Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving music auto-tagging by intra-song instance bagging

Bagging is one the most classic ensemble learning techniques in the machine learning literature. The idea is to generate multiple subsets of the training data via bootstrapping (random sampling with replacement), and then aggregate the output of the models trained from each subset via voting or averaging. As music is a temporal signal, we propose and study two bagging methods in this paper: the inter-song instance bagging that bootstraps song-level features, and the intra-song instance bagging that draws bootstrapping samples directly from short-time features for each training song. In particular, we focus on the latter method, as it better exploits the temporal information of music signals. The bagging methods result in surprisingly effective models for music auto-tagging: incorporating the idea to a simple linear support vector machine (SVM) based system yields accuracies that are comparable or even superior to state-of-the-art, possibly more sophisticated methods for three different datasets. As the bagging method is a meta algorithm, it holds the promise of improving other MIR systems.
Cerebral blood flow in the ventromedial prefrontal cortex correlates with treatment response to low‐frequency right prefrontal repetitive transcranial magnetic stimulation in the treatment of depression

Aims: Low‐frequency right prefrontal repetitive transcranial magnetic stimulation (rTMS) is effective in treating depression, and its antidepressant effects have proven to correlate with decreases in cerebral blood flow (CBF) in the orbitofrontal cortex and subgenual cingulate cortex. However, a predictor of treatment response to low‐frequency right prefrontal rTMS in depression has not been identified yet. The aim of this study was to estimate regional CBF in the frontal regions and investigate the correlation with treatment response to low‐frequency right prefrontal rTMS in depression.
Residential Satisfaction in Housing Estates in European Cities: A Multi-level Research Approach

This paper mainly focuses on the determinants of housing and estate satisfaction in post-Second World War housing estates. Multi-level linear regression models were applied to estimate the impacts of individual, dwelling and estate characteristics on resident satisfaction levels, using a unique dataset from 25 post-Second World War estates in nine European countries. It is concluded that satisfaction with the dwelling is higher for the elderly and residents with higher incomes, and in situations where the dwelling has been renovated and is sufficiently large. The presence of children and a longer duration of stay have negative impacts, and renters are generally more negative than owners. Estate satisfaction is highest among immigrant households or when the dwelling is renovated, and among lower educated inhabitants. Individual characteristics and opinions on the estate are more important than estate characteristics in explaining estate satisfaction. The overall conclusion is that attempts to improve post-Second World War areas may result in new areas, but does not necessarily improve the overall satisfaction.
Fast retransmission mechanism for VoIP in IEEE 802.11e wireless LANs

IEEE 802.11 wireless LANs provide a cheap and high data rate mobile service. Voice service through wireless LANs has became an important issue. The new IEEE 802.11e standard provides a framework to support quality of service (QoS) according to traffic type. The enhanced distributed channel access (EDCA) mechanism, which is contention-based access, has several access parameters which are able to give a higher access probability to the high priority service. Since a real time service, such as voice over IP (VoIP), has a delay constraint to guarantee QoS, fast collision resolution is required. We review the IEEE 802.11e standard and propose a new fast retransmission mechanism for VoIP packets to reduce the delay and improve the channel utilization by eliminating the overhead time for backoff procedures. Simulation results show that the proposed scheme reduces the delay and dropping probability of VoIP packets.
Pass-Band Optimal Reconstruction on the Body-Centered Cubic Lattice

In this paper, a pass-band optimal reconstruction technique is adapted to the Body-Centered Cubic (BCC) lattice. In order to perform the frequencydomain preprocessing, we derive a practical Discrete Fourier Transform (DFT) for BCC-sampled data. In the discrete frequency domain our DFT provides a natural isotropic periodicity on a FaceCentered Cubic (FCC) pattern, unlike the previous method, which leads to periodicity on a sheared Cartesian pattern. One of the most important advantages of our approach is that no specialized FFT implementation is required, as basically the well-known traditional FFT libraries can be directly used for calculating the discrete frequency coefficients. Furthermore, the proposed DFT can be easily adapted to FCC-sampled data as well. We show that a prefiltered pass-band optimal reconstruction based on our DFT can capture the high-frequency details much better than the previously proposed generalized interpolation method.
What’s in the E YES for Attentive Input

limited to the user’s explicit actions through the mouse and the keyboard. These input channels are rather impoverished in comparison to the increasingly rich multimedia output of computer systems, resulting in the asymmetrical bandwidth problem [3]. In such systems a single mousedriven cursor conveys the user’s attention, goal, action, and perhaps, even his or her frustration. This situation is likely to change in future Attentive User Interfaces (AUIs) with new channels of computer input based on computer vision and other technologies. AUIs may sense and track the user’s presence, movement, body posture, head direction, and other environmental information (See the sidebar “How to Track What People Do”). A particularly rich source of information for AUIs is the user’s visual attention as indicated by his or her eye gaze. Our hypothesis is that where the user looks contextualizes much of that user’s action and hence can be applied to facilitate human-computer interaction (HCI). By Shumin Zhai
Knowledge Management in Distributed Agile Software Development

