Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-lingual talker discrimination

This paper describes a talker discrimination experiment in which native English listeners were presented with two sentences spoken by bilingual talkers (English/German and English/Finnish) and were asked to judge whether they thought the sentences were spoken by the same person or not. Equal amounts of cross-lingual and matched-language trials were presented. The experiments showed that listeners are able to complete this task well, they can discriminate between talkers significantly better than chance. However, listeners are significantly less accurate on cross-lingual talker trials than on matched-language pairs. No significant differences were found on this task between German and Finnish. Bias (B 00 ) and Sensitivity (A 0 ) values are presented to analyse the listeners’ behaviour in more detail. The results are promising for the evaluation of EMIME, a project covering speech-to-speech translation with speaker adaptation. Index Terms: evaluation, talker discrimination, cross-lingual
Acquisition and invasiveness of different serotypes of Streptococcus pneumoniae in young children

Summary Rates of acquisition and mean duration of nasal carriage of different serotypes of Streptococcus pneumoniae have been estimated by fitting a stochastic model to longitudinal carriage data in children from Papua New Guinea. Immunogenicity and two indices of relative invasiveness were determined for each serotype. Immunogenic serotypes were less frequently acquired and were carried for shorter periods, but no relationship between immunogenicity and invasiveness was apparent using either index of invasiveness. Frequent invasion was associated with a high acquisition rate and high frequency and prolonged duration of carriage. Carriage studies can provide a broad indication of which serotypes cause invasive disease but not the proportion of disease due to individual serotypes; some serotypes which cause invasive disease (e.g. serotype 46) are not found even in extensive carriage studies. The antibiotic resistance of carriage organisms, however, does approximate the resistance patterns of invasive organisms and thus may be used to monitor changing patterns of antimicrobial susceptibility in the community.
A parallel compiler for multi-core microcontrollers

Due to resource limitation of multi-core microcontrollers, programming them is difficult. This work presents a simple parallel compiler that can exploit multi-core to speed up parallel tasks on a multi-core microcontroller. The parallel constructors are introduced. A scheme to use compiler directives to hint the compiler is discussed. Experiments on the real processors are performed to validate the scheme. The results show that the compiler can exploit multi-core to speed up the computation tasks on the target microcontroller.
Cuckoo: a Language for Implementing Memory- and Thread-safe System Services

This paper is centered around the design of a threadand memory-safe language, primarily for the compilation of application-specific services for extensible operating systems. We describe various issues that have influenced the design of our language, called Cuckoo, that guarantees safety of programs with potentially asynchronous flows of control. Comparisons are drawn between Cuckoo and related software safety techniques, including Cyclone and software-based fault isolation (SFI), and performance results suggest our prototype compiler is capable of generating safe code that executes with low runtime overheads, even without potential code optimizations. Compared to Cyclone, Cuckoo is able to safely guard accesses to memory when programs are multithreaded. Similarly, Cuckoo is capable of enforcing memory safety in situations that are potentially troublesome for techniques such as SFI.
Inhibition of the Proton-Coupled Folate Transporter (PCFT-SLC46A1) by Bicarbonate and Other Anions

The proton-coupled folate transporter (PCFT) plays a key role in intestinal folate absorption, and loss-of-function mutations in the gene encoding this transporter are the molecular basis for hereditary folate malabsorption. Using a stable transfectant with high expression of PCFT, physiologic levels of bicarbonate produced potent and rapidly reversible inhibition of PCFT-mediated transport at neutral pH. Bisulfite and nitrite also inhibited PCFT function at neutral pH, whereas sulfate, nitrate, and phosphate had no impact at all. At weakly acidic pH (6.5), bisulfite and nitrite exhibited much stronger inhibition of PCFT-mediated transport, whereas sulfate and nitrate remained noninhibitory. Inhibition by bisulfite and nitrite at pH 6.5 was associated with a marked decrease in the influx Vmax and collapse of the transmembrane proton gradient attributed to the diffusion of the protonated forms into these cells. Monocarboxylates such as pyruvate and acetate also collapsed the pH gradient and were also inhibitory, whereas citrate and glycine neither altered the proton gradient nor inhibited PCFT-mediated transport. These observations add another dimension to the unfavorable pH environment for PCFT function in systemic tissues: the presence of high concentrations of bicarbonate.
Somatostatin receptor biology in neuroendocrine and pituitary tumours: part 1 – molecular pathways

