Search

Neural vocoders often struggle with aliasing in latent feature spaces, caused by time-domain nonlinear operations and resampling layers. Aliasing folds high-frequency components into the low-frequency range, making aliased and original frequency components indistinguishable and introducing two practical issues. First, aliasing complicates the waveform generation process, as the subsequent layers must address these aliasing effects, increasing the computational complexity. Second, it limits extrapolation performance, particularly in handling high fundamental frequencies, which degrades the perceptual quality of generated speech waveforms. This paper demonstrates that 1) time-domain nonlinear operations inevitably introduce aliasing but provide a strong inductive bias for harmonic generation, and 2) time-frequency-domain processing can achieve aliasing-free waveform synthesis but lacks the inductive bias for effective harmonic generation. Building on this insight, we propose Wavehax, an aliasing-free neural WAVEform generator that integrates 2D convolution and a HArmonic prior for reliable Complex Spectrogram estimation. Experimental results show that Wavehax achieves speech quality comparable to existing high-fidelity neural vocoders and exhibits exceptional robustness in scenarios requiring high fundamental frequency extrapolation, where aliasing effects become typically severe. Moreover, Wavehax requires less than 5% of the multiply-accumulate operations and model parameters compared to HiFi-GAN V1, while achieving over four times faster CPU inference speed., Comment: 13 pages, 5 figures, Submitted to IEEE/ACM Trans. ASLP

We have been developing an x-ray imaging system, Multi-Image X-ray Interferometer Module (MIXIM), to achieve a high angular resolution with a compact system size. MIXIM is comprised of a mask with equally-spaced apertures and an x-ray detector. The aperture size and mask-detector distance determine the system's angular resolution. Although a smaller aperture gives a better resolution, the degree of improvement is limited by a diffraction effect. MIXIM circumvents this problem by utilizing the Talbot effect. Our experiment with the previous model equipped with a multi-pinhole mask obtained an angular resolution of 0.5" with a mask-detector distance of 92 cm. A major downside of the multi-pinhole mask is, however, that it has a very low opening fraction, which results in a very low effective area. Here, we newly adopt to MIXIM a multiple coded aperture (MCA) mask, an array of coded aperture patterns. Our proof-of-concept experiment demonstrates that the Talbot effect works even for the MCA mask with a high opening fraction of ~50% at 12.4 keV. Consequently, the new MIXIM realizes about 25 times as large an effective area as that of the previous model, while maintaining a high angular resolution of 0.2" and a compact size of ~1.5 m., Comment: 27 pages, 11 figures, accepted for publication in JATIS

Xtend is one of the two telescopes onboard the X-ray imaging and spectroscopy mission (XRISM), which was launched on September 7th, 2023. Xtend comprises the Soft X-ray Imager (SXI), an X-ray CCD camera, and the X-ray Mirror Assembly (XMA), a thin-foil-nested conically approximated Wolter-I optics. A large field of view of $38^{\prime}\times38^{\prime}$ over the energy range from 0.4 to 13 keV is realized by the combination of the SXI and XMA with a focal length of 5.6 m. The SXI employs four P-channel, back-illuminated type CCDs with a thick depletion layer of 200 $\mu$m. The four CCD chips are arranged in a 2$\times$2 grid and cooled down to $-110$ $^{\circ}$C with a single-stage Stirling cooler. Before the launch of XRISM, we conducted a month-long spacecraft thermal vacuum test. The performance verification of the SXI was successfully carried out in a course of multiple thermal cycles of the spacecraft. About a month after the launch of XRISM, the SXI was carefully activated and the soundness of its functionality was checked by a step-by-step process. Commissioning observations followed the initial operation. We here present pre- and post-launch results verifying the Xtend performance. All the in-orbit performances are consistent with those measured on ground and satisfy the mission requirement. Extensive calibration studies are ongoing., Comment: 10 pages, 8 figures. Proceedings of SPIE Astronomical Telescopes and Instrumentation 2024

