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Previous research contributions on blind lossy compression identification report near perfect performance metrics on their test set, across a variety of codecs and bit rates. However, we show that such results can be deceptive and may not accurately represent true ability of the system to tackle the task at hand. In this article, we present an investigation into the robustness and generalisation capability of a lossy audio identification model. Our contributions are as follows. (1) We show the lack of robustness to codec parameter variations of a model equivalent to prior art. In particular, when naively training a lossy compression detection model on a dataset of music recordings processed with a range of codecs and their lossless counterparts, we obtain near perfect performance metrics on the held-out test set, but severely degraded performance on lossy tracks produced with codec parameters not seen in training. (2) We propose and show the effectiveness of an improved training strategy to significantly increase the robustness and generalisation capability of the model beyond codec configurations seen during training. Namely we apply a random mask to the input spectrogram to encourage the model not to rely solely on the training set's codec cutoff frequency., Comment: Accepted to be published in the Proceedings of the 25th International Society for Music Information Retrieval Conference 2024

We consider the verification of neural network policies for reach-avoid control tasks in stochastic dynamical systems. We use a verification procedure that trains another neural network, which acts as a certificate proving that the policy satisfies the task. For reach-avoid tasks, it suffices to show that this certificate network is a reach-avoid supermartingale (RASM). As our main contribution, we significantly accelerate algorithmic approaches for verifying that a neural network is indeed a RASM. The main bottleneck of these approaches is the discretization of the state space of the dynamical system. The following two key contributions allow us to use a coarser discretization than existing approaches. First, we present a novel and fast method to compute tight upper bounds on Lipschitz constants of neural networks based on weighted norms. We further improve these bounds on Lipschitz constants based on the characteristics of the certificate network. Second, we integrate an efficient local refinement scheme that dynamically refines the state space discretization where necessary. Our empirical evaluation shows the effectiveness of our approach for verifying neural network policies in several benchmarks and trained with different reinforcement learning algorithms.

We present an A*-based algorithm to compute policies for finite-horizon Dec-POMDPs. Our goal is to sacrifice optimality in favor of scalability for larger horizons. The main ingredients of our approach are (1) using clustered sliding window memory, (2) pruning the A* search tree, and (3) using novel A* heuristics. Our experiments show competitive performance to the state-of-the-art. Moreover, for multiple benchmarks, we achieve superior performance. In addition, we provide an A* algorithm that finds upper bounds for the optimum, tailored towards problems with long horizons. The main ingredient is a new heuristic that periodically reveals the state, thereby limiting the number of reachable beliefs. Our experiments demonstrate the efficacy and scalability of the approach., Comment: 19 pages, 3 figures. Extended version (with appendix) of the paper to appear in IJCAI 2024

Creating a trailer requires carefully picking out and piecing together brief enticing moments out of a longer video, making it a challenging and time-consuming task. This requires selecting moments based on both visual and dialogue information. We introduce a multi-modal method for predicting the trailerness to assist editors in selecting trailer-worthy moments from long-form videos. We present results on a newly introduced soap opera dataset, demonstrating that predicting trailerness is a challenging task that benefits from multi-modal information. Code is available at https://github.com/carlobretti/cliffhanger, Comment: MMM24

Computational harmony analysis is important for MIR tasks such as automatic segmentation, corpus analysis and automatic chord label estimation. However, recent research into the ambiguous nature of musical harmony, causing limited inter-rater agreement, has made apparent that there is a glass ceiling for common metrics such as accuracy. Commonly, these issues are addressed either in the training data itself by creating majority-rule annotations or during the training phase by learning soft targets. We propose a novel alternative approach in which a human and an autoregressive model together co-create a harmonic annotation for an audio track. After automatically generating harmony predictions, a human sparsely annotates parts with low model confidence and the model then adjusts its predictions following human guidance. We evaluate our model on a dataset of popular music and we show that, with this human-in-the-loop approach, harmonic analysis performance improves over a model-only approach. The human contribution is amplified by the second, constrained prediction of the model., Comment: Accepted at MMRP23. 7 pages, 5 figures, 2 tables

