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In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensing, which necessitates quantization to be deployed and are subject to noise and perturbations due to experimental conditions. Our method allows assessing the robustness of ML models to such effects as a function of quantization precision and under different regularization techniques -- two crucial concerns that remained underexplored so far. By investigating the interplay between performance, efficiency, and robustness by means of loss landscape analysis, we both established a strong correlation between gently-shaped landscapes and robustness to input and weight perturbations and observed other intriguing and non-obvious phenomena. Our method allows a systematic exploration of such trade-offs a priori, i.e., without training and testing multiple models, leading to more efficient development workflows. This work also highlights the importance of incorporating robustness into the Pareto optimization of ML models, enabling more reliable and adaptive scientific sensing systems., Comment: Under review

HL Tau is one of the most well-studied Class I young stellar objects, including frequent observations at near- and mid-infrared, (sub-) millimeter, and X-ray wavelengths. We present the results of an X-ray variability monitoring campaign with XMM-Newton in 2020 and X-ray gratings spectroscopy from Chandra/HETGS in 2018. We find that the X-ray spectrum of HL Tau is consistently hot (with characteristic plasma temperatures $T \gtrsim 30$ MK) over 31 epochs spanning 20 years, which is consistent in temperature with most Class I YSOs. The high-resolution HETG spectrum indicates the presence of some cooler plasma. We characterize the variability of the star across the 31 observations and find a subset of observations with significant variability on a $\sim$21-day timescale in the observed count rate and flux. We discuss the possible origins of this variability, and identify further observations that would better constrain the nature of the changes., Comment: 16 pages, nine figures. Accepted to the Astronomical Journal

We demonstrate, both theoretically and experimentally, that arbitrary scatterers preserving parity-time-duality ($\mathcal{P}\cdot\mathcal{T}\cdot\mathcal{D}$) symmetry inherently produce a backscattered wave whose electric field is the mirror-symmetric counterpart of the incident electric field, up to an amplitude factor, with respect to the system's characteristic mirror plane. Specifically, we establish that a general elliptically polarized wave, when reflected from such structures, exhibits a polarization state related to the polarization ellipse of the incident wave by a parity transformation. Notably, a circularly polarized wave reflects with spin angular momentum opposite to that of the incident field, in stark contrast to reflection from conventional conducting screens. These findings enable several applications such as reflective polarizers., Comment: 11 pages, 10 figures

We present the first interferometric imaging of molecular line emission from the Ring Nebula, NGC~6720, in the form of Submillimeter Array (SMA) observations of CO $J=2\rightarrow 1$ emission. The SMA $^{12}$CO(2--1) mapping data, with $\sim$3$''$ spatial resolution and 2 km s$^{-1}$ velocity resolution, provide an unprecedentedly detailed, 3D view of the Ring's clumpy molecular envelope. The morphology of the velocity-integrated SMA $^{12}$CO(2--1) image closely resembles those of near-IR H$_2$ and PAH emission in JWST/NIRCam imaging of NGC~6720, with the molecular gas forming a geometrically thin layer surrounding the ionized gas imaged by HST and JWST. A simple, geometrical model of the $^{12}$CO(2--1) data shows that the intrinsic structure of NGC~6720's molecular envelope closely resembles a truncated, triaxial ellipsoid that is viewed close to pole-on, and that the dynamical age of the molecular envelope is $\sim$6000 yr. The SMA $^{12}$CO(2--1) data furthermore reveal that filamentary features seen projected in the Ring's interior in JWST imaging are in fact fast-moving polar knots or bullets with radial velocities of $\pm$45--50 km s$^{-1}$ relative to systemic, and that the hot progenitor star remnant is positioned at the precise geometric center of the clumpy, ellipsoidal molecular shell. We assert that the Ring's molecular envelope was formed via a relatively sudden, AGB-terminating mass ejection event $\sim$6000 yr ago, and that this ellipsoidal envelope was then punctured by fast, collimated polar outflows resulting from interactions between the progenitor and one or more companion stars. Such an evolutionary scenario may describe most molecule-rich, ``Ring-like'' planetary nebulae., Comment: 17 pages, 10 figures; to appear in The Astrophysical Journal (accepted 20 Jan. 2025)

A key tenet of the Transactional Interpretation of Quantum Mechanics is the idea that photon absorption localizes the absorbing material system. In doing so, it measures the location of the absorber and hence reduces information entropy, which in turn needs to be balanced by appropriate entropy production, if there is a link between information entropy and thermodynamic entropy. Based on a critical analysis of the physics of information erasure, we clarify the link between information and thermodynamic entropy and develop a rigorous model of entropy production in photon absorption processes. Links to the intepretation of quantum probabilities and Maxwell's Demon are made., Comment: Published In Scientific Reports (Springer Nature)

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