We introduce a new amortized likelihood ratio estimator for likelihood-free simulation-based inference (SBI). Our estimator is simple to train and estimates the likelihood ratio using a single forward pass of the neural estimator. Our approach directly computes the likelihood ratio between two competing parameter sets which is different from the previous approach of comparing two neural network output values. We refer to our model as the direct neural ratio estimator (DNRE). As part of introducing the DNRE, we derive a corresponding Monte Carlo estimate of the posterior. We benchmark our new ratio estimator and compare to previous ratio estimators in the literature. We show that our new ratio estimator often outperforms these previous approaches. As a further contribution, we introduce a new derivative estimator for likelihood ratio estimators that enables us to compare likelihood-free Hamiltonian Monte Carlo (HMC) with random-walk Metropolis-Hastings (MH). We show that HMC is equally competitive, which has not been previously shown. Finally, we include a novel real-world application of SBI by using our neural ratio estimator to design a quadcopter. Code is available at https://github.com/SRI-CSL/dnre.

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains $27{,}714$ diverse air vehicle designs - the largest corpus of designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design, and more generally, in the emerging field of deep learning for scientific design. AircraftVerse is accompanied by a datasheet as suggested in the recent literature, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset with baseline models are hosted at http://doi.org/10.5281/zenodo.6525446, code at https://github.com/SRI-CSL/AircraftVerse, and the dataset description at https://uavdesignverse.onrender.com/.

KB_Bio_101 contains knowledge about processes and mechanisms, and was created from an introductory textbook in biology. We give an overview of its content, summarize the key concepts represented, and give some examples of problems requiring further ontology research.

Speaker adaptation by means of adjustment of speaker characteristic properties, such as vocal tract length, has the important advantage compared to conventional adaptation techniques that the adapted models are guaranteed to be realistic if the description of the properties are. One problem with this approach is that the search procedure to estimate them is computationally heavy. We address the problem by using a multi-dimensional, hierarchical tree of acoustic model sets. The leaf sets are created by transforming a conventionally trained model set using leaf-specific speaker profile vectors. The model sets of non-leaf nodes are formed by merging the models of their child nodes, using a computationally efficient algorithm. During recognition, a maximum likelihood criterion is followed to traverse the tree. Studies of one- (VTLN) and four-dimensional speaker profile vectors (VTLN, two spectral slope parameters and model variance scaling) exhibit a reduction of the computational load to a fraction compared to that of an exhaustive grid search. In recognition experiments on children's connected digits using adult and male models, the one-dimensional tree search performed as well as the exhaustive search. Further reduction was achieved with four dimensions. The best recognition results are 0.93% and 10.2% WER in TIDIGITS and PF-Star-Sw, respectively, using adult models.

This paper describes the corpus of recordings of children's speech which was collected as part of the EU FP5 PF STAR project.The corpus contains more than 60 hours of speech, including read and imitated native-language speech in British English, German and Swedish, read and imitated non-nativelanguage English speech from German, Italian and Swedish children, and native-language spontaneous and emotional speech in English and German. 1 This work was conducted as part of EU FP5 PF STAR (Preparing Future Multisensorial Interaction Research)

An experimental offline investigation of the performance of connected digits recognition was performed on children in the age range four to eight years. Poor performance using adult models was improved significantly by adaptation and vocal tract length normalisation but not to the same level as training on children. Age dependent models were tried with limited advantage. A combined adult and child training corpus maintained the performance for the separately trained categories. Linear frequency compression for vocal tract length normalization was attempted but estimation of the warping factor was sensitive to non-speech segments and background noise. Phoneme-based word modeling outperformed the whole word models, even though the vocabulary only consisted of digits. 1.

normally determined by the false reject (FRR) and false accept (FAR) rates as averages on a population of test speakers.