Miracle-dz/UniFAR-FTUs huggingface dataset:
Monetary policy under import price shocks: the case of Hungary

The general task of inflation targeting for central banks is to find an interest rate path that ensures the achievement of the inflation target by trading off the possible economic sacrifices. Commodities such as oil or food appear directly in the consumer basket, and also serve as input in the production process, especially in the case of oil. Central banks of small, open economies now and again face the problem of dealing with imported price pressures, i.e. terms-of-trade shocks. They often need to judge whether these pressures affect the production side of the economy or inflation expectations, or induce substitution in demand. Imported inflationary pressure can be counterbalanced by nominal exchange rate appreciation; this, however, does not constitute a free lunch, as it can temporarily contract activity in the tradable sector. In this respect, there is a question of whether monetary policies that work under a large terms-of-trade shift would target “domestic” inflation (as in Clarida et al (2001)) sometimes proxied by the “core inflation”, which excludes food and energy prices from the consumer basket, or whether it is optimal to focus on total inflation.
Throughput optimization of general non-linear computations

This paper addresses an optimal technique for throughput optimization of general non-linear data flow computations using a set of transformations. Throughput is widely recognized as the most important design metric of the modern DSP and communication applications. Numerous approaches have been proposed for throughput optimization, but most were restricted to limited classes of computations. They have limited effectiveness when applied to large complex non-linear DSP and communication computations. The new technique is used as an optimization engine in a divide-and-conquer global approach for throughput optimization. We demonstrate the effectiveness of the new technique on numerous real-life non-linear designs.
Empirical modelling in support of constructionism : a case study

Conventional programming paradigms have limitations where support for constructionist learning is concerned. This paper illustrates the merits of an alternative approach to giving support for constructionist learning, based on the principles of Empirical Modelling (EM), with reference to an algorithm for determining whether a decomposition of a particular relational schema is lossless. Model-building that is to be effective for constructionist learning has to support activities relating to three distinct roles: that of a student, a teacher and a developer. Our aim is to demonstrate that EM brings far greater conceptual unity to the interactions of the student, the teacher and the developer than is typically found in using a conventional approach to educational software development.
Lightweight monitoring of MPI programs in real time

Current technologies allow efficient data collection by several sensors to determine an overall evaluation of the status of a cluster. However, no previous work of which we are aware analyzes the behavior of the parallel programs themselves in real time. In this paper, we perform a comparison of different artificial intelligence techniques that can be used to implement a lightweight monitoring and analysis system for parallel applications on a cluster of Linux workstations. We study the accuracy and performance of deterministic and stochastic algorithms when we observe the flow of both library‐function and operating‐system calls of parallel programs written with C and MPI. We demonstrate that monitoring of MPI programs can be achieved with high accuracy and in some cases with a false‐positive rate near 0% in real time, and we show that the added computational load on each node is small. As an example, the monitoring of function calls using a hidden Markov model generates less than 5% overhead. The proposed system is able to automatically detect deviations of a process from its expected behavior in any node of the cluster, and thus it can be used as an anomaly detector, for performance monitoring to complement other systems or as a debugging tool. Copyright © 2005 John Wiley & Sons, Ltd.
Corrosion Behavior of Titanium in Artificial Saliva by Lactic Acid

As one of the main products produced by oral microorganisms, the role of lactic acid in the corrosion of titanium is very important. In this study, the corrosion behavior of titanium in artificial saliva with and without lactic acid were investigated by open-circuit potentials (OCPs), polarization curves and electrochemical impedance spectroscopy (EIS). OCP firstly increased with the amount of lactic acid from 0 to 3.2 g/L and then tended to decrease from 3.2 to 5.0 g/L. The corrosion of titanium was distinctly affected by lactic acid, and the corrosion rate increased with increasing the amount of lactic acid. At each concentration of lactic acid, the corrosion rate clearly increased with increasing the immersing time. Results of scanning electron microscopy (SEM) also indicated that lactic acid accelerated the pitting corrosion in artificial saliva. A probable mechanism was also proposed to explain the experimental results.
Robust formation and reconfiguration control of multiple VTOL UAVs: Design and flight test

In this paper, a distributed robust feedback control strategy with inter-vehicle collision avoidance is proposed for formation and reconfiguration control of a team of VTOL UAVs. A potential-field approach is used to generate a desired velocity for each UAV which ensures that the team of UAVs can perform formation and reconfiguration, avoid inter-vehicle collision as well as track a specified virtual leader. Each UAV is controlled to track its desired velocity subject to dynamic constraints. The proposed feedback control is robust against error disturbances due to dynamic constraints and measurement noise. A formation flight test of three quadrotor UAVs demonstrates the effectiveness and robustness of the proposed formation control strategy.
An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions

Reverse immunogenetic approaches attempt to optimize the selection of candidate epitopes, and thus minimize the experimental effort needed to identify new epitopes. When predicting cytotoxic T cell epitopes, the main focus has been on the highly specific MHC class I binding event. Methods have also been developed for predicting the antigen‐processing steps preceding MHC class I binding, including proteasomal cleavage and transporter associated with antigen processing (TAP) transport efficiency. Here, we use a dataset obtained from the SYFPEITHI database to show that a method integrating predictions of MHC class I binding affinity, TAP transport efficiency, and C‐terminal proteasomal cleavage outperforms any of the individual methods. Using an independent evaluation dataset of HIV epitopes from the Los Alamos database, the validity of the integrated method is confirmed. The performance of the integrated method is found to be significantly higher than that of the two publicly available prediction methods BIMAS and SYFPEITHI. To identify 85% of the epitopes in the HIV dataset, 9% and 10% of all possible nonamers in the HIV proteins must be tested when using the BIMAS and SYFPEITHI methods, respectively, for the selection of candidate epitopes. This number is reduced to 7% when using the integrated method. In practical terms, this means that the experimental effort needed to identify an epitope in a hypothetical protein with 85% probability is reduced by 20–30% when using the integrated method.
A Distributed Cross-Layer Adaptive Scheduling Algorithm for Wireless Mesh Networks

In this paper, we propose a cross-layer scheduling scheme named DCLASA for the coordinated distributed scheduling of IEEE 802.16 WMN. This scheme considers the channel quality information (CQI) in physical layer when designing the timeslot allocation algorithm in MAC layer, and adjusts the node's holdoff exponent according to CQI. Meanwhile multi-user diversity and adaptive modulation and coding technique are adopted to improve system performance. Simulation shows that DCLASA can increase the network total throughput, and reduce link transmission delay.
Aspect-oriented generation of the API documentation for AspectJ

Through the development of a framework or a class library, writing the document on their application programming interface (API) is essential. The document on the API, which we call the API documentation, is mainly read by programmers who want to develop their applications on top of that framework or library. In this paper, we present a tool named CommentWeaver, which generates the API documentation of a framework/library written in AspectJ. CommentWeaver extracts the descriptions for the API documentation from both classes and aspects in the source files, and then it weaves them for generating the API documentation in HTML. Although ajdoc similarly generates the API documentation for AspectJ, the generated API documentation does not directly present the exact behavior of the API if it is affected by aspects.
HEATSMART: An interactive application aware thermal management framework for MPSoC embedded systems

Multiprocessor System-on-Chip (MPSoC) is emerging as an integral component in embedded systems, such as mobile phones, PDAs, handheld medical devices, etc. Due to its immense processing power and incumbent hardware resources the users are able to execute multiple concurrent applications. The more applications the MPSoC executes the more heat it dissipates. Such eminent heat dissipation damages the device and induces faults, causing a shorter life span. Thus, thermal aware designs are of utmost important for durable and dependable embedded devices. The MPSoC could potentially control and manage the heat dissipation using techniques such as computation migration. However, this would be based on the operational behavior of the hardware, but not on the behavior of the applications mapped and executed on the hardware. We propose an interactive temperature management where the users are allowed to provide inputs to effectively manage the heat dissipation and at the same time are able to operate the applications according to their will. We propose and demonstrate a formal design methodology to assist the people involved during the design process.
Instability modelling of drumlin formation incorporating lee-side cavity growth

It is proposed that the formation of the subglacial bedforms known as drumlins occurs through an instability associated with the flow of ice over a wet deformable till. We pose a mathematical model that describes this instability, and we solve a simplified version of the model numerically in order to establish the form of finite-amplitude two-dimensional waveforms. A feature of the solutions is that cavities frequently form downstream of the bedforms; we allow the model to cater for this possibility and we provide an efficient numerical method to solve the resulting free boundary problem.
Solving Optimal Power Flow Problems with Improved Particle Swarm Optimization