In this paper, we explore the application of language and speech technology to open-ended questions in a Dutch panel survey. In an experimental wave respondents could choose to answer open questions via speech or keyboard. Automatic speech recognition (ASR) was used to process spoken responses. We evaluated answers from these input modalities to investigate differences between spoken and typed answers.We report the errors the ASR system produces and investigate the impact of these errors on downstream analyses. Open-ended questions give more freedom to answer for respondents, but entail a non-trivial amount of work to analyse. We evaluated the feasibility of using transformer-based models (e.g. BERT) to apply sentiment analysis and topic modelling on the answers of open questions. A big advantage of transformer-based models is that they are trained on a large amount of language materials and do not necessarily need training on the target materials. This is especially advantageous for survey data, which does not contain a lot of text materials. We tested the quality of automatic sentiment analysis by comparing automatic labeling with three human raters and tested the robustness of topic modelling by comparing the generated models based on automatic and manually transcribed spoken answers., Comment: 7 pages

Multiclass multilabel classification is the task of attributing multiple labels to examples via predictions. Current models formulate a reduction of the multilabel setting into either multiple binary classifications or multiclass classification, allowing for the use of existing loss functions (sigmoid, cross-entropy, logistic, etc.). Multilabel classification reductions do not accommodate for the prediction of varying numbers of labels per example and the underlying losses are distant estimates of the performance metrics. We propose a loss function, sigmoidF1, which is an approximation of the F1 score that (1) is smooth and tractable for stochastic gradient descent, (2) naturally approximates a multilabel metric, and (3) estimates label propensities and label counts. We show that any confusion matrix metric can be formulated with a smooth surrogate. We evaluate the proposed loss function on text and image datasets, and with a variety of metrics, to account for the complexity of multilabel classification evaluation. sigmoidF1 outperforms other loss functions on one text and two image datasets and several metrics. These results show the effectiveness of using inference-time metrics as loss functions for non-trivial classification problems like multilabel classification., Comment: Published at TMLR

Machine learning is challenging the way we make music. Although research in deep generative models has dramatically improved the capability and fluency of music models, recent work has shown that it can be challenging for humans to partner with this new class of algorithms. In this paper, we present findings on what 13 musician/developer teams, a total of 61 users, needed when co-creating a song with AI, the challenges they faced, and how they leveraged and repurposed existing characteristics of AI to overcome some of these challenges. Many teams adopted modular approaches, such as independently running multiple smaller models that align with the musical building blocks of a song, before re-combining their results. As ML models are not easily steerable, teams also generated massive numbers of samples and curated them post-hoc, or used a range of strategies to direct the generation, or algorithmically ranked the samples. Ultimately, teams not only had to manage the "flare and focus" aspects of the creative process, but also juggle them with a parallel process of exploring and curating multiple ML models and outputs. These findings reflect a need to design machine learning-powered music interfaces that are more decomposable, steerable, interpretable, and adaptive, which in return will enable artists to more effectively explore how AI can extend their personal expression., Comment: 6 pages + 3 pages of references

Context. The star formation rates and stellar densities found in young massive clusters suggest that these stellar systems originate from gas at densities n(H$_2$) $>10^7$ cm$^{-3}$. Until today, however, the physical characterization of this ultra high density material remains largely unconstrained in observations. Aims. We investigated the density properties of the star-forming gas in the OMC-1 region located in the vicinity of the Orion Nebula Cluster (ONC). Methods. We mapped the molecular emission at 652 GHz in OMC-1 as part of the APEX-SEPIA660 Early Science. Results. We detect bright and extended N$_2$H$^+$ (J=7-6) line emission along the entire OMC-1 region. Comparisons with previous ALMA data of the (J=1-0) transition and radiative transfer models indicate that the line intensities observed in this N$_2$H$^+$ (7-6) line are produced by large mass reservoirs of gas at densities n(H$_2$) $>10^7$ cm$^{-3}$. Conclusions. The first detection of this N$_2$H$^+$ (7-6) line at parsec-scales demonstrates the extreme density conditions of the star-forming gas in young massive clusters such as the ONC. Our results highlight the unique combination of sensitivity and mapping capabilities of the new SEPIA660 receiver for the study of the ISM properties at high frequencies., Comment: 8 pages, 5 Figures. Accepted for publication by A&A

