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The global climate has underscored the need for effective policies to reduce greenhouse gas emissions from all sources, including those resulting from agricultural expansion, which is regulated by the Common Agricultural Policy (CAP) across the European Union (EU). To assess the effectiveness of these mitigation policies, statistical methods must account for the heterogeneous impact of policies across different countries. We propose a Bayesian approach that combines the multinomial logit model, which is suitable for compositional land-use data, with a Bayesian nonparametric (BNP) prior to cluster regions with similar policy impacts. To simultaneously control for other relevant factors, we distinguish between cluster-specific and global covariates, coining this approach the Bayesian nonparametric partial clustering model. We develop a novel and efficient Markov Chain Monte Carlo (MCMC) algorithm, leveraging recent advances in the Bayesian literature. Using economic, geographic, and subsidy-related data from 22 EU member states, we examine the effectiveness of policies influencing land-use decisions across Europe and highlight the diversity of the problem. Our results indicate that the impact of CAP varies widely across the EU, emphasizing the need for subsidies to be tailored to optimize their effectiveness.

Text-to-image diffusion models sometimes depict blended concepts in the generated images. One promising use case of this effect would be the nonword-to-image generation task which attempts to generate images intuitively imaginable from a non-existing word (nonword). To realize nonword-to-image generation, an existing study focused on associating nonwords with similar-sounding words. Since each nonword can have multiple similar-sounding words, generating images containing their blended concepts would increase intuitiveness, facilitating creative activities and promoting computational psycholinguistics. Nevertheless, no existing study has quantitatively evaluated this effect in either diffusion models or the nonword-to-image generation paradigm. Therefore, this paper first analyzes the conceptual blending in a pretrained diffusion model, Stable Diffusion. The analysis reveals that a high percentage of generated images depict blended concepts when inputting an embedding interpolating between the text embeddings of two text prompts referring to different concepts. Next, this paper explores the best text embedding space conversion method of an existing nonword-to-image generation framework to ensure both the occurrence of conceptual blending and image generation quality. We compare the conventional direct prediction approach with the proposed method that combines $k$-nearest neighbor search and linear regression. Evaluation reveals that the enhanced accuracy of the embedding space conversion by the proposed method improves the image generation quality, while the emergence of conceptual blending could be attributed mainly to the specific dimensions of the high-dimensional text embedding space., Comment: Paper accepted at ACM MM 2024 (doi: 10.1145/3664647.3681202) with supplementary materials concatenated

Ian McGilchrist's works present the thesis that the two hemispheres of the brain have radically different modes of interacting with the world, and that their respective perceptions and functions must be properly integrated for a viable way forward. This proper integration requires restoring the right-brain to its proper place as "Master." I discuss a parallel to this insight in the dichotomous "worlds" of quantum and classical physics. In addition, I discuss the relevance of Whitehead's process philosophy, as well as the Taoist concepts of Yin and Yang, with particular attention to the importance and primacy of Yin underlying the quantum level as "Master.", Comment: Invited talk given at Conference: "Metaphysics and the Matter With Things," February, 2024. Center for Process Studies and CA Institute of Integral Studies

Gaia Data Release 3 (DR3) has provided the largest and most astrometrically precise catalogue of nearby stars to date, allowing for a more complete membership census of nearby, young stellar moving groups. These loose associations of young (age $<$100 Myr) stars within $\sim$100 pc are vital laboratories for the study of the early evolution of low-mass stars and planetary systems. We have exploited DR3 data to examine the boundary region between two of the youngest nearby moving groups, the $\sim$3--8 Myr-old $\epsilon$ Cha Association (ECA) and an $\sim$8 Myr-old sub-population of the sprawling Lower Centaurus Crux (LCC) young star complex. Using spatio-kinematic and color-magnitude criteria designed to select stars in the ECA, we identify $\sim$54 new young-star candidates that extend from the ECA core to the southern edge of the LCC. Included among our new candidates are six previously unidentified ultra-low-mass, mid- to late-M stars, lying near the future hydrogen-burning limit, that display significant infrared excesses. Our spatial, kinematic, and CMD analysis of these new candidates and previously established LCC and ECA members blurs the boundary between these groups and provides evidence for a wave of continuous star formation extending from north (LCC) to south (ECA). We discuss the factors which studies of nearby young moving groups must consider when constraining the ages of stars in these groups., Comment: Accepted by AJ

