Search

Architected materials achieve unique mechanical properties through precisely engineered microstructures that minimize material usage. However, a key challenge of low-density materials is balancing high stiffness with stable deformability up to large strains. Current microstructures, which employ slender elements such as thin beams and plates arranged in periodic patterns to optimize stiffness, are largely prone to instabilities, including buckling and brittle collapse at low strains. This challenge is here addressed by introducing a new class of aperiodic architected materials inspired by quasicrystalline lattices. Beam networks derived from canonical quasicrystalline patterns, such as the Penrose tiling in 2D and icosahedral quasicrystals in 3D, are shown to create stiff, stretching-dominated topologies with non-uniform force chain distributions, effectively mitigating the global instabilities observed in periodic designs. Numerical and experimental results confirm the effectiveness of these designs in combining stiffness and stable deformability at large strains, representing a significant advancement in the development of low-density metamaterials for applications requiring high impact resistance and energy absorption. Our results demonstrate the potential of deterministic quasi-periodic topologies to bridge the gap between periodic and random structures, while branching towards uncharted territory in the property space of architected materials.

We introduce HyperCAN, a machine learning framework that utilizes hypernetworks to construct adaptable constitutive artificial neural networks for a wide range of beam-based metamaterials exhibiting diverse mechanical behavior under finite deformations. HyperCAN integrates an input convex network that models the nonlinear stress-strain map of a truss lattice, while ensuring adherence to fundamental mechanics principles, along with a hypernetwork that dynamically adjusts the parameters of the convex network as a function of the lattice topology and geometry. This unified framework demonstrates robust generalization in predicting the mechanical behavior of previously unseen metamaterial designs and loading scenarios well beyond the training domain. We show how HyperCAN can be integrated into multiscale simulations to accurately capture the highly nonlinear responses of large-scale truss metamaterials, closely matching fully resolved simulations while significantly reducing computational costs. This offers new efficient opportunities for the multiscale design and optimization of truss metamaterials.

We report the discovery of the transiting planet GJ 238 b, with a radius of $0.566\pm0.014$ R$_{\oplus}$ ($1.064\pm0.026$ times the radius of Mars) and an orbital period of 1.74 day. The transit signal was detected by the TESS mission and designated TOI-486.01. The star's position close to the Southern ecliptic pole allows for almost continuous observations by TESS when it is observing the Southern sky. The host star is an M2.5 dwarf with $V=11.57\pm0.02$ mag, $K=7.030\pm0.023$ mag, a distance of $15.2156\pm0.0030$ pc, a mass of $0.4193_{-0.0098}^{+0.0095}$ M$_{\odot}$, a radius of $0.4314_{-0.0071}^{+0.0075}$ R$_{\odot}$, and an effective temperature of $3{,}485\pm140$ K. We validate the planet candidate by ruling out or rendering highly unlikely each of the false positive scenarios, based on archival data and ground-based follow-up observations. Validation was facilitated by the host star's small size and high proper motion, of $892.633\pm0.025$ mas yr$^{-1}$., Comment: Published in AJ

We report the discovery and validation of HD 21520 b, a transiting planet found with TESS and orbiting a bright G dwarf (V=9.2, $T_{eff} = 5871 \pm 62$ K, $R_{\star} = 1.04\pm 0.02\, R_{\odot}$). HD 21520 b was originally alerted as a system (TOI-4320) consisting of two planet candidates with periods of 703.6 and 46.4 days. However, our analysis supports instead a single-planet system with an orbital period of $25.1292\pm0.0001$ days and radius of $2.70 \pm 0.09\, R_{\oplus}$. Three full transits in sectors 4, 30 and 31 match this period and have transit depths and durations in agreement with each other, as does a partial transit in sector 3. We also observe transits using CHEOPS and LCOGT. SOAR and Gemini high-resolution imaging do not indicate the presence of any nearby companions, and MINERVA-Australis and CORALIE radial velocities rule out an on-target spectroscopic binary. Additionally, we use ESPRESSO radial velocities to obtain a tentative mass measurement of $7.9^{+3.2}_{-3.0}\, M_{\oplus}$, with a 3-$\sigma$ upper limit of 17.7 $M_{\oplus}$. Due to the bright nature of its host and likely significant gas envelope of the planet, HD 21520 b is a promising candidate for further mass measurements and for atmospheric characterization., Comment: Submitted to MNRAS