Automatic Chord Estimation (ACE) is a fundamental task in Music Information Retrieval (MIR) and has applications in both music performance and MIR research. The task consists of segmenting a music recording or score and assigning a chord label to each segment. Although it has been a task in the annual benchmarking evaluation MIREX for over 10 years, ACE is not yet a solved problem, since performance has stagnated and modern systems have started to tune themselves to subjective training data. We propose DECIBEL, a new ACE system that exploits widely available MIDI and tab representations to improve ACE from audio only. From an audio file and a set of MIDI and tab files corresponding to the same popular music song, DECIBEL first estimates chord sequences. For audio, state-of-the-art audio ACE methods are used. MIDI files are aligned to the audio, followed by a MIDI chord estimation step. Tab files are transformed into untimed chord sequences and then aligned to the audio. Next, DECIBEL uses data fusion to integrate all estimated chord sequences into one final output sequence. DECIBEL improves all tested state-of-the-art ACE methods by over 3 percent on average. This result shows that the integration of musical knowledge from heterogeneous symbolic music representations is a suitable strategy for addressing challenging MIR tasks such as ACE., Comment: 81 pages, 47 figures

The installation process of offshore wind turbines requires the use of expensive jack-up vessels. These vessels regularly report their position via the Automatic Identification System (AIS). This paper introduces a novel approach of applying machine learning to AIS data from jack-up vessels. We apply the new method to 13 offshore wind farms in Danish, German and British waters. For each of the wind farms we identify individual turbine locations, individual installation times, time in transit and time in harbor for the respective vessel. This is done in an automated way exclusively using AIS data with no prior knowledge of turbine locations, thus enabling a detailed description of the entire installation process., Comment: Offshore WindEurope conference 2019

We study the probabilistic distribution of the confirmation time of Bitcoin transactions, conditional on the current memory pool (i.e., the queue of transactions awaiting confirmation). The results of this paper are particularly interesting for users that want to make a Bitcoin transaction during `heavy-traffic situations', when the transaction demand exceeds the block capacity. In such situations, Bitcoin users tend to bid up the transaction fees, in order to gain priority over other users that pay a lower fee. We argue that the time until a Bitcoin transaction is confirmed can be modelled as a particular stochastic fluid queueing process (to be precise: a Cram\'er-Lundberg process). We approximate the queueing process in two different ways. The first approach leads to a lower bound on the confirmation probability, which becomes increasingly tight as traffic decreases. The second approach relies on a diffusion approximation with a continuity correction, which becomes increasingly accurate as traffic intensifies. The accuracy of the approximations under different traffic loads are evaluated in a simulation study.

This paper provides a mathematical framework for estimation of the service time distribution and the expected service time of an infinite-server queueing system with a nonhomogeneous Poisson arrival process, in the case of partial information, where only the number of busy servers are observed over time. The problem is reduced to a statistical deconvolution problem, which is solved by using Laplace transform techniques and kernels for regularization. Upper bounds on the mean squared error of the proposed estimators are derived. Some concrete simulation experiments are performed to illustrate how the method can be applied and to provide some insight in the practical performance.

