Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Singer Identification Based on Speech-Derived Models

Automatic singer identification (SNID) aims to determine who among a set of singers perform a given music recording. So far, most existing SNID methods follow a framework stemming from speaker identification (SPID) research, which models each person's characteristics using his/her voice data. This framework, however, is impractical in many SNID applications, because acquiring solo a cappella from each singer is usually not as feasible as collecting spoken data from each speaker in SPID applications. In view of the easy availability of spoken data, this work investigates the possibility of modeling singers' voices using spoken data instead of singing data. However, our experiment found it difficult to replace singing data fully by using spoken data in singer voice modeling, due to the significant difference between singing and speaking for most people. Thus, we propose an alternative solution based on the use of few available singing data. The idea is to modify speech-derived voice models using MAP adaptation on few singing data, so that the adapted voice models can cover performers' singing characteristics. Our experiments found that most of the singing clips can be correctly identified using the adapted voice models.
Robust Parsing: More with Less

Covering as many phenomena as possible is a traditional goal of parser development, but the broader a grammar is made, the blunter it may become, as rare constructions inﬂuence the behaviour on simple sentences that were already solved correctly. We observe the effects of intentionally removing support for speciﬁc constructions from a broad-coverage grammar of German. We show that accuracy of analysing sentences from the NEGRA corpus can be improved not only for sentences that do not need the extra coverage, but even when including those that do.
Effect of Sintering Temperature on Microstructure, Chemical Stability, and Electrical Properties of Transition Metal or Yb-Doped BaZr0.1Ce0.7Y0.1M0.1O3−δ (M = Fe, Ni, Co, and Yb)

Perovskite-type BaZr0.1Ce0. 7Y0.1M0. 1O3-δ (M = Fe, Ni, Co and Yb) (BZCY-M) oxides were synthesized using the conventional solid-state reaction method at 1350-1550 oC in air in order to investigate the effect of dopants on sintering, crystal structure, chemical stability under CO2 and H2S, and electrical transport properties. The formation of the single-phase perovskite-type structure with an orthorhombic space group Imam was confirmed by Rietveld refinement using powder X-ray diffraction (PXRD) for the Fe, Co, Ni and Yb-doped samples. The BZCY-Co and BZCY-Ni oxides show a total electrical conductivity of 0.01 and 8 × 10-3 Scm-1 at 600 oC in wet H2 with an activation energy of 0.36 and 0.41 eV, respectively. Scanning electron microscopy (SEM) and energy-dispersive X-ray analysis (EDX) revealed Ba and Co rich secondary phase at the grain-boundaries, which may explain the enhancement in the total conductivity of the BZCY-Co. However, ex-solution of Ni at higher sintering temperatures, especially at 1550 oC, decreases the total conductivity of the BZCY-Ni material. The Co and Ni dopants act as a sintering aid and form dense pellets at a lower sintering temperature of 1250 oC. The Fe, Co and Ni-doped BZCY-M samples synthesized at 1350 oC show stability in 30 ppm H2S/H2 at 800 oC, and increasing the firing temperature to 1550 oC, enhanced the chemical stability in CO2 / N2 (1: 2) at 25-900 oC. The BZCY-Co and Ni compounds with high conductivity in wet H2 could be considered as possible anodes for intermediate temperature solid oxide fuel cells (IT-SOFCs).
Linear levels through n-grams

We show that novel, linear game levels can be created using n-grams that have been trained on a corpus of existing levels. The method is fast and simple, and produces levels that are recognisably in the same style as those in the corpus that it has been trained on. We use Super Mario Bros. as an example domain, and use a selection of the levels from the original game as a training corpus. We treat Mario levels as a left-to-right sequence of vertical level slices, allowing us to perform level generation in a setting with some formal similarities to n-gram-based text generation and music generation. In empirical results, we investigate the effects of corpus size and n (sequence length). While the applicability of the method might seem limited to the relatively narrow domain of 2D games, we argue that many games in effect have linear levels and n-grams could be used to good effect, given that a suitable alphabet can be found.
Two novel Ant Colony Optimization approaches for Bayesian network structure learning

Learning Bayesian networks from data is an NP-hard problem with important practical applications. Several researchers have designed algorithms to overcome the computational complexity of this task. Difficult challenges remain however in reducing computation time for structure learning in networks of medium to large size and in understanding problem-dependent aspects of performance. In this paper, we present two novel algorithms (ChainACO and K2ACO) that use Ant Colony Optimization (ACO). Both algorithms search through the space of orderings of data variables. The ChainACO approach uses chain structures to reduce computational complexity of evaluation but at the expense of ignoring the richer structure that is explored in the K2ACO approach. The novel algorithms presented here are ACO versions of previously published GA approaches. We are therefore able to compare ACO vs GA algorithms and Chain vs K2 evaluations. We present a series of experiments on three well-known benchmark problems. Our results show problem-specific trade-offs between solution quality and computational effort. However it seems that the ACO-based approaches might be favored for larger problems, achieving better fitnesses and success rate than their GA counterparts on the largest network studied in our experiments.
X-ray optical depth diagnostics of T Tauri accretion shocks