Software development teams need highly valuable knowledge to carry out knowledge-intensive development activities. Agile teams are cross-functional teams that promote sharing of project-specific knowledge through frequent faceto-face interaction, effective communication and customer collaboration. Knowledge sharing is difficult for distributed Agile teams due to spatial, temporal, and cultural barriers, which negatively affect face-to-face interaction, communication and collaboration. There seems to be very few studies that focus on knowledge management in distributed Agile teams. Through a Grounded Theory study that involved 45 participants from 28 different software companies in the USA, India and Australia, we investigate distributed software development from the specific perspective of Agile teams. In this paper, we describe how Agile teams gather, store, share and use knowledge in distributed software development.
Estimating spatial correlations of the channel elements of a multiple input multiple output system based on outdoor to indoor channel measurements

At high SNR, strong correlations in the elements of the MIMO channel can lead to a decrease in channel capacity. In this paper we propose a new methodology to estimate these spatial correlations based on the analysis of the most significant eigenvalues of transmit and receive covariance matrices of the MIMO channel. Our study is based on measurements performed in a university campus with the following characteristics: (4 × 4) MIMO single user channels with non line of sight, outdoor to indoor and wideband for fixed wireless applications. These type of scenarios are of interest because they can be used to deploy high speed wireless internet applications.
Distributional measures of concept-distance: A task-oriented evaluation

We propose a framework to derive the distance between concepts from distributional measures of word co-occurrences. We use the categories in a published thesaurus as coarse-grained concepts, allowing all possible distance values to be stored in a concept--concept matrix roughly . 01% the size of that created by existing measures. We show that the newly proposed concept-distance measures outperform traditional distributional word-distance measures in the tasks of (1) ranking word pairs in order of semantic distance, and (2) correcting real-word spelling errors. In the latter task, of all the WordNet-based measures, only that proposed by Jiang and Conrath outperforms the best distributional concept-distance measures.
The role of mentoring and project characteristics for onboarding in open source software projects

Context: Onboarding is a process that helps newcomers become integrated members of their organisation. Successful onboarding programs can result in increased performance in conventional organisations, but there is little guidance on how to onboard new developers in Open Source Software (OSS) projects. Goal: In this study, we examine how mentoring and project characteristics influence the effectiveness and efficiency of the onboarding process. We study a collaboration program involving a total of nine Open Source Software projects and more than 120 students from different universities around the world as part of Facebook's Education Modernization Program. Method: We use quantitative measurements of source code repositories, issue tracking systems, and discussion fora to examine how newcomers become contributing members of their OSS projects. Results: We found that developers receiving deliberate onboarding support through mentoring were more active at an earlier stage than developers entering projects through conventional means. Also, we found that project size and lifetime influenced onboarding. Conclusion: Empirical decision support can contribute to a more effective onboarding process in OSS projects. Mentor support in critical stages can accelerate the process, but project maturity is also a significant factor that increases the effect of onboarding.
Glycemic Variability and Oxidative Stress: A Link between Diabetes and Cardiovascular Disease?

Diabetes is associated with a two to three-fold increase in risk of cardiovascular disease. However, intensive glucose-lowering therapy aiming at reducing HbA1c to a near-normal level failed to suppress cardiovascular events in recent randomized controlled trials. HbA1c reflects average glucose level rather than glycemic variability. In in vivo and in vitro studies, glycemic variability has been shown to be associated with greater reactive oxygen species production and vascular damage, compared to chronic hyperglycemia. These findings suggest that management of glycemic variability may reduce cardiovascular disease in patients with diabetes; however, clinical studies have shown conflicting results. This review summarizes the current knowledge on glycemic variability and oxidative stress, and discusses the clinical implications.
A Material Model for Prediction of Fatigue Damage and Degradation of CFRP Materials

Objective of the present study is the definition of a material model accounting for fatigue damage and degradation. The model is formulated as a brittle damage model in the otherwise linear elastic framework. A stress driven damage evolution equation is derived from microplasticity considerations. The model is implemented as a user-defined material model into a commercial finite element program. In a comparison with experimental data in the low cycle fatigue regime, a good agreement with the numerical prediction is obtained.
Designing a task for SENSEVAL-2