III-V semiconductor nanowires have shown great potential in various quantum transport experiments. However, realizing a scalable high-quality nanowire-based platform that could lead to quantum information applications has been challenging. Here, we study the potential of selective area growth by molecular beam epitaxy of InAs nanowire networks grown on GaAs-based buffer layers. The buffered geometry allows for substantial elastic strain relaxation and a strong enhancement of field effect mobility. We show that the networks possess strong spin-orbit interaction and long phase coherence lengths with a temperature dependence indicating ballistic transport. With these findings, and the compatibility of the growth method with hybrid epitaxy, we conclude that the material platform fulfills the requirements for a wide range of quantum experiments and applications.

Context: We describe the new SEPIA (Swedish-ESO PI Instrument for APEX) receiver, which was designed and built by the Group for Advanced Receiver Development (GARD), at Onsala Space Observatory (OSO) in collaboration with ESO. It was installed and commissioned at the APEX telescope during 2015 with an ALMA Band 5 receiver channel and updated with a new frequency channel (ALMA Band 9) in February 2016. Aims: This manuscript aims to provide, for observers who use the SEPIA receiver, a reference in terms of the hardware description, optics and performance as well as the commissioning results. Methods: Out of three available receiver cartridge positions in SEPIA, the two current frequency channels, corresponding to ALMA Band 5, the RF band 158--211 GHz, and Band 9, the RF band 600--722 GHz, provide state-of-the-art dual polarization receivers. The Band 5 frequency channel uses 2SB SIS mixers with an average SSB noise temperature around 45K with IF (intermediate frequency) band 4--8 GHz for each sideband providing total 4x4 GHz IF band. The Band 9 frequency channel uses DSB SIS mixers with a noise temperature of 75--125K with IF band 4--12 GHz for each polarization. Results: Both current SEPIA receiver channels are available to all APEX observers., Comment: Accepted for publication in Astronomy & Astrophysics

In this paper we study the number of customers in infinite-server queues with a self-exciting (Hawkes) arrival process. Initially we assume that service requirements are exponentially distributed and that the Hawkes arrival process is of a Markovian nature. We obtain a system of differential equations that characterizes the joint distribution of the arrival intensity and the number of customers. Moreover, we provide a recursive procedure that explicitly identifies (transient and stationary) moments. Subsequently, we allow for non-Markovian Hawkes arrival processes and non-exponential service times. By viewing the Hawkes process as a branching process, we find that the probability generating function of the number of customers in the system can be expressed in terms of the solution of a fixed-point equation. We also include various asymptotic results: we derive the tail of the distribution of the number of customers for the case that the intensity jumps of the Hawkes process are heavy-tailed, and we consider a heavy-traffic regime. We conclude the paper by discussing how our results can be used computationally and by verifying the numerical results via simulations., Comment: v3: some minor corrections

The increasing accuracy of automatic chord estimation systems, the availability of vast amounts of heterogeneous reference annotations, and insights from annotator subjectivity research make chord label personalization increasingly important. Nevertheless, automatic chord estimation systems are historically exclusively trained and evaluated on a single reference annotation. We introduce a first approach to automatic chord label personalization by modeling subjectivity through deep learning of a harmonic interval-based chord label representation. After integrating these representations from multiple annotators, we can accurately personalize chord labels for individual annotators from a single model and the annotators' chord label vocabulary. Furthermore, we show that chord personalization using multiple reference annotations outperforms using a single reference annotation., Comment: Proceedings of the First International Conference on Deep Learning and Music, Anchorage, US, May, 2017 (arXiv:1706.08675v1 [cs.NE])

We consider a linear stochastic fluid network under Markov modulation, with a focus on the probability that the joint storage level attains a value in a rare set at a given point in time. The main objective is to develop efficient importance sampling algorithms with provable performance guarantees. For linear stochastic fluid networks without modulation, we prove that the number of runs needed (so as to obtain an estimate with a given precision) increases polynomially (whereas the probability under consideration decays essentially exponentially); for networks operating in the slow modulation regime, our algorithm is asymptotically efficient. Our techniques are in the tradition of the rare-event simulation procedures that were developed for the sample-mean of i.i.d. one-dimensional light-tailed random variables, and intensively use the idea of exponential twisting. In passing, we also point out how to set up a recursion to evaluate the (transient and stationary) moments of the joint storage level in Markov-modulated linear stochastic fluid networks.