Positron emission tomography (PET) is a well-established functional imaging technique for diagnosing brain disorders. However, PET's high costs and radiation exposure limit its widespread use. In contrast, magnetic resonance imaging (MRI) does not have these limitations. Although it also captures neurodegenerative changes, MRI is a less sensitive diagnostic tool than PET. To close this gap, we aim to generate synthetic PET from MRI. Herewith, we introduce PASTA, a novel pathology-aware image translation framework based on conditional diffusion models. Compared to the state-of-the-art methods, PASTA excels in preserving both structural and pathological details in the target modality, which is achieved through its highly interactive dual-arm architecture and multi-modal condition integration. A cycle exchange consistency and volumetric generation strategy elevate PASTA's capability to produce high-quality 3D PET scans. Our qualitative and quantitative results confirm that the synthesized PET scans from PASTA not only reach the best quantitative scores but also preserve the pathology correctly. For Alzheimer's classification, the performance of synthesized scans improves over MRI by 4%, almost reaching the performance of actual PET. Code is available at https://github.com/ai-med/PASTA.

The nucleation and propagation of disconnections play an essential role during twin growth. Atomistic methods can reveal such small structural features on twin facets and model their motion, yet are limited by the simulation length and time scales. Alternatively, mesoscale modeling approaches (such as the phase field method) address these constraints of atomistic simulations and can maintain atomic-level accuracy when integrated with atomic-level information. In this work, a phase field model is used to simulate the disconnection-mediated twinning, informed by molecular dynamics (MD) simulations. This work considers the specific case of the growth of $\{10\bar{1}2\}$ twin in magnesium. MD simulations are first conducted to obtain the orientation-dependent interface mobility and motion threshold, and to simulate twin embryo growth and collect facet velocities, which can be used for calibrating the continuum model. The phase field disconnections model, based on the principle of minimum dissipation potential, provides the theoretical framework. This model incorporates a nonconvex grain boundary energy, elasticity and shear coupling, and simulates disconnections as a natural emergence under the elastic driving force. The phase field model is further optimized by including the anisotropic interface mobility and motion threshold suggested by MD simulations. Results agree with MD simulations of twin embryo growth in the aspects of final twin thickness, twin shape, and twin size, as well as the kinetic behavior of twin boundaries and twin tips. The simulated twin microstructure is also consistent with experimental observations, demonstrating the fidelity of the model.

Studying the relative orientations of the orbits of exoplanets and wide-orbiting binary companions (semimajor axis greater than 100 AU) can shed light on how planets form and evolve in binary systems. Previous observations by multiple groups discovered a possible alignment between the orbits of visual binaries and the exoplanets that reside in them. In this study, using data from \textit{Gaia} DR3 and TESS, we confirm the existence of an alignment between the orbits of small planets $(R<6 R_\oplus)$ and binary systems with semimajor axes below 700 AU ($p=10^{-6}$). However, we find no statistical evidence for alignment between planet and binary orbits for binary semimajor axes greater than 700 AU, and no evidence for alignment of large, closely-orbiting planets (mostly hot Jupiters) and binaries at any separation. The lack of orbital alignment between our large planet sample and their binary companions appears significantly different from our small planet sample, even taking into account selection effects. Therefore, we conclude that any alignment between wide-binaries and our sample of large planets (predominantly hot Jupiters) is probably not as strong as what we observe for small planets in binaries with semimajor axes less than 700 AU. The difference in the alignment distribution of hot Jupiters and smaller planets may be attributed to the unique evolutionary mechanisms occuring in systems that form hot Jupiters, including potentially destabilizing secular resonances that onset as the protoplanetary disk dissipates and high-eccentricity migration occurring after the disk is gone., Comment: 16 pages, 10 figures, submitted to AJ. Email corresponding author for data files

Incorporating scientific knowledge into deep learning (DL) models for materials-based simulations can constrain the network's predictions to be within the boundaries of the material system. Altering loss functions or adding physics-based regularization (PBR) terms to reflect material properties informs a network about the physical constraints the simulation should obey. The training and tuning process of a DL network greatly affects the quality of the model, but how this process differs when using physics-based loss functions or regularization terms is not commonly discussed. In this manuscript, several PBR methods are implemented to enforce stress equilibrium on a network predicting the stress fields of a high elastic contrast composite. Models with PBR enforced the equilibrium constraint more accurately than a model without PBR, and the stress equilibrium converged more quickly. More importantly, it was observed that independently fine-tuning each implementation resulted in more accurate models. More specifically, each loss formulation and dataset required different learning rates and loss weights for the best performance. This result has important implications on assessing the relative effectiveness of different DL models and highlights important considerations when making a comparison between DL methods.