This document provides guidelines for setting up and runnin g a task for SENSEVAL-2. These guidelines are based on experience gathered from SENSEVAL-1, which involved ind ividually organized tasks in English (Kilgarriff and Rosenzweig 2000), French (Segond 2000), and Italian (Calzo lari and Corazzari 2000), and on the evaluation protocol proposed by Resnik and Yarowsky (1999). Although Resnik and Yarowsky’s proposal assumes that a very large corpus is available and that the same type of evaluation is run repea t dly over several years, we suggest that a variation of it be adopted, as outlined below. As these are merely guideline s, f your circumstances require you to adopt a different task design, we still encourage you to participate in SENSEV AL-2. The following steps are involved in designing/organizing a SENSEVAL-2 task:
Dimension Reduction Techniques for Distributional Symbolic Data

In the framework of symbolic data analysis (SDA), distribution-valued data are defined as multivalued data, where each unit is described by a distribution (e.g., a histogram, a density, or a quantile function) of a quantitative variable. SDA provides different methods for analyzing multivalued data. Among them, the most relevant techniques proposed for a dimensional reduction of multivalued quantitative variables is principal component analysis (PCA). This paper gives a contribution in this context of analysis. Starting from new association measures for distributional variables based on a peculiar metric for distributions, the squared Wasserstein distance, a PCA approach is proposed for distribution-valued data, represented by quantile-variables. An application of the proposed PCA method, performed on simulated distribution-valued data, shows interesting interpretative results in terms of location, variability, and shape of the distributions on the factorial planes.
EarFieldSensing: A Novel In-Ear Electric Field Sensing to Enrich Wearable Gesture Input through Facial Expressions

EarFieldSensing (EarFS) is a novel input method for mobile and wearable computing using facial expressions. Facial muscle movements induce both electric field changes and physical deformations, which are detectable with electrodes placed inside the ear canal. The chosen ear-plug form factor is rather unobtrusive and allows for facial gesture recognition while utilizing the close proximity to the face. We collected 25 facial-related gestures and used them to compare the performance levels of several electric sensing technologies (EMG, CS, EFS, EarFS) with varying electrode setups. Our developed wearable fine-tuned electric field sensing employs differential amplification to effectively cancel out environmental noise while still being sensitive towards small facial-movement-related electric field changes and artifacts from ear canal deformations. By comparing a mobile with a stationary scenario, we found that EarFS continues to perform better in a mobile scenario. Quantitative results show EarFS to be capable of detecting a set of 5 facial gestures with a precision of 90% while sitting and 85.2% while walking. We provide detailed instructions to enable replication of our low-cost sensing device. Applying it to different positions of our body will also allow to sense a variety of other gestures and activities.
Randomized algorithm based entropy norm computation in hardware for anomaly detection of IP data streams

Entropy measurements have been used for anomaly detection purposes in IP data streams. Randomized algorithms with efficient storage requirements for estimating entropy of data streams are now available in the literature. The main challenge in software implementation for IP data streams has been in storing large volumes of data, along with, the requirement of analysis at high speed. Although hardware architectures for executing such algorithms would be several orders of magnitude faster than software, no attempts to explore hardware for this purpose have been made so far. In this paper, a randomized algorithm available in literature is evaluated for hardware implementation. First the chosen randomized algorithm is improved to obtain a 30.0% reduction in memory requirements. Next two hardware architectural designs are described and analyzed for possible FPGA implementation. The corresponding trade-offs, limitations and resource requirements are discussed.
Improved Envisat Altimetry Ice Sheet Elevation Change Data Processing Algorithms Using Repeat-Track Analysis

Repeat-track analysis is commonly utilized to generate elevation change time series from satellite radar altimetry over ice sheets. It requires surface gradient (SG) correction due primarily to orbital drifts and radar-related empirical corrections caused by radar scatters from ice surface and potential subsurface. In this letter, two approaches, namely, the use of a digital elevation model (DEM) and the modified repeat-track analysis, which uses the accumulated Envisat altimetry profiles, are applied to correct the SG over both Greenland ice sheet (GrIS) and Antarctic ice sheet (AIS). By comparing the root mean square (rms) of elevation change time series after SG correction, the percentage of data (rms<; 1 m) obtained by using modified repeat-track analysis is found to be 85% and 88% for the GrIS and AIS, respectively, as opposed to 45% and 44% if the DEM method is used. Furthermore, three cases are studied to assess empirical corrections for elevation retrieved from both ice-1 and ice-2 algorithms over the AIS. We conclude that the modified repeat-track analysis is more effective to remove topographic induced error. For the ice-2 algorithm, waveform shape parameters are needed in addition to applying corrections from changes in backscatter coefficients. The trend of elevation changes from the ice-1 algorithm with only backscatter analysis agrees with that from the ice-2 algorithm with corrections from backscatter coefficient changes and waveform shape parameters. This study could provide a potential data processing recipe for generating improved satellite radar altimetry elevation time series over ice sheets.
Changes in Corporate Governance of German Corporations: Convergence to the Anglo-American Model?

