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Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as it progressively degrades the performance of machine learning models over time. Our findings indicate that the standard cross-validation method used in existing model training overestimates performance by inadequately accounting for drift. This is primarily because typical cross-validation techniques allow data instances to appear in both training and testing sets, thereby distorting the accuracy of the predictive evaluation. As a result, these models are unable to precisely predict future drift effects, compromising their ability to generalize and adapt to evolving data conditions. This paper presents two solutions: (1) a novel sensor drift compensation learning paradigm for validating models, and (2) automated machine learning (AutoML) techniques to enhance classification performance and compensate sensor drift. By employing strategies such as data balancing, meta-learning, automated ensemble learning, hyperparameter optimization, feature selection, and boosting, our AutoML-DC (Drift Compensation) model significantly improves classification performance against sensor drift. AutoML-DC further adapts effectively to varying drift severities., Comment: To be published in Measurement Journal

In recent years, quantum computers have emerged as promising candidates for implementing kernels. Quantum Embedding Kernels embed data points into quantum states and calculate their inner product in a high-dimensional Hilbert Space by computing the overlap between the resulting quantum states. Variational Quantum Circuits (VQCs) are typically used for this end, with Kernel Target Alignment (KTA) as cost function. The optimized kernels can then be deployed in Support Vector Machines (SVMs) for classification tasks. However, both classical and quantum SVMs scale poorly with increasing dataset sizes. This issue is exacerbated in quantum kernel methods, as each inner product requires a quantum circuit execution. In this paper, we investigate KTA-trained quantum embedding kernels and employ a low-rank matrix approximation, the Nystr\"om method, to reduce the quantum circuit executions needed to construct the Kernel Matrix. We empirically evaluate the performance of our approach across various datasets, focusing on the accuracy of the resulting SVM and the reduction in quantum circuit executions. Additionally, we examine and compare the robustness of our model under different noise types, particularly coherent and depolarizing noise., Comment: 10 pages, 7 figures

Quantum Reinforcement Learning (QRL) has emerged as a promising research field, leveraging the principles of quantum mechanics to enhance the performance of reinforcement learning (RL) algorithms. However, despite its growing interest, QRL still faces significant challenges. It is still uncertain if QRL can show any advantage over classical RL beyond artificial problem formulations. Additionally, it is not yet clear which streams of QRL research show the greatest potential. The lack of a unified benchmark and the need to evaluate the reliance on quantum principles of QRL approaches are pressing questions. This work aims to address these challenges by providing a comprehensive comparison of three major QRL classes: Parameterized Quantum Circuit based QRL (PQC-QRL) (with one policy gradient (QPG) and one Q-Learning (QDQN) algorithm), Free Energy based QRL (FE-QRL), and Amplitude Amplification based QRL (AA-QRL). We introduce a set of metrics to evaluate the QRL algorithms on the widely applicable benchmark of gridworld games. Our results provide a detailed analysis of the strengths and weaknesses of the QRL classes, shedding light on the role of quantum principles in QRL and paving the way for future research in this field., Comment: 10 pages, accepted at ICAART 2025

Motivated by ongoing pump-probe spectroscopy experiments, this work develops a theoretical framework for describing the rovibrational wave packet dynamics that ensues when a single weakly-bound van der Waals trimer is exposed to a short, sub-picosecond linearly polarized pump laser pulse. The intensity I of the pump laser is chosen such that excitation and ionization of the electronic degrees of freedom are negligible while excitation of the wavepacket in the nuclear degrees of freedom is non-negligible. The numerical treatment, which takes advantage of the fact that the laser pulse is very short compared to typical molecular time scales, is based on a wave packet decomposition that utilizes hyperspherical coordinates. The framework is applied to the extremely floppy bosonic helium trimer. A convergence analysis of the partial wave decomposition is conducted. The kinetic energy release and orientation dynamics are presented. While the dynamics of more strongly-bound van der Waals trimers such as, e.g., the argon trimer display negligible coupling between vibrational and rotational degrees of freedom, rendering a description within a rigid-body picture appropriate, those of weakly-bound trimers display non-negligible coupling between vibrational and rotational degrees of freedom, rendering a description within a rigid-body picture inappropriate. It is shown that a model that constructs the helium trimer dynamics from the dynamics of the helium dimer captures a number of key characteristics of the alignment signal, including the interference between different angular momentum wave packet components., Comment: 22 pages, 11 figures

