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Using normalizing flows and reweighting, Boltzmann Generators enable equilibrium sampling from a Boltzmann distribution, defined by an energy function and thermodynamic state. In this work, we introduce Thermodynamic Interpolation (TI), which allows for generating sampling statistics in a temperature-controllable way. We introduce TI flavors that work directly in the ambient configurational space, mapping between different thermodynamic states or through a latent, normally distributed reference state. Our ambient-space approach allows for the specification of arbitrary target temperatures, ensuring generalizability within the temperature range of the training set and demonstrating the potential for extrapolation beyond it. We validate the effectiveness of TI on model systems that exhibit metastability and non-trivial temperature dependencies. Finally, we demonstrate how to combine TI-based sampling to estimate free energy differences through various free energy perturbation methods and provide corresponding approximated kinetic rates estimated through generator extended dynamic mode decomposition (gEDMD)., Comment: 36 pages, 8 figures

We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most major branches of modern mathematics -- from computationally intensive problems in number theory and real analysis to abstract questions in algebraic geometry and category theory. Solving a typical problem requires multiple hours of effort from a researcher in the relevant branch of mathematics, and for the upper end questions, multiple days. FrontierMath uses new, unpublished problems and automated verification to reliably evaluate models while minimizing risk of data contamination. Current state-of-the-art AI models solve under 2% of problems, revealing a vast gap between AI capabilities and the prowess of the mathematical community. As AI systems advance toward expert-level mathematical abilities, FrontierMath offers a rigorous testbed that quantifies their progress.

Reconfigurable devices have garnered significant attention for alleviating the scaling requirements of conventional CMOS technology, as they require fewer components to construct circuits with similar function. Prior works required continuous voltage application for programming gate terminal(s) in addition to the primary gate terminal, which undermines the advantages of reconfigurable devices in realizing compact and power-efficient integrated circuits. Here, we realize reconfigurable devices based on a single-gate field-effect transistor (FET) architecture by integrating semiconducting channels consisting of a monolayer film of highly aligned SWCNTs with a ferroelectric AlScN gate dielectric, all compatible with CMOS back-end-of-line (BEOL) processing. We demonstrated these SWCNT ferroelectric FETs (FeFETs) in a centimeter-scale array (~1 cm^2) comprising ~735 devices, with high spatial uniformity in device characteristics across the array. The devices exhibited ambipolar transfer characteristics with high on-state currents and current on/off ratios exceeding 10^5, demonstrating an excellent balance between electron and hole conduction (~270 {\mu}A/{\mu}m at a drain voltage of 3 V. When functioning as a non-volatile memory, the SWCNT FeFETs demonstrated large memory windows of 0.26 V/nm and 0.08 V/nm in the hole and electron conduction regions, respectively, combined with excellent retention behavior for up to 10^4 s. Repeated reconfiguration between p-FET and n-FET modes was also enabled by switching the spontaneous polarization in AlScN and operating the transistor within a voltage range below the coercive voltage. We revealed through circuit simulations that reconfigurable SWCNT transistors can realize ternary content-addressable memory (TCAM) with far fewer devices compared to circuits based on silicon CMOS technology or based on resistive non-volatile devices., Comment: 37 pages, 16 figures (5 main and 11 supplementary figures)

General state-space models (SSMs) are widely used in statistical machine learning and are among the most classical generative models for sequential time-series data. SSMs, comprising latent Markovian states, can be subjected to variational inference (VI), but standard VI methods like the importance-weighted autoencoder (IWAE) lack functionality for streaming data. To enable online VI in SSMs when the observations are received in real time, we propose maximising an IWAE-type variational lower bound on the asymptotic contrast function, rather than the standard IWAE ELBO, using stochastic approximation. Unlike the recursive maximum likelihood method, which directly maximises the asymptotic contrast, our approach, called online sequential IWAE (OSIWAE), allows for online learning of both model parameters and a Markovian recognition model for inferring latent states. By approximating filter state posteriors and their derivatives using sequential Monte Carlo (SMC) methods, we create a particle-based framework for online VI in SSMs. This approach is more theoretically well-founded than recently proposed online variational SMC methods. We provide rigorous theoretical results on the learning objective and a numerical study demonstrating the method's efficiency in learning model parameters and particle proposal kernels.