The GALEX Nearby Young Star Search (GALNYSS) yielded the identification of more than 2000 late-type stars that, based on their ultraviolet and infrared colors and pre-Gaia proper motions, are potentially of age < 200 Myr and lie within ~120 pc of Earth. We present the results of a campaign of medium- and high-resolution optical spectroscopy of 471 GALNYSS stars aimed at confirming their youth and their potential membership in nearby young stellar moving groups. We present radial velocity (RV), Li absorption, and H-$\alpha$ emission measurements for these spectroscopically observed GALNYSS stars, and assess their Li absorption and optical emission-line properties and infrared excesses. Our RV measurements are combined with literature and Gaia DR3 RV measurements and Gaia DR3 astrometry and photometry to obtain the spatiokinematics and color-magnitude positions of GALNYSS stars. We use these results to assess membership in the TW Hya, Tuc-Hor, Carina, Columba, and Argus Associations and the $\beta$-PMG and AB Dor moving groups. We have identified 132 stars as candidate members of these seven groups; roughly half of these candidates are newly identified on the basis of data presented here. At least one-third of the 132 candidates are spectroscopic and/or photometric binaries and/or have comoving (visual) binary companions in Gaia DR3. The contingent of young, low-mass stars in the solar vicinity we identify here should provide excellent subjects for future direct imaging exoplanet surveys and studies of the early evolution of low-mass stars and their planetary progeny., Comment: The paper is accepted by the Open Journal of Astrophysics

In this paper, we introduce a zero-shot Voice Transfer (VT) module that can be seamlessly integrated into a multi-lingual Text-to-speech (TTS) system to transfer an individual's voice across languages. Our proposed VT module comprises a speaker-encoder that processes reference speech, a bottleneck layer, and residual adapters, connected to preexisting TTS layers. We compare the performance of various configurations of these components and report Mean Opinion Score (MOS) and Speaker Similarity across languages. Using a single English reference speech per speaker, we achieve an average voice transfer similarity score of 73% across nine target languages. Vocal characteristics contribute significantly to the construction and perception of individual identity. The loss of one's voice, due to physical or neurological conditions, can lead to a profound sense of loss, impacting one's core identity. As a case study, we demonstrate that our approach can not only transfer typical speech but also restore the voices of individuals with dysarthria, even when only atypical speech samples are available - a valuable utility for those who have never had typical speech or banked their voice. Cross-lingual typical audio samples, plus videos demonstrating voice restoration for dysarthric speakers are available here (google.github.io/tacotron/publications/zero_shot_voice_transfer)., Comment: Submitted to ICASSP

As mass-losing asymptotic giant branch (AGB) stars evolve to planetary nebulae (PNe), the mass outflow geometries transform from nearly spherical to extreme aspherical. The physical mechanisms governing this transformation are widely believed to be linked to binarity and the associated production of disks and fast jets during transitional (post-AGB) evolutionary stages. We are carrying out a systematic ALMA survey ($P$re-planet$A$ry $N$ebulae high-angular-res$O$lution su$R$vey with $A$L$MA$ or PANORAMA) of a representative sample of bipolar and multipolar post-AGB objects. We have obtained high angular-resolution (0".1-0".4) observations of the CO(3--2) and/or 6--5 emission in order to probe the spatio-kinematic structure of the collimated outflows and the central disk/torii. The results are remarkable, generally showing the presence of bipolar or multipolar high-velocity outflows, dense toroidal waists, and in one case, a geometrically-thin circular ring around the central bipolar nebula. A high degree of point-symmetry characterizes the morphology of the mass ejecta. In this contribution, we present these and other highlights from our survey. We aim to use 2D/3D radiative transfer modeling in order to derive accurate outflow momenta, masses and mass-loss rates for our sample, and build hydrodynamical models that can explain the observed spatio-kinematic structures. These results will then be used to distinguish between different classes of PN-shaping binary interaction models.

Scientific edge computing increasingly relies on hardware-accelerated neural networks to implement complex, near-sensor processing at extremely high throughputs and low latencies. Existing frameworks like HLS4ML are effective for smaller models, but struggle with larger, modern neural networks due to their requirement of spatially implementing the neural network layers and storing all weights in on-chip memory. CGRA4ML is an open-source, modular framework designed to bridge the gap between neural network model complexity and extreme performance requirements. CGRA4ML extends the capabilities of HLS4ML by allowing off-chip data storage and supporting a broader range of neural network architectures, including models like ResNet, PointNet, and transformers. Unlike HLS4ML, CGRA4ML generates SystemVerilog RTL, making it more suitable for targeting ASIC and FPGA design flows. We demonstrate the effectiveness of our framework by implementing and scaling larger models that were previously unattainable with HLS4ML, showcasing its adaptability and efficiency in handling complex computations. CGRA4ML also introduces an extensive verification framework, with a generated runtime firmware that enables its integration into different SoC platforms. CGRA4ML's minimal and modular infrastructure of Python API, SystemVerilog hardware, Tcl toolflows, and C runtime, facilitates easy integration and experimentation, allowing scientists to focus on innovation rather than the intricacies of hardware design and optimization.