• Introduction • The somatostatin receptor as a GPCR • Anti‐tumour effects of somatostatin • Post‐receptor signalling pathways ‐ Phosphotyrosine phosphatases and their action on MAPK and PI3K/Akt pathways ‐ Anti‐secretory action of SST through cAMP, K+ and Ca++ channels ‐ Other post‐receptor pathways
Efficient Error Estimating Coding: Feasibility and Applications

Motivated by recent emerging systems that can leverage partially correct packets in wireless networks, this paper proposes the novel concept of error estimating coding (EEC). Without correcting the errors in the packet, EEC enables the receiver of the packet to estimate the packet's bit error rate, which is perhaps the most important meta-information of a partially correct packet. Our EEC design provides provable estimation quality with rather low redundancy and computational overhead. To demonstrate the utility of EEC, we exploit and implement EEC in two wireless network applications, Wi-Fi rate adaptation and real-time video streaming. Our real-world experiments show that these applications can significantly benefit from EEC.
Cardioprotection by N-Acetylglucosamine Linkage to Cellular Proteins

Background— The modification of proteins with O-linked &bgr;-N-acetylglucosamine (O-GlcNAc) represents a key posttranslational modification that modulates cellular function. Previous data suggest that O-GlcNAc may act as an intracellular metabolic or stress sensor, linking glucose metabolism to cellular function. Considering this, we hypothesized that augmentation of O-GlcNAc levels represents an endogenously recruitable mechanism of cardioprotection. Methods and Results— In mouse hearts subjected to in vivo ischemic preconditioning, O-GlcNAc levels were significantly elevated. Pharmacological augmentation of O-GlcNAc levels in vivo was sufficient to reduce myocardial infarct size. We investigated the influence of O-GlcNAc levels on cardiac injury at the cellular level. Lethal oxidant stress of cardiac myocytes produced a time-dependent loss of cellular O-GlcNAc levels. This pathological response was largely reversible by pharmacological augmentation of O-GlcNAc levels and was associated with improved cardiac myocyte survival. The diminution of O-GlcNAc levels occurred synchronously with the loss of mitochondrial membrane potential in isolated cardiac myocytes. Pharmacological enhancement of O-GlcNAc levels attenuated the loss of mitochondrial membrane potential. Proteomic analysis identified voltage-dependent anion channel as a potential target of O-GlcNAc modification. Mitochondria isolated from adult mouse hearts with elevated O-GlcNAc levels had more O-GlcNAc–modified voltage-dependent anion channel and were more resistant to calcium-induced swelling than cardiac mitochondria from vehicle mice. Conclusions— O-GlcNAc signaling represents a unique endogenously recruitable mechanism of cardioprotection that may involve direct modification of mitochondrial proteins critical for survival such as voltage-dependent anion channel.
Impact of Urbanization on Economic Growth: Four Sector Analysis in Case of Pakistan

July-Dec 2015 JISR-MSSE Number 2 Volume 13 The study analyses the impact of urbanization on economic growth of Pakistan from 1975-2012. Conceptual framework of the present theory is based on the theory of Lewis (1954) and Tadaro (1976). Co-integration analysis is used to examine the impact of urbanization on economic growth of four sectors namely agriculture, manufacturing, industrial and services sector of the economy. Results show that urbanization affect economic growth significantly and they have a long run relationship. It was found that urbanization is an important determinant of economic growth but it has negative impact on economic growth in agriculture sector. However, it has positive impact on economic growth in industrial, manufacturing and services sector. Impact of Urbanization on Economic Growth: Four Sector Analysis in Case of Pakistan
ADARM: An Application-Driven Adaptive Resource Management Framework for Data Centers

ADARM is a framework for application-driven and adaptive resource allocation to enable large scale service-oriented cloud DCs. It builds and utilizes real-time application profiles and models to understand application requirements and performance characteristics. In addition, service-orientation enables ADARM to use uniform and flexible REST services to abstract, organize, monitor and control the fine-grained cloud resources, and it separates the resource control logic from the datacenter infrastructure. The combination of application-driven and service-orientation enables ADARM to incorporate various adaptive optimization techniques to optimize resource allocations for large scale distributed DCs. This paper discuses the design rationales behind ADARM, and describes its architecture and main benefits. Prototypical validations on several core components in ADARM are presented to demonstrate the potential business value and the feasibility of such a system framework.
Combining gradient ascent search and support vector machines for effective autofocus of a field emission–scanning electron microscope