Deep Brain Stimulation (DBS) is a therapy widely used for treating the symptoms of neurological disorders. Electrical pulses are chronically delivered in DBS to a disease-specific brain target via a surgically implanted electrode. The stimulating contact configuration, stimulation polarity, as well as amplitude, frequency, and pulse width of the DBS pulse sequence are utilized to optimize the therapeutic effect. In this paper, the utility of therapy individualization by means of patient-specific mathematical modeling is investigated with respect to a specific case of a patient diagnosed with Essential Tremor (ET). Two computational models are compared in their ability to elucidate the impact of DBS stimulation on the dentato-rubrothalamic tract: (i) a conventional model of Volume of Tissue Activated (VTA) and (ii) a well-established neural fiber activation modeling framework known as OSS-DBS. The simulation results are compared with tremor measured in the patient under different DBS settings using a smartphone application. The findings of the study highlight that temporally static VTA models do not adequately describe the differences in the outcomes of bipolar stimulation settings with switched polarity, whereas neural fiber activation models hold potential in this regard. However, it is noted that neither of the investigated models fully accounts for the measured symptom pattern, particularly regarding a bilateral effect produced by unilateral stimulation., Comment: 7 pages, 5 figures, 5 tables

Accurate prediction of thermodynamic properties is essential in drug discovery and materials science. Molecular dynamics (MD) simulations provide a principled approach to this task, yet they typically rely on prohibitively long sequential simulations. Implicit Transfer Operator (ITO) Learning offers a promising approach to address this limitation by enabling stable simulation with time steps orders of magnitude larger than MD. However, to train ITOs, we need extensive, unbiased MD data, limiting the scope of this framework. Here, we introduce Boltzmann Priors for ITO (BoPITO) to enhance ITO learning in two ways. First, BoPITO enables more efficient data generation, and second, it embeds inductive biases for long-term dynamical behavior, simultaneously improving sample efficiency by one order of magnitude and guaranteeing asymptotically unbiased equilibrium statistics. Further, we showcase the use of BoPITO in a new tunable sampling protocol interpolating ITO models trained on off-equilibrium simulation data and an unbiased equilibrium distribution to solve inverse problems in molecular science.

Diffusion models have recently shown considerable potential in solving Bayesian inverse problems when used as priors. However, sampling from the resulting denoising posterior distributions remains a challenge as it involves intractable terms. To tackle this issue, state-of-the-art approaches formulate the problem as that of sampling from a surrogate diffusion model targeting the posterior and decompose its scores into two terms: the prior score and an intractable guidance term. While the former is replaced by the pre-trained score of the considered diffusion model, the guidance term has to be estimated. In this paper, we propose a novel approach that utilises a decomposition of the transitions which, in contrast to previous methods, allows a trade-off between the complexity of the intractable guidance term and that of the prior transitions. We validate the proposed approach through extensive experiments on linear and nonlinear inverse problems, including challenging cases with latent diffusion models as priors, and demonstrate its effectiveness in reconstructing electrocardiogram (ECG) from partial measurements for accurate cardiac diagnosis.

We introduce a novel weather-adaptive approach for multi-step forecasting of multi-scale SOP changes in aerial fiber links. By harnessing the discrete wavelet transform and incorporating weather data, our approach improves forecasting accuracy by over 65% in RMSE and 63% in MAPE compared to baselines., Comment: ECOC 2024

We propose a novel unsupervised anomaly detection approach using generative adversarial networks and SOP-derived spectrograms. Demonstrating remarkable efficacy, our method achieves over 97% accuracy on SOP datasets from both submarine and terrestrial fiber links, all achieved without the need for labelled data., Comment: ECOC 2024

The program ftint is introduced which numerically evaluates dimensionally regulated integrals as they occur in the perturbative approach to the gradient-flow formalism in quantum field theory. It relies on sector decomposition in order to determine the coefficients of the individual orders in $\epsilon=(4-D)/2$, where $D$ is the space-time dimension. For that purpose, it implements an interface to the public library pySecDec. The current version works for massive and massless integrals up to three-loop level with vanishing external momenta, but the underlying method is extendable to more general cases., Comment: 30 pages, 1 figure; v2: matches published version; the program ftint is available from https://gitlab.com/ftint/ftint

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high spatial frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional (2D) images of vector magnetic fields acquired by a quantum diamond microscope (QDM) utilizing a surface layer of Nitrogen Vacancy (NV) centers in diamond. Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower SNR data, reducing the data collection time by a factor of about 400 and permitting reconstructions of weaker and three-dimensional current sources., Comment: 17 pages, 10 figures. Includes Supplemental Information

