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The ability to manipulate complex systems, such as the brain, to modify specific outcomes has far-reaching implications, particularly in the treatment of psychiatric disorders. One approach to designing appropriate manipulations is to target key features of predictive models. While generative latent variable models, such as probabilistic principal component analysis (PPCA), is a powerful tool for identifying targets, they struggle incorporating information relevant to low-variance outcomes into the latent space. When stimulation targets are designed on the latent space in such a scenario, the intervention can be suboptimal with minimal efficacy. To address this problem, we develop a novel objective based on supervised variational autoencoders (SVAEs) that enforces such information is represented in the latent space. The novel objective can be used with linear models, such as PPCA, which we refer to as generative principal component regression (gPCR). We show in simulations that gPCR dramatically improves target selection in manipulation as compared to standard PCR and SVAEs. As part of these simulations, we develop a metric for detecting when relevant information is not properly incorporated into the loadings. We then show in two neural datasets related to stress and social behavior in which gPCR dramatically outperforms PCR in predictive performance and that SVAEs exhibit low incorporation of relevant information into the loadings. Overall, this work suggests that our method significantly improves target selection for manipulation using latent variable models over competitor inference schemes.

Astrophysical simulations are computation, memory, and thus energy intensive, thereby requiring new hardware advances for progress. Stony Brook University recently expanded its computing cluster "SeaWulf" with an addition of 94 new nodes featuring Intel Sapphire Rapids Xeon Max series CPUs. We present a performance and power efficiency study of this hardware performed with FLASH: a multi-scale, multi-physics, adaptive mesh-based software instrument. We extend this study to compare performance to that of Stony Brook's Ookami testbed which features ARM-based A64FX-700 processors, and SeaWulf's AMD EPYC Milan and Intel Skylake nodes. Our application is a stellar explosion known as a thermonuclear (Type Ia) supernova and for this 3D problem, FLASH includes operators for hydrodynamics, gravity, and nuclear burning, in addition to routines for the material equation of state. We perform a strong-scaling study with a 220 GB problem size to explore both single- and multi-node performance. Our study explores the performance of different MPI mappings and the distribution of processors across nodes. From these tests, we determined the optimal configuration to balance runtime and energy consumption for our application., Comment: Accepted to PEARC '24 (Practice and Experience in Advanced Research Computing)

Traditional one-shot medical image segmentation (MIS) methods use registration networks to propagate labels from a reference atlas or rely on comprehensive sampling strategies to generate synthetic labeled data for training. However, these methods often struggle with registration errors and low-quality synthetic images, leading to poor performance and generalization. To overcome this, we introduce a novel one-shot MIS framework based on knowledge distillation, which allows the network to directly 'see' real images through a distillation process guided by image reconstruction. It focuses on anatomical structures in a single labeled image and a few unlabeled ones. A registration-based data augmentation network creates realistic, labeled samples, while a feature distillation module helps the student network learn segmentation from these samples, guided by the teacher network. During inference, the streamlined student network accurately segments new images. Evaluations on three public datasets (OASIS for T1 brain MRI, BCV for abdomen CT, and VerSe for vertebrae CT) show superior segmentation performance and generalization across different medical image datasets and modalities compared to leading methods. Our code is available at https://github.com/NoviceFodder/OS-MedSeg.

All modern theories of gravitation, starting with Newton's, predict that gravity will affect the speed of light propagation. Einstein's theory of General Relativity famously predicted that the effect is twice the Newtonian value, a prediction that was verified during the 1919 solar eclipse. Recent theories of vector gravity can be interpreted to imply that gravity will have a different effect on pulsed light versus continuous-wave (CW) light propagating between the two mirrors of an optical cavity. Interestingly, we are not aware of any previous experiments to determine the relative effect of gravity on the propagation of pulsed versus CW light. In order to observe if there are small differences, we use a 6 GHz electro-optic frequency comb and low-noise CW laser to make careful measurements of the resonance frequencies of a high-finesse optical cavity. Once correcting for the effects of mirror dispersion, we determine that the cavity resonance frequencies for pulsed and CW light are the same to within our experimental error, which is on the order of $10^{-12}$ of the optical frequency, and one part in 700 of the expected gravitational shift., Comment: 12 pages

Oxidative potential (OP), which measures particulate matter's (PM) capacity to induce oxidative stress in the lungs, is increasingly recognized as an indicator of PM toxicity. Since OP is not routinely monitored, it can be challenging to estimate exposure and health impacts. Remote sensing data are commonly used to estimate PM mass concentration, but have never been used to estimate OP. In this study, we evaluate the potential of satellite images to estimate OP as measured by acellular ascorbic acid (OP AA) and dithiothreitol (OP DTT) assays of 24-hour PM10 sampled periodically over five years at three locations around Grenoble, France. We use a deep convolutional neural network to extract features of daily 3 m/pixel PlanetScope satellite images and train a multilayer perceptron to estimate OP at a 1 km spatial resolution based on the image features and common meteorological variables. The model captures more than half of the variation in OP AA and almost half of the variation in OP DTT (test set R2 = 0.62 and 0.48, respectively), with relative mean absolute error (MAE) of about 32%. Using only satellite images, the model still captures about half of the variation in OP AA and one third of the variation in OP DTT (test set R2 = 0.49 and 0.36, respectively) with relative MAE of about 37%. If confirmed in other areas, our approach could represent a low-cost method for expanding the temporal or spatial coverage of OP estimates.