Autofocus is an important issue in electron microscopy, particularly at high magnification. It consists in searching for sharp image of a specimen, that is corresponding to the peak of focus. The paper presents a machine learning solution to this issue. From seven focus measures, support vector machines fitting is used to compute the peak with an initial guess obtained from a gradient ascent search, that is search in the direction of higher gradient of focus. The solution is implemented on a Carl Zeiss Auriga FE‐SEM with a three benchmark specimen and magnification ranging from x300 to x160 000. Based on regularized nonlinear least squares optimization, the solution overtakes the literature nonregularized search and Fibonacci search methods: accuracy improvement ranges from 1.25 to 8 times, fidelity improvement ranges from 1.6 to 28 times, and speed improvement ranges from 1.5 to 4 times. Moreover, the solution is practical by requiring only an off‐line easy automatic train with cross‐validation of the support vector machines.
Pushing constraints into data streams

One important challenge in data mining is the ability to deal with complex, voluminous and dynamic data. Indeed, due to the great advances in technology, in many real world applications data appear in the form of continuous data streams, as opposed to traditional static datasets. Several techniques have been proposed to explore data streams, in particular for the discovery of frequent co-occurrences in data. However, one of the common criticisms pointed out to frequent pattern mining is the fact that it generates a huge number of patterns, independent of user expertise, making it very hard to analyze and use the results. These bottlenecks are even more evident when dealing with data streams, since new data are continuously and endlessly arriving, and many intermediate results must be kept in memory. The use of constraints to filter the results is the most common and used approach to focus the discovery on what is really interesting. In this sense, there is a need for the integration of data stream mining with constrained mining. In this work we describe a set of strategies for pushing constraints into data stream mining, through the use of a pattern tree structure that captures a summary of the current possible patterns. We also propose an algorithm that discovers patterns in data streams that satisfy any user defined constraint.
A Fast and Accurate Numerical Scheme for the Primitive Equations of the Atmosphere

We present a fast and accurate numerical scheme for the approximation of the primitive equations of the atmosphere. The temporal variable is discretized by using a special semi-implicit scheme which requires only to solve a Helmholtz equation and a nonlocal Stokes problem at each time step; the spatial variables are discretized by a spectral-Galerkin procedure with the horizontal components of vectorial spherical harmonics for the horizontal variables and Legendre or Chebyshev polynomials for the vertical variable. The new scheme has two distinct features: (i) it is unconditionally stable given fixed physical parameters, and (ii) the Helmholtz equation and the nonlocal Stokes problem which need to be solved at each time step can be decomposed into a sequence of one-dimensional equations (in the vertical variable) which can be solved by a spectral-Galerkin method with optimal computational complexity.
Novel Design of Gaits on Space Station for Dynamic Disturbance Minimization

In this paper, we propose a novel design of gaits for space manipulators so as to minimize the dynamic disturbance on a space station. Without modifying their joint configuration, we first propose the use of our previous developed gripping methodology. It combines the concepts of wheels motion in parallel grippers to reduce the swing motions in conventional gaits. For the proposed gaits, three major aspects are considered including 1) linear motion; 2) turning; and 3) exterior transition. Based on the conventional modeling approach for fixed base space manipulators, we extend the method to formulate the dynamic coupling during wheels motion. It is to visualize the manipulator as a prismatic joint with its gripping base moving with wheels motion. Also, we develop an experimental platform for verification. For disturbance analysis, various simulations have been conducted. Results have validated that the proposed gaits generate the lowest disturbance on a space station.
A Subjective Probability Based Deductive Approach to Global Trust Evaluation in Composite Services

In Service-Oriented Computing (SOC) environments, the trustworthiness of each service provider is critical for a service client when selecting one from a large pool of service providers. The trust value of a service provider is usually in the range of [0, 1] and is evaluated from the ratings given by service clients, which represent the subjective belief of service clients on the satisfaction of delivered services. So a trust value can be taken as a subjective probability, by which one party believes that another party can perform an action in a certain situation. Hence, subjective probability theory should be adopted in trust evaluation. In addition, in SOC environments, a service provider usually can invoke the services from other service providers forming a composite service. Thus, the global trust of a composite service should be evaluated based on both the subjective probability property of trust and complex invocation structures. In this paper, we first interpret the trust dependency caused by direct service invocations as conditional probability. Then, on the basis of trust dependency, we propose a Subjective probability based deductive (SELECTIVE) approach to evaluate the subjective global trustworthiness of a composite service. All these processes follow subjective probability theory and keep the subjective probability property of trust in evaluations. Our experimental results demonstrate that when compared with existing approaches our proposed SELECTIVE approach can yield more reasonable results.
Population Models of Temporal Differentiation

