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Inflammation of the umbilical cord can be seen as a result of ascending intrauterine infection or other inflammatory stimuli. Acute fetal inflammatory response (FIR) is characterized by infiltration of the umbilical cord by fetal neutrophils, and can be associated with neonatal sepsis or fetal inflammatory response syndrome. Recent advances in deep learning in digital pathology have demonstrated favorable performance across a wide range of clinical tasks, such as diagnosis and prognosis. In this study we classified FIR from whole slide images (WSI). We digitized 4100 histological slides of umbilical cord stained with hematoxylin and eosin(H&E) and extracted placental diagnoses from the electronic health record. We build models using attention-based whole slide learning models. We compared strategies between features extracted by a model (ConvNeXtXLarge) pretrained on non-medical images (ImageNet), and one pretrained using histopathology images (UNI). We trained multiple iterations of each model and combined them into an ensemble. The predictions from the ensemble of models trained using UNI achieved an overall balanced accuracy of 0.836 on the test dataset. In comparison, the ensembled predictions using ConvNeXtXLarge had a lower balanced accuracy of 0.7209. Heatmaps generated from top accuracy model appropriately highlighted arteritis in cases of FIR 2. In FIR 1, the highest performing model assigned high attention to areas of activated-appearing stroma in Wharton's Jelly. However, other high-performing models assigned attention to umbilical vessels. We developed models for diagnosis of FIR from placental histology images, helping reduce interobserver variability among pathologists. Future work may examine the utility of these models for identifying infants at risk of systemic inflammatory response or early onset neonatal sepsis.

We consider the problem of estimating the transition dynamics $T^*$ from near-optimal expert trajectories in the context of offline model-based reinforcement learning. We develop a novel constraint-based method, Inverse Transition Learning, that treats the limited coverage of the expert trajectories as a \emph{feature}: we use the fact that the expert is near-optimal to inform our estimate of $T^*$. We integrate our constraints into a Bayesian approach. Across both synthetic environments and real healthcare scenarios like Intensive Care Unit (ICU) patient management in hypotension, we demonstrate not only significant improvements in decision-making, but that our posterior can inform when transfer will be successful.

The placenta forms a critical barrier to infection through pregnancy, labor and, delivery. Inflammatory processes in the placenta have short-term, and long-term consequences for offspring health. Digital pathology and machine learning can play an important role in understanding placental inflammation, and there have been very few investigations into methods for predicting and understanding Maternal Inflammatory Response (MIR). This work intends to investigate the potential of using machine learning to understand MIR based on whole slide images (WSI), and establish early benchmarks. To that end, we use Multiple Instance Learning framework with 3 feature extractors: ImageNet-based EfficientNet-v2s, and 2 histopathology foundation models, UNI and Phikon to investigate predictability of MIR stage from histopathology WSIs. We also interpret predictions from these models using the learned attention maps from these models. We also use the MIL framework for predicting white blood cells count (WBC) and maximum fever temperature ($T_{max}$). Attention-based MIL models are able to classify MIR with a balanced accuracy of up to 88.5% with a Cohen's Kappa ($\kappa$) of up to 0.772. Furthermore, we found that the pathology foundation models (UNI and Phikon) are both able to achieve higher performance with balanced accuracy and $\kappa$, compared to ImageNet-based feature extractor (EfficientNet-v2s). For WBC and $T_{max}$ prediction, we found mild correlation between actual values and those predicted from histopathology WSIs. We used MIL framework for predicting MIR stage from WSIs, and compared effectiveness of foundation models as feature extractors, with that of an ImageNet-based model. We further investigated model failure cases and found them to be either edge cases prone to interobserver variability, examples of pathologist's overreach, or mislabeled due to processing errors.

Within batch reinforcement learning, safe policy improvement (SPI) seeks to ensure that the learnt policy performs at least as well as the behavior policy that generated the dataset. The core challenge in SPI is seeking improvements while balancing risk when many state-action pairs may be infrequently visited. In this work, we introduce Decision Points RL (DPRL), an algorithm that restricts the set of state-action pairs (or regions for continuous states) considered for improvement. DPRL ensures high-confidence improvement in densely visited states (i.e. decision points) while still utilizing data from sparsely visited states. By appropriately limiting where and how we may deviate from the behavior policy, we achieve tighter bounds than prior work; specifically, our data-dependent bounds do not scale with the size of the state and action spaces. In addition to the analysis, we demonstrate that DPRL is both safe and performant on synthetic and real datasets.