Context. In classical T Tauri stars, X-rays are produced by two plasma components: a hot low-density plasma, with frequent flaring activity, and a high-density lower temperature plasma. The former is coronal plasma related to the stellar magnetic act ivity. The latter component, never observed in non-accreting stars, could be plasma heated by the shock formed by the accretion process. However its nature is still being debated. Aims. Our aim is to probe the soft X-ray emission from the high-density plasma component in classical T Tauri stars to check whether this is plasma heated in the accretion shock or whether it is coronal plasma. Methods. High-resolution X-ray spectroscopy allows us to measure individual line fluxes. We analyze X-ray spectra of the classic al T Tauri star MP Muscae and TW Hydrae. Our aim is to evaluate line ratios to search for optical depth effects, which are expected in the accretion-driven scenario. We also derive the plasma emission measure distributions EMD, to investigate whether and how the EMD of accreting and non accreting young stars differ. The results are compared to those obtained for the non-accreting weak-line T Tauri stars TWA 5. Results. We find evidence of resonance scattering in the strongest lin es of MP Mus, supporting the idea that soft X-rays are produced by plasma heated in the accretion shock. We also find that the EMD of MP Mus has two peaks: a cool peak at temperatures expected by plasma heated in the accretion shock, and a hot peak typical of coronal plasma. The shape of the EMD of MP Mus appears to be the superposition of the EMD of a pure coronal source, like TWA 5, and an EMD alike that of TW Hydrae, which is instead dominated by shock-heated plasma.
Blended learning in independent high schools: an interpretative phenomenological analysis of faculty perceptions.

The purpose of this interpretative phenomenological analysis was to understand the perceptions and lived experiences of faculty teaching blended classes in independent high schools. Blended learning is defined as inquiry that combines both face-to-face and online modalities (Halverson, Graham, Spring, & Drysdale, 2012). Following trends set at the college level, the blended platform has proliferated in high schools, yet scant research has been conducted in this area and little is known about the perceptions and experiences of teachers delivering blended courses in high schools. Data collected through multiple interviews with five independent high school faculty led to findings covering four superordinate themes, including the resistance of faculty to the blended platform, faculty respect for the contributions of blended learning to teaching, faculty appreciation of the learning opportunities for students, and student hesitance to embrace learning communities. The study revealed that faculty perceive that the blended platform can be an effective pedagogical platform that better prepares students for college and beyond by providing students with increased ownership over their learning and a greater opportunity to explore their academic passions. At the same time, concerns were raised regarding an unwillingness of some students to create communities of inquiry as well as a possible deterioration to the faculty-student relationship as a result of faculty losing some control over the learning process in blended courses.
Mental Health Consequences of Pre- and Peri-Military Violence Victimization among United States Army Soldiers: The Moderating Effect of Resilience

Several factors must be considered when examining violence victimization and mental health consequences among military service members: the prevalence of pre-military victimization, the risk of subsequent victimization conferred by prior victimization, the cumulative or multiplicative effect of adverse events, and the prevalence of trauma exposure and mental health issues in the military, as well as protective factors, such as resilience. The present study seeks to examine the differential impact of several types of victimization both pre- and peri-military on current mental health symptoms, as well as the potential buffering effect of resilience, in a sample of United States Army Soldiers (N = 947). To address our research aims, we conducted multiple regression analyses to examine associations of violence victimization with PTSD and depression symptoms in separate models, and a simple moderation analysis was performed. Higher ratings of minor forms of victimization in the military, weapon victimization in the military, sexual victimization both pre- and peri-military, being beat up pre-military, and life events were associated with more of PTSD symptoms, while higher levels of resilience were associated with fewer PTSD symptoms. Higher ratings of minor victimization in the military, sexual victimization pre-military, being beat up pre-military, life events, and combat were associated with higher levels of depression symptoms, while resilience was associated with lower ratings of depression symptoms. We found that psychological resilience buffered against the effects of victimization history on current symptoms of both PTSD and depression. Even at low levels of victimization, resilience was protective against PTSD and depressive symptoms. Our results support the deleterious effects of sexual assault victimization while in the military, and highlight the importance of several pre-military forms of victimization as continuing to predict current mental health among service members. Finally, in addition to the prevention of violence in the military, these findings suggest that efforts to increase resilience may be worthwhile.
Parallel group independent component analysis for massive fMRI data sets

Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.
A 3-D Scanner Capturing Range and Color : Design and Calibration

We present a simple, low-cost and easy to reconfigure 3-D color-range scanner-CRS, which is able to capture the geometry and color of the measured object. The CRS utilizes a scanning laser sheet and the triangulation principle to acquire the geometry of the measured object. The projected laser stripe is detected using a matrix array pixel processor MAPP2200, which makes the scanner system compact and eliminates the need for any additional signal processing hardware. The color values for each 3-D point are acquired using a standard color CCD camera. For accurate range measurements and color value acquisition the physical geometry of the scanning system has to be determined and camera-to-camera calibration made between MAPP2200 and CCD camera. In-house camera calibration toolbox for MATLAB is used for both of these tasks. To maximize port-ability the CRS is connected to the RS-232 port of the host computer and can be easily controlled by using simple AS-CII commands. Finally we show some 3-D images taken with CRS.
Stratospheric dryness

Stratospheric dryness J. Lelieveld, C. Brühl, P. Jöckel, B. Steil, P. J. Crutzen, H. Fischer, M. A. Giorgetta, P. Hoor, M. G. Lawrence, M. Milz, R. Sausen, G. P. Stiller, and H. Tost Max Planck Institute for Chemistry, J.J. Becherweg 27, 55128 Mainz, Germany Scripps Institution of Oceanography, UCSD, La Jolla, CA 92093-0221, USA Max Planck Institute for Meteorology, Bundesstrasse 53, 20146 Hamburg, Germany FZK Institute for Meteorology and Climate Research, 76021 Karlsruhe, Germany DLR-Institut für Physik der Atmosphäre, Oberpfaffenhofen, 82234 Wessling, Germany Received: 20 October 2006 – Accepted: 9 November 2006 – Published: 14 November 2006 Correspondence to: J. Lelieveld (lelieveld@mpch-mainz.mpg.de)
Manipulating Machines: Designing Robots to Grasp Our World

Manipulation requires anticipatory actions. We preshape our grasp before making contact with an object and stiffen our arm in anticipation of lifting a heavy one. These actions precondition the manipulation engagement to minimize the effects of short timescale dynamics. The goal of the research proposed here is to contribute an approach to robot manipulation encompassing three principal components: force sensing and compliant manipulation in unstructured environments, a decentralized, behavior based cognitive architecture, and the integration of anticipatory sensorimotor cues into the robot’s behavioral repertoire. A control architecture, pARC, is proposed as a framework for incorporating anticipatory information into a behavior based decomposition. The approach will be demonstrated through a series of manipulation scenarios conducted on a new 29 degree-of-freedom force controlled humanoid robot named Domo.
Re: Magnetic resonance imaging and mammography in women with a hereditary risk of breast cancer.

BACKGROUND: Although breast cancer screening is recommended to start at a younger age for women with a hereditary risk of breast cancer, the sensitivity of mammography for these women is reduced. We compared magnetic resonance imaging (MRI) with mammography to determine which is more sensitive and whether MRI could play a role in the early detection of breast cancer for these women. METHODS: We constructed a retrospective cohort of all breast MRI and mammography surveillance reports made in our department from November 1994 to February 2001. All of the 179 women in the cohort had received biannual palpation in addition to annual imaging by MRI, mammography, or both. The 258 MRI images and the 262 mammograms were classified with the use of the BI-RADS (i.e., Breast Imaging Reporting and Data System) scoring system, which has five categories to indicate the level of suspicion of a lesion. Receiver operator characteristic curves were generated for MRI and mammography, and the area under each curve (AUC) was assessed for the entire cohort of 179 women and for a subset of 75 women who had received both an MRI and a mammographic examination within a 4-month period. All statistical tests were two-sided. RESULTS: In the cohort of 179 women, we detected 13 breast cancers. Seven cancers were not revealed by mammography, but all were detected by MRI. For the entire cohort, the AUC for mammography was 0.74 (95% confidence interval [CI] = 0.68 to 0.79), and the AUC for MRI was 0.99 (95% CI = 0.98 to 1.0). For the subset of women who had both examinations, the AUC for mammography was 0.70 (95% CI = 0.60 to 0.80), and the AUC for MRI was 0.98 (95% CI = 0.95 to 1.0). CONCLUSION: MRI was more accurate than mammography in annual breast cancer surveillance of women with a hereditary risk of breast cancer. Larger prospective studies to examine the role of MRI in screening programs are justified.
Flat Combining Synchronized Global Data Structures