The keyword spotting (KWS) problem requires large amounts of real speech training data to achieve high accuracy across diverse populations. Utilizing large amounts of text-to-speech (TTS) synthesized data can reduce the cost and time associated with KWS development. However, TTS data may contain artifacts not present in real speech, which the KWS model can exploit (overfit), leading to degraded accuracy on real speech. To address this issue, we propose applying an adversarial training method to prevent the KWS model from learning TTS-specific features when trained on large amounts of TTS data. Experimental results demonstrate that KWS model accuracy on real speech data can be improved by up to 12% when adversarial loss is used in addition to the original KWS loss. Surprisingly, we also observed that the adversarial setup improves accuracy by up to 8%, even when trained solely on TTS and real negative speech data, without any real positive examples., Comment: to be published in a Workshop at Interspeech 2024, Synthetic Data's Transformative Role in Foundational Speech Models

Recent Atacama Large Millimeter/submillimeter Array (ALMA) observations of protoplanetary disks in the millimeter continuum have shown a variety of radial gaps, cavities, and spiral features. These substructures may be signposts for ongoing planet formation, and therefore these systems are promising targets for direct imaging planet searches in the near-infrared. To this end, we present results from a deep imaging survey in the $L'$-band (3.8 $\mu$m) with the Keck/NIRC2 vortex coronagraph to search for young planets in 43 disks with resolved features in the millimeter continuum or evidence for gaps/central cavities from their spectral energy distributions. Although we do not detect any new point sources, using the vortex coronagraph allows for high sensitivity to faint sources at small angular separations (down to ${\sim}$0$^{\prime\prime}$.1), allowing us to place strong upper limits on the masses of potential gas giant planets. We compare our mass sensitivities to the masses of planets derived using ALMA observations, and while we are sensitive to $\sim$1 M$_{Jup}$ planets in the gaps in some of our systems, we are generally not sensitive to planets of the masses expected from the ALMA observations. In addition to placing upper limits on the masses of gas giant planets that could be interacting with the dust in the disks to form the observed millimeter substructures, we are also able to map the micron-sized dust as seen in scattered light for 8 of these systems. Our large sample of systems also allows us to investigate limits on planetary accretion rates and disk viscosities., Comment: 23 pages, 14 figures, 3 tables, accepted for publication in AJ

This paper explores the use of TTS synthesized training data for KWS (keyword spotting) task while minimizing development cost and time. Keyword spotting models require a huge amount of training data to be accurate, and obtaining such training data can be costly. In the current state of the art, TTS models can generate large amounts of natural-sounding data, which can help reducing cost and time for KWS model development. Still, TTS generated data can be lacking diversity compared to real data. To pursue maximizing KWS model accuracy under the constraint of limited resources and current TTS capability, we explored various strategies to mix TTS data and real human speech data, with a focus on minimizing real data use and maximizing diversity of TTS output. Our experimental results indicate that relatively small amounts of real audio data with speaker diversity (100 speakers, 2k utterances) and large amounts of TTS synthesized data can achieve reasonably high accuracy (within 3x error rate of baseline), compared to the baseline (trained with 3.8M real positive utterances)., Comment: to be published in a Workshop at Interspeech 2024, Synthetic Data's Transformative Role in Foundational Speech Models

AGB stars are not expected to be X-ray-emitters, yet a small fraction of them, the so-called X-AGBs, show X-ray emission that can be attributed to coronal activity of a companion or accretion onto it. By searching the recently released eROSITA-DE eRASS1 source catalog, we aim to increase the sample of known X-AGBs. So far, 36 X-AGBs have been reported, including 21 previous ROSAT RASS, Chandra and XMM detections and 15 more eROSITA eRASS1 recent detections. We cross-correlated the position of sources in the eROSITA-DE eRASS1 catalog with those of AGB stars to find possible X-ray counterparts. We carefully checked these by comparing X-ray and near-IR $K$ images, disregarding those affected by optical loading, diffuse sources, or unreliable positional associations. Seven high-confidence X-AGBs and another seven possible ones are found. Accounting for previous X-ray detections, the sample of X-AGBs is increased from 36 up to 47, of which eROSITA-DE has so far discovered 26 new X-AGBs, more than doubling the number of known X-AGBs. This demonstrates eROSITA's capability to detect X-AGBs despite the challenge posed by the optical loading caused by their near-IR brightness. The eRASS1 X-AGBs tend to have higher X-ray luminosity than previously detected X-AGBs, suggesting a bias toward brighter sources. A comparison of the X-ray and far-UV luminosity of X-AGBs with those of X-ray-emitter symbiotic stars (X-SySts) revealed an overlap in the X-ray luminosity range $10^{29.5}

This paper presents verification-guided development (VGD), a software engineering process we used to build Cedar, a new policy language for expressive, fast, safe, and analyzable authorization. Developing a system with VGD involves writing an executable model of the system and mechanically proving properties about the model; writing production code for the system and using differential random testing (DRT) to check that the production code matches the model; and using property-based testing (PBT) to check properties of unmodeled parts of the production code. Using VGD for Cedar, we can build fast, idiomatic production code, prove our model correct, and find and fix subtle implementation bugs that evade code reviews and unit testing. While carrying out proofs, we found and fixed 4 bugs in Cedar's policy validator, and DRT and PBT helped us find and fix 21 additional bugs in various parts of Cedar.

Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for performance validation in experimental software frameworks, verifying those ML models are robust under extreme quantization and pruning, and enabling ultra-fine-grained model inspection for efficient fault tolerance. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements in extreme experimental environments. We study metrics for developing robust algorithms, present preliminary results and mitigation strategies, and conclude with an outlook of these and future directions of research towards the longer-term goal of developing autonomous scientific experimentation methods for accelerated scientific discovery., Comment: IEEE VLSI Test Symposium 2024 (VTS)

Quantum theory in its conventional formulation is notoriously subject to various measurement-related paradoxes, as exemplified by the "Schrodinger's Cat" and "Wigner's Friend" thought experiments. It has been shown, for example by Frauchiger and Renner, that nested measurements such as those occurring in the Wigner's Friend experiment can lead to inconsistencies concerning the putative outcomes of measurements. Such inconsistencies are commonly presumed to remain private and incommensurable, but this is not the case. A counterexample, in which the inconsistencies can be revealed among the observers, is reviewed. The implications for a recent attempt to shield Relational Quantum Mechanics from such inconsistencies are considered, and it is concluded that the attempt is not successful. Further implications for the state of the debate concerning the viability of quantum theory in its various formulations are discussed., Comment: Final version, to appear in Philosophy of Physics

We investigate the impact of auxiliary learning tasks such as observation reconstruction and latent self-prediction on the representation learning problem in reinforcement learning. We also study how they interact with distractions and observation functions in the MDP. We provide a theoretical analysis of the learning dynamics of observation reconstruction, latent self-prediction, and TD learning in the presence of distractions and observation functions under linear model assumptions. With this formalization, we are able to explain why latent-self prediction is a helpful \emph{auxiliary task}, while observation reconstruction can provide more useful features when used in isolation. Our empirical analysis shows that the insights obtained from our learning dynamics framework predicts the behavior of these loss functions beyond the linear model assumption in non-linear neural networks. This reinforces the usefulness of the linear model framework not only for theoretical analysis, but also practical benefit for applied problems.

Scanning probe techniques are popular, non-destructive ways to visualize the real space structure of Van der Waals moir\'es. The high lateral spatial resolution provided by these techniques enables extracting the moir\'e lattice vectors from a scanning probe image. We have found that the extracted values, while precise, are not necessarily accurate. Scan-to-scan variations in the behavior of the piezos which drive the scanning probe, and thermally-driven slow relative drift between probe and sample, produce systematic errors in the extraction of lattice vectors. In this Letter, we identify the errors and provide a protocol to correct for them. Applying this protocol to an ensemble of ten successive scans of near-magic-angle twisted bilayer graphene, we are able to reduce our errors in extracting lattice vectors to less than 1%. This translates to extracting twist angles with a statistical uncertainty less than 0.001{\deg} and uniaxial heterostrain with uncertainty on the order of 0.002%., Comment: 20 pages including supplementary material and 3 main figures

During neural network training, the sharpness of the Hessian matrix of the training loss rises until training is on the edge of stability. As a result, even nonstochastic gradient descent does not accurately model the underlying dynamical system defined by the gradient flow of the training loss. We use an exponential Euler solver to train the network without entering the edge of stability, so that we accurately approximate the true gradient descent dynamics. We demonstrate experimentally that the increase in the sharpness of the Hessian matrix is caused by the layerwise Jacobian matrices of the network becoming aligned, so that a small change in the network preactivations near the inputs of the network can cause a large change in the outputs of the network. We further demonstrate that the degree of alignment scales with the size of the dataset by a power law with a coefficient of determination between 0.74 and 0.98.

A triangulation of a point configuration is regular if it can be given by a height function, that is every point gets lifted to a certain height and projecting the lower convex hull gives the triangulation. Checking regularity of a triangulation usually is done by solving a linear program. However when checking many flip-connected triangulations for regularity, one can instead ask which flips preserve regularity. When traversing the flip graph for enumerating all regular triangulations, this allows for vast reduction of the linear programs needing to be solved. At the same time the remaining linear programs will be much smaller., Comment: 8 pages, 2 figures, 2 tables