The Laser Interferometer Space Antenna (LISA) will be capable of detecting gravitational waves (GWs) in the milli-Hertz band. Among various sources, LISA will detect the coalescence of supermassive black hole binaries (SMBHBs). Accurate and rapid inference of parameters for such sources will be important for potential electromagnetic follow-up efforts. Rapid Bayesian inference with LISA includes additional complexities as compared to current generation terrestrial detectors in terms of time and frequency dependent antenna response functions. In this work, we extend a recently developed, computationally efficient technique that uses meshfree interpolation methods to accelerate Bayesian reconstruction of compact binaries. Originally developed for second-generation terrestrial detectors, this technique is now adapted for LISA parameter estimation. Using the full inspiral, merger, and ringdown waveform (PhenomD) and assuming rigid adiabatic antenna response function, we show faithful inference of SMBHB parameters from GW signals embedded in stationary, Gaussian instrumental noise. We discuss the computational cost and performance of the meshfree approximation method in estimating the GW source parameters., Comment: 18 pages, 6 figure

Evolution is often understood through genetic mutations driving changes in an organism's fitness, but there is potential to extend this understanding beyond the genetic code. We propose that natural products - complex molecules central to Earth's biochemistry can be used to uncover evolutionary mechanisms beyond genes. By applying Assembly Theory (AT), which views selection as a process not limited to biological systems, we can map and measure evolutionary forces in these molecules. AT enables the exploration of the assembly space of natural products, demonstrating how the principles of the selfish gene apply to these complex chemical structures, selecting vastly improbable and complex molecules from a vast space of possibilities. By comparing natural products with a broader molecular database, we can assess the degree of evolutionary contingency, providing insight into how molecular novelty emerges and persists. This approach not only quantifies evolutionary selection at the molecular level but also offers a new avenue for drug discovery by exploring the molecular assembly spaces of natural products. Our method provides a fresh perspective on measuring the evolutionary processes both, shaping and being read out, by the molecular imprint of selection., Comment: 25 pages, 7 figures, 30 references

Crystallisation is an important phenomenon which facilitates the purification as well as structural and bulk phase material characterisation using crystallographic methods. However, different conditions can lead to a vast set of different crystal structure polymorphs and these often exhibit different physical properties, allowing materials to be tailored to specific purposes. This means the high dimensionality that can result from variations in the conditions which affect crystallisation, and the interaction between them, means that exhaustive exploration is difficult, time-consuming, and costly to explore. Herein we present a robotic crystal search engine for the automated and efficient high-throughput approach to the exploration of crystallisation conditions. The system comprises a closed-loop computer crystal-vision system that uses machine learning to both identify crystals and classify their identity in a multiplexed robotic platform. By exploring the formation of a well-known polymorph, we were able to show how a robotic system could be used to efficiently search experimental space as a function of relative polymorph amount and efficiently create a high dimensionality phase diagram with minimal experimental budget and without expensive analytical techniques such as crystallography. In this way, we identify the set of polymorphs possible within a set of experimental conditions, as well as the optimal values of these conditions to grow each polymorph., Comment: 18 pages, 6 figures, 20 references

We investigate the statistical behavior of a system comprising fore-aft symmetric rods confined between two vertically vibrating plates using numerical simulations closely resembling the experimental setup studied by Narayan et al., Science 317, 105 (2007). Our focus lies in studying the phase transition of the system from an isotropic phase to a nematic phase as we increase either the rod length or the rod concentration. Our simulations confirm the presence of long-range ordering in the ordered phase. Furthermore, we identify a phase characterized by a periodic ordering profile that disrupts translation symmetry. We also provide a detailed analysis of the translational and rotational diffusive dynamics of the rods. Interestingly, the translational diffusivity of the rods is found to increase with the rod concentration., Comment: 13 pages, 18 figures. Soft Matter, 2024, Advance Article