Differential diagnosis of dementia is challenging due to overlapping symptoms, with structural magnetic resonance imaging (MRI) being the primary method for diagnosis. Despite the clinical value of computer-aided differential diagnosis, research has been limited, mainly due to the absence of public datasets that contain diverse types of dementia. This leaves researchers with small in-house datasets that are insufficient for training deep neural networks (DNNs). Self-supervised learning shows promise for utilizing unlabeled MRI scans in training, but small batch sizes for volumetric brain scans make its application challenging. To address these issues, we propose Triplet Training for differential diagnosis with limited target data. It consists of three key stages: (i) self-supervised pre-training on unlabeled data with Barlow Twins, (ii) self-distillation on task-related data, and (iii) fine-tuning on the target dataset. Our approach significantly outperforms traditional training strategies, achieving a balanced accuracy of 75.6%. We further provide insights into the training process by visualizing changes in the latent space after each step. Finally, we validate the robustness of Triplet Training in terms of its individual components in a comprehensive ablation study. Our code is available at https://github.com/ai-med/TripletTraining., Comment: Accepted for presentation at MIDL 2024

Generative models such as denoising diffusion models are quickly advancing their ability to approximate highly complex data distributions. They are also increasingly leveraged in scientific machine learning, where samples from the implied data distribution are expected to adhere to specific governing equations. We present a framework to inform denoising diffusion models of underlying constraints on such generated samples during model training. Our approach improves the alignment of the generated samples with the imposed constraints and significantly outperforms existing methods without affecting inference speed. Additionally, our findings suggest that incorporating such constraints during training provides a natural regularization against overfitting. Our framework is easy to implement and versatile in its applicability for imposing equality and inequality constraints as well as auxiliary optimization objectives., Comment: 15 pages, 4 figures; added further theoretical motivation, new residual estimation mechanism and additional experimental study

Predicting the mechanics of large structural networks, such as beam-based architected materials, requires a multiscale computational strategy that preserves information about the discrete structure while being applicable to large assemblies of struts. Especially the fracture properties of such beam lattices necessitate a two-scale modeling strategy, since the fracture toughness depends on discrete beam failure events, while the application of remote loads requires large simulation domains. As classical homogenization techniques fail in the absence of a separation of scales at the crack tip, we present a concurrent multiscale technique: a fully-nonlocal quasicontinuum (QC) multi-lattice formulation for beam networks, based on a conforming mesh. Like the original atomistic QC formulation, we maintain discrete resolution where needed (such as around a crack tip) while efficiently coarse-graining in the remaining simulation domain. A key challenge is a suitable model in the coarse-grained domain, where classical QC uses affine interpolations. This formulation fails in bending-dominated lattices, as it overconstrains the lattice by preventing bending without stretching of beams. Therefore, we here present a beam QC formulation based on mixed-order interpolation in the coarse-grained region -- combining the efficiency of linear interpolation where possible with the accuracy advantages of quadratic interpolation where needed. This results in a powerful computational framework, which, as we demonstrate through our validation and benchmark examples, overcomes the deficiencies of previous QC formulations and enables, e.g., the prediction of the fracture toughness and the diverse nature of stress distributions of stretching- and bending-dominated beam lattices in two and three dimensions.

