Miracle-dz/UniFAR-FTUs huggingface dataset:
Voice conversion method based on State-Space Model

This paper presents a novel method based on State Space Model (SSM) for voice conversion. The SSM has the ability to model the spectral parameter trajectory of speech which is desirable in voice conversion. Traditional training method for SSM is EM (Expectation Maximum) algorithm. Due to its low convergence speed, the training time is very long. Moreover increasing the spectral parameter order which helps to improve the speech quality will also cause SSM training difficulties. These problems are the underlying motivations of our work. In this paper we develop an efficient and robust training procedure based on LSF (Line Spectral Frequencies) segmentation method and subspace training algorithm. The objective and subjective experiments demonstrate the superior performance of the training approach as compared with EM method.
Reliability Analysis of Ku-Band 5-bit Phase Shifters Using MEMS SP4T and SPDT Switches

This work presents a Ku-band microelectromechanical systems (MEMS) based 5-bit phase shifter using dc contact single-pole-four-throw (SP4T) and single-pole-double-throw switches. The design is implemented using a coplanar waveguide transmission line. Two individual 2-bit phase shifters and one 1-bit phase shifter are cascaded to develop the complete 5-bit phase shifter. The phase shifters are fabricated on 635- μm alumina substrate using a surface micromachining process. The 5-bit phase shifter demonstrates an average insertion loss of 2.65 dB in the 13-18-GHz band with a return loss better than 22 dB and average phase error less than 0.68 ° at 17 GHz. Total area of the fabricated 5-bit phase shifter is 4.7 × 2.8 mm2. The reliability of the single-pole-single-throw and SP4T switches show more than 10 million cycles with an RF power of 0.1-2 W. Furthermore, reliability of the MEMS phase shifter is extensively investigated and presented with cold and hot switched conditions. To the best of our knowledge, this is the first reported MEMS 5-bit phase shifter in the literature that has undergone different reliability and qualification testing including 3-axis vibrations.
Imaging of activated complement using ultrasmall superparamagnetic iron oxide particles (USPIO)-conjugated vectors: an in vivo in utero non-invasive method to predict bad pregnancy and fetal outcomes

In the current study, we have developed a magnetic resonance imaging (MRI)-based method for noninvasive detection of complement activation in placenta and fetal brain in vivo in utero. Using this method, we found that anti-complement C3-targeted ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles bind within the inflamed placenta and fetal brain cortical tissue, causing a shortening of the T2* relaxation time. We used two mouse models of pregnancy complications: a mouse model of obstetrics antiphospholipid syndrome (APS) and a mouse model of preterm birth (PTB). We found that detection of C3 deposition in the placenta in the APS model was associated with placental insufficiency characterised by increased oxidative stress, decreased vascular endothelial growth factor (VEGF) and placental growth factor (PlGF) levels and intrauterine growth restriction. We also found that fetal brain C3 deposition was associated with cortical axonal cytoarchitecture disruption and increased neurodegeneration in the mouse model of APS and in the PTB model. In the APS-model, fetuses that showed increased C3 in their brains additionally expressed anxiety-related behaviour after birth. Importantly, USPIO did not affect pregnancy outcomes and liver function in the mother and the offspring , suggesting that this method may be useful for detecting complement activation in vivo in utero and predicting placental insufficiency and abnormal fetal neurodevelopment that leads to neuropsychiatric disorders. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature. com/authors/editorial_policies/license.html#terms All the Authors declare not to have any financial or other relationships that might lead to a conflict of interest. Europe PMC Funders Group Author Manuscript Mol Psychiatry. Author manuscript; available in PMC 2016 February 01. Published in final edited form as: Mol Psychiatry. 2015 August ; 20(8): 1017–1026. doi:10.1038/mp.2014. 110. E uope PM C Fuders A uhor M ancripts E uope PM C Fuders A uhor M ancripts
Do Student Conduct Administrators Discriminate against Black students? An Analysis of Drug Sanctions Using Vignettes