Temporal derivatives are computed by a wide variety of neural circuits, but the problem of performing this computation accurately has received little theoretical study. Here we systematically compare the performance of diverse networks that calculate derivatives using cell-intrinsic adaptation and synaptic depression dynamics, feedforward network dynamics, and recurrent network dynamics. Examples of each type of network are compared by quantifying the errors they introduce into the calculation and their rejection of high-frequency input noise. This comparison is based on both analytical methods and numerical simulations with spiking leaky-integrate-and-fire (LIF) neurons. Both adapting and feedforward-network circuits provide good performance for signals with frequency bands that are well matched to the time constants of postsynaptic current decay and adaptation, respectively. The synaptic depression circuit performs similarly to the adaptation circuit, although strictly speaking, precisely linear differentiation based on synaptic depression is not possible, because depression scales synaptic weights multiplicatively. Feedback circuits introduce greater errors than functionally equivalent feedforward circuits, but they have the useful property that their dynamics are determined by feedback strength. For this reason, these circuits are better suited for calculating the derivatives of signals that evolve on timescales outside the range of membrane dynamics and, possibly, for providing the wide range of timescales needed for precise fractional-order differentiation.
A Context-Aware Spontaneous Mobile Social Network

Context-awareness has become an essential concept in the field of pervasive computing and mobile systems, because of the emerging of online social networks, along with the worldwide diffusion of smartphones. Recent efforts and relevant research regarding mobile social network (MSN) aim at connecting people in smart environments considering not only their social behavior but also their context. In this area, this work presents a novel MSN model and discusses related applications. The main scientific contribution of this model, called Spontaneous Social Network (SSN), is the possibility of creating dynamic social networks based on a combination of multiple context-aware data, including location, profile and data obtained from popular online social networks. We also developed some scenarios to evaluate the proposed model. The results showed the feasibility of the proposed model and some possible applications in a University Campus.
Minimax Nonparametric Classification—Part I: Rates of Convergence

— This paper studies minimax aspects of nonparametric classification. We first study minimax estimation of the conditional probability of a class label, given the feature variable. This function, say f, is assumed to be in a general nonparametric class. We show the minimax rate of convergence under square L2 loss is determined by the massiveness of the class as measured by metric entropy. The second part of the paper studies minimax classification. The loss of interest is the difference between the probability of misclassification of a classifier and that of the Bayes decision. As is well known, an upper bound on risk for estimating f gives an upper bound on the risk for classification, but the rate is known to be suboptimal for the class of monotone functions. This suggests that one does not have to estimate f well in order to classify well. However, we show that the two problems are in fact of the same difficulty in terms of rates of convergence under a sufficient condition, which is satisfied by many function classes including Besov (Sobolev), Lipschitz, and bounded variation. This is somewhat surprising in view of a result of Devroye, Gyorfi, ¨ and Lugosi (1996).
Model-Driven Instrumentation of Graphical User Interfaces

In today's continuously changing markets newly developed products often do not meet the demands and expectations of customers. Research on this problem identified a large gap between developer and user expectations. Approaches to bridge this gap are to provide the developers with better information on product usage and to create a fast feedback cycle that helps tackling usage problems. Therefore, the user interface of the product, the central point of human-computer interaction, has to be instrumented to collect accurate usage data which serves as basis for further improvement steps. This paper presents a novel engineering approach that combines model-driven user interface development and flexible instrumentation with runtime monitoring. In its application, it enables observation integration into products which provides comprehensive data about usage and thus allows for fast feedback cycles and consequently increased software quality. A case-study demonstrates the applicability of this approach.
Logical Partitions on Many-Core Platforms

Cloud platforms that use logical partitions to allocate dedicated resources to VMs can benefit from small and therefore secure hypervisors. Many-core platforms, with their abundant resources, are an attractive basis to create and deploy logical partitions on a large scale. However, many-core platforms are designed for efficient cross-core data sharing rather than isolation, which is a key requirement for logical partitions. Typically, logical partitions leverage hardware virtualization extensions that require complex CPU core enhancements. These extensions are not optimal for many-core platforms, where it is preferable to keep the cores as simple as possible. In this paper, we show that a simple address-space isolation mechanism, that can be implemented in the Network-on-Chip of the many-core processor, is sufficient to enable logical partitions. We implement the proposed change for the Intel Single-Chip Cloud Computer (SCC). We also design a cloud architecture that relies on a small and disengaged hypervisor for the security-enhanced Intel SCC. Our prototype hypervisor is 3.4K LOC which is comparable to the smallest hypervisors available today. Furthermore, virtual machines execute bare-metal avoiding runtime interaction with the hypervisor and virtualization overhead.