Grain boundary (GB) properties greatly influence the mechanical, electrical, and thermal response of polycrystalline materials. Most computational studies of GB properties at finite temperatures use molecular dynamics (MD), which is computationally expensive, limited in the range of accessible timescales, and requires cumbersome techniques like thermodynamic integration to estimate free energies. This restricts the reasonable computation (without incurring excessive computational expense) of GB properties to regimes that are often unrealistic, such as zero temperature or extremely high strain rates. Consequently, there is a need for simulation methodology that avoids the timescale limitations of MD, while providing reliable estimates of GB properties. The Gaussian Phase-Packet (GPP) method is a temporal coarse-graining technique that can predict relaxed atomic structures at finite temperature in the quasistatic limit. This work applies GPP, combined with the quasiharmonic approximation for computing the free energy, to the problem of determining the free energy and shear coupling factor of grain boundaries in metals over a range of realistic temperatures. Validation is achieved by comparison to thermodynamic integration, which confirms that the presented approach captures relaxed-energy GB structures and shear coupling factors at finite temperature with a high degree of accuracy.

The effect of stellar multiplicity on planetary architecture and orbital dynamics provides an important context for exoplanet demographics. We present a volume-limited catalog up to 300 pc of 66 stars hosting planets and planet candidates from Kepler, K2 and TESS with significant Hipparcos-Gaia proper motion anomalies, which indicate the presence of companions. We assess the reliability of each transiting planet candidate using ground-based follow-up observations, and find that the TESS Objects of Interest (TOIs) with significant proper motion anomalies show nearly four times more false positives due to Eclipsing Binaries compared to TOIs with marginal proper motion anomalies. In addition, we find tentative evidence that orbital periods of planets orbiting TOIs with significant proper motion anomalies are shorter than those orbiting TOIs without significant proper motion anomalies, consistent with the scenario that stellar companions can truncate planet-forming disks. Furthermore, TOIs with significant proper motion anomalies exhibit lower Gaia differential velocities in comparison to field stars with significant proper motion anomalies, suggesting that planets are more likely to form in binary systems with low-mass substellar companions or stellar companions at wider separation. Finally, we characterize the three-dimensional architecture of LTT 1445 ABC using radial velocities, absolute astrometry from Gaia and Hipparcos, and relative astrometry from imaging. Our analysis reveals that LTT 1445 is a nearly flat system, with a mutual inclination of 2.88 deg between the orbit of BC around A and that of C around B. The coplanarity may explain why multiple planets around LTT 1445 A survive in the dynamically hostile environment of this system., Comment: 28 pages, 9 figures, published by AJ

We present ground and space-based photometric observations of TOI-270 (L231-32), a system of three transiting planets consisting of one super-Earth and two sub-Neptunes discovered by TESS around a bright (K-mag=8.25) M3V dwarf. The planets orbit near low-order mean-motion resonances (5:3 and 2:1), and are thus expected to exhibit large transit timing variations (TTVs). Following an extensive observing campaign using 8 different observatories between 2018 and 2020, we now report a clear detection of TTVs for planets c and d, with amplitudes of $\sim$10 minutes and a super-period of $\sim$3 years, as well as significantly refined estimates of the radii and mean orbital periods of all three planets. Dynamical modeling of the TTVs alone puts strong constraints on the mass ratio of planets c and d and on their eccentricities. When incorporating recently published constraints from radial velocity observations, we obtain masses of $M_{\mathrm{b}}=1.48\pm0.18\,M_\oplus$, $M_{c}=6.20\pm0.31\,M_\oplus$ and $M_{\mathrm{d}}=4.20\pm0.16\,M_\oplus$ for planets b, c and d, respectively. We also detect small, but significant eccentricities for all three planets : $e_\mathrm{b} =0.0167\pm0.0084$, $e_{c} =0.0044\pm0.0006$ and $e_{d} = 0.0066\pm0.0020$. Our findings imply an Earth-like rocky composition for the inner planet, and Earth-like cores with an additional He/H$_2$O atmosphere for the outer two. TOI-270 is now one of the best-constrained systems of small transiting planets, and it remains an excellent target for atmospheric characterization., Comment: 22 pages, 7 figures