The electronic properties of moir\'e heterostructures depend sensitively on the relative orientation between layers of the stack. For example, near-magic-angle twisted bilayer graphene (TBG) commonly shows superconductivity, yet a TBG sample with one of the graphene layers rotationally aligned to a hexagonal Boron Nitride (hBN) cladding layer provided the first experimental observation of orbital ferromagnetism. To create samples with aligned graphene/hBN, researchers often align edges of exfoliated flakes that appear straight in optical micrographs. However, graphene or hBN can cleave along either zig-zag or armchair lattice directions, introducing a 30 degree ambiguity in the relative orientation of two flakes. By characterizing the crystal lattice orientation of exfoliated flakes prior to stacking using Raman and second-harmonic generation for graphene and hBN, respectively, we unambiguously align monolayer graphene to hBN at a near-0 degree, not 30 degree, relative twist angle. We confirm this alignment by torsional force microscopy (TFM) of the graphene/hBN moir\'e on an open-face stack, and then by cryogenic transport measurements, after full encapsulation with a second, non-aligned hBN layer. This work demonstrates a key step toward systematically exploring the effects of the relative twist angle between dissimilar materials within moir\'e heterostructures., Comment: 40 pages, 15 figures

Our understanding of how exoplanets form and evolve relies on analyses of both the mineralogy of protoplanetary disks and their detailed structures; however, these key complementary aspects of disks are usually studied separately. We present initial results from a hybrid model that combines the empirical characterization of the mineralogy of a disk, as determined from its mid-infrared spectral features, with the MCFOST radiative transfer disk model, a combination we call the EaRTH Disk Model. With the results of the mineralogy detection serving as input to the radiative transfer model, we generate mid-infrared spectral energy distributions (SEDs) that reflect both the mineralogical and structural parameters of the corresponding disk. Initial fits of the SED output by the resulting integrated model to Spitzer Space T elescope mid-infrared (IRS) spectra of the protoplanetary disk orbiting the nearby T Tauri star MP Mus demonstrate the potential advantages of this approach by revealing details like the dominance of micron-sized olivine and micron-sized forsterite in this dusty disk. The simultaneous insight into disk composition and structure provided by the EaRTH Disk methodology should be directly applicable to the interpretation of mid-infrared spectra of protoplanetary disks that will be produced by the James Webb Space Telescope., Comment: Accepted for publication in ApJ, 38 pages, 11 figures, 6 tables

Supply chain security has become a very important vector to consider when defending against adversary attacks. Due to this, more and more developers are keen on improving their supply chains to make them more robust against future threats. On March 7th, 2024 researchers from the Secure Software Supply Chain Center (S3C2) gathered 14 industry leaders, developers and consumers of the open source ecosystem to discuss the state of supply chain security. The goal of the summit is to share insights between companies and developers alike to foster new collaborations and ideas moving forward. Through this meeting, participants were questions on best practices and thoughts how to improve things for the future. In this paper we summarize the responses and discussions of the summit. The panel questions can be found in the appendix., Comment: This is our WIP paper on the Summit. More versions will be released soon

The Orion Nebula Cluster (ONC) is the closest site of very young ($\sim$ 1 Myrs) massive star formation. The ONC hosts more than 1600 young and X-ray bright stars with masses ranging from $\sim$ 0.1 to 35 $M_\odot$. The Chandra HETGS Orion Legacy Project observed the ONC with the Chandra high energy transmission grating spectrometer (HETGS) for $2.1\,$Ms. We describe the spectral extraction and cleaning processes necessary to separate overlapping spectra. We obtained 36 high resolution spectra which includes a high brilliance X-ray spectrum of $\theta^1$ Ori C with over 100 highly significant X-ray lines. The lines show Doppler broadening between 300 and $400\;\mathrm{km}\;\mathrm{s}^{-1}$. Higher spectral diffraction orders allow us to resolve line components of high Z He-like triplets in $\theta^1$ Ori C with unprecedented spectral resolution. Long term light curves spanning $\sim$20 years show all stars to be highly variable, including the massive stars. Spectral fitting with thermal coronal emission line models reveals that most sources show column densities of up to a few times $10^{22}\,$cm$^{-2}$ and high coronal temperatures of 10 to 90 MK. We observe a bifurcation of the high temperature component where some stars show a high component of 40 MK, while others show above 60 MK indicating heavy flaring activity. Some lines are resolved with Doppler broadening above our threshold of $\sim200\;\mathrm{km}\;\mathrm{s}^{-1}$, up to $500\;\mathrm{km}\;\mathrm{s}^{-1}$. This data set represents the largest collection of HETGS high resolution X-ray spectra from young pre-MS stars in a single star-forming region to date., Comment: Accepted by ApJ, fixed typo in the xlabel of Fig 12 (left panel)