We present selected examples demonstrating an alternative approach to contour deformation for numerically computing loop integrals in the Minkowski regime. This method focuses on identifying singular hypersurfaces (varieties of the $\mathscr{F}$ polynomial) and mapping them to known points which can then be resolved by employing blow-ups/sector decomposition techniques, thereby avoiding the need for contour deformation. Using this technique, we achieve improved convergence properties without the need for contour deformation, which is known to significantly increase the complexity of the integrand by introducing, for example, derivatives of the $\mathscr{F}$ polynomial and complicated Jacobians. We highlight that while we have only tested the approach on selected one-, two- and three-loop massless and one-loop massive examples, it shows promise for practical applications, offering potential benefits over the traditional approach. Evaluation times are compared with existing contour deformation implementations to illustrate the performance of this alternative method., Comment: Contribution to proceedings of the Loops and Legs in Quantum Field Theory conference (LL2024)

We explain how to define an embedding of a tame stack over a noetherian ring into a certain generalization of a weighted projective stack using a notion of ample vector bundle on the stack. As applications we construct algebraic moduli stacks of tame stacks equipped with an ample vector bundle and algebraic stacks of tame orbicurves.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

We extend the notions of Hochschild and cyclic homology to morphisms from algebraic spaces to algebraic stacks. Using this, we obtain generalizations to log schemes in the sense of Fontaine and Illusie of these homology theories.

Generative models for 3D drug design have gained prominence recently for their potential to design ligands directly within protein pockets. Current approaches, however, often suffer from very slow sampling times or generate molecules with poor chemical validity. Addressing these limitations, we propose Semla, a scalable E(3)-equivariant message passing architecture. We further introduce a molecular generation model, SemlaFlow, which is trained using flow matching along with scale optimal transport, a novel extension of equivariant optimal transport. Our model produces state-of-the-art results on benchmark datasets with just 100 sampling steps. Crucially, SemlaFlow samples high quality molecules with as few as 20 steps, corresponding to a two order-of-magnitude speed-up compared to state-of-the-art, without sacrificing performance. Furthermore, we highlight limitations of current evaluation methods for 3D generation and propose new benchmark metrics for unconditional molecular generators. Finally, using these new metrics, we compare our model's ability to generate high quality samples against current approaches and further demonstrate SemlaFlow's strong performance., Comment: Preprint. Code to be released upon full publication

Ferroelectric field-effect transistors (FeFET) with two-dimensional (2D) semiconductor channels are promising low-power, embedded non-volatile memory (NVM) candidates for next-generation in-memory computing. However, the performance of FeFETs can be limited by a charge imbalance between the ferroelectric layer and the channel, and for low-dimensional semiconductors, also by a high contact resistance between the metal electrodes and the channel. Here, we report a significant enhancement in performance of contact-engineered FeFETs with a 2D MoS2 channel and a ferroelectric Al0.68Sc0.32N (AlScN) gate dielectric. Replacing Ti with In contact electrodes results in a fivefold increase in on-state current (~120 uA/um at 1 V) and on-to-off ratio (~2*10^7) in the FeFETs. In addition, the high carrier concentration in the MoS2 channel during the on-state (> 10^14 cm^-2) facilitates the observation of a metal-to-insulator phase transition in monolayer MoS2 permitting observation of high field effect mobility (> 100 cm^2V^-1s^-1) at cryogenic temperatures. Our work and devices broaden the potential of FeFETs and provides a unique platform to implement high-carrier-density transport in a 2D channel., Comment: 42 pages, 5 main figures