JWST has ushered in an era of unprecedented ability to characterize exoplanetary atmospheres. While there are over 5,000 confirmed planets, more than 4,000 TESS planet candidates are still unconfirmed and many of the best planets for atmospheric characterization may remain to be identified. We present a sample of TESS planets and planet candidates that we identify as "best-in-class" for transmission and emission spectroscopy with JWST. These targets are sorted into bins across equilibrium temperature $T_{\mathrm{eq}}$ and planetary radius $R{_\mathrm{p}}$ and are ranked by transmission and emission spectroscopy metric (TSM and ESM, respectively) within each bin. In forming our target sample, we perform cuts for expected signal size and stellar brightness, to remove sub-optimal targets for JWST. Of the 194 targets in the resulting sample, 103 are unconfirmed TESS planet candidates, also known as TESS Objects of Interest (TOIs). We perform vetting and statistical validation analyses on these 103 targets to determine which are likely planets and which are likely false positives, incorporating ground-based follow-up from the TESS Follow-up Observation Program (TFOP) to aid the vetting and validation process. We statistically validate 23 TOIs, marginally validate 33 TOIs to varying levels of confidence, deem 29 TOIs likely false positives, and leave the dispositions for 4 TOIs as inconclusive. 14 of the 103 TOIs were confirmed independently over the course of our analysis. We provide our final best-in-class sample as a community resource for future JWST proposals and observations. We intend for this work to motivate formal confirmation and mass measurements of each validated planet and encourage more detailed analysis of individual targets by the community., Comment: Submitted to AJ. Machine-readable versions of Tables 2 and 3 are included. 40 pages, 7 figures, 3 tables

Although metamaterials have been widely used for controlling elastic waves through bandgap engineering, the directed guidance of stress waves in non-periodic structures has remained a challenge. This work demonstrates that spatially graded metamaterials based on conformal mappings present a rich design space for controlling and attenuating wave motion - without the need for bandgaps. Conformal mappings transform an elementary unit cell by scaling and rotation into graded lattices with approximately geometrically similar unit cells. This self-similarity allows for control over the local wave dispersion throughout the metamaterial. As a key mechanism, it is shown that elastic waves cannot propagate through graded unit cells with significant size differences, except at low frequencies. This is exploited to create low-pass elastic wave guides, extending beyond classical bandgap engineering, since bandgaps are not required to achieve wave guiding and attenuation. Experiments confirm the low-pass elastic wave filtering capability of a planar truss metamaterial with conformal grading. Finally, a systematic design of curved metamaterial surfaces is presented, providing a flexible framework for programming low-pass attenuation and wave guiding in three dimensions.

The rise of machine learning has fueled the discovery of new materials and, especially, metamaterials--truss lattices being their most prominent class. While their tailorable properties have been explored extensively, the design of truss-based metamaterials has remained highly limited and often heuristic, due to the vast, discrete design space and the lack of a comprehensive parameterization. We here present a graph-based deep learning generative framework, which combines a variational autoencoder and a property predictor, to construct a reduced, continuous latent representation covering an enormous range of trusses. This unified latent space allows for the fast generation of new designs through simple operations (e.g., traversing the latent space or interpolating between structures). We further demonstrate an optimization framework for the inverse design of trusses with customized mechanical properties in both the linear and nonlinear regimes, including designs exhibiting exceptionally stiff, auxetic, pentamode-like, and tailored nonlinear behaviors. This generative model can predict manufacturable (and counter-intuitive) designs with extreme target properties beyond the training domain.

We report the discovery of two warm sub-Neptunes transiting the bright (G = 9.5 mag) K-dwarf HD 15906 (TOI 461, TIC 4646810). This star was observed by the Transiting Exoplanet Survey Satellite (TESS) in sectors 4 and 31, revealing two small transiting planets. The inner planet, HD 15906 b, was detected with an unambiguous period but the outer planet, HD 15906 c, showed only two transits separated by $\sim$ 734 days, leading to 36 possible values of its period. We performed follow-up observations with the CHaracterising ExOPlanet Satellite (CHEOPS) to confirm the true period of HD 15906 c and improve the radius precision of the two planets. From TESS, CHEOPS and additional ground-based photometry, we find that HD 15906 b has a radius of 2.24 $\pm$ 0.08 R$_\oplus$ and a period of 10.924709 $\pm$ 0.000032 days, whilst HD 15906 c has a radius of 2.93$^{+0.07}_{-0.06}$ R$_\oplus$ and a period of 21.583298$^{+0.000052}_{-0.000055}$ days. Assuming zero bond albedo and full day-night heat redistribution, the inner and outer planet have equilibrium temperatures of 668 $\pm$ 13 K and 532 $\pm$ 10 K, respectively. The HD 15906 system has become one of only six multiplanet systems with two warm ($\lesssim$ 700 K) sub-Neptune sized planets transiting a bright star (G $\leq$ 10 mag). It is an excellent target for detailed characterisation studies to constrain the composition of sub-Neptune planets and test theories of planet formation and evolution., Comment: 30 pages, 20 figures, 11 tables (including appendix). Published in MNRAS