This work introduces a novel Augmented Reality (AR) approach to visualize material data alongside real objects in order to facilitate detailed material analyses based on spatial non-destructive testing (NDT) data as generated in X-ray computed tomography (XCT) imaging. For this purpose, we introduce a framework that leverages the potential of AR devices, visualization and interaction techniques to seamlessly explore complex primary and secondary XCT data matched with real-world objects. The overall goal of the proposed analysis scheme is to enable researchers and analysts to inspect material properties and structures onsite and in-place. Coupling immersive visualization techniques with real physical objects allows for highly intuitive workflows in material analysis and inspection, which enables the identification of anomalies and accelerates informed decision making. As a result, this framework generates an immersive experience, which provides a more engaging and more natural analysis of material data. A case study on fiber-reinforced polymer datasets was used to validate the AR framework and its new workflow. Initial results revealed positive feedback from experts, in particular regarding improved understanding of spatial data and a more natural interaction with material samples, which may have significant potential when combined with conventional analysis systems., Comment: 16 pages, 4 figures

We discuss what is special about the reproducibility of workflows in computer algebra. It is emphasized how the programming language Julia and the new computer algebra system OSCAR support such a reproducibility, and how users can benefit for their own work., Comment: 15 pages

Eruptive stars are a class of young stellar objects that show an abrupt increase in their luminosity. These burst-like episodes are thought to dominate the stellar accretion process during the class 0/class I stage. We present an overview of a survey of seven episodically accreting protostars aimed at studying their potentially complex circumstellar surroundings. The observations were performed with the instrument SPHERE, mounted at the VLT. We observed the eruptive stars in $H$-band with the near-infrared imager IRDIS and used the polarimeter to extract the polarized light scattered from the stars' surroundings. We produced polarized light images for three FUor objects, Z CMa, V960 Mon, and FU Ori, and four EXor objects, XZ Tau, UZ Tau, NY Ori, and EX Lup. We calculated the intrinsic polarization fraction for all the observed stars. In all systems we registered scattered light from around the primary star. FU Ori and V960 Mon are surrounded by complex structures including spiral-like features. In Z CMa, we detected a point source 0.7 arcsec to the northeast of the primary. Based on the astrometric measurements from archival Keck/NIRC2 data, we find this source to be a third member of the system. Further, Z CMa displays an outflow extending thousands of au. Unlike the other EXor objects in our sample, XZ Tau shows bright, extended scattered light structures, also associated with an outflow on a scale of hundreds of au. The other EXors show relatively faint disk-like structures in the immediate vicinity of the coronagraph. Asymmetric arms were only found around FUor objects, while faint disks seem to predominantly occur around EXors. Importantly, for Z CMa the detection of the faint extended structure questions previous interpretations of the system's dynamic state. The streamer which was associated with a fly-by object turned out to be part of a huge outflow extending 6000 au., Comment: Accepted for publication in A&A. 14 pages, 7 figures, 2 tables

With more scientific fields relying on neural networks (NNs) to process data incoming at extreme throughputs and latencies, it is crucial to develop NNs with all their parameters stored on-chip. In many of these applications, there is not enough time to go off-chip and retrieve weights. Even more so, off-chip memory such as DRAM does not have the bandwidth required to process these NNs as fast as the data is being produced (e.g., every 25 ns). As such, these extreme latency and bandwidth requirements have architectural implications for the hardware intended to run these NNs: 1) all NN parameters must fit on-chip, and 2) codesigning custom/reconfigurable logic is often required to meet these latency and bandwidth constraints. In our work, we show that many scientific NN applications must run fully on chip, in the extreme case requiring a custom chip to meet such stringent constraints.

We present early results from our program of ALMA Band 6 (1.3mm) molecular line mapping of a sample of nearby, well-studied examples of high-excitation, bipolar/pinched-waist and molecule-rich planetary nebulae (Hubble 5 and NGC 2440, 2818, 2899, 6302, and 6445). We have mapped these planetary nebulae (PNe) in isotopologues of CO as well as various molecular line tracers of high-energy irradiation, such as HCN, CN, HNC, and HCO+, with the complementary goals of establishing nebular kinematics as well as the zones of UV-heated and X-ray-ionized molecular gas within each nebula. The resulting high-resolution ALMA molecular emission-line maps reveal the regions of high-excitation bipolar PNe in which molecular gas, presumably ejected during asymptotic giant branch stages of the PN progenitor stars, survives and evolves chemically. We present a summary of molecular species detected to date in the sample nebulae, and we use example results for one PN (NGC 6455) to demonstrate the power of the ALMA data in revealing the structures, kinematics, and compositions of the equatorial molecular tori that are a common feature of the sample objects., Comment: 6 pages, 4 figures, accepted for IAU Proceedings Series (IAUS 384)

We introduce a protocol that gives access to out-of-time-ordered correlation functions in many-body quantum systems. Unlike other such protocols, our proposal, which can be applied to arbitrary initial states, neither requires ancilla degrees of freedom to the quantum system of interest, nor has the need for randomized measurements. Nontrivial experimental capabilities required to implement the protocol are single-site measurements, single-site rotations, and backwards time evolution. To exemplify the implementation of the protocol, we put forward a strategy for Hamiltonian sign inversion $H\to-H$ in arrays of Rydberg-dressed atoms. In this way, a complete and practical toolbox is obtained for the measurement of out-of-time-ordered correlations in equilibrium and nonequilibrium situations., Comment: 8 pages, 4 figures