The implementation of scalable synchronized data structures is notoriously difficult. Recent work in shared-memory multicores introduced a new synchronization paradigm called flat combining that allows many concurrent accessors to cooperate efficiently to reduce contention on shared locks. In this work we introduce this paradigm to a domain where reducing communication is paramount: distributed memory systems. We implement a flat combining framework for Grappa, a latency-tolerant PGAS runtime, and show how it can be used to implement synchronized global data structures. Even using simple locking schemes, we find that these flat-combining data structures scale out to 64 nodes with 2x-100x improvement in throughput. We also demonstrate that this translates to application performance via two simple graph analysis kernels. The higher communication cost and structured concurrency of Grappa lead to a new form of distributed flat combining that drastically reduces the amount of communication necessary for maintaining global sequential consistency.
Joint RNN-Based Greedy Parsing and Word Composition

This paper introduces a greedy parser based on neural networks, which leverages a new compositional sub-tree representation. The greedy parser and the compositional procedure are jointly trained, and tightly depends on each-other. The composition procedure outputs a vector representation which summarizes syntactically (parsing tags) and semantically (words) sub-trees. Composition and tagging is achieved over continuous (word or tag) representations, and recurrent neural networks. We reach F1 performance on par with well-known existing parsers, while having the advantage of speed, thanks to the greedy nature of the parser. We provide a fully functional implementation of the method described in this paper.
A simple mathematical model applied to selection of the sodium profile during profiled haemodialysis.

BACKGROUND Among dialysis patients in the last 10 years the incidence of intradialytic dysequilibrium syndrome and symptomatic hypotension has increased significantly. Profiled haemodialysis (PHD), a new dialysis technique based on intradialytic modulation of the dialysate sodium concentration according to pre-elaborated individual profiles, has been set up to reduce intradialytic imbalances and the incidence of dysequilibrium syndrome and symptomatic hypotension. The present paper illustrates a new mathematical model for solute kinetics, single-compartment for sodium and two-compartment for urea, aimed at improving the use of PHD. The model allows the sodium profile to be elaborated a priori, before each dialysis session, according to the patient's clinical needs and respecting the individual sodium mass removal and weight gain. METHOD The mathematical model was first derived and then applied to determining a rational dialysate sodium profile. A procedure which allows the method to be tuned to individual clinical needs on the basis of routine measurements performed before each session is also presented. The proposed method was validated in vivo during seven dialysis sessions, each performed on a different patient. RESULTS The comparison between data predicted by the model and those obtained in vivo shows a good correspondence in particular concerning the time pattern of blood urea and sodium. The comparison between the model prediction and in vivo determined sodium and urea plasma curves showed standard deviations (2.25 mEq/l for sodium and 0.87 mmol/l for urea) only slightly higher than those attributable to laboratory measurement errors. Moreover, in vivo implementation of PHD by our model enables one to remove an amount of sodium mass comparable with the a priori quantity predicted by the model.
Performance Analysis of Clustering Algorithms for 3 d Medical Image Rendering and Its Volume Calculation

Three dimensional reconstructions of abnormal regions from MR brain images is an important operation as it helps the radiologist in the diagnosis, surgical planning and biological research.This paper evaluates and analyse the clustering algorithms involved in segmenting abnormal region from MR brain images. Volumetric calculation is also presented with the extracted region to assist the radiologist in estimating the stage of cancer.
A Taxation attribute reduction based on genetic algorithm and rough set theory

Selection of taxation attributes is one difficult question in analyzing the sources of taxation. This paper introduces genetic-algorithm-based rough set attribute reduction algorithm into the job of taxation attribute reduction. By referring to the concept of dependability in rough set, this method optimizes the configuration of fitness function, improves the convergence of original algorithm and changes the limitation of current attribute reduction in genetic algorithm. This algorithm fundamentally realizes the selection of comparatively small attribute sets with the presupposition that the data classification ability is not changed. It is valid after being tested.
Explaining the Emergence of Private Standards in Food Supply Chains

This paper provides an original theoretical framework to better understand the raise of private standards in agrifood chains. Reasons for the emergence and conditions for the effectiveness of private standards are identified, by investigating retailers' strategic behaviour and, more precisely, both interactions among retailers and upstream producers and the role of consumer behaviour vis-avis the food safety risk. We show that a relatively strict Minimum Quality Standard (MQS) may incentive the retailer to develop an even more demanding private standard, when market-driven incentive is relatively high; this result crucially depends on consumer risk misperception. Setting a private standard may improve market access for upstream producers. In addition, it may reduce food safety risk and, at the same time, improve consumer surplus.