Bulk Acoustic Wave (BAW) filters find applications in radio frequency (RF) communication systems for Wi-Fi, 3G, 4G, and 5G networks. In the beyond-5G (potential 6G) era, high frequency bands (>8 GHz) are expected to require resonators with high-quality factor (Q) and electromechanical coupling (k_t^2) to form filters with low insertion loss and high selectivity. However, both the Q and k_t^2 of resonator devices formed in traditional uniform polarization piezoelectric films of aluminum nitride (AlN) and aluminum scandium nitride (AlScN) decrease when scaled beyond 8 GHz. In this work, we utilized 4-layer AlScN periodically poled piezoelectric films (P3F) to construct high frequency (~17-18 GHz) resonators and filters. The resonator performance is studied over a range of device geometries, with the best resonator achieving a k_t^2 of 11.8% and a Q_p of 236.6 at the parallel resonance frequency (fp) of 17.9 GHz. These resulting figures of merit are ((FoM)_1=(k_t^2 Q)_p and (FoM_2=f_p(FoM)_1x10^-9) ) 27.9 and 500 respectively. These and the k_t^2 are significantly higher than previously reported An/AlScN-based resonators operating at similar frequencies. Fabricated 3-element and 6-element filters formed from these resonators demonstrated low insertion losses (IL) of 1.86 dB and 3.25 dB, and -3 dB bandwidths (BW) of 680 MHz (fractional BW of 3.9%) and 590 MHz (fractional BW of 3.3%) at ~17.4 GHz center frequency. The 3-element and 6-element filters achieved excellent linearity with in-band input third-order intercept point (IIP3) values of +36 dBm and +40 dBm, respectively, which are significantly higher than previously reported acoustic filters operating at similar frequencies.

Transition metal dichalcogenide (TMD) materials have attracted substantial interest due to their remarkable excitonic, optical, electrical, and mechanical properties, which are highly dependent on their crystal structure. Controlling the crystal structure of these materials is essential for fine-tuning their performance, $\textit{e.g.}$, linear and nonlinear optical, as well as charge transport properties. While various phase-switching TMD materials, like molybdenum telluride (MoTe$_2$), are available, their transitions are often irreversible. Here, we investigate the mechanism of a light-induced reversible phase transition in mono- and bilayer flakes of rhenium disulfide (ReS$_2$). Our observations, based on scanning transmission electron microscopy, nonlinear spectroscopy, and density functional theory calculations, reveal a transition from the ground T$''$ (double distorted T) to the metastable H$'$ (distorted H) phase under femtosecond laser irradiation or influence of highly-energetic electrons. We show that the formation of sulfur vacancies facilitates this phenomenon. Our findings pave the way towards actively manipulating the crystal structure of ReS$_2$ and possibly its heterostructures.

Magic-angle twisted bilayer graphene (MATBG) stands as one of the most versatile materials in condensed-matter physics due to its hosting of a wide variety of exotic phases while also offering convenient tunability. However, the fabrication of MATBG is still manual, and remains to be a challenging and inefficient process, with devices being highly dependent on specific fabrication methods, that often result in inconsistency and variability. In this work, we present an optimized protocol for the fabrication of MATBG samples, for which we use deterministic graphene anchoring to stabilize the twist-angle, and a careful bubble removal techniques to ensure a high twist-angle homogeneity. We use low-temperature transport experiments to extract the average twist-angle between pairs of leads. We find that up to 38 percent of the so fabricated devices show micrometer square sized regions with a twist-angle in the range 1.1 plus/minus 0.1 degrees, and a twist-angle variation of only 0.02 degrees, where in some instances such regions were up to 36 micrometer square large. We are certain that the discussed protocols can be directly transferred to non-graphene materials, and will be useful for the growing field of moire materials.

Commercial production of integrated photonic devices is limited by scalability of desirable material platforms. We explore a relatively new photonic material, AlScN, for its use in electro-optic modulation. Its CMOS-compatibility could facilitate large-scale production of integrated photonic modulators, and it exhibits an enhanced second-order optical nonlinearity compared to intrinsic AlN, indicating the possibility for efficient modulation. Here, we measure the electro-optic effect in AlScN-based modulators, demonstrating $V_{\pi}L$ around 750 V$\cdot$cm. Since the electro-optic response is smaller than expected, we discuss potential causes for the reduced response and future outlook for AlScN-based photonics., Comment: 9 pages, 3 figures

Self-supervised pretraining (SSP) has been recognized as a method to enhance prediction accuracy in various downstream tasks. However, its efficacy for DNA sequences remains somewhat constrained. This limitation stems primarily from the fact that most existing SSP approaches in genomics focus on masked language modeling of individual sequences, neglecting the crucial aspect of encoding statistics across multiple sequences. To overcome this challenge, we introduce an innovative deep neural network model, which incorporates collaborative learning between a `student' and a `teacher' subnetwork. In this model, the student subnetwork employs masked learning on nucleotides and progressively adapts its parameters to the teacher subnetwork through an exponential moving average approach. Concurrently, both subnetworks engage in contrastive learning, deriving insights from two augmented representations of the input sequences. This self-distillation process enables our model to effectively assimilate both contextual information from individual sequences and distributional data across the sequence population. We validated our approach with preliminary pretraining using the human reference genome, followed by applying it to 20 downstream inference tasks. The empirical results from these experiments demonstrate that our novel method significantly boosts inference performance across the majority of these tasks. Our code is available at https://github.com/wiedersehne/FinDNA.

