Miracle-dz/UniFAR-FTUs huggingface dataset:
The neural processing of foreign-accented speech and its relationship to listener bias

Foreign-accented speech often presents a challenging listening condition. In addition to deviations from the target speech norms related to the inexperience of the nonnative speaker, listener characteristics may play a role in determining intelligibility levels. We have previously shown that an implicit visual bias for associating East Asian faces and foreignness predicts the listeners' perceptual ability to process Korean-accented English audiovisual speech (Yi et al., 2013). Here, we examine the neural mechanism underlying the influence of listener bias to foreign faces on speech perception. In a functional magnetic resonance imaging (fMRI) study, native English speakers listened to native- and Korean-accented English sentences, with or without faces. The participants' Asian-foreign association was measured using an implicit association test (IAT), conducted outside the scanner. We found that foreign-accented speech evoked greater activity in the bilateral primary auditory cortices and the inferior frontal gyri, potentially reflecting greater computational demand. Higher IAT scores, indicating greater bias, were associated with increased BOLD response to foreign-accented speech with faces in the primary auditory cortex, the early node for spectrotemporal analysis. We conclude the following: (1) foreign-accented speech perception places greater demand on the neural systems underlying speech perception; (2) face of the talker can exaggerate the perceived foreignness of foreign-accented speech; (3) implicit Asian-foreign association is associated with decreased neural efficiency in early spectrotemporal processing.
Animals versus Animats: or Why Not the Real Iguana?

The field of Artificial Life (ALife) has focussed attention on bottom­up explanations of adaptive behaviour, and how this can result from relatively simple systems interacting dynamically with their environments. Two differing approaches in developing these ideas are: to build models of simple sensorimotor systems in specific animals, assessed within complete behaviour­environments loops; and to explore the behaviour of invented artificial animals (often called ‘animats’) under similar conditions. But how can we learn about real biology from simulation of a non­existent animal? I will argue that most animat research, to the extent that it is relevant to biology, should also be considered as model building. Many claims are made for the animat approach: that it involves instantiation and is thus not simulation; that it uses idealised rather than realistic models; that the aim is theory exploration and existence proof rather than accounting for biological data; that the abstraction of the models makes the conclusions drawn from them more general. Few of these claims hold up under analysis, and it is suggested that we will learn more by choosing real, and not made­up, targets for our models.
Title : Time-Sensitive Planning Using Point-Interval Logic

This paper presents an application of Point-Interval logic (PIL) for the problem of planning timesensitive aspects of a mission. The logic incorporates both point and interval structures of time associated with mission activities and also provides for qualitative and quantitative descriptions of temporal requirements among mission activities. An algorithm is presented that extends the inference engine of PIL for mission planning application. The planning approach is demonstrated with the help of a small illustrative mission planning problem with non-trivial temporal constraints. The temporal formalism with its descriptive input language, an inference engine for reasoning about generated plans, and the planning algorithm has been implemented in the form of a software tool.
Named Entity Resolution Using Automatically Extracted Semantic Information

One major problem in text mining and semantic retrieval is that detected entity mentions have to be assigned to the true underlying entity. The ambiguity of a name results from both the polysemy and synonymy problem, as the name of a unique entity may be written in variant ways and different unique entities may have the same name. The term “bush” for instance may refer to a woody plant, a mechanical fixing, a nocturnal primate, 52 persons and 8 places covered in Wikipedia and thousands of other persons. For the first time, according to our knowledge we apply a kernel entity resolution approach to the German Wikipedia as reference for named entities. We describe the context of named entities in Wikipedia and the context of a detected name phrase in a new document by a context vector of relevant features. These are designed from automatically extracted topic indicators generated by an LDA topic model. We use kernel classifiers, e.g. rank classifiers, to determine the right matching entity but also to detect uncovered entities. In comparison to a baseline approach using only text similarity the addition of topics approach gives a much higher f-value, which is comparable to the results published for English. It turns out that the procedure also is able to detect with high reliability if a person is not covered by the Wikipedia.
Large-scale yeast transformation in low-percentage agarose medium.