Abstract:Recent scholarly work indicates Black students in K-12 are significantly more likely to be suspended or expelled than their White peers. However, little empirical work exists in the post-secondary environment, raising questions about the discrimination Black students encounter in the university student conduct process. This study explored racial disparities in postsecondary student discipline through the use of vignettes randomly assigned to a national sample of student conduct administrators. Our vignettes asked administrators to recommend student conduct sanctions for individuals found responsible for marijuana violations. Vignettes differed only in the student name provided, and names were purposely selected to suggest a particular racial identity. We found no pattern of racial bias in sanction assignments given to Black students compared to White students, nor did we observe differences in the total number of sanctions assigned.
Identification of Novel Saccharomyces cerevisiaeProteins with Nuclear Export Activity: Cell Cycle-Regulated Transcription Factor Ace2p Shows Cell Cycle-Independent Nucleocytoplasmic Shuttling

ABSTRACT Nuclear export of proteins containing leucine-rich nuclear export signals (NESs) is mediated by the NES receptor CRM1/Crm1p. We have carried out a yeast two-hybrid screen with Crm1p as a bait. The Crm1p-interacting clones were subscreened for nuclear export activity in a visual assay utilizing the Crm1p-inhibitor leptomycin B (LMB). This approach identified three Saccharomyces cerevisiaeproteins not previously known to have nuclear export activity. These proteins are the 5′ RNA triphosphatase Ctl1p, the cell cycle-regulated transcription factor Ace2p, and a protein encoded by the previously uncharacterized open reading frame YDR499W. Mutagenesis analysis show that YDR499Wp contains an NES that conforms to the consensus sequence for leucine-rich NESs. Mutagenesis of Ctl1p and Ace2p were unable to identify specific NES residues. However, a 29-amino-acid region of Ace2p, rich in hydrophobic residues, contains nuclear export activity. Ace2p accumulates in the nucleus at the end of mitosis and activates early-G1-specific genes. We now provide evidence that Ace2p is nuclear not only in late M-early G1 but also during other stages of the cell cycle. This feature of Ace2p localization explains its ability to activate genes such as CUP1, which are not expressed in a cell cycle-dependent manner.
Semantic Grid Resource Discovery using DHTs in Atlas

For the Semantic Grid vision [13] to become a reality, high quality of service must be offered to users and applications at all levels of the Grid fabric. In this position paper, we concentrate on high quality of service in the provision of resource discovery services in Semantic Grids. Resource discovery is an important problem in Grids in general, and Semantic Grids in particular. We discuss how to achieve high-performance, scalability, resilience to failures, robustness and adaptivity in the provision of resource discovery services in Semantic Grids, and especially in OntoKit, the Semantic Grid toolkit currently under development in project OntoGrid [22]. OntoGrid (http://www.ontogrid.net) is a Semantic Grid project funded by the Grid Technologies unit of the European Commission under the strategic objective “Grid-based systems for Complex Problem Solving” of the Information Society Technologies programme of FP6. Our basic assumption in this paper is that Semantic Grid resources (e.g., machines, services or ontologies) will be annotated by RDF(S) metadata. Metadata pervades the Semantic Grid and is used to describe Grid resources, the environment, provenance and trust information etc. [13]. The Resource Description Framework (RDF) and RDF Schema (RDFS) are frameworks for representing information about Web resources. RDF(S) consists of W3C recommendations that enable the encoding, exchange and reuse of structured metadata, providing the means for publishing both human-readable and machine-processable information and vocabularies for semantically describing things on the Web. Although RDF(S) was originally proposed in the context of the Semantic Web, it is also a very natural framework for representing information about Grid resources. As a result, it is used heavily in various Semantic Grid projects e.g., Grid (http://www.mygrid.org.uk) or OntoGrid.
Microwave Radar/radiometer for Arctic Clouds MiRAC: First insights from the ACLOUD campaign