XRISM (X-Ray Imaging and Spectroscopy Mission) is an astronomical satellite with the capability of high-resolution spectroscopy with the X-ray microcalorimeter, Resolve, and wide field-of-view imaging with the CCD camera, Xtend. The Xtend consists of the mirror assembly (XMA: X-ray Mirror Assembly) and detector (SXI: Soft X-ray Imager). The components of SXI include CCDs, analog and digital electronics, and a mechanical cooler. After the successful launch on September 6th, 2023 (UT) and subsequent critical operations, the mission instruments were turned on and set up. The CCDs have been kept at the designed operating temperature of $-110^\circ$C ~after the electronics and cooling system were successfully set up. During the initial operation phase, which continued for more than a month after the critical operations, we verified the observation procedure, stability of the cooling system, all the observation options with different imaging areas and/or timing resolutions, and operations for protection against South Atlantic Anomaly. We optimized the operation procedure and observation parameters including the cooler settings, imaging areas for the specific modes with higher timing resolutions, and event selection algorithm. We summarize our policy and procedure of the initial operations for SXI. We also report on a couple of issues we faced during the initial operations and lessons learned from them., Comment: 10 pages, 8 figures, Proceedings of SPIE Astronomical Telescopes + Instrumentation 2024

We present an analysis of X-ray observations of the Ultraluminous X-ray source (ULX) in IZw18 based on archival data taken with Chandra, XMM-Newton, and Suzaku. This ULX is considered to be an intermediate-mass black hole candidate simply because it is in the lowest metallicity environment among ULXs, where formation of heavy black holes is facilitated. However, actual study of the ULX based on observations spanning for a long period has been too limited to determine its nature. In this study, we investigate the spectral evolution of the ULX up to 2014, combining the previously-unpublished Suzaku data with those from the other two satellites. We derive a positive correlation of $L\propto T_{\rm in}^{2.1\pm0.4}$ between the bolometric luminosity $L$ and inner-disk temperature $T_{\rm in}$ on the basis of the multi-color disk-blackbody model, where we exclude the Chandra data, which has the lowest luminosity and systematic residuals in the fitting. The nominal relation $L\propto T_{\rm in}^{4}$ for the standard disk is rejected at a significance level of 1.5 %. These results suggest that the ULX was in the slim-disk state during these observations except at the time of the Chandra observation, in which the ULX was likely to be in a different state. The apparent inner-disk radius appears negatively correlated with the inner-disk temperature. Moreover, we find a radial dependence of the disk temperature of $T (r)\propto r^{-p}$ with $p<0.75$, which also supports the hypothesis that the ULX has a slim disk. In consequence, the IZw18 ULX is most likely to be powered by a stellar-mass compact object in supercritical accretion., Comment: 13 pages, 5 figures, and 2 tables, accepted for publication in ApJ

This study proposes social navigation metrics for autonomous agents in air combat, aiming to facilitate their smooth integration into pilot formations. The absence of such metrics poses challenges to safety and effectiveness in mixed human-autonomous teams. The proposed metrics prioritize naturalness and comfort. We suggest validating them through a user study involving military pilots in simulated air combat scenarios alongside autonomous loyal wingmen. The experiment will involve setting up simulations, designing scenarios, and evaluating performance using feedback from questionnaires and data analysis. These metrics aim to enhance the operational performance of autonomous loyal wingmen, thereby contributing to safer and more strategic air combat.

