Miracle-dz/UniFAR-FTUs huggingface dataset:
The Supply and Demand for Federal Reserve Deposits

This paper argues that the following ingredients are necessary in order to draw causal inference from historical correlations: explication of the real-world mechanism that is supposed to have produced a proposed structural relation; the existence of a natural experiment through which the data were generated; and extensive statistical corroboration of the econometric model. This research strategy is employed in an effort to identify the sources of disturbances to the supply and demand for Federal Reserve deposits. The effect of a shock in the supply of reserves on the federal funds rate depends on the marginal benefits banks perceive to holding excess reserves and the marginal costs of discount window borrowing. We find empirically that a temporary shock to the supply of reserves will only induce banks to borrow at the discount window if it occurs on settlement Wednesday or the last day of the quarter. The structural estimates presented here suggest that a $1 billion loss in reserves will raise the federal funds rate by 6.6 basis points if it occurs on settlement Wednesday or the last day of a quarter and by 2.6 basis points if it occurs on other days. A number of alternative sources of evidence are used to corroborate these structural estimates and to provide independent statistically significant confirmation that a reduction in the supply of reserves clearly raises the overnight interest rate.
An Ant Colony Algorithm with Stochastic Local Search for the VRP

In recent years there has been growing interest in algorithms inspired by the observation of natural phenomena to define computational procedures which can solve complex problems. In this paper, through an analysis of the constructive procedure of the solution in the ant colony system (ACS), a vehicle routing problem (VRP) is examined and a hybrid ant colony system coupled with a stochastic local search algorithm(SLSACS), is proposed. In SLSACS, only partial customers are randomly chosen to compute the transition probability. Experiments on various aspects of the algorithm and computational results for fourteen benchmark problems are reported. We compare our approach with ACS, some other classic, powerful meta-heuristics and show that our results are competitive.
Curbing the epidemic: governments and the economics of tobacco control

Below we reprint the excecutive summary of an important new report published by the World Bank. This report has its origins in the converging eVorts of several partners to address a shared problem: the relative neglect of economic contributions to the debate on tobacco control. In 1997, at the tenth world conference on tobacco in Beijing,China, the World Bank organised a consultation session on the economics of tobacco control. The meeting was part of an ongoing review of the Bank’s own control policies. There was clear recognition at this meeting that insufficient global attention was being paid to the economics of smokingrelated deaths. The meeting’s participants also agreed that the discipline of economics was not being applied to tobacco control in many countries, and that even where economic approaches were being used, their methodology was of variable quality. At the same time that the World Bank began reviewing its policies, economists at the University of Cape Town, South Africa, had begun a project on the economics of tobacco control for southern Africa. These initiatives were brought together, in partnership with economists at the University of Lausanne, Switzerland, and others, to form a wider review. The work culminated in a conference in Cape Town in February 1998.The proceedings of the conference have been published separately. The collaboration led to a broader analysis of the economics of tobacco control, involving economists and others from a wide range of countries and institutions. This report summarises the findings of those studies as they are relevant to policymakers.
SmartScale: Automatic Application Scaling in Enterprise Clouds

Enterprise clouds today support an on demand resource allocation model and can provide resources requested by applications in a near online manner using virtual machine resizing or cloning. However, in order to take advantage of an on demand resource model, enterprise applications need to be automatically scaled in a way that makes the most efficient use of resources. In this work, we present the SmartScale automated scaling framework. SmartScale uses a combination of vertical (adding more resources to existing VM instances) and horizontal (adding more VM instances) scaling to ensure that the application is scaled in a manner that optimizes both resource usage and the reconfiguration cost incurred due to scaling. The SmartScale methodology is proactive and ensures that the application converges quickly to the desired scaling level even when the workload intensity changes significantly. We evaluate SmartScale using real production traces on Olio, an emerging cloud benchmark, running on a kvm-based cloud testbed. We present both theoretical and experimental evidence that comprehensively establish the effectiveness of SmartScale.
Artificial Neural Networks-Growth & Learn: a Survey

-Incremental Learning using Constructive algorithms help us to change the structure of the neural network by adding or removing the links. These algorithms start with a small network which grows dynamically by the addition of hidden layers/units. Thus we need to overcome the problem of over-fitting and get a network with high generalization performance.
Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion

Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also hard to build a good model without diagnosing discriminative labels. Can we reduce the label costs and improve the ability to train a good model for multi-label learning simultaneously? Active learning addresses the less training samples problem by querying the most valuable samples to achieve a better performance with little costs. In multi-label active learning, some researches have been done for querying the relevant labels with less training samples or querying all labels without diagnosing the discriminative information. They all cannot effectively handle the outlier labels for the measurement of uncertainty. Since maximum correntropy criterion (MCC) provides a robust analysis for outliers in many machine learning and data mining algorithms, in this paper, we derive a robust multi-label active learning algorithm based on an MCC by merging uncertainty and representativeness, and propose an efficient alternating optimization method to solve it. With MCC, our method can eliminate the influence of outlier labels that are not discriminative to measure the uncertainty. To make further improvement on the ability of information measurement, we merge uncertainty and representativeness with the prediction labels of unknown data. It cannot only enhance the uncertainty but also improve the similarity measurement of multi-label data with labels information. Experiments on benchmark multi-label data sets have shown a superior performance than the state-of-the-art methods.
Hunting the Chameleon: Structural Conformations of the Intrinsically Disordered Protein Alpha‐Synuclein

The human a-synuclein protein (ASYN) plays a central role in the etiology of Parkinson’s disease, and forms fibrillar aggregates that are found in Lewy bodies and Lewy neurites in the brain, structures which are the hallmark of the disease. Three point mutations (A30P, A53T, and E46K) are associated with early-onset Parkinson’s disease. Modifications such as phosphorylation of the serine residue at position 129, and truncations of the protein, are reported to play an important role in the toxicity of ASYN. ASYN displays remarkable structural versatility: it has long been considered an intrinsically disordered or “natively unfolded” protein at physiological conditions, but can readily adopt b-sheet structure in aggregates or a-helical structure when bound to lipids. Very recent reports suggest that at physiological conditions in vivo, ASYN adopts a helical tetrameric structure, although this observation remains controversial. The intriguing reports about the ASYN tetramer could imply that these structures are yet another face of the chameleon-like nature of the protein in vivo. The notion of a-synuclein as a protein chameleon was introduced in 2003 by Uversky, and the ensuing years have only served to reinforce this view of the protein. The function of ASYN is unknown, but it is thought to involve lipid-binding in vesicles and synaptic membranes. The intrinsically disordered nature of the protein renders it intractable to standard high-resolution structural biology methods, making single-molecule Fçrster resonance energy transfer (FRET) and spectroscopic approaches that yield distance constraints invaluable in establishing structural parameters for the protein. In general for amyloid diseases, early aggregation intermediates are suspected of playing a key role in cell damage, although the structures and mechanisms of action of these early intermediates are unknown. There is evidence that amyloid toxicity may be caused by membrane permeabilization by pore-like early intermediates leading to disruption of calcium homeostasis and cell-death; such pores have been detected for ASYN, 45–48] but the hypothesis remains to be unequivocally verified. Thus, it is very likely that the structural diversity of ASYN is a key element in the pathology of the synucleinopathies.
DEPENDENCY PARSING OF SPOKEN SWEDISH

The tremendous improvement in robustness and accuracy of natural language parsing that we have witnessed during the last decade has almost exclusively been concerned with the analysis of written texts. The development of equally accurate syntactic parsers for spoken language is one of the greatest challenges for the parsing community. In this paper, we report the first results on parsing spoken Swedish with a data-driven dependency parser previously evaluated on written texts from a wide variety of languages. We compare two different algorithms, one restricted to projective dependency structures and one that allows non-projective structures, and compare the results to those obtained for written Swedish using the same methodology. The results show that parsing accuracy is still lower for spoken language than for written language, although part of the difference can be explained by properties of the transcribed spoken corpus used in the experiments. The results also show that the capacity to derive non-projective dependency structures is more crucial for spoken Swedish than for written Swedish.
A 90-Day Dietary Toxicity Study of Genetically Modified Rice T1C-1 Expressing Cry1C Protein in Sprague Dawley Rats

In a 90-day study, Sprague Dawley rats were fed transgenic T1C-1 rice expressing Cry1C protein and were compared with rats fed non-transgenic parental rice Minghui 63 and rats fed a basal diet. No adverse effects on animal behavior or weight gain were observed during the study. Blood samples were collected and analyzed, and standard hematological and biochemical parameters were compared. A few of these parameters were found to be significantly different, but were within the normal reference intervals for rats of this breed and age, and were thus not considered to be treatment-related. Following sacrifice, a large number of organs were weighed, and macroscopic and histopathological examinations were performed with no changes reported. The aim of this study was to use a known animal model to determine the safety of the genetically modified (GM) rice T1C-1. The results showed no adverse or toxic effects due to T1C-1 rice when tested in this 90-day study.
Preventive start-time optimization against network failure