We study the propagation of light bundles in non-empty spacetime, as most of the Universe is filled by baryonic matter in the form of a (dilute) plasma. Here we restrict to the case of a cold (i.e., pressureless) and non-magnetised plasma. Then the influence of the medium on the light rays is encoded in the spacetime dependent plasma frequency. Our result for a general spacetime generalises the Sachs equations to the case of a cold plasma Universe. We find that the reciprocity law (Etherington theorem), the relation that connects area distance with luminosity distance, is modified. Einstein's field equation is not used, i.e., our results apply independently of whether or not the plasma is self-gravitating. As an example, our findings are applied to a homogeneous plasma in a Robertson-Walker spacetime. We find small modifications of the cosmological redshift of frequencies and of the Hubble law., Comment: minor reformulations; 26 pages, 3 figures

We describe the design, construction, and characterization of the Band 9 heterodyne receivers (600-720 GHz) for the Atacama Large Millimeter / submillimeter Array (ALMA). The ALMA Band 9 receiver units ("cartridges"), which are installed in the telescope's front end, have been designed to detect and down-convert two orthogonal linear polarization components of the light collected by the ALMA antennas. The light entering the front end is refocused with a compact arrangement of mirrors, which is fully contained within the cartridge. The arrangement contains a grid to separate the polarizations and two beam splitters to combine each resulting beam with a local oscillator signal. The combined beams are fed into independent double-sideband mixers, each with a corrugated feedhorn coupling the radiation by way of a waveguide with backshort cavity into an impedance-tuned SIS junction that performs the heterodyne down-conversion. Finally, the generated intermediate frequency signals are amplified by cryogenic and room-temperature HEMT amplifiers and exported to the telescope's back end for further processing and, finally, correlation. The receivers have been constructed and tested in the laboratory and they show excellent performance, complying with ALMA requirements. Performance statistics on all 73 Band 9 receivers are reported. On-sky characterization and tests of the performance of the Band 9 cartridges are presented using commissioning data.

We study infinite-server queues in which the arrival process is a Cox process (or doubly stochastic Poisson process), of which the arrival rate is given by shot noise. A shot-noise rate emerges as a natural model, if the arrival rate tends to display sudden increases (or: shots) at random epochs, after which the rate is inclined to revert to lower values. Exponential decay of the shot noise is assumed, so that the queueing systems are amenable for analysis. In particular, we perform transient analysis on the number of customers in the queue jointly with the value of the driving shot-noise process. Additionally, we derive heavy-traffic asymptotics for the number of customers in the system by using a linear scaling of the shot intensity. First we focus on a one dimensional setting in which there is a single infinite-server queue, which we then extend to a network setting.

In this paper we study the stationary workload distribution of a fluid tandem queue in heavy traffic. We consider different types of L\'evy input, covering compound Poisson, $\alpha$-stable L\'evy motion (with $1<\alpha<2$), and Brownian motion. In our analysis we separately deal with L\'evy input processes with increments that have finite and infinite variance. A distinguishing feature of this paper is that we do not only consider the usual heavy-traffic regime, in which the load at one of the nodes goes to unity, but also a regime in which we simultaneously let the load of both servers tend to one, which, as it turns out, leads to entirely different heavy-traffic asymptotics. Numerical experiments indicate that under specific conditions the resulting simultaneous heavy-traffic approximation significantly outperforms the usual heavy-traffic approximation.