Silicon microelectronics, consisting of complementary metal oxide semiconductor (CMOS) technology, have changed nearly all aspects of human life from communication to transportation, entertainment, and healthcare. Despite the widespread and mainstream use, current silicon-based devices suffer significant reliability issues at temperatures exceeding 125 C. The emergent technological frontiers of space exploration, geothermal energy harvesting, nuclear energy, unmanned avionic systems, and autonomous driving will rely on control systems, sensors, and communication devices which operate at temperatures as high as 500 C and beyond. At these extreme temperatures, active (heat exchanger, phase change cooling) or passive (fins and thermal interface materials) cooling strategies add significant mass and complication which is often infeasible. Thus, new material solutions beyond conventional silicon CMOS devices are necessary for high temperature, resilient electronic systems. Accomplishing this will require a united effort to explore development, integration, and ultimately manufacturing of non-silicon-based logic and memory technologies, non-traditional metals for interconnects, and ceramic packaging technology., Comment: 6 Figures

The growth in data generation necessitates efficient data processing technologies to address the von Neumann bottleneck in conventional computer architecture. Memory-driven computing, which integrates non-volatile memory (NVM) devices in a 3D stack, is gaining attention, with CMOS back-end-of-line (BEOL) compatible ferroelectric (FE) diodes being ideal due to their two-terminal design and inherently selector-free nature, facilitating high-density crossbar arrays. Here, we demonstrate BEOL-compatible, high-performance FE-diodes scaled to 5, 10, and 20 nm FE Al0.72Sc0.28N/Al0.64Sc0.36N films. Through interlayer (IL) engineering, we show substantial improvements in the ON/OFF ratios (>166 times) and rectification ratios (>176 times) in these scaled devices. The superlative characteristics also enables 5-bit multi-state operation with a stable retention. We also experimentally and theoretically demonstrate the counterintuitive result that the inclusion of an IL can lead to a decrease in the ferroelectric switching voltage of the device. An in-depth analysis into the device transport mechanisms is performed, and our compact model aligns seamlessly with the experimental results. Our results suggest the possibility of using scaled AlxSc1-xN FE-diodes for high performance, low-power, embedded NVM.

Recent advancements in solving Bayesian inverse problems have spotlighted denoising diffusion models (DDMs) as effective priors. Although these have great potential, DDM priors yield complex posterior distributions that are challenging to sample. Existing approaches to posterior sampling in this context address this problem either by retraining model-specific components, leading to stiff and cumbersome methods, or by introducing approximations with uncontrolled errors that affect the accuracy of the produced samples. We present an innovative framework, divide-and-conquer posterior sampling, which leverages the inherent structure of DDMs to construct a sequence of intermediate posteriors that guide the produced samples to the target posterior. Our method significantly reduces the approximation error associated with current techniques without the need for retraining. We demonstrate the versatility and effectiveness of our approach for a wide range of Bayesian inverse problems. The code is available at \url{https://github.com/Badr-MOUFAD/dcps}, Comment: Updated version with significant updates

The H1 Collaboration at HERA reports the first measurement of groomed event shape observables in deep inelastic electron-proton scattering (DIS) at $\sqrt{s}=319$ GeV, using data recorded between the years 2003 and 2007 with an integrated luminosity of $351$ pb$^{-1}$. Event shapes provide incisive probes of perturbative and non-perturbative QCD. Grooming techniques have been used for jet measurements in hadronic collisions; this paper presents the first application of grooming to DIS data. The analysis is carried out in the Breit frame, utilizing the novel Centauro jet clustering algorithm that is designed for DIS event topologies. Events are required to have squared momentum-transfer $Q^2 > 150$ GeV$^2$ and inelasticity $ 0.2 < y < 0.7$. We report measurements of the production cross section of groomed event 1-jettiness and groomed invariant mass for several choices of grooming parameter. Monte Carlo model calculations and analytic calculations based on Soft Collinear Effective Theory are compared to the measurements., Comment: 32 pages, 17 tables, 7 figures, version as accepted by EPJ C