This paper reviews research on preventive start-time optimization (PSO). PSO determines a suitable set of link weights in an open shortest path network at the network operation start time that can handle any link failure scenario preventively. PSO minimizes the worst-case congestion ratio in case of failure. PSO can avoid both unexpected network congestion and network instability. We formulate the original PSO problem as a mixed integer liner programming problem by extending the starttime optimization model and describe heuristic approaches. We also introduce mathematical programming models for the PSO variations. In addition, we expand PSO into generalized preventive start-time optimization (GPSO) to find a link weight set that balances both congestion ratios under no failure and the worst-case failure.
Family dynamics in pre- and post-transition Romania: a life-table description

This study presents a life-table decription of a wide range of events concerning family formation and fertility in Romania, by contrasting two different periods in Romanian history: a period of authoritarian regime and centrally planned economy (1980-1989) and a period of democratic political regime and market-oriented economy (1996-2005). A large number of single- and multi-decrement life tables deals with leaving home, separating from the parental family, forming first union, direct marriage, cohabitation, childbearing in the context of cohabitation or marriage, separation and divorce, parenting (first birth and second birth). Perspectives of men and women, as well of couples and children, are considered. The main data source we used here is Generations and Gender Survey carried out in Romania in 2005. Our life-table analysis confirms the early stage of the Second Demographic Transition in Romania. However, in European context it still remains a society highly valuing marriage and childbearing, despite the evidence in family behaviour changes which have accompanied political and socio-economical transformations after the fall of communist regime.
What is the Conversation About?: A Topic-Model-Based Approach for Analyzing Customer Sentiments in Twitter

In Social Commerce customers evolve to be an important information source for companies. Customers use the communication platforms of Web 2.0, for example Twitter, in order to express their sentiments about products or discuss their experiences with them. These sentiments can be very important for the development of products or the enhancement of marketing strategies. The research goal is to analyze customer sentiments in Twitter. The first step in the research is the detection of topics in Twitter entries which contain patterns of interest. For the topic detection, the authors use Latent Dirichlet Allocation for topic modeling. The authors found event based topics in the exemplary context of Sony's 3D TV sets. In future work, the authors will implement sentiment analysis algorithms in order to determine sentiments in the entries corresponding to the detected topics.
Detection of Duplicate Objects in Semi Structured Data like XML

Duplicate detection is the process of finding the duplicate objects in the data. This is the important part of data cleansing step of data mining. Duplication occurs when some real world object has multiple representations in data source. Significant amount of work has been done in duplicate detection of relational data, but only recently the researchers have shifted their focus towards duplicate detection in hierarchical and semi-structured data e.g. XML. A novel method called XMLDup uses Bayesian network to effectively detect the duplicates in XML. However this method does not consider the XML objects with different structure. In this work, we will propose a modified version of XMLDup so that it can also detect the objects with structural differences. Keywords-Data cleansing, duplicate detection, hierarchical data, XML, Bayesian networks.
PEP-1-HO-1 prevents MPTP-induced degeneration of dopaminergic neurons in a Parkinson’s disease mouse model

Heme oxygenase-1 (HO-1) degrades heme to carbon dioxide, biliverdin, and Fe2+, which play important roles in various biochemical processes. In this study, we examined the protective function of HO-1 against oxidative stress in SH-SY5Y cells and in a Parkinson’s disease mouse model. Western blot and fluorescence microscopy analysis demonstrated that PEP-1-HO-1, fused with a PEP-1 peptide can cross the cellular membranes of human neuroblastoma SH-SY5Y cells. In addition, the transduced PEP-1-HO-1 inhibited generation of reactive oxygen species (ROS) and cell death caused by 1-methyl-4-phenylpyridinium ion (MPP+). In contrast, HO-1, which has no ability to transduce into SH-SY5Y cells, failed to reduce MPP+-induced cellular toxicity and ROS production. Furthermore, intraperitoneal injected PEP-1-HO-1 crossed the blood-brain barrier in mouse brains. In a PD mouse model, PEP-1-HO-1 significantly protected against 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced toxicity and dopaminergic neuronal death. Therefore, PEP-1-HO-1 could be a useful agent in treating oxidative stress induced ailments including PD. [BMB Reports 2014; 47(10): 569-574]
A comparative study of linear and nonlinear time-frequency filters

Various methods for time-varying filtering with prescribed time-frequency (TF) pass region are compared analytically and numerically. The Weyl correspondence permits a unified TF analysis of apparently unrelated concepts of linear TF filtering, including STFT-based filters, Weyl filters, and TF projection filters. The linear TF filters are compared to nonlinear TF analysis/masking/synthesis methods based on the Wigner distribution (WD) or the smoothed WD. The study of TF (signal separation problems permits the objective evaluation of TF filter methods. Linear methods yield improved performance and had reduced cost as compared to nonlinear methods. <<ETX>>
Public-key Cryptanalysis