BENCHMARKS The use of yeast as a research tool became especially popular with the development of the yeast two-hybrid (Y2H) assay, which is a powerful tool for the detection of specific protein-protein interactions. Several versions of the Y2H system have been developed. All variations of this method demand the large-scale transformation of yeast with cDNA library plasmids (1). Transformation of yeast with plas-mid DNA can be achieved by several methods, such as agitation with glass beads (2), spheroplast preparation (3), treatment with lithium acetate (4), or electroporation (5). The most popular and commonly used is lithium acetate transformation was developed by Ito et al. (6) and modified by Gietz and Woods (7). The standard Y2H library protocols for an average mammalian cDNA library (10 6 primary independent clones) and maximal colony density 5/mm 2 require plating of more than fifty 150-mm plates (8). In addition to being laborious and having problems in maintaining sterility, the inevitable overgrowth of marginal colonies near the plate edge causes loss of uniformity across the library. Here we present a protocol for the large-scale transformation of yeast in a semi-solid agarose medium that does not require plating. The protocol allows for the growth of transformed cells as separate colonies in the volume of semi-solid medium. The protocol is optimized for lithium acetate transformation , gives high transformation efficiency , and provides an equal growth environment for each colony, thereby faithfully preserving the integrity of the primary library. It also allows yeast library propagation with a significantly higher colony density. The protocol was developed on the basis of several published techniques (7–10) and requires fewer reagents when compared to other approaches. Working with DupLEX-A™, a LexA-based Y2H system (OriGene Technologies, Rockville, MD, USA), we successfully used this protocol for the EGY48 strain (MATα, his3, trp1, ura3, 6 LexAop-LEU2) transformed with different pEG202-based bait plas-mids and a reporter plasmid pSH18-34 (10). Using this protocol, we managed to achieve transformation efficiencies of 0.25–1 × 10 6 /µg of library DNA or 2–8 million independent colonies/400 mL of the semi-solid agarose medium. Therefore, this protocol allowed us to cover an average cDNA library with just one or two bottles of semi-solid medium (400 mL each). All reagents used were from Sigma (St. Louis, MO, USA) except the low melting point agarose). Culture media reagents as well as carrier DNA were from Bio101 (Vista, CA, USA). Procedures were performed according …
An Adiabatic Register File Based on Improved CAL Circuits Using Single-Phase Power Clock

This paper presents an adiabatic register file based on improved CAL (Clocked Adiabatic Logic) circuits, which can operate in a single-phase power clock. All the circuits except for the storage cells use improved CAL circuits. The storage cell is based on the conventional memory one. For a comparison, a conventional register file is also realized. Full-custom layouts are drawn, which is realized using TSMC 0.18um process, and the net-lists are extracted from their layouts. The post-layout simulations have been performed. The results show that the single-phase adiabatic register file can work very well, illustrating an overall power reduction of about 63% compared with the conventional one at 100 MHz.
Rat meets iRat

Biorobotics has the potential to provide an integrated understanding from neural systems to behavior that is neither ethical nor technically feasible with living systems. Robots that can interact with animals in their natural environment open new possibilities for empirical studies in neuroscience. However, designing a robot that can interact with a rodent requires considerations that span a range of disciplines. For the rat's safety, the body form and movements of the robot need to take into consideration the safety of the animal, an appropriate size for the rodent arenas, and behaviors for interaction. For the robot's safety, its form must be robust in the face of typically inquisitive and potentially aggressive behaviors by the rodent, which can include chewing on exposed parts, including electronics, and deliberate or accidental fouling. We designed a rat-sized robot, the iRat (intelligent rat animat technology) for studies in neuroscience. The iRat is about the same size as a rat and has the ability to navigate autonomously around small environments. In this study we report the first interactions between the iRat and real rodents in a free exploration task. Studies with five rats show that the rats and iRat interact safely for both parties.
Facial Feature Alignment by Manifold Learning of Active Appearance Model

Extracting accurate positions of eyes, nose and mouth, is a crucial process for face recognition and facial expression recognition. Classical methods such as Active Appearance Model (AAM) use the principal component analysis to reduce the dimensionality of appearance data, and an iterative search to find facial features by minimizing an error criteria of the reduced appearance data. In this paper, we propose a facial feature extraction approach by manifold learning. The manifold learning method, locality preserving projection (LPP), projects appearance data into low-dimensional data by considering neighborhood relation but not variance. The LPP can preserve local structure of appearance data, and remain most of the important characteristics of the appearance data. During search phase, AdaBoost face detection algorithm is utilized to locate the face localization, which can improve the search. Experimental data includes 870 images from AR face database which includes variations of illumination and expression, and 200 images from CMU PIE face database which includes different poses. Experimental results show that the proposed method has better performance than that of the AAM method.
Using corpus linguistic techniques in (critical) discourse studies reduces but does not remove bias: Evidence from an Arabic corpus about refugees