We introduce TartanAviation, an open-source multi-modal dataset focused on terminal-area airspace operations. TartanAviation provides a holistic view of the airport environment by concurrently collecting image, speech, and ADS-B trajectory data using setups installed inside airport boundaries. The datasets were collected at both towered and non-towered airfields across multiple months to capture diversity in aircraft operations, seasons, aircraft types, and weather conditions. In total, TartanAviation provides 3.1M images, 3374 hours of Air Traffic Control speech data, and 661 days of ADS-B trajectory data. The data was filtered, processed, and validated to create a curated dataset. In addition to the dataset, we also open-source the code-base used to collect and pre-process the dataset, further enhancing accessibility and usability. We believe this dataset has many potential use cases and would be particularly vital in allowing AI and machine learning technologies to be integrated into air traffic control systems and advance the adoption of autonomous aircraft in the airspace., Comment: 8 pages, 6 figures, 2 tables

Surface-to-Air Missiles (SAMs) are crucial in modern air defense systems. A critical aspect of their effectiveness is the Engagement Zone (EZ), the spatial region within which a SAM can effectively engage and neutralize a target. Notably, the EZ is intrinsically related to the missile's maximum range; it defines the furthest distance at which a missile can intercept a target. The accurate computation of this EZ is essential but challenging due to the dynamic and complex factors involved, which often lead to high computational costs and extended processing times when using conventional simulation methods. In light of these challenges, our study investigates the potential of machine learning techniques, proposing an approach that integrates machine learning with a custom-designed simulation tool to train supervised algorithms. We leverage a comprehensive dataset of pre-computed SAM EZ simulations, enabling our model to accurately predict the SAM EZ for new input parameters. It accelerates SAM EZ simulations, enhances air defense strategic planning, and provides real-time insights, improving SAM system performance. The study also includes a comparative analysis of machine learning algorithms, illuminating their capabilities and performance metrics and suggesting areas for future research, highlighting the transformative potential of machine learning in SAM EZ simulations.

OBJECTIVES: Quantitative MRI techniques such as T2 and T1$\rho$ mapping are beneficial in evaluating cartilage and meniscus. We aimed to evaluate the MIXTURE (Multi-Interleaved X-prepared Turbo-Spin Echo with IntUitive RElaxometry) sequences that provide morphologic images with clinical turbo spin-echo (TSE) contrasts and additional parameter maps versus reference TSE sequences in an in-situ model of human cartilage defects. MATERIALS AND METHODS: Prospectively, standardized cartilage defects of 8mm, 5mm, and 3mm diameter were created in the lateral femora of 10 human cadaveric knee specimens (81$\pm$10 years, nine male/one female). Using a clinical 3T MRI scanner and knee coil, MIXTURE sequences combining (i) proton-density weighted fat-saturated (PD-w FS) images and T2 maps and (ii) T1-weighted images and T1$\rho$ maps were acquired before and after defect creation, alongside the corresponding 2D TSE and 3D TSE reference sequences. Defect delineability, bone texture, and cartilage relaxation times were quantified. Inter-sequence comparisons were made using appropriate parametric and non-parametric tests. RESULTS: Overall, defect delineability and texture features were not significantly different between the MIXTURE and reference sequences. After defect creation, relaxation times increased significantly in the central femur (for T2) and all regions combined (for T1$\rho$). CONCLUSION: MIXTURE sequences permit time-efficient simultaneous morphologic and quantitative joint assessment based on clinical image contrasts. While providing T2 or T1$\rho$ maps in clinically feasible scan time, morphologic image features, i.e., cartilage defect delineability and bone texture, were comparable between MIXTURE and corresponding reference sequences., Comment: 12 pages (main body), 3 tables, 6 figures