Here we demonstrate the first biochemistry-agnostic approach to map evolutionary relationships at the molecular scale, allowing the construction of phylogenetic models using mass spectrometry (MS) and Assembly Theory (AT) without elucidating molecular identities. AT allows us to estimate the complexity of molecules by deducing the amount of shared information stored within them when . By examining 74 samples from a diverse range of biotic and abiotic sources, we used tandem MS data to detect 24102 analytes (9262 unique) and 59518 molecular fragments (6755 unique). Using this MS dataset, together with AT, we were able to infer the joint assembly spaces (JAS) of samples from molecular analytes. We show how JAS allows agnostic annotation of samples without fingerprinting exact analyte identities, facilitating accurate determination of their biogenicity and taxonomical grouping. Furthermore, we developed an AT-based framework to construct a biochemistry-agnostic phylogenetic tree which is consistent with genome-based models and outperforms other similarity-based algorithms. Finally, we were able to use AT to track colony lineages of a single bacterial species based on phenotypic variation in their molecular composition with high accuracy, which would be challenging to track with genomic data. Our results demonstrate how AT can expand causal molecular inference to non-sequence information without requiring exact molecular identities, thereby opening the possibility to study previously inaccessible biological domains., Comment: 26 pages, 8 figures, 45 references

Positioning patients for scanning and interventional procedures is a critical task that requires high precision and accuracy. The conventional workflow involves manually adjusting the patient support to align the center of the target body part with the laser projector or other guiding devices. This process is not only time-consuming but also prone to inaccuracies. In this work, we propose an automated patient positioning system that utilizes a camera to detect specific hand gestures from technicians, allowing users to indicate the target patient region to the system and initiate automated positioning. Our approach relies on a novel multi-stage pipeline to recognize and interpret the technicians' gestures, translating them into precise motions of medical devices. We evaluate our proposed pipeline during actual MRI scanning procedures, using RGB-Depth cameras to capture the process. Results show that our system achieves accurate and precise patient positioning with minimal technician intervention. Furthermore, we validate our method on HaGRID, a large-scale hand gesture dataset, demonstrating its effectiveness in hand detection and gesture recognition.

We introduce a novel bottom-up approach for human body mesh reconstruction, specifically designed to address the challenges posed by partial visibility and occlusion in input images. Traditional top-down methods, relying on whole-body parametric models like SMPL, falter when only a small part of the human is visible, as they require visibility of most of the human body for accurate mesh reconstruction. To overcome this limitation, our method employs a "Divide and Fuse (D&F)" strategy, reconstructing human body parts independently before fusing them, thereby ensuring robustness against occlusions. We design Human Part Parametric Models (HPPM) that independently reconstruct the mesh from a few shape and global-location parameters, without inter-part dependency. A specially designed fusion module then seamlessly integrates the reconstructed parts, even when only a few are visible. We harness a large volume of ground-truth SMPL data to train our parametric mesh models. To facilitate the training and evaluation of our method, we have established benchmark datasets featuring images of partially visible humans with HPPM annotations. Our experiments, conducted on these benchmark datasets, demonstrate the effectiveness of our D&F method, particularly in scenarios with substantial invisibility, where traditional approaches struggle to maintain reconstruction quality., Comment: Accepted by ECCV2024

Spintronic-based neuromorphic hardware offers high-density and rapid data processing at nanoscale lengths by leveraging magnetic configurations like skyrmion and domain walls. Here, we present the maximal hardware implementation of a convolutional neural network (CNN) based on a compact multi-bit skyrmion-based synapse and a hybrid CMOS domain wall-based circuit for activation and max-pooling functionalities. We demonstrate the micromagnetic design and operation of a circular bilayer skyrmion system mimicking a scalable artificial synapse, demonstrated up to 6-bit (64 states) with an ultra-low energy consumption of 0.87 fJ per state update. We further show that the synaptic weight modulation is achieved by the perpendicular current interaction with the labyrinth-maze like uniaxial anisotropy profile, inducing skyrmionic gyration, thereby enabling long-term potentiation (LTP) and long-term depression (LTD) operations. Furthermore, we present a simultaneous rectified linear (ReLU) activation and max pooling circuitry featuring a SOT-based domain wall ReLU with a power consumption of 4.73 $\mu$W. The ReLU function, stabilized by a parabolic uniaxial anisotropy profile, encodes domain wall positions into continuous resistance states coupled with the HSPICE circuit simulator. Our integrated skyrmion and domain wall-based spintronic hardware achieves 98.07% accuracy in convolutional neural network (CNN) based pattern recognition task, consuming 110 mW per image., Comment: 15 pages, 10 figures