In 1976, Diffie and Hellman introduced the revolutionary concept of public-key cryptography, also known as asymmetric cryptography. Today, asymmetric cryptography is routinely used to secure the Internet. The most famous and most widely used asymmetric cryptosystem is RSA, invented by Rivest, Shamir and Adleman. Surprisingly, there are very few alternatives known, and most of them are also based on number theory. How secure are those asymmetric cryptosystems? Can we attack them in certain settings? Should we implement RSA the way it was originally described thirty years ago? Those are typical questions that cryptanalysts have tried to answer since the appearance of public-key cryptography. In these notes, we present the main techniques and principles used in public-key cryptanalysis, with a special emphasis on attacks based on lattice basis reduction, and more generally, on algorithmic geometry of numbers. To simplify our exposition, we focus on the two most famous asymmetric cryptosystems: RSA and Elgamal. Cryptanalysis has played a crucial rôle in the way cryptosystems are now implemented, and in the development of modern security notions. Interestingly, it also introduced in cryptology several mathematical objects which have since proved very useful in cryptographic design. This is for instance the case of Euclidean lattices, elliptic curves and pairings.
HYDROMETEOR IDENTIFICATION FROM W-BAND POLARIMETRIC MEASUREMENTS

Automatic classifications of radar polarimetric measurements into hydrometeor types using fuzzy-logic or other similar algorithms are now routinely done using ground-based cm-wavelength polarimetric radars (e.g., Vivekanandan et al. 1999; Lim. et al. 2005). In most of these algorithms, the polarimetric thresholds for various hydrometeor types are based on computational studies. A comprehensive review of polarimetric thresholds used for hydrometeor identification from cm-wavelength radar is given by Straka et al. (2000). Most of the cm-wavelength based algorithms are focused on large-scale systems and typically lump ice crystals into 2-3 broad categories. However, as both observational and computational studies for mm-radar show (e.g., Wolde and Vali, 2001; Tang and Aydin 1995), ice crystals have diverse polarimetric signatures that depend on crystal shape, size, density and fall patterns. By using near coincident in-situ and airborne radar data, it is possible to refine the threshold obtained from computational studies and add more classes (mixed phase, mixed particle types, rimed vs. pristine crystals etc). This paper uses nearcoincident (within 100 m) W-band polarimetric radar and in-situ cloud microphysics measurements in developing fuzzy-logic based hydrometeor classification beyond what has been reported in the past.
Frequency and time-domain analysis of partial discharge measurements in PWM inverter-fed induction motors

This work presents a new PD (partial discharge) measuring system for inverter-fed motors based on a bridge circuit topology. The bridge circuit was adopted in the measuring system for picking up PD signal and suppressing interference. The experimental results show that it is possible to detect and record PD notwithstanding the large interference due to inverter commutations if measuring frequencies are selected adequately.
Effects of Compression Ratio and EGR on Performance, Combustion and Emissions of Di Injection Diesel Engine

The performance of a Diesel engine is influenced by various parameters like compression ratio, fuel air ratio, speed etc. The performance of a diesel engine increases with increase in compression ratio. Variable compression technologies in IC engines are used to increase fuel efficiency under variable loads. Cooled exhaust gas recirculation is a common way to control in-cylinder NOx production and is used in most modern high speed direct injection diesel engines. However the effect of EGR on performance, combustion and emissions production at different compression ratios are difficult to depict. In the present work an attempt was made to study the effects of cooled exhaust gas recirculation on performance, combustion and emissions of a variable compression Diesel Engine. The test was conducted at different compression ratios with different loads (no load to full load) and for different EGR rates (0-10%).
Hybrid neurofuzzy computing with nullneurons

In this paper we address a new type of elementary neurofuzzy unit called nullneuron. A nullneuron is a generalization of and/or neurons based on the concept of nullnorm, a category of fuzzy sets operators that generalizes triangular norms and conorms. The nullneuron model is parametrized by an element u, called the absorbing element. If the absorbing element u = 0, then the nullneuron becomes a and neuron and if u = 1, then the nullneuron becomes a dual or neuron. Also, we introduce a new learning scheme for hybrid neurofuzzy networks based on nullneurons using reinforcement learning. This learning scheme adjusts the weights associated with the individual inputs of the nullneurons, and learns the role of the nullneuron in the network (and or or) by individually adjusting the parameter u of each nullneuron. Nullneuron-based neural networks and the associated learning scheme is more general than similar neurofuzzy networks because they allow different triangular norms in the same network structure. Experimental results show that nullneuron-based networks provide accurate results with low computational effort.