Introduction: Quantitative MRI techniques such as T2 and T1\r{ho} mapping are beneficial in evaluating knee joint pathologies; however, long acquisition times limit their clinical adoption. MIXTURE (Multi-Interleaved X-prepared Turbo-Spin Echo with IntUitive RElaxometry) provides a versatile turbo spin-echo (TSE) sequence platform for simultaneous morphologic and quantitative joint imaging yet lacks comparative evaluation in basic and translational research contexts. Methods: Two MIXTURE sequences were designed along clinical requirements: (i) MIX1, combining proton density (PD)-weighted fat-saturated (FS) images and quantitative T2 mapping (acquisition time: 4:59 min), and (ii) MIX2, combining T1-weighted images with quantitative T1\r{ho} mapping (6:38 min). MIXTURE sequences and their reference 2D and 3D TSE counterparts were acquired from ten human cadaveric knee joints using a clinical 3T MRI scanner and knee coil. Contrast, contrast-to-noise ratios, and coefficients of variation were comparatively evaluated using parametric tests. Clinical radiologists (n=3) assessed diagnostic quality as a function of sequence and anatomic structure using 5-point Likert scales and ordinal regression. The significance level was set to {\alpha}=0.01. Results: MIX1 and MIX2 had at least equal diagnostic quality compared to the 2D and 3D TSE sequences of the same image weighting. Contrast, contrast-to-noise ratios, and coefficients of variation were largely similar for the PD-weighted FS and T1-weighted images. Discussion: In clinically feasible scan times, the MIXTURE sequence platform yields (i) morphologic images of diagnostic quality and adjustable TSE-based contrasts and (ii) quantitative parameter mapping with additional insights on soft tissue composition and ultrastructure., Comment: 13 pages (main text), 7 figures, 3 tables

This paper presents our systems (denoted as T13) for the singing voice conversion challenge (SVCC) 2023. For both in-domain and cross-domain English singing voice conversion (SVC) tasks (Task 1 and Task 2), we adopt a recognition-synthesis approach with self-supervised learning-based representation. To achieve data-efficient SVC with a limited amount of target singer/speaker's data (150 to 160 utterances for SVCC 2023), we first train a diffusion-based any-to-any voice conversion model using publicly available large-scale 750 hours of speech and singing data. Then, we finetune the model for each target singer/speaker of Task 1 and Task 2. Large-scale listening tests conducted by SVCC 2023 show that our T13 system achieves competitive naturalness and speaker similarity for the harder cross-domain SVC (Task 2), which implies the generalization ability of our proposed method. Our objective evaluation results show that using large datasets is particularly beneficial for cross-domain SVC., Comment: Accepted to ASRU 2023

This work explores the use of military simulations in predicting and evaluating the outcomes of potential scenarios. It highlights the evolution of military simulations and the increased capabilities that have arisen due to the advancement of artificial intelligence. Also, it discusses the various applications of military simulations, such as developing tactics and employment doctrines, training decision-makers, evaluating new acquisitions, and developing new technologies. The paper then focuses on the Brazilian Air Force's efforts to create its own simulation tool, the Aerospace Simulation Environment (Ambiente de Simula\c{c}\~ao Aeroespacial -- ASA in Portuguese), and how this cloud-based service called ASA Simulation as a Service (ASA-SimaaS) can provide greater autonomy and economy for the military force. The main contribution of this work is to present the ASA-SimaaS solution as a means of empowering digital transformation in defense scenarios, establishing a partnership network, and improving the military's simulation capabilities and competitiveness.

AsaPy is a custom-made Python library designed to simplify and optimize the analysis of aerospace simulation data. Instead of introducing new methodologies, it excels in combining various established techniques, creating a unified, specialized platform. It offers a range of features, including the design of experiment methods, statistical analysis techniques, machine learning algorithms, and data visualization tools. AsaPy's flexibility and customizability make it a viable solution for engineers and researchers who need to quickly gain insights into aerospace simulations. AsaPy is built on top of popular scientific computing libraries, ensuring high performance and scalability. In this work, we provide an overview of the key features and capabilities of AsaPy, followed by an exposition of its architecture and demonstrations of its effectiveness through some use cases applied in military operational simulations. We also evaluate how other simulation tools deal with data science, highlighting AsaPy's strengths and advantages. Finally, we discuss potential use cases and applications of AsaPy and outline future directions for the development and improvement of the library.