Genetic variation in the tomato clade was explored by sequencing a selection of 84 tomato accessions and related wild species representative for the Lycopersicon, Arcanum, Eriopersicon, and Neolycopersicon groups. We present a reconstruction of three new reference genomes in support of our comparative genome analyses. Sequence diversity in commercial breeding lines appears extremely low, indicating the dramatic genetic erosion of crop tomatoes. This is reflected by the SNP count in wild species which can exceed 10 million i.e. 20 fold higher than in crop accessions. Comparative sequence alignment reveals group, species, and accession specific polymorphisms, which explain characteristic fruit traits and growth habits in tomato accessions. Using gene models from the annotated Heinz reference genome, we observe a bias in dN/dS ratio in fruit and growth diversification genes compared to a random set of genes, which probably is the result of a positive selection. We detected highly divergent segments in wild S. lycopersicum species, and footprints of introgressions in crop accessions originating from a common donor accession. Phylogenetic relationships of fruit diversification and growth specific genes from crop accessions show incomplete resolution and are dependent on the introgression donor. In contrast, whole genome SNP information has sufficient power to resolve the phylogenetic placement of each accession in the four main groups in the Lycopersicon clade using Maximum Likelihood analyses. Phylogenetic relationships appear correlated with habitat and mating type and point to the occurrence of geographical races within these groups and thus are of practical importance for introgressive hybridization breeding. Our study illustrates the need for multiple reference genomes in support of tomato comparative genomics and Solanum genome evolution studies., Comment: 4 Figure, 10 Supplementary Figures, 2 Supplementary Figures This is the pre-peer reviewed version of the following article: The Plant Journal 80.1 (2014): 136-148, which has been published in final form at http://doi.org/10.1111/tpj.12616

Heat engines provide most of our mechanical power and are essential for transportation on macroscopic scale. However, although significant progress has been made in the miniaturization of electrostatic engines, it has proven difficult to reduce the size of liquid or gas driven heat engines below 10^7 um^3. Here we demonstrate that a crystalline silicon structure operates as a cyclic piezoresistive heat engine when it is driven by a sufficiently high DC current. A 0.34 um^3 engine beam draws heat from the DC current using the piezoresistive effect and converts it into mechanical work by expansion and contraction at different temperatures. This mechanical power drives a silicon resonator of 1.1x10^3 um^3 into sustained oscillation. Even below the oscillation threshold the engine beam continues to amplify the resonator's Brownian motion. When its thermodynamic cycle is inverted, the structure is shown to reduce these thermal fluctuations, therefore operating as a refrigerator., Comment: Updated version after publication to make it almost identical to the Nature Physics article. During the review process the preprint v1 was merged with part of the results from arXiv:0904.3748 (please check this manuscript for more details on the measurements and simulations)

There is a large interest to decrease the size of mechanical oscillators since this can lead to miniaturization of timing and frequency referencing devices, but also because of the potential of small mechanical oscillators as extremely sensitive sensors. Here we show that a single crystal silicon resonator structure spontaneously starts to oscillate when driven by a constant direct current (DC). The mechanical oscillation is sustained by an electrothermomechanical feedback effect in a nanobeam, which operates as a mechanical displacement amplifier. The displacement of the resonator mass is amplified, because it modulates the resistive heating power in the nanobeam via the piezoresistive effect, which results in a temperature variation that causes a thermal expansion feedback-force from the nanobeam on the resonator mass. This self-amplification effect can occur in almost any conducting material, but is particularly effective when the current density and mechanical stress are concentrated in beams of nano-scale dimensions.

We used angle-resolved photoemission spectroscopy to study the shadow Fermi surface in one layer Bi2Sr1.6La0.4CuO6+delta and two layer (Bi,Pb)2Sr2CaCu2O8+delta. We find the shadow band to have the same peakwidth and dispersion as the main band. In addition, the shadow band/main band intensity ratio is found to be binding energy independent. Consequently, it is concluded that the shadow bands in Bi-based HTSC do not originate from antiferromagnetic interactions but have a structural origin., Comment: 10 pages, 2 figures