In this contribution, we introduce a general class of car-following models with an input-state-output port-Hamiltonian structure. We derive stability conditions and long-term behavior of the finite system with periodic boundaries and quadratic interaction potential by spectral analysis and using asymptotic properties of multivariate Ornstein-Uhlenbeck processes. The uncontrolled dynamics exhibit instability and random collective behavior under stochastic perturbations. By implementing an open-loop speed control, the system stabilizes and weakly converges to Gaussian limit distributions. The convergence is unconditional for constant speed control. However, a stability condition arises for the closed-loop system where the speed control acts as a dynamic feedback depending on the distance ahead. The results are illustrated by numerical simulations. Interestingly, only the closed-loop system is able to reproduce, at least transiently, realistic stop-and-go behavior that can be resolved using the Hamiltonian component of the model., Comment: 10 pages, 3 figures

Recent advancements in large language models (LLMs) have shown potential for transforming data processing in healthcare, particularly in understanding complex clinical narratives. This study evaluates the efficacy of zero-shot LLMs in summarizing long clinical texts that require temporal reasoning, a critical aspect for comprehensively capturing patient histories and treatment trajectories. We applied a series of advanced zero-shot LLMs to extensive clinical documents, assessing their ability to integrate and accurately reflect temporal dynamics without prior task-specific training. While the models efficiently identified key temporal events, they struggled with chronological coherence over prolonged narratives. The evaluation, combining quantitative and qualitative methods, highlights the strengths and limitations of zero-shot LLMs in clinical text summarization. The results suggest that while promising, zero-shot LLMs require further refinement to effectively support clinical decision-making processes, underscoring the need for enhanced model training approaches that better capture the nuances of temporal information in long context medical documents.

We consider two complex SYK models entangled in a thermofield double (TFD) state and investigate the effect of one-sided projective measurements. As measurement operator we choose single site charge operators. Performing a measurement results in a non-zero $U(1)$ charge. The entropy curve differs from the previously studied SYK model due to a thermodynamic phase transition that takes place after a certain charge is reached. We also match our results to a dual bulk description. Finally, a teleportation protocol is provided to support the notion of a traversable wormhole being formed., Comment: 55 pages, 26 figures, v2: reference added

Spatial networks are widely used in various fields to represent and analyze interactions or relationships between locations or spatially distributed entities.There is a network science concept known as the 'rich club' phenomenon, which describes the tendency of 'rich' nodes to form densely interconnected sub-networks. Although there are established methods to quantify topological, weighted, and temporal rich clubs individually, there is limited research on measuring the rich club effect in spatially-weighted temporal networks, which could be particularly useful for studying dynamic spatial interaction networks. To address this gap, we introduce the spatially-weighted temporal rich club (WTRC), a metric that quantifies the strength and consistency of connections between rich nodes in a spatiotemporal network. Additionally, we present a unified rich club framework that distinguishes the WTRC effect from other rich club effects, providing a way to measure topological, weighted, and temporal rich club effects together. Through two case studies of human mobility networks at different spatial scales, we demonstrate how the WTRC is able to identify significant weighted temporal rich club effects, whereas the unweighted equivalent in the same network either fails to detect a rich club effect or inaccurately estimates its significance. In each case study, we explore the spatial layout and temporal variations revealed by the WTRC analysis, showcasing its particular value in studying spatiotemporal interaction networks. This research offers new insights into the study of spatiotemporal networks, with critical implications for applications such as transportation, redistricting, and epidemiology., Comment: 25 pages, 14 figures

Living systems are composed of discrete units, assembled through a hierarchy of structures, and active, by locally extracting energy from their environment to produce mechanical work. Hydrodynamic theories have been successfully applied to describe the large scale dynamics of active materials. Yet, the hydrodynamic limit requires a separation of scales which is not necessarily fulfilled among living systems. In this work, we propose a novel agent-based model of flexible rods exchanging active force dipoles with nematic symmetry, allowing us to explore their behavior down to the sub-agent scale. We obtain spontaneous flows and self-propulsion of $+1/2$ topological defects, hallmarks of the hydrodynamic theory of active nematics, even on scales smaller than the individual agent! Moreover, our results go beyond the hydrodynamic framework, identifying novel correlations between orientation and flows or strong asymmetries between contractile and extensile activity. Finally, we show the versatility of our agent-based model by presenting spontaneous flows in three dimensions and nematic tissue growth. Because living systems like cell tissues often exhibit several sources of activity, our framework opens the way for more integrated descriptions of living materials.