We have synthesized new Tsai-type Yb-based intermediate-valence approximant crystals (ACs) with different degree of approximation to quasicrystal, Zn--Au--Yb 1/1 and 2/1 AC, and studied the external pressure effect on their Yb mean-valence $\nu$. Whereas 1/1 AC distinctly exhibits a first-order-like jump in $\nu$ at a transition pressure $P_{\rm v}$, 2/1 AC only shows an indistinct anomaly at $P_{\rm v}$. We have also studied the pressure dependence of the $\nu$ of Au--Al--Yb 1/1 AC, which is a prototypal AC exhibiting pressure-induced quantum criticality. It shows a continuous valence anomaly at a critical pressure $P_{\rm c}$ where the magnetic susceptibility diverges toward zero temperature, in contrast to the valence jump in the Zn--Au--Yb 1/1 AC. These results are discussed based on a theoretical model of quantum critical valence fluctuation.

We propose a novel x-ray imaging system, Multi-Image X-ray Interferometer Module (MIXIM), with which a very high angular resolution can be achieved even with a small system size. MIXIM is composed of equally-spaced multiple slits and an x-ray detector, and its angular resolution is inversely proportional to the distance between them. Here we report our evaluation experiments of MIXIM with a newly adopted CMOS sensor with a high spatial resolution of 2.5 {\mu}m. Our previous experiments with a prototype MIXIM were limited to one-dimensional imaging, and more importantly, the achieved angular resolution was only {\sim}1", severely constrained due to the spatial resolution of the adopted sensor with a pixel size of 4.25 {\mu}m. By contrast, one-dimensional images obtained in this experiment had a higher angular resolution of 0.5" when a configured system size was only {\sim}1 m, which demonstrates that MIXIM can simultaneously realize a high angular resolution and compact size. We also successfully obtained a two-dimensional profile of an x-ray beam for the first time for MIXIM by introducing a periodic pinhole mask. The highest angular resolution achieved in our experiments is smaller than 0.1" with a mask-sensor distance of 866.5 cm, which shows the high scalability of MIXIM., Comment: 33 pages, 14 figures, 2 tables, accepted for publication in JATIS

This work contributes to developing an agent based on deep reinforcement learning capable of acting in a beyond visual range (BVR) air combat simulation environment. The paper presents an overview of building an agent representing a high-performance fighter aircraft that can learn and improve its role in BVR combat over time based on rewards calculated using operational metrics. Also, through self-play experiments, it expects to generate new air combat tactics never seen before. Finally, we hope to examine a real pilot's ability, using virtual simulation, to interact in the same environment with the trained agent and compare their performances. This research will contribute to the air combat training context by developing agents that can interact with real pilots to improve their performances in air defense missions.

Xtend is a soft X-ray imaging telescope developed for the X-Ray Imaging and Spectroscopy Mission (XRISM). XRISM is scheduled to be launched in the Japanese fiscal year 2022. Xtend consists of the Soft X-ray Imager (SXI), an X-ray CCD camera, and the X-ray Mirror Assembly (XMA), a thin-foil-nested conically approximated Wolter-I optics. The SXI uses the P-channel, back-illuminated type CCD with an imaging area size of 31 mm on a side. The four CCD chips are arranged in a 2$\times$2 grid and can be cooled down to $-120$ $^{\circ}$C with a single-stage Stirling cooler. The XMA nests thin aluminum foils coated with gold in a confocal way with an outer diameter of 45~cm. A pre-collimator is installed in front of the X-ray mirror for the reduction of the stray light. Combining the SXI and XMA with a focal length of 5.6m, a field of view of $38^{\prime}\times38^{\prime}$ over the energy range from 0.4 to 13 keV is realized. We have completed the fabrication of the flight model of both SXI and XMA. The performance verification has been successfully conducted in a series of sub-system level tests. We also carried out on-ground calibration measurements and the data analysis is ongoing., Comment: 11 pages, 13 figures. Proceedings of SPIE Astronomical Telescopes and Instrumentation 2022