Cedar is a new authorization policy language designed to be ergonomic, fast, safe, and analyzable. Rather than embed authorization logic in an application's code, developers can write that logic as Cedar policies and delegate access decisions to Cedar's evaluation engine. Cedar's simple and intuitive syntax supports common authorization use-cases with readable policies, naturally leveraging concepts from role-based, attribute-based, and relation-based access control models. Cedar's policy structure enables access requests to be decided quickly. Cedar's policy validator leverages optional typing to help policy writers avoid mistakes, but not get in their way. Cedar's design has been finely balanced to allow for a sound and complete logical encoding, which enables precise policy analysis, e.g., to ensure that when refactoring a set of policies, the authorized permissions do not change. We have modeled Cedar in the Lean programming language, and used Lean's proof assistant to prove important properties of Cedar's design. We have implemented Cedar in Rust, and released it open-source. Comparing Cedar to two open-source languages, OpenFGA and Rego, we find (subjectively) that Cedar has equally or more readable policies, but (objectively) performs far better.

Collecting high-quality studio recordings of audio is challenging, which limits the language coverage of text-to-speech (TTS) systems. This paper proposes a framework for scaling a multilingual TTS model to 100+ languages using found data without supervision. The proposed framework combines speech-text encoder pretraining with unsupervised training using untranscribed speech and unspoken text data sources, thereby leveraging massively multilingual joint speech and text representation learning. Without any transcribed speech in a new language, this TTS model can generate intelligible speech in >30 unseen languages (CER difference of <10% to ground truth). With just 15 minutes of transcribed, found data, we can reduce the intelligibility difference to 1% or less from the ground-truth, and achieve naturalness scores that match the ground-truth in several languages., Comment: To appear in ICASSP 2024. Demo page: https://google.github.io/tacotron/publications/extending_tts/

The secondary fan $\Sigma(k,n)$ is a polyhedral fan which stratifies the regular subdivisions of the hypersimplices $\Delta(k,n)$. We find new infinite families of rays of $\Sigma(k,n)$, and we compute the fans $\Sigma(2,7)$ and $\Sigma(3,6)$. In the special case $k=2$ the fan $\Sigma(2,n)$ is closely related to the metric fan $\mathop{MF}(n)$, which forms a natural parameter space for the metric spaces on $n$ points. So our results yield a classification of the finite metric spaces on seven points., Comment: 21 pages, 2 figures

We give an elementary proof that in a Borel family of games, the set of games for which player II has a winning strategy is Baire measurable, universally measurable, and completely Ramsey in the case where $X = [\mathbb{N}]^{\aleph_0}$.

We present Submillimeter Array (SMA) mapping of $^{12}$CO $J=2\rightarrow 1$, $^{13}$CO $J=2\rightarrow 1$, and CN $N=2\rightarrow 1$ emission from the Ring-like planetary nebula (PN) NGC 3132, one of the subjects of JWST Early Release Observation (ERO) near-infrared imaging. The $\sim$5$''$ resolution SMA data demonstrate that the Southern Ring's main, bright, molecule-rich ring is indeed an expanding ring, as opposed to a limb-brightened shell, in terms of its intrinsic (physical) structure. This suggests that NGC 3132 is a bipolar nebula viewed more or less pole-on (inclination $\sim$15--30$^\circ$). The SMA data furthermore reveal that the nebula harbors a second expanding molecular ring that is aligned almost orthogonally to the main, bright molecular ring. We propose that this two-ring structure is the remnant of an ellipsoidal molecular envelope of ejecta that terminated the progenitor star's asymptotic giant branch evolution and was subsequently disrupted by a series of misaligned fast, collimated outflows or jets resulting from interactions between the progenitor and one or more companions., Comment: 20 pages, 12 figures; accepted by The Astrophysical Journal

The question of where the inertial properties of matter come from has been open for a long time. Isaac Newton considered inertia an intrinsic property of matter. Ernst Mach held a different view whereby the inertia of a body comes from its interaction with the rest of the universe. This idea is known today as Mach's principle. We discuss Mach's principle based on transactional gravity, the recently developed completion of the entropic gravity program by the physics of quantum events induced by transactions. A consequence of the analysis is a fundamental relation between the gravitational constant G and the total mass in the causal universe, derived by means of entropic principles., Comment: arXiv admin note: text overlap with arXiv:2209.04025