The H1 Collaboration reports the first measurement of the 1-jettiness event shape observable $\tau_1^b$ in neutral-current deep-inelastic electron-proton scattering (DIS). The observable $\tau_1^b$ is equivalent to a thrust observable defined in the Breit frame. The data sample was collected at the HERA $ep$ collider in the years 2003-2007 with center-of-mass energy of $\sqrt{s}=319\,\text{GeV}$, corresponding to an integrated luminosity of $351.1\,\text{pb}^{-1}$. Triple differential cross sections are provided as a function of $\tau_1^b$, event virtuality $Q^2$, and inelasticity $y$, in the kinematic region $Q^2>150\,\text{GeV}^{2}$. Single differential cross section are provided as a function of $\tau_1^b$ in a limited kinematic range. Double differential cross sections are measured, in contrast, integrated over $\tau_1^b$ and represent the inclusive neutral-current DIS cross section measured as a function of $Q^2$ and $y$. The data are compared to a variety of predictions and include classical and modern Monte Carlo event generators, predictions in fixed-order perturbative QCD where calculations up to $\mathcal{O}(\alpha_s^3)$ are available for $\tau_1^b$ or inclusive DIS, and resummed predictions at next-to-leading logarithmic accuracy matched to fixed order predictions at $\mathcal{O}(\alpha_s^2)$. These comparisons reveal sensitivity of the 1-jettiness observable to QCD parton shower and resummation effects, as well as the modeling of hadronization and fragmentation. Within their range of validity, the fixed-order predictions provide a good description of the data. Monte Carlo event generators are predictive over the full measured range and hence their underlying models and parameters can be constrained by comparing to the presented data., Comment: 45 pages, 38 tables, 13 figures

The Breit frame provides a natural frame to analyze lepton-proton scattering events. In this reference frame, the parton model hard interactions between a quark and an exchanged boson defines the coordinate system such that the struck quark is back-scattered along the virtual photon momentum direction. In Quantum Chromodynamics (QCD), higher order perturbative or non-perturbative effects can change this picture drastically. As Bjorken-$x$ decreases below one half, a rather peculiar event signature is predicted with increasing probability, where no radiation is present in one of the two Breit-frame hemispheres and all emissions are to be found in the other hemisphere. At higher orders in $\alpha_s$ or in the presence of soft QCD effects, predictions of the rate of these events are far from trivial, and that motivates measurements with real data. We report on the first observation of the empty current hemisphere events in electron-proton collisions at the HERA collider using data recorded with the H1 detector at a center-of-mass energy of 319 GeV. The fraction of inclusive neutral-current DIS events with an empty hemisphere is found to be $0.0112 \pm 3.9\,\%_\text{stat} \pm 4.5\,\%_\text{syst} \pm 1.6\,\%_\text{mod}$ in the selected kinematic region of $150< Q^2<1500$ GeV$^2$ and inelasticity $0.14< y<0.7$. The data sample corresponds to an integrated luminosity of 351.1 pb$^{-1}$, sufficient to enable differential cross section measurements of these events. The results show an enhanced discriminating power at lower Bjorken-$x$ among different Monte Carlo event generator predictions., Comment: 13 pages, 5 figures, 2 Tables. This version as accepted for publication

We systematically investigate the influence of the fabrication process on dielectric loss in aluminum-on-silicon superconducting coplanar waveguide resonators with internal quality factors ($Q_i$) of about one million at the single-photon level. These devices are essential components in superconducting quantum processors; they also serve as proxies for understanding the energy loss of superconducting qubits. By systematically varying several fabrication steps, we identify the relative importance of reducing loss at the substrate-metal and the substrate-air interfaces. We find that it is essential to clean the silicon substrate in hydrogen fluoride (HF) prior to aluminum deposition. A post-fabrication removal of the oxides on the surface of the silicon substrate and the aluminum film by immersion in HF further improves the $Q_i$. We observe a small, but noticeable, adverse effect on the loss by omitting either standard cleaning (SC1), pre-deposition heating of the substrate to 300$\deg$C, or in-situ post-deposition oxidation of the film's top surface. We find no improvement due to excessive pumping meant to reach a background pressure below $6{\times} 10^{-8}$ mbar. We correlate the measured loss with microscopic properties of the substrate-metal interface through characterization with X-ray photoelectron spectroscopy (XPS), time-of-flight secondary ion mass spectroscopy (ToF-SIMS), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS), and atomic force microscopy (AFM)., Comment: 22 pages, 11 figures