Abstract Critical Discourse Analysis (CDA) investigates the relationship between language, power, and society. Corpus linguistics (CL) is the study of language based on examples of real life language use. Over the last two decades, various scholars have combined some approaches and notions of CDA with the analytical framework of CL to examine the representation of several phenomena in relatively large texts. This study follows a corpus-assisted (critical) discourse analysis approach to investigate a 2.5 million word corpus of Arabic news articles by Jordan’s News Agency (PETRA). It demonstrates how some researchers following this approach may make some decisions, at some stages of their analysis, which are likely to affect their findings. These potential decisions may include selecting what statistical measures to use, what threshold to consider, what terms from the frequency, cluster, and collocation results to further investigate, which concordance lines to include in their study, and some others. In this study, I argue that some of these decisions can be made to suit the researchers’ preconceived assumptions and pre-existing hypotheses. The study concludes that using corpus linguistic techniques to discursively analyze large data reduces but not completely removes researchers’ bias.
Decision support systems design as knowledge-based tool integration

Decision making is always a topical problem. In order to make a decision, a decision maker needs criteria and has to analyze the problem with these criteria. But it is not simple to take into account a great deal of criteria in order to make a decision. In general, one tool gives one analysis for one criterion (except the multicriteria tools). The paper aims to present a method of designing DSS based on most analysis tools. A main feature of this method is to decompose the problem into subproblems in order to distinguish the tools associated to each subproblem. The main goal is to reuse existing tools corresponding to these subproblems. The interesting feature of our method is that the DSS is not dependent on the components. So, other components could be added to the system in the future without having to rethink the system's architecture. This method is currently being applied to DSS in railway investment. We see that the resulting synthesis of tools is difficult to make in order to give a real decision support.
Why is Volatility Greater during Recessions ? Theory and Evidence

During recession, many macroeconomic variables display higher levels of volatility. This paper shows how introducing an AR(1)-ARCH(1) driving process into the canonical Lucas consumption CAPM framework can account for the empirically observed greater volatilty of asset returns during recessions. In particular, agents’ joint forecasting of levels and time-varying second moments transforms symmetric-volatility driving processes into asymmetric-volatility endogenous variables. Numerical examples show that the model can indeed account for the degree of cyclical variation in the U.S. data. Importantly, the underlying mechanism is not speci.c to . nancial markets, and can be applied to a wide variety of macroeconomic variables.
Self-Employment and the Role of Health Insurance

We investigate the effect of health insurance on labor market transitions in and out of self-employment as well as on the likelihood of being self-employed. We consider the role of individual health insurance coverage along with that from a spouse. Next, we examine a series of tax deductions granted to the self-employed through amendments made to the 1986 Tax Reform Act. Using data from the Current Population Survey for 1996-2007, we find significant but small effects of the after-tax health insurance premium on the entry rate, with no effect on exits from self-employment or the likelihood of being self-employed.
Using Bayesian Logistic Regression with High-Order Interactions to Model Radiation-Induced Toxicities Following Radiotherapy

Radiotherapy treatments of cancer patients are planned using dose-volume constraints. These constraints limit the volume of organs receiving a given threshold dose. We propose a new framework to predict radiation-induced toxicities and evaluate dosimetric constraints using Bayesian logistic regression with high-order interactions. The predictive power of 2 sets of rectal dose-volume constraints proposed in the recent literature was evaluated using follow-up data from the RT01 prostate radiotherapy trial. Toxicities considered were rectal bleeding and loose stools. Furthermore we derived a new type of geometrical dosimetric constraint and assessed the predictive power. % using the Bayesian logistic regression model. Bayesian logistic regression with high-order interactions using dosimetric constraints successfully predicted radiation-induced rectal bleeding and loose stools. Literature-based dose-volume constraints had less predictive power than our new type of geometrical constraint. Imposing the latter type of constraints when generating a treatment plan would be beneficial for outcome.
Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.
Documento de Trabajo-2017 / 07 Narrative Sign Restrictions for SVARs Juan Antolín-Díaz ( Fulcrum Asset Management )

We identify structural vector autoregressions using narrative sign restrictions. Narrative sign restrictions constrain the structural shocks and the historical decomposition around key historical events, ensuring that they agree with the established narrative account of these episodes. Using models of the oil market and monetary policy, we show that narrative sign restrictions are highly informative. We highlight that adding a single narrative sign restriction dramatically sharpens and even changes the inference of SVARs originally identified via traditional sign restrictions. Our approach combines the appeal of narrative methods with the popularized usage of traditional sign restrictions.
People-Sensing Spatial Characteristics of RF Sensor Networks