Abstract. The Microwave Radar/radiometer for Arctic Clouds (MiRAC) is a novel instrument package developed to study the vertical structure and characteristics of clouds and precipitation onboard the Polar 5 research aircraft. MiRAC combines a frequency modulated continuous wave (FMCW) radar at 94 GHz including a 89 GHz passive channel (MiRAC-A) and an eight channel radiometer with frequencies between 175 and 340 GHz (MiRAC-P). The radar can be flexibly operated using different chirp sequences to provide measurements of the equivalent radar reflectivity with different vertical resolution down to 5 m. MiRAC is mounted for down-looking geometry on Polar 5 to enable the synergy with lidar and radiation measurements. To mitigate the influence of the strong surface backscatter the radar is mounted with an inclination of about 25° backward in a belly pod under the Polar 5 aircraft. Procedures for filtering ground return and range side-lobes have been developed. MiRAC-P frequencies are especially adopted for low humidity conditions typical for the Arctic to provide information on water vapor and hydrometeor content. MiRAC has been operated on 19 research flights during the ACLOUD campaign in the vicinity of Svalbard in May/June 2017 providing in total 48 hours of measurements from flight altitudes > 2300 m. The radar measurements have been carefully quality controlled and corrected for surface clutter, mounting of the instrument, and aircraft orientation to provide measurements on a unified, geo-referenced vertical grid allowing the combination with the other nadir pointing instruments. An intercomparison with CloudSat shows good agreement in terms of cloud top height of 1.5 km and radar reflectivity up to −5 dBz and demonstrates that MiRAC is able to fill the gap in observing low level clouds with its more than ten times higher vertical resolution down to about 150 m above the surface. This is especially important for the Arctic as about 45 % of the clouds during ACLOUD showed cloud tops below 1200 m, i.e., the blind zone of CloudSat.
Extreme Learning Machine combining matrix factorization for collaborative filtering

Collaborative Filtering (CF) is one of the most popular techniques for information filtering in recommendation systems. Currently, there are many linear and nonlinear regression algorithms for CF. However, to our knowledge, these regression algorithms may not give satisfactory results in some practical applications. In this paper, Extreme Learning Machine (ELM), which is famous with its fast speed and good performance in generalization, is firstly employed to build a nonlinear regression model for CF, namely ELM for CF (ELMCF) algorithm. Then by combining ELM and Weighted Nonnegative Matrix Tri-Factorization (WNMTF), which can alleviate the data sparsity problem of the user-item matrix, a new nonlinear regression model is proposed, namely Extreme Learning Machine Combining Matrix Factorization for Collaborative Filtering (CELMCF) algorithm, to construct regression based CF algorithms and improve the performance of recommendation systems. Experiments are conducted on several benchmark datasets from different application domains. Experimental results show that the proposed CELMCF algorithm outperforms some state-of-the-art regression based CF algorithms (including ELMCF algorithm, Linear Regression for CF (LRCF) algorithm and Memory based CF (MemCF) algorithm) more efficiently with the competitive effectiveness.
Development and studies of novel microfabricated radiation hard scintillation detectors with high spatial resolution

A new type of scintillation detector is being developed with standard microfabrication techniques. It consists of a dense array of scintillating waveguides obtained by coupling microfluidic channels filled with liquid scintillator to photodetectors. Easy manipulation of liquid scintillators inside microfluidic devices allow their flushing, renewal and exchange making the active medium intrinsically radiation hard. The detectors have been fabricated by photostructuration of a radiation hard epoxy resin (SU-8) deposited on silicon wafers and coupled to a multi-anode photomultiplier tube (MAPMT) to read out the scintillation light. They have been characterized by exciting the liquid scintillator in the 200 micrometers thick microchannels with electrons from a 90Sr yielding approximately 1 photoelectron per impinging Minimum Ionizing Particle (MIP). These promising results demonstrate the concept of microfluidic scintillating detection and are very encouraging for future developments.
Study on Fractal of Silt Floc Pores and the Settling Character of Silt Floc