Ensuring predictability in modern real-time Systems-on-Chip (SoCs) is an increasingly critical concern for many application domains such as automotive, robotics, and industrial automation. An effective approach involves the modeling and development of hardware components, such as interconnects and shared memory resources, to evaluate or enforce their deterministic behavior. Unfortunately, these IPs are often closed-source, and these studies are limited to the single modules that must later be integrated with third-party IPs in more complex SoCs, hindering the precision and scope of modeling and compromising the overall predictability. With the coming-of-age of open-source instruction set architectures (RISC-V) and hardware, major opportunities for changing this status quo are emerging. This study introduces an innovative methodology for modeling and analyzing State-of-the-Art (SoA) open-source SoCs for low-power cyber-physical systems. Our approach models and analyzes the entire set of open-source IPs within these SoCs and then provides a comprehensive analysis of the entire architecture. We validate this methodology on a sample heterogenous low-power RISC-V architecture through RTL simulation and FPGA implementation, minimizing pessimism in bounding the service time of transactions crossing the architecture between 28% and 1%, which is considerably lower when compared to similar SoA works.

Automatic speech recognition (ASR) systems can suffer from poor recall for various reasons, such as noisy audio, lack of sufficient training data, etc. Previous work has shown that recall can be improved by retrieving rewrite candidates from a large database of likely, contextually-relevant alternatives to the hypothesis text using nearest-neighbors search over embeddings of the ASR hypothesis text to correct and candidate corrections. However, ASR-hypothesis-based retrieval can yield poor precision if the textual hypotheses are too phonetically dissimilar to the transcript truth. In this paper, we eliminate the hypothesis-audio mismatch problem by querying the correction database directly using embeddings derived from the utterance audio; the embeddings of the utterance audio and candidate corrections are produced by multimodal speech-text embedding networks trained to place the embedding of the audio of an utterance and the embedding of its corresponding textual transcript close together. After locating an appropriate correction candidate using nearest-neighbor search, we score the candidate with its speech-text embedding distance before adding the candidate to the original n-best list. We show a relative word error rate (WER) reduction of 6% on utterances whose transcripts appear in the candidate set, without increasing WER on general utterances.

This essay offers a meta-level analysis in the sociology and history of physics in the context of the so-called "Arrow of Time Problem" or "Two Times Problem," which asserts that the empirically observed directionality of time is in conflict with physical theory. I argue that there is actually no necessary conflict between physics and the arrow of time, and that the observed directionality of time is perfectly consistent with physics unconstrained by certain optional metaphysical, epistemological and methodological beliefs and practices characterizing the conventional or Received View., Comment: Invited contribution to Proceedings of Conference on the Arrow of Time, Buenos Aires, February 2023; final accepted version

We prove that every Schreier graph of a free Borel action of a finitely generated non-amenable group has a Baire measurable perfect matching. This result was previously only known in the bipartite setting. We also prove that every Borel non-amenable bounded degree graph with only even degrees has a Baire measurable balanced orientation.

Modeling and simulation of electrical power systems are becoming increasingly important approaches for the development and operation of novel smart grid functionalities -- especially with regard to data-driven applications as data of certain operational states or misconfigurations can be next to impossible to obtain. The DeMaDs framework allows for the simulation and modeling of electric power grids and malfunctions therein. Furthermore, it serves as a testbed to assess the applicability of various data-driven malfunction detection methods. These include data mining techniques, traditional machine learning approaches as well as deep learning methods. The framework's capabilities and functionality are laid out here, as well as explained by the means of an illustrative example., Comment: 2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)

The changes in the electric energy system toward a sustainable future are inevitable and already on the way today. This often entails a change of paradigm for the electric energy grid, for example, the switch from central to decentralized power generation which also has to provide grid-supporting functionalities. However, due to the scarcity of distributed sensors, new solutions for grid operators for monitoring these functionalities are needed. The framework presented in this work allows to apply and assess data-driven detection methods in order to implement such monitoring capabilities. Furthermore, an approach to a multi-stage detection of misconfigurations is introduced. Details on implementations of the single stages as well as their requirements are also presented. Furthermore, testing and validation results are discussed. Due to its feature of being seamlessly integrable into system operators' current metering infrastructure, clear benefits of the proposed solution are pointed out., Comment: 27th International Conference on Electricity Distribution (CIRED 2023)

Behavioural metrics have been shown to be an effective mechanism for constructing representations in reinforcement learning. We present a novel perspective on behavioural metrics for Markov decision processes via the use of positive definite kernels. We leverage this new perspective to define a new metric that is provably equivalent to the recently introduced MICo distance (Castro et al., 2021). The kernel perspective further enables us to provide new theoretical results, which has so far eluded prior work. These include bounding value function differences by means of our metric, and the demonstration that our metric can be provably embedded into a finite-dimensional Euclidean space with low distortion error. These are two crucial properties when using behavioural metrics for reinforcement learning representations. We complement our theory with strong empirical results that demonstrate the effectiveness of these methods in practice., Comment: Published in TMLR