An “RF sensor” network can monitor RSS values on links in the network and perform device-free localization, i.e., locating a person or object moving in the area in which the network is deployed. This paper provides a statistical model for the RSS variance as a function of the person’s position w.r. t. the transmitter (TX) and receiver (RX). We show that the ensemble mean of the RSS variance has an approximately linear relationship with the expected total affected power (ETAP). We then use analysis to derive approximate expressions for the ETAP as a function of the person’s position, for both scattering and reflection. Counterintuitively, we show that reflection, not scattering, causes the RSS variance contours to be shaped like Cassini ovals. Experimental tests reported here and in past literature are shown to validate the analysis.
Chapter 8 Fouling in Nanofiltration

Environmental Engineering, University of Wollongong, Wollongong, NSW 2522, Australia ph +61 2 4221 3385, fax +61 2 4221 4738, A.Schaefer@uow.edu. au Chemical Process Engineering Research Institute, P.O. Box 361, 57001, Thermi, Greece ph ++302310498181, fax ++302310498189 andritso@cperi.certh. gr karabaj@cperi.certh. gr Chemical & Environmental Engineering, University of California, Riverside, CA 92521, USA, ph: +011-909-787-7345, fax: +011-909-787-5696, email: hoek@engr.ucr. edu Departamento de Microbiologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, Brazil ph +55 11 3091 7745, fax +55 11 3091 7354, schneide@icb.usp. br Lappeenranta University of Technology, Laboratory of Membrane Technology and Technical Polymer Chemistry, P.O. Box 20, FIN-53851 Lappeenranta, Finland ph. +358-5-621 2160, fax +358-5-621 2199, marianne.nystrom@lut.fi Chapter 8 – Fouling in Nanofiltration
Trusted routing for VANET

Trust establishment in VANET is a particularly challenging task due to the lack of infra-structure, openness of wireless links and the usually highly dynamic network topology. To overcome these difficulties, we propose a trusted routing framework that provides message authentication, node-to-node trust and routability verification, without online assistance of Certificate Authorities (CA). Our approach prevents identity impersonation, false link availability indication by compromised nodes as well as some of routing protocol specific misbehaviours. By applying this framework to the Optimised Link State Routing Protocol (OLSR), we demonstrate how this mechanism can be used to establish trusted routes.
Understanding senior executives' use of information technology and the internet

The Internet and information technology (IT) have received considerable attention from senior executives, yet they still have not committed themselves fully to these technologies. Consequently, they are not reaping the full benefits. Recent studies investigated the factors that influence senior executives' use of IT and the Internet. Surprisingly, only a few factors had a significant influence on usage, directly or indirectly. A better understanding of these factors can facilitate the design of interventions that address the adoption and acceptance of a new IT tool, an information system or the Internet. This chapter proposes a limited number of interventions aimed at improving usage. The Internet will change the way information is being gathered, evaluated, and communicated. This also applies for senior executives and their main constituencies. Now is the time for senior executives to hone their Internet skills so that they can receive the right information in the right way, leading to enhanced decision-making.
Jordan Wto Accession: A Quantitative Investigation And Estimation Of A Gravity Model 1994-2009

In light of Jordan’s accession to the World Trade Organization (WTO) in 2000, this paper explores the impact of this accession on trade flows to investigate whether and how WTO accession facilitates trade growth by applying a standard gravity model. Interestingly, our results on the WTO’s impact on trade are exactly in line with what they should be. The gravity model fits the data well. The results explain how Jordan’s WTO accession- among other changes in Jordan's trade policy, enhanced Jordan trade by creating commitment to the multilateral trade agreements which in turn results in increasing Jordan trade with countries all over the world.
Holistic Indices for Productivity Control Assessment, Applied to the Comparative Analysis of PID and Fuzzy Controllers Within an Isasmelt Furnace

This research aims to contribute to the analysis of control performance assessment in extractive metallurgy. Productivity-based indices are proposed in addition to current measuring techniques. Such criteria are employed to compare conventional PID and fuzzy-based controllers in copper smelting. This process is mathematically modeled in order to be simulated. The comparison confirms a better performance of the fuzzy controller in dealing with the molten bath temperature within an Isasmelt furnace. In normal operating conditions (online tests), the proposed controller achieves a consistent mean square relative error reduction of 72% between measured values and the temperature setpoint and standard deviation of approximately 60% (from 27.8 °C to 11.1 °C). The productivity criteria establish a lower consumption of raw materials (13%) and energy supply (29%).