This paper examines the many changes which have transformed the German system of corporate governance during the last seven odd years. It concludes that it is in the process of converging towards the Anglo-American model and that this has fundamentally affected the way strategic decisions are made in firms. Convergence is not seen as a functional necessity, nor is it viewed as inevitable. The paper offers both a theoretical exploration of institutional and system transformation and an empirical study which substantiates the theoretical position taken with evidence about recent trends in capital markets, banks, government and firms. Empirical evidence from the pharmaceutical/chemical industry is supplemented by data on firms in other sectors, including the financial sector. The theoretical examination of institutional change focuses on the notions of system logic, institutional complementarity, functional conversion and hybridisation. It examines both external sources of change and internal powerful actors who promote the process of transformation. The notions of hybridisation of the German business system, as well as claims about functional conversion and the evolution of a new complementarity between institutions, are rejected in favour of a trend towards convergence. The transformation in capital markets and the rise to dominance of the notion of shareholder value is particularly affecting large international and quoted firms, but is gradually spreading also to other parts of the economy. This transformation is affecting labour and industrial relations in negative ways, as well as posing a threat to the German production model of diversified quality production.
EWS-FLI-1-Targeted Cytotoxic T-cell Killing of Multiple Tumor Types Belonging to the Ewing Sarcoma Family of Tumors

Purpose: The Ewing sarcoma family of tumors (ESFT) comprises a group of aggressive, malignant bone, and soft tissue tumors that predominantly affect children and young adults. These tumors frequently share expression of the EWS-FLI-1 translocation, which is central to tumor survival but not present in healthy cells. In this study, we examined EWS-FLI-1 antigens for their capacity to induce immunity against a range of ESFT types. Design: Computer prediction analysis of peptide binding, HLA-A2.1 stabilization assays, and induction of cytotoxic T-lymphocytes (CTL) in immunized HLA-A2.1 transgenic mice were used to assess the immunogenicity of native and modified peptides derived from the fusion region of EWS-FLI-1 type 1. CTL-killing of multiple ESFT family members in vitro, and control of established xenografts in vivo, was assessed. We also examined whether these peptides could induce human CTLs in vitro. Results: EWS-FLI-1 type 1 peptides were unable to stabilize cell surface HLA-A2.1 and induced weak CTL activity against Ewing sarcoma cells. In contrast, peptides with modified anchor residues induced potent CTL killing of Ewing sarcoma cells presenting endogenous (native) peptides. The adoptive transfer of CTL specific for the modified peptide YLNPSVDSV resulted in enhanced survival of mice with established Ewing sarcoma xenografts. YLNPSVDSV-specific CTL displayed potent killing of multiple ESFT types in vitro: Ewing sarcoma, pPNET, Askin's Tumor, and Biphenotypic sarcoma. Stimulation of human peripheral blood mononuclear cells with YLNPSVDSV peptide resulted in potent CTL-killing. Conclusions: These data show that YLNPSVDSV peptide is a promising antigen for ESFT immunotherapy and warrants further clinical development. Clin Cancer Res; 18(19); 5341–51. ©2012 AACR.
Evolution of the Cluster Correlation Function

We study the evolution of the cluster correlation function and its richness dependence from z = 0 to z = 3 using large-scale cosmological simulations. A standard flat LCDM model with Ωm = 0.3 and, for comparison, a tilted Ωm = 1 model (TSCDM) are used. The evolutionary predictions are presented in a format suitable for direct comparisons with observations. We find that the cluster correlation strength increases with redshift: high-redshift clusters are clustered more strongly (on a comoving scale) than low-redshift clusters of the same mass. The increased correlation with redshift, in spite of the decreasing mass correlation strength, is caused by the strong increase in cluster bias with redshift: clusters represent higher density peaks of the mass distribution as the redshift increases. The richness-dependent cluster correlation function, presented as the correlation scale versus cluster mean separation relation, R0-d, is found to be, remarkably, independent of redshift to z ≲ 2 for LCDM and z ≲ 1 for TSCDM for a fixed correlation function slope and a cluster mass within a fixed comoving radius. The nonevolving R0-d relation implies that both the comoving clustering scale and the cluster mean separation increase with redshift for the same mass clusters, so that the R0-d relation remains essentially unchanged. For LCDM, this relation is R0(z) ≃ 2.6 1/2 for z ≲ 2 in comoving h-1 Mpc scales. The TSCDM model has smaller correlation scales, as expected. Evolution in the relation is seen at z ≳ 2 for LCDM and z ≳ 1 for TSCDM, where the amplitude of the relations declines. The evolution of the R0-d relation from z ~ 0 to z ~ 3 provides an important new tool in cosmology; it can be used to break degeneracies that exist at z ~ 0 and provide precise determination of cosmological parameters.