The paper proposes an improved particle swarm optimization algorithm with preserving feasibility strategy and neighbor selection scheme for solving optimal power flow (OPF) problems. In the proposed algorithm, fitness function and constraints are handled separately. Fitness function is used for guiding particles flying and constraints for checking the feasibility of particles. When updating the memories, all the particles only keep feasible solutions in their memory. Constraint handling with preserving feasibility strategy overcomes the deficiency of penalty function, provides advantages of faster convergence and simpler manipulation, and greatly improves the performance of the algorithm. Neighbor selection scheme ensures that every particle adjusts its flying based on the information of its better performers in the neighborhood and enhances local exploitation ability of the algorithm. The proposed algorithm was tested on the IEEE 30 bus system. The test results show that the proposed algorithm is applicable and effective in the solution of OPF problems. Compared with evolutionary programming, the proposed algorithm has faster convergence speed and more stable convergence performance
Development of Material Learning Based Multimedia for Early Childhood Inclusion Education

This study aims to develop learning materials based multimedia for Early Childhood Inclusion Education courses as the development of a multimedia learning model for student based on early childhood education.Method of research is R n D, development model used was the Merger of Dick & Carey, Borg & Gall Learning Models and Instructional Development Models. Result of research general conclusion, the validation of learning design experts 94.94% is feasible, 92.8% of material experts are decent and 93.5% of media experts are eligible, so recommending learning materials can be used. In the 93.07% individual trial, it was feasible, 92.7% small group trials were feasible and in the field trial 87.52% the development of multimediabased learning materials could be used. The conclusion of expert validation and testing, the learning material for Early Childhood Incusion Education Based Multimedia is worthy of use. Keywords—learning material, inclusion education, multimedia, childhood education.
Scaling of hybrid electric vehicle powertrain components for hardware-in-the-loop simulation

Hardware in the loop (HIL) simulation enables experimental study of prototype hardware systems or control algorithms via real-time interaction between physical hardware and virtual simulations. As a result, this method is a particularly valuable tool for hybrid vehicle powertrain analysis. In the case where novel or prototype hardware is being examined, it is often necessary to scale the signals in and out of the prototype system in order to represent production-sized components. This scaling process is usually done in an ad-hoc manner. In this work, a formal method is presented that derives appropriate input/output signal conditioning to correctly scale electric vehicle components, namely the electric motor and battery subsystems.
Prediction of Discharge Capacity of Lithium Battery Based on Cloud Neural Network

The prediction of discharge capacity of lithium batteries was one of the main tasks of battery management system. The discharge capacity of lithium batteries was related with many parameters, including discharge current, voltage, temperature, and the past charge and discharge history. The prediction methods of existing lithium battery discharge capacity mostly have no learning capabilities and nonlinear prediction ability, in order to predict the discharge capacity of lithium battery more accurately, an algorithm Based on cloud neural network (CNN) was presented. On the basis of the analysis of the actual data of NASA, determine the related influence factors of discharge capacity, set up a corresponding CNN prediction model using cloud model, and use the cloud model for adaptive adjustment of the learning speed. Comparing with the traditional NN method, the simulation result demonstrates that the CNN prediction model has smaller prediction error.
Neurogenesis in humans

Since 1944 increasing evidence has been emerging that the adult human brain harbours progenitor cells with the potential to produce neuroblasts. However, it was not until 1998 that this fact was confirmed in the adult human brain. With the purpose of human neurogenesis being hotly debated, many research groups have focussed on the effect of neurodegenerative diseases in the brain to determine the strength of the endogenous regenerative response. Although most of the human studies have focussed on the hippocampus, there is a groundswell of evidence that there is greater plasticity in the subventricular zone and in the ventriculo‐olfactory neurogenic system. In this review, we present the evidence for increased or decreased plasticity and neurogenesis in different diseases and with different drug treatments in the adult human brain. Whilst there is a paucity of studies on human neurogenesis, there are sufficient to draw some conclusions about the potential of plasticity in the human brain.
Network-aware service function chaining placement in a data center

Network function virtualization (NFV) has drawn much attention in recent years, where some network functions that used to be deployed on specific hardware have become virtualized instances on general servers to achieve more scalability and flexibility. In a data center, service function chaining (SFC) makes a workflow traverse different network functions in a specific order to provide different levels of service for its customer. Because the distance between any adjacent network functions in a service chain will decide the total bandwidth consumption for that chain, the placement of the virtualized network functions in a data center becomes an important problem. In this study, this placement problem is treated as a multi-layer bin packing problem. Two greedy algorithms are proposed for the tree-like network topology: Multi-layer Worst-Fit (MWF) and Multilayer Best Fit (MBF). Furthermore, the placement problem is formulated as an integer linear programming. The experimental results show that MWF can reduce bandwidth consumption by 15% while only increasing the number of used servers by 1% compared to the traditional Best-fit algorithm.
Metastable vacua in flux compactifications and their phenomenology