Realizing lattices of exciton polariton condensates has been of much interest owing to the potential of such systems to realize analog Hamiltonian simulators and physical computing architectures. Prior work on polariton condensate lattices has primarily been on GaAs-based systems, with the recent advent of organic molecules and perovskite systems allowing room-temperature operation. However, in most of these room temperature systems, the lattices are defined using a bottom-up approach by patterning the bottom mirrors, significantly limiting the types of lattices and refractive index contrast that can be realized. Here, we report a direct write approach that uses a Focused Ion Beam (FIB) to etch 2D lattice into a planar microcavity. Such etching of the cavity allows for realizing high refractive index contrast lattices. We realize the polariton condensate lattice using the highly photostable host-guest Frenkel excitons of an organic dye small molecular ionic lattice (SMILES).1,2 The lattice structures are defined on a planar microcavity embedded with SMILES using FIB, allowing the realization of lattices with different geometries, including defect sites on demand. The band structure of the lattice and the emergence of condensation are imaged using momentum-resolved spectroscopy. The present approach allows us to study periodic, quasi-periodic, and disordered polariton condensate lattices at room temperature using a top-down approach without compromising on the quantum yield of the organic excitonic material embedded in the cavity.

We study the jamming transition in a model of elastic particles under shear at zero temperature, with a focus on the relaxation time $\tau_1$. This relaxation time is from two-step simulations where the first step is the ordinary shearing simulation and the second step is the relaxation of the energy after stopping the shearing. $\tau_1$ is determined from the final exponential decay of the energy. Such relaxations are done with many different starting configuration generated by a long shearing simulation in which the shear varible $\gamma$ slowly increases. We study the correlations of both $\tau_1$, determined from the decay, and the pressure, $p_1$, from the starting configurations as a function of the difference in $\gamma$. We find that the correlations of $p_1$ are more long lived than the ones of $\tau_1$ and find that the reason for this is that the individual $\tau_1$ is controlled both by $p_1$ of the starting configuration and a random contribution which depends on the relaxation path length -- the average distance moved by the particles during the relaxation. We further conclude that it is $\gammatau$, determined from the correlations of $\tau_1$, which is the relevant one when the aim is to generate data that may be used for determining the critical exponent that characterizes the jamming transition.

We present numerical results for the two-loop virtual amplitude entering the NNLO corrections to Higgs boson production in association with a top quark pair at the LHC, focusing, as a proof of concept of our method, on the part of the quark-initiated channel containing loops of massless or massive quarks. Results for the UV renormalised and IR subtracted two-loop amplitude for each colour structure are given at selected phase-space points and visualised in terms of surfaces as a function of two-dimensional slices of the full phase space., Comment: Correction of LO event distribution in Fig.2

This article addresses online variational estimation in state-space models. We focus on learning the smoothing distribution, i.e. the joint distribution of the latent states given the observations, using a variational approach together with Monte Carlo importance sampling. We propose an efficient algorithm for computing the gradient of the evidence lower bound (ELBO) in the context of streaming data, where observations arrive sequentially. Our contributions include a computationally efficient online ELBO estimator, demonstrated performance in offline and true online settings, and adaptability for computing general expectations under joint smoothing distributions.

Acousto-optic integration offers numerous applications including low-loss microwave signal processing, nonreciprocal light propagation, frequency comb generation, and broadband acousto-optic modulation. State-of-the-art acousto-optic systems are mainly implemented entirely using in-house fabrication processes, which despite excellent performance typically suffer from low yield and are not compatible with mass production through foundry processes. Here, we demonstrate a highly efficient wideband acousto-optic modulator (AOM) implemented on a silicon photonics foundry process enabling high-yield low-cost mass production of AOMs with other photonic and electronic devices on the same substrate. In the reported structure, a 150 ${\mu}$m long AlScN-based acoustic transducer launches surface acoustic waves (SAW), which modulate the light passing through a silicon optical waveguide. A modulation efficiency of -18.3 dB over a bandwidth of 112 MHz is achieved, which to our knowledge is the highest reported efficiency and bandwidth combination among silicon based AOMs, resulting in about an order of magnitude $BW(V_{\pi}L)^{-1}$ figure-of-merit improvement compared to the state-of-the-art CMOS compatible AOMs. The monolithically integrated acousto-optic platform developed in this work will pave the way for low-cost, miniature microwave filters, true time delays, frequency combs, and other signal processors with the advanced functionality offered by foundry-integrated photonic circuits.