Friesland College (FC) in the province of Friesland, the Netherlands, provides students with an array of vocational education, including full-time senior secondary vocational education in 3- or 4-year programs; 2-year programs of full-time short courses; and an apprenticeship system, in which students learn a trade at a company while attending the college part-time. The college also offers basic adult education, training for unemployed workers, and in-company training. FC is located in an agricultural province of roughly 600,000 people with the highest unemployment rate in the Netherlands. Other factors affecting the college are decreasing numbers of young people, a shortage of 60,000 people per year nationwide in clerical and technical fields, and a 30-40% college dropout rate. To address these issues and in preparation for new legislation on education and vocational training effective in 1996, the province has undertaken the "Veranderplan," or Change Program. The program is designed to transform FC from one large institution to a group, or "flotilla," of 17 smaller units in in following seven stages: (1) five separate vocational colleges and one apprenticeship college existing at the "point of departure" (1989-90); (2) merger into one college, FC (1990-91); (3) tentative, informal merger between FC, one college for general adult education, eight centers for basic education, and an institute for socio-cultural education (1991-92); (4) formal merger, creating a Regional Education Centre (1992-93); (5) internal merger, establishing 17 individual academic units (1993-94); (6) first evaluation and introduction of total quality management (1994-96); and (7) final evaluation of process and results in time to meet the legislative requirement (January 1, 1996). (PAA)

From measurements of the far-infrared reflectivity at grazing angles of incidence with p-polarized light we determined the c-axis Josephson plasma frequencies of the single layer high T_c cuprates Tl_2Ba_2CuO_6 and La_{2-x}Sr_xCuO_4. We detected a strong plasma resonance at 50 cm^{-1} for La_{2-x}Sr_xCuO_4 in excellent agreement with previously published results. For Tl_2Ba_2CuO_6 we were able to determine an upper limit of the unscreened c-axis Josephson plasma frequency 100 cm^{-1} or a c-axis penetration depth > 15 \mu m. The small value of $\omega_J$ stands in contrast to recent a prediction based on the inter-layer tunneling mechanism of superconductivity., Comment: 4 pages, Phys. Rev. B, in press, Revtex, 4 postscript figures

The purpose of this qualitative case study was to investigate the musical "place" of the family vehicle by describing the music making of nine young children, ages 10 months to 4.5 years, that occurred in vehicles over the course of 9 weeks during which the children were enrolled in a researcher-led early childhood music course. Research questions examined the qualities of children's music making in the car, optimal activities, and comparison of in-car and at-home music spaces. Data included parent journals on music activities that occurred in a vehicle, parent-filmed videos of children's music-making activities both in and out of vehicles, videos of early childhood music classes, researcher field notes of music classes, and exit interviews with parents regarding their perceptions of music-making in the vehicles. The children sang, moved, listened to music, composed, and improvised while in the car, with activities mostly similar to those that occurred inside of the home. The family vehicle provided several advantageous characteristics as compared with the home, including reduced distractions; proximity to siblings, leading to increased sibling interaction at times; and opportunity for parent and child reflection.

Conducted a meta-analytic review of 41 studies to explore the relation between children's aggressive behavior and hostile attribution of intent to peers. Found a significant association between hostile attribution of intent and aggressive behavior, with larger effects associated with more severe aggressive behavior, rejection by peers as a selection criteria, inclusion of 8- to 12-year-olds, and absence of control for intelligence. (JPB)

Ongoing NASA-funded and privately funded studies continue to indicate that an opportunity exists for a second generation supersonic commercial airliner, or High-Speed Civil Transport (HSCT), to become a key part of the 21st century international air transportation system. Long distance air travel is projected to increase at about 5 percent per annum over the next two decades. This projection suggests that by the year 2015, more than 600,000 passengers per day will be traveling long distances, predominantly over water. These routes would be among the most desirable for an HSCT as part of the international air transportation system. Beyond the year 2000, this portion of the air transportation market is projected to be the fastest growing segment.

A study conducted with kindergarten and elementary school children showed that Piagetian tasks which measured seriation, conservation, and multiple classification were equal or superior to traditional intelligence tests in predicting number language, number line comprehension, and verbal arithmetic. (GC)