A new method, using fractal dimension of silt floc pores to study the settling performance of silt floc, was put forward. Firstly, the fractal characteristics of silt floc pores were descried qualitatively by the software of image-pro plus6.0, which was taken from Shahu Lake in Wuhan city. Secondly, two-dimensional fractal dimension of silt floc pores was calculated by image analysis. At last, the settling velocity of silt floc was estimated by the fractal dimension of silt floc pores, which compared with the traditional method. The conclusion given in this paper showed that porosity of silt floc may be reflected by the fractal properties, and the settling velocity of silt floc reduced with the fractal dimension of silt floc pores increasing.
Automating and Simplifying Memory Corruption Attack Response Using Failure-Aware Computing

GAURIAR, PRACHI. Automating and Simplifying Memory Corruption Attack Response Using Failure-Aware Computing. (Under the direction of Assistant Professor Jun Xu). Over the last two decades, advances in software engineering have produced new ways of creating robust, reliable software. Unfortunately, the dream of bug-free software still eludes us. When bugs are discovered in deployed software, software failures and service disruption can lead to significant losses, both monetary and otherwise. The typical failure response process is composed of three phases: failure detection, cause analysis, and solution formulation. To minimize the impact of software failures, it is critical that each of these phases be completed as quickly as possible. This thesis is separated into two parts. In the first part, we propose a general conceptual approach called failure-aware computing that aims to automate as much of the failure response process as possible. We describe the architecture of this proposed framework, some possible applications, and challenges if it were implemented. We then describe how this framework can be applied to responding to memory corruption errors. In the second part, we describe and evaluate an implementation of part of this framework for diagnosing memory corruption failures. In particular, we discuss a root cause analysis tool we have created that analyzes a program’s source code to determine which memory-related program events potentially lead to a memory corruption error. Our tool then monitors the afflicted program’s execution and outputs useful information to aid the developer in understanding the root cause of the failure. We finally evaluate our tool’s effectiveness in identifying the root cause of memory access errors in both self-written and open-source code. Automating and Simplifying Memory Corruption Attack Response Using Failure-Aware Computing
EVOLUTION AND STABILITY OF THE G‐MATRIX ON A LANDSCAPE WITH A MOVING OPTIMUM

Abstract In quantitative genetics, the genetic architecture of traits, described in terms of variances and covariances, plays a major role in determining the trajectory of evolutionary change. Hence, the genetic variance‐covariance matrix (G‐matrix) is a critical component of modern quantitative genetics theory. Considerable debate has surrounded the issue of G‐matrix constancy because unstable G‐matrices provide major difficulties for evolutionary inference. Empirical studies and analytical theory have not resolved the debate. Here we present the results of stochastic models of G‐matrix evolution in a population responding to an adaptive landscape with an optimum that moves at a constant rate. This study builds on the previous results of stochastic simulations of G‐matrix stability under stabilizing selection arising from a stationary optimum. The addition of a moving optimum leads to several important new insights. First, evolution along genetic lines of least resistance increases stability of the orientation of the G‐matrix relative to stabilizing selection alone. Evolution across genetic lines of least resistance decreases G‐matrix stability. Second, evolution in response to a continuously changing optimum can produce persistent maladaptation for a correlated trait, even if its optimum does not change. Third, the retrospective analysis of selection performs very well when the mean G‐matrix (Ḡ) is known with certainty, indicating that covariance between G and the directional selection gradient (3 is usually small enough in magnitude that it introduces only a small bias in estimates of the net selection gradient. Our results also show, however, that the contemporary Ḡ‐matrix only serves as a rough guide to Ḡ. The most promising approach for the estimation of G is probably through comparative phylogenetic analysis. Overall, our results show that directional selection actually can increase stability of the G‐matrix and that retrospective analysis of selection is inherently feasible. One ? riajor remaining challenge is to gain a sufficient understanding of the G‐matrix to allow the confident estimation of Ḡ.
HogWild++: A New Mechanism for Decentralized Asynchronous Stochastic Gradient Descent