In the context of flux compactifications, metastable vacua with a small positive cosmological constant are obtained by combining a sector where supersymmetry is broken dynamically with the sector responsible for moduli stabilization, which is known as the F-uplifting. We analyze this procedure in a model-independent way and study phenomenological properties of the resulting vacua.
Evolutionary and Functional Features of Copy Number Variation in the Cattle Genome

Genomic structural variations are an important source of genetic diversity. Copy number variations (CNVs), gains and losses of large regions of genomic sequence between individuals of a species, have been associated with a wide variety of phenotypic traits. However, in cattle, as well as many other species, relatively little is understood about CNV, including frequency of CNVs in the genome, sizes, and locations, chromosomal properties, and evolutionary processes acting to shape CNV. In this work, we focused on copy number variation in the bovine genome, with the aim to detect CNVs in Bos taurus coding sequence and explore potential evolutionary mechanisms shaping these CNV. We identified and characterized CNV regions by utilizing exome sequence from 175 influential sires used in the Germplasm Evaluation project, representing 10 breeds. We examined various evolutionary and functional aspects of these CNVs, including selective constraint on CNV-overlapped genes, centrality of CNV genes in protein-protein interaction networks, and tissue-specific expression of CNV genes. Patterns of CNV in the Bos taurus genome reveal that reduced functional constraint and mutational bias may play a prominent role in shaping this type of structural variation.
A New Approach for Finite-Length Framework of Multi-Input Multi-Output MMSE

In this paper this framework is introduced for analyzing the performance of a finite-length minimum-mean-square error decision feedback equalizer (MMSE-DFE) in a multi-input multi-output (MIMO) environment. The framework includes transmit and receive diversity systems as special cases and quantifies the diversity performance improvement as a function of the number of transmit/receive antennas and equalizer taps. Fast and parallelizable algorithms for computing the finitelength MIMO MMSE-DFE are presented for three common multi-user detection scenarios.
Computer-aided detection of Pulmonary Nodules based on SVM in thoracic CT images

Computer-Aided diagnosis of Solitary Pulmonary Nodules using the method of X-ray CT images is the early detection of lung cancer. In this study, a computer-aided system for detection of pulmonary nodules on CT scan based support vector machine classifier is provided for the diagnosis of solitary pulmonary nodules. So at the first step, by data mining techniques, volume of data are reduced. Then divided by the area of the chest, the suspicious nodules are identified and eventually nodules are detected. In comparison with the threshold-based methods, support vector machine classifier to classify more accurately describes areas of the lungs. In this study, the false positive rate is reduced by combination of threshold with support vector machine classifier. Experimental results based on data from 147 patients with lung LIDC image database show that the proposed system is able to obtained sensitivity of 89.9% and false positive of 3.9 per scan. In comparison to previous systems, the proposed system demonstrates good performance.
Justice Lost! The Failure of International Human Rights Law To Matter Where Needed Most

International human rights treaties have been ratified by many nation-states, including those ruled by repressive governments, raising hopes for better practices in many corners of the world. Evidence increasingly suggests, however, that human rights laws are most effective in stable or consolidating democracies or in states with strong civil society activism. If so, treaties may be failing to make a difference in those states most in need of reform — the world's worst abusers — even though they have been the targets of the human rights regime from the very beginning. The authors address this question of compliance by focusing on the behavior of repressive states in particular. Through a series of cross-national analyses on the impact of two key human rights treaties, the article demonstrates that (1) governments, including repressive ones, frequently make legal commitments to human rights treaties, subscribing to recognized norms of protection and creating opportunities for socialization and capacity-building necessary for lasting reforms; (2) these commitments mostly have no effects on the world's most terrible repressors even long into the future; (3) recent findings that treaty effectiveness is conditional on democracy and civil society do not explain the behavior of the world's most abusive governments; and (4) realistic institutional reforms will probably not help to solve this problem.
Use of Concurrent Wavelet Transform (WT) to Image Compression and a Verification Analysis of Communicating Threads for Concurrent WT

— Wavelet Transform (WT) has been proved to be a very useful tool for image processing in the recent years. However, WT is very computationally rigorous process requiring novel and computationally competent method to achieve image compression. The concurrent transformation of the image might be one of the solutions to this problem. In this paper, we investigate and analyze the concurrency in communicating threads that perform the wavelet transformation concurrently. The grammar and its syntax are formulated to specify the communication among threads. Moreover, the system modeled in Symbolic Model Verifier (SMV) is formally verified.