Many uses of lasers place the highest importance on access to specific wavelength bands. For example, mobilizing optical-atomic clocks for a leap in sensing requires compact lasers at frequencies spread across the visible and near infrared. Integrated photonics enables high-performance, scalable laser platforms, however, customizing laser-gain media to support wholly new bands is challenging and often prohibitively mismatched in scalability to early quantum-based sensing and information systems. Here, we demonstrate a microresonator optical-parametric oscillator (OPO) that converts a pump laser to an output wave within a frequency span exceeding an octave. We achieve phase matching for oscillation by nanopatterning the microresonator to open a photonic-crystal bandgap on the mode of the pump laser. By adjusting the nanophotonic pattern and hence the bandgap, the ratio of output OPO wave frequency span to required pump laser tuning is more than 10,000. We also demonstrate tuning the oscillator in free-spectral-range steps, more finely with temperature, and minimal additive frequency noise of the laser-conversion process. Our work shows that nanophotonics offers control of laser conversion in microresonators, bridging phase-matching of nonlinear optics and application requirements for laser designs., Comment: 8 pages, 3 figures

Control of nonlinear interactions in microresonators enhances access to classical and quantum field states across nearly limitless bandwidth. A recent innovation has been to leverage coherent scattering of the intraresonator pump as a control of group-velocity dispersion and nonlinear frequency shifts, which are precursors for the dynamical evolution of new field states. A uniform periodicity nanostructure addresses backscattering with one resonator mode, and pumping that mode enables universal phase-matching for four-wave mixing with control by the bandgap. Yet, since nonlinear-resonator phenomena are intrinsically multimode and exhibit complex modelocking, here we demonstrate a new approach to controlling nonlinear interactions by creating bandgap modes completely separate from the pump laser. We explore this bandgap-detuned excitation regime through generation of benchmark optical parametric oscillators (OPOs) and soliton microcombs. Indeed, we show that mode-locked states are phase matched more effectively in the bandgap-detuned regime in which we directly control the modal Kerr shift with the bandgaps without perturbing the pump field. In particular, bandgap-detuned excitation enables an arbitrary control of backscattering as a versatile tool for mode-locked state engineering. Our experiments leverage nanophotonic resonators for phase matching of OPOs and solitons, leading to control over threshold power, conversion efficiency, and emission direction that enable application advances in high-capacity signaling and computing, signal generation, and quantum sensing.

Laser sources power extreme data transmission as well as computing acceleration, access to ultrahigh-speed signaling, and sensing for chemicals, distance, and pattern recognition. The ever-growing scale of these applications drives innovation in multi-wavelength lasers for massively parallel processing. We report a nanophotonic Kerr-resonator circuit that consumes the power of an input laser and generates a soliton frequency comb at approaching unit efficiency. By coupling forward and backward propagation, we realize a bidirectional Kerr resonator that supports universal phase matching but also opens excess loss by double-sided emission. Therefore, we induce reflection of the resonator's forward, external-coupling port to favor backward propagation, resulting in efficient, one-sided soliton formation. Coherent backscattering with nanophotonics provides the control to put arbitrary phase-matching and efficient laser-power consumption on equal footing in Kerr resonators. In the overcoupled-resonator regime, we measure 65% conversion efficiency of a 40 mW input pump laser, and the nonlinear circuit consumes 97% of the pump, generating the maximum possible comb power. Our work opens up high-efficiency soliton formation in integrated photonics, exploring how energy flows in nonlinear circuits and enabling laser sources for advanced transmission, computing, quantum sensing, and artificial-intelligence applications.

The dielectric loss of silicon nitride (Si$_3$N$_4$) limits the performance of microwave-frequency devices that rely on this material for sensing, signal processing, and quantum communication. Using superconducting resonant circuits, we measure the cryogenic loss tangent of either as-deposited or high-temperature annealed stoichiometric Si$_3$N$_4$ as a function of drive strength and temperature. The internal loss behavior of the electrical resonators is largely consistent with the standard tunneling model of two-level systems (TLS), including damping caused by resonant energy exchange with TLS and by the relaxation of non-resonant TLS. We further supplement the TLS model with a self-heating effect to explain an increase in the loss observed in as-deposited films at large drive powers. Critically, we demonstrate that annealing remedies this anomalous power-induced loss, reduces the relaxation-type damping by more than two orders of magnitude, and reduces the resonant-type damping by a factor of three. Employing infrared absorption spectroscopy, we find that annealing reduces the concentration of hydrogen in the Si$_3$N$_4$, suggesting that hydrogen impurities cause substantial dissipation., Comment: 11 pages, 7 figures