This work proposes a method to accelerate the acquisition of high-quality edited magnetic resonance spectroscopy (MRS) scans using machine learning models taking the sample covariance matrix as input. The method is invariant to the number of transients and robust to noisy input data for both synthetic as well as in-vivo scenarios.

The accelerated inverse design of complex material properties - such as identifying a material with a given stress-strain response over a nonlinear deformation path - holds great potential for addressing challenges from soft robotics to biomedical implants and impact mitigation. While machine learning models have provided such inverse mappings, they are typically restricted to linear target properties such as stiffness. To tailor the nonlinear response, we here show that video diffusion generative models trained on full-field data of periodic stochastic cellular structures can successfully predict and tune their nonlinear deformation and stress response under compression in the large-strain regime, including buckling and contact. Unlike commonly encountered black-box models, our framework intrinsically provides an estimate of the expected deformation path, including the full-field internal stress distribution closely agreeing with finite element simulations. This work has thus the potential to simplify and accelerate the identification of materials with complex target performance., Comment: 21 pages, 3 figures

This literature review presents a comprehensive overview of machine learning (ML) applications in proton magnetic resonance spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the magnetic resonance field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in-vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.

We present the discovery and confirmation of a transiting hot, bloated Super-Neptune using photometry from TESS and LCOGT and radial velocity measurements from HARPS. The host star TOI-2498 is a V = 11.2, G-type (T$_{eff}$ = 5905 $\pm$ 12K) solar-like star with a mass of 1.12 $\pm$ 0.02 M$_{\odot}$ and a radius of 1.26 $\pm$ 0.04 R$_{\odot}$. The planet, TOI-2498 b, orbits the star with a period of 3.7 days, has a radius of 6.1 $\pm$ 0.3 R$_{\oplus}$, and a mass of 35 $\pm$ 4 M$_{\oplus}$. This results in a density of 0.86 $\pm$ 0.25 g cm$^{-3}$. TOI-2498 b resides on the edge of the Neptune desert; a region of mass-period parameter space in which there appears to be a dearth of planets. Therefore TOI-2498 b is an interesting case to study to further understand the origins and boundaries of the Neptune desert. Through modelling the evaporation history, we determine that over its $\sim$3.6 Gyr lifespan, TOI-2498 b has likely reduced from a Saturn sized planet to its current radius through photoevaporation. Moreover, TOI-2498 b is a potential candidate for future atmospheric studies searching for species like water or sodium in the optical using high-resolution, and for carbon based molecules in the infra-red using JWST., Comment: 13 pages, 11 figures, accepted for publication in MNRAS

We report the detection and validation of two planets orbiting TOI-2095 (TIC 235678745). The host star is a 3700K M1V dwarf with a high proper motion. The star lies at a distance of 42 pc in a sparsely populated portion of the sky and is bright in the infrared (K=9). With data from 24 Sectors of observation during TESS's Cycles 2 and 4, TOI-2095 exhibits two sets of transits associated with super-Earth-sized planets. The planets have orbital periods of 17.7 days and 28.2 days and radii of 1.30 and 1.39 Earth radii, respectively. Archival data, preliminary follow-up observations, and vetting analyses support the planetary interpretation of the detected transit signals. The pair of planets have estimated equilibrium temperatures of approximately 400 K, with stellar insolations of 3.23 and 1.73 times that of Earth, placing them in the Venus zone. The planets also lie in a radius regime signaling the transition between rock-dominated and volatile-rich compositions. They are thus prime targets for follow-up mass measurements to better understand the properties of warm, transition radius planets. The relatively long orbital periods of these two planets provide crucial data that can help shed light on the processes that shape the composition of small planets orbiting M dwarfs., Comment: Submitted to AAS Journals