This paper uses a reactive flow large eddy simulation (LES) and decomposition techniques to study combustion instabilities in a methane-oxygen combustor. This work examines two case scenarios to elucidate the significance of injector-chamber frequency coupling as the cause of thermo-acoustic instability. Initial investigation in a well-known benchmark case of the continuously variable resonance combustor (CVRC) reports the potential instability mechanisms and the role of injector-chamber frequency coupling in thermo-acoustic instability. Subsequently, the multi-element rocket combustor case study identifies the critical resonant modes and highlights potential frequency coupling between the injector and the chamber region. The interplay between longitudinal pressure oscillations in the oxidizer post and transverse pressure waves in the chamber is responsible for the enhanced pressure dynamics in the combustor. The present work uses the dynamic mode decomposition (DMD) technique to reveal the evolution of acoustic modes in injector and chamber for CVRC and multi-element combustor. The dominant pressure mode forms found by DMD analysis also showcase the role of injector-chamber frequency coupling in amplified combustion dynamics. The results demonstrate how the predominant cause of combustion instability in rocket combustors can be effectively determined using the high-fidelity LES framework in conjunction with the modal decomposition technique.

Lablets are autonomous microscopic particles with programmable CMOS electronics that can control electrokinetic phenomena and electrochemical reactions in solution via actuator and sensor microelectrodes. In this paper, we describe the design and fabrication of optimized singulated lablets (CMOS3) with dimensions 140x140x50 micrometers carrying an integrated coplanar encapsulated supercapacitor as a rechargeable power supply. The lablets are designed to allow docking to one another or to a smart surface for interchange of energy, electronic information, and chemicals. The paper focusses on the digital and analog design of the lablets to allow significant programmable functionality in a microscopic footprint, including the control of autonomous actuation and sensing up to the level of being able to support a complete lablet self-reproduction life cycle, although experimentally this remains to be proven. The potential of lablets in autonomous sensing and control and for evolutionary experimentation are discussed., Comment: This article was originally submitted (2016) for review as one of a number of preprints as supporting information for the final review of the EU MICREAgents Project # 318671 (2012-2016). Here it is presented in slightly revised form. The version, v2 contains a reference to the verilog source code on GitHub

Lablets are autonomous microscopic particles with programmable CMOS electronics that canvcontrol electrokinetic phenomena and electrochemical reactions in solution via actuator and sensor microelectrodes. The lablets are designed to be rechargeable using an integrated supercapacitor, and to allow docking to one another or to a smart surface for interchange of energy, electronic information and chemicals. In this paper, we describe the design and fabrication of singulated lablets (CMOS2) at the scale of 100 by 200 {\mu}m, with the supercap adjacent to the functional lablet and occupying half the space. In other works, we have characterized the supercap and described the electronic design and proven functionality using arrays of these lablets. Here we present fabrication details for integrating functional coatings and the supercap and demonstrate electronic functionality of the lablets following singulation., Comment: This article was originally submitted (2016) for review as supporting information for the final review of the EU MICREAgents Project # 318671 (2012-2016). Here it is presented in slightly revised form

Understanding how structural flexibility affects the properties of metal-organic frameworks (MOFs) is crucial for the design of better MOFs for targeted applications. Flexible MOFs can be studied with molecular dynamics simulations, whose accuracy depends on the force-field used to describe the interatomic interactions. Density functional theory (DFT) and quantum-chemistry methods are highly accurate, but the computational overheads limit their use in long time-dependent simulations for large systems. In contrast, classical force fields usually struggle with the description of coordination bonds. In this work we develop a DFT-accurate machine-learning spectral neighbor analysis potential, trained on DFT energies, forces and stress tensors, for two representative MOFs, namely ZIF-8 and MOF-5. Their structural and vibrational properties are then studied as a function of temperature and tightly compared with available experimental data. Most importantly, we demonstrate an active-learning algorithm, based on mapping the relevant internal coordinates, which drastically reduces the number of training data to be computed at the DFT level. Thus, the workflow presented here appears as an efficient strategy for the study of flexible MOFs with DFT accuracy, but at a fraction of the DFT computational cost.

