluxes as well as in-stream net uptake rates for nitrate (NO3−), ammonium (NH4+), and soluble reactive phosphorus (SRP) along a 3.7 km reach on an annual basis. Chloride concentrations (used as conservative tracer) indicated a strong hydrological connection at the riparian–stream interface. However, stream and riparian groundwater nutrient concentrations showed a moderate to null correlation, suggesting high in-stream biogeochemical processing. In-stream net nutrient uptake (Fsw) was highly variable across contiguous segments and over time, but its temporal variation was not related to the vegetative period of the riparian forest. For NH4+, the occurrence of Fsw > 0 μg N m−1 s−1 (gross uptake > release) was high along the reach, while for NO3−, the occurrence of Fsw
Automating the Testing of OpenFlow Applications

Software-defined networking, and the emergence of OpenFlow-capable switches, enables a wide range of new network functionality. However, enhanced programmability inevitably leads to more software faults (or bugs). We believe that tools for testing OpenFlow programs are critical to the success of the new technology. However, the way OpenFlow applications interact with the data plane raises several challenges. First, the space of possible inputs (e.g., packet headers and inter-packet timings) is huge. Second, the centralized controller has a indirect view of the traffic and experiences unavoidable delays in installing rules in the switches. Third, external factors like user behavior (e.g., mobility) and higher-layer protocols (e.g., the TCP state machine) affect the correctness of OpenFlow programs. In this work-in-progress paper, we extend techniques for symbolic execution to generate inputs that systematically explore the space of system executions. First, we analyze controller applications to identify equivalence classes of packets that exercise different parts of the code. Second, we propose several network models with increasing precision, ranging from simple traffic models to live testing on the target network. Initial experiences with our prototype, which symbolically executes OpenFlow applications written in Python, suggest that our techniques can help programmers identify bugs in their OpenFlow programs.
Patch Clamp: A Powerful Technique for Studying the Mechanism of Acupuncture

Cellular and molecular events can be investigated using electrophysiological techniques. In particular, the patch-clamp method provides detailed information. In addition, the patch-clamp technique has become a powerful method for investigating the mechanisms underlying the effects of acupuncture. In this paper, recent researches on how acupuncture might modulate electrophysiological responses in the central nervous system (CNS) and affect peripheral structures are reviewed.
Fast and exact direct illumination

Rendering high quality soft shadows from area light sources is necessary to increase the level of realism. Quality soft shadows are closely related to the visibility computation. An accurate visibility information improves the shadows realism. However, this remains a challenging problem since even small visibility approximations can lead to unacceptable errors in a picture. We propose a new approach to this problem, based on an exact visibility pre-computation, done in the Plucker space. It takes advantage of this first step to provide an exact from-point visibility query algorithm. We propose several results in a ray tracing application, where the direct illumination at any given point is provided with a fast computation, and with a high degree of quality.
Aalborg Universitet Performance Comparison of Reconstruction Algorithms in Discrete Blind Multi-Coset Sampling

This paper investigates the performance of different reconstruction algorithms in discrete blind multi-coset sampling. Multi-coset scheme is a promising compressed sensing architecture that can replace traditional Nyquist-rate sampling in the applications with multi-band frequency sparse signals. The performance of the existing compressed sensing reconstruction algorithms have not been investigated yet for the discrete multi-coset sampling. We compare the following algorithms – orthogonal matching pursuit, multiple signal classification, subspace-augmented multiple signal classification, focal under-determined system solver and basis pursuit denoising. The comparison is performed via numerical simulations for different sampling conditions. According to the simulations, focal under-determined system solver outperforms all other algorithms for signals with low signal-to-noise ratio. In other cases, the multiple signal classification algorithm is more beneficial. Keywords— compressed sensing, multi-band signals, multi-coset sampling, multiple-measurement vectors.
Intelligent System for Assessing Human Stress Using EEG Signals and Psychoanalysis Tests

This paper presents a results of designing an intelligent system to evaluate human stress level using Electroencephalogram (EEG) signals and Psychoanalysis tests. The questionnaires for Psychoanalysis tests were created based on Cohen's Perceived Stress Scale (PSS). EEG signals were captured using wireless EEG equipment. The Graphical User Interface (GUI) for the Psychoanalysis tests and EEG signals were created. The system was evaluated for 12 healthy subjects (7 females and 5 males). The results show that the intelligent system able to display the stress score, stress level and dominant index of EEG signals simultaneously. Thus, users can use the results of the system to take necessary action in order to improve their lifestyle.
The Duodenal Mucosal Bicarbonate Secretion

The duodenal lumen is exposed to aggressive factors with a high potential to cause damage to the mucosa. Bicarbonate secretion by the duodenal mucosa is accepted as the primary important defense mechanism against the hydrochloric acid intermittently expelled from the stomach. The present work concerns both the influence of the central nervous system and the effects of the hormone melatonin on duodenal bicarbonate secretion in anesthetized rats in vivo as well as effects of melatonin on intracellular calcium signaling by duodenal enterocyte in vitro examined in tissues of both human and rat origin. The main findings were as follows: Melatonin is a potent stimulant of duodenal mucosal bicarbonate secretion and also seems to be involved in the acid-induced stimulation of the secretion. Stimulation elicited in the central nervous system by the α1-adrenoceptor agonist phenylephrine induced release of melatonin from the intestinal mucosa and a four-fold increase in alkaline secretion. The melatonin antagonist luzindole abolished the duodenal secretory response to administered melatonin and to central nervous phenylephrine but did not influence the release of intestinal melatonin. Central nervous stimulation was also abolished by synchronous ligation of the vagal trunks and the sympathetic chains at the sub-laryngeal level. Melatonin induced release of calcium from intracellular stores and also influx of extracellular calcium in isolated duodenal enterocytes. Enterocytes in clusters functioned as a syncytium. Overnight fasting rapidly and profoundly down-regulated the responses to the duodenal secretagogue orexin-A and the muscarinic agonist bethanechol but not those to melatonin or vasoactive intestinal polypeptide.