Stochastic Gradient Descent (SGD) is a popular technique for solving large-scale machine learning problems. In order to parallelize SGD on multi-core machines, asynchronous SGD (Hogwild! ) has been proposed, where each core updates a global model vector stored in a shared memory simultaneously, without using explicit locks. We show that the scalability of Hogwild! on modern multi-socket CPUs is severely limited, especially on NUMA (Non-Uniform Memory Access) system, due to the excessive cache invalidation requests and false sharing. In this paper we propose a novel decentralized asynchronous SGD algorithm called HogWild++ that overcomes these drawbacks and shows almost linear speedup on multi-socket NUMA systems. The main idea in HogWild++ is to replace the global model vector with a set of local model vectors that are shared by a cluster (a set of cores), keep them synchronized through a decentralized token-based protocol that minimizes remote memory access conflicts and ensures convergence. We present the design and experimental evaluation of HogWild++ on a variety of datasets and show that it outperforms state-of-the-art parallel SGD implementations in terms of efficiency and scalability.
Contribution of airborne full-waveform lidar and image data for urban scene classification

Airborne lidar systems have become an alternative source for the acquisition of altimeter data. In addition to multi-echo laser scanner systems, full-waveform systems are able to record the whole backscattered signal for each emitted laser pulse. These data provide more information about the structure and the physical properties of the surface. This paper is focused on the classification of full-waveform lidar and airborne image data on urban scenes. Random forests are used since they provide an accurate classification and run efficiently on large datasets. Moreover, they provide measures of variable importance for each class. This is crucial to analyze the relevance of each feature for the classification of urban scenes. Random Forests provide more accurate results than Support Vector Machines with an overall accuracy of 95.75%. The most relevant features show the contribution of lidar waveforms for classifying dense urban scenes and improve the classification accuracy for all classes.
Experimental Study of Flow Field and Movement of Sediment over a Ramp

Applications of hydraulic structures in low dams and diversion structures in countries like India, where sediment is a serious concern, demands a good expanse of research on the flow field and movement of sediment. The present study was focused on the non-uniformity in the velocity distribution along the flow direction and the movement of sediment over a ramp. The velocity distribution of the flow near and over the ramp was studied with the help of Acoustic Doppler Velocimeter. Experiments were conducted to study the mechanics and kinematics of movement of sediment particles. The movement of sediment particles over the smooth ramp was studied with the help of high-speed camera system. The particle velocity, acceleration, applied force and the angle of velocity vector were processed and analyzed from the captured images using digital image analysis techniques. The path of the movement of sediment particle was tracked. The vector results of the flow pattern indicated a very high amount of increase of the uplift velocity (y – component of the instantaneous flow velocity) component over a ramp. The sediment had very little movement for about 3.8 seconds at the beginning of the ramp and accelerated as it moved along the ramp in the downstream side. The path of the sediment movement over the ramp was found to be inclined by 25.6 to 35.7 degree to the horizontal axis which was almost parallel to the ramp (29.055 degree).
New Method for Finding Initial Cluster Centroids in K-means Algorithm

Data Mining is special field of computer science concerned with the automated extraction of patterns of knowledge implicitly stored in large databases, data warehouses and other large data repositories. Clustering is one of the Data Mining tasks which is used to cluster objects on the basis of their nearness to the central value. It is a method of grouping objects automatically. In clustering elements within same cluster are more similar than the elements in other clusters. KMeans is one the method of clustering which is used widely because it is simple and efficient. The output of the K Means depends upon the chosen central values for clustering. So accuracy of the K Means algorithm depends much on the chosen central values. The original K Means method chooses the initial cluster centroids randomly which affects its performance. This paper presents a new method for finding initial cluster centroids for K Means. General Terms Centroids, Complexity, Dataset, Modified K-Means, K-Means