Spintronic nano-oscillators can generate tunable microwave signals that find a wide range of applications in the field of telecommunication to modern neuromorphic computing systems. Among other spintronic devices, a magnetic skyrmion is a promising candidate for the next generation of low-power devices due to its small size and topological stability. In this work, we propose a multi-channel oscillator design based on the synthetic anti-ferromagnetic (SAF) skyrmion pair. The mitigation of the skyrmion Hall effect in SAF and the associated decimation of the Magnus force endows the proposed oscillator with an ultra-high frequency of 41GHz and a multi-channel frequency output driven by the same current. The ultrahigh operational frequency represents an $\sim$342 times improvement compared to the monolayer single skyrmion oscillator featuring a constant uniaxial anisotropy profile. Using micromagnetic simulations, we demonstrate the effectiveness of our proposed multi-channel oscillator design by introducing multi-channel nanotracks along with multiple skyrmions for enhanced frequency operation. The ultrahigh operational frequency and multi-channel output are attributed to three key factors: The oscillator design accounting for a finite spin-flip length of the spacer (such as Ru) material, tangential velocity proportionality on input spin current along with weak dependence on the radius of rotation of the skyrmion-pair, skyrmion interlocking in the channel enabled by the multi-channel high Ku rings and skyrmion-skyrmion repulsion, therefore resulting ultrahigh frequency and multi-channel outputs.

Over the last two decades, non-trivial magnetic textures, especially the magnetic skyrmion family, have been extensively explored out of fundamental interest, and diverse possible applications. Given the possible technological and scientific ramifications of skyrmion-texture on magnetic tunnel junction (ST-MTJ), in this work, we present non-equilibrium Green's function (NEGF) based description of ST-MTJs both for N\'eel and Bloch textures, to capture the spin/charge current across different voltages, temperatures, and sizes. We predict the emergence of a textured spin current from the uniform layer of the ST-MTJs, along with a radially varying, asymmetrical voltage dependence of spin torque. We delineate the voltage-induced rotation of the spin current texture, coupled with the appearance of helicity in spin current, particularly in the case of N\'eel skyrmions on MTJs. We describe the TMR roll-off in ST-MTJ with lower cross-sectional area and higher temperature based on transmission spectra analysis. We also introduce a computationally efficient coupled spatio-eigen framework of NEGF to address the 3D-NEGF requirement of the ST-MTJs. With analytical underpinning, we establish the generic nature of the spatio-eigen framework of NEGF, alleviating the sine-qua-non of the 3D-NEGF for systems that lack transnational invariance and simultaneous eigen-basis in the transverse directions.

We present spintronic devices based hardware implementation of UNet for segmentation tasks. Our approach involves designing hardware for convolution, deconvolution, rectified activation function (ReLU), and max pooling layers of the UNet architecture. We designed the convolution and deconvolution layers of the network using the synaptic behavior of the domain wall MTJ. We also construct the ReLU and max pooling functions of the network utilizing the spin hall driven orthogonal current injected MTJ. To incorporate the diverse physics of spin-transport, magnetization dynamics, and CMOS elements in our UNet design, we employ a hybrid simulation setup that couples micromagnetic simulation, non-equilibrium Green's function, SPICE simulation along with network implementation. We evaluate our UNet design on the CamVid dataset and achieve segmentation accuracies of 83.71$\%$ on test data, on par with the software implementation with 821mJ of energy consumption for on-chip training over 150 epochs. We further demonstrate nearly one order $(10\times)$ improvement in the energy requirement of the network using unstable ferromagnet ($\Delta$=4.58) over the stable ferromagnet ($\Delta$=45) based ReLU and max pooling functions while maintaining the similar accuracy. The hybrid architecture comprising domain wall MTJ and unstable FM-based MTJ leads to an on-chip energy consumption of 85.79mJ during training, with a testing energy cost of 1.55 $\mu J$.