Importance sampling is a rare event simulation technique used in Monte Carlo simulations to bias the sampling distribution towards the rare event of interest. By assigning appropriate weights to sampled points, importance sampling allows for more efficient estimation of rare events or tails of distributions. However, importance sampling can fail when the proposal distribution does not effectively cover the target distribution. In this work, we propose a method for more efficient sampling by updating the proposal distribution in the latent space of a normalizing flow. Normalizing flows learn an invertible mapping from a target distribution to a simpler latent distribution. The latent space can be more easily explored during the search for a proposal distribution, and samples from the proposal distribution are recovered in the space of the target distribution via the invertible mapping. We empirically validate our methodology on simulated robotics applications such as autonomous racing and aircraft ground collision avoidance., Comment: Accepted at AAAI 2025

The fusion energy research and development landscape has seen significant advances in recent years, with important scientific and technological breakthroughs and a rapid rise of investment in the private sector. The workforce needs of the nascent fusion industry are growing at a rate that academic workforce development programs are not currently able to match. This paper presents the findings of the Workforce Accelerator for Fusion Energy Development Conference held in Hampton, Virginia, United States of America (USA), on May 29-30 2024, which was funded by the US National Science Foundation (NSF). A major goal of the conference was to focus on bringing public and private stakeholders together to identify opportunities for partnership in fusion research and education with the goal of meeting the needs for a talented and diverse workforce. Representatives from industry, academia, and national laboratories participated in the conference through the preparation of white papers, presentations, and group discussions, and the production of recommendations to address the challenges facing the US fusion workforce.

As LLM-based applications reach millions of customers, ensuring their scalability and continuous quality improvement is critical for success. However, the current workflows for developing, maintaining, and operating (DevOps) these applications are predominantly manual, slow, and based on trial-and-error. With this paper we introduce the Generative AI Toolkit, which automates essential workflows over the whole life cycle of LLM-based applications. The toolkit helps to configure, test, continuously monitor and optimize Generative AI applications such as agents, thus significantly improving quality while shortening release cycles. We showcase the effectiveness of our toolkit on representative use cases, share best practices, and outline future enhancements. Since we are convinced that our Generative AI Toolkit is helpful for other teams, we are open sourcing it on and hope that others will use, forward, adapt and improve, Comment: 16 pages, 6 figures. For source code see https://github.com/awslabs/generative-ai-toolkit

We present OpenNER 1.0, a standardized collection of openly available named entity recognition (NER) datasets. OpenNER contains 34 datasets spanning 51 languages, annotated in varying named entity ontologies. We correct annotation format issues, standardize the original datasets into a uniform representation, map entity type names to be more consistent across corpora, and provide the collection in a structure that enables research in multilingual and multi-ontology NER. We provide baseline models using three pretrained multilingual language models to compare the performance of recent models and facilitate future research in NER.

Optimal control problems with state distribution constraints have attracted interest for their expressivity, but solutions rely on linear approximations. We approach the problem of driving the state of a dynamical system in distribution from a sequential decision-making perspective. We formulate the optimal control problem as an appropriate Markov decision process (MDP), where the actions correspond to the state-feedback control policies. We then solve the MDP using Monte Carlo tree search (MCTS). This renders our method suitable for any dynamics model. A key component of our approach is a novel, easy to compute, distance metric in the distribution space that allows our algorithm to guide the distribution of the state. We experimentally test our algorithm under both linear and nonlinear dynamics., Comment: Submitted to the IEEE Robotics and Automation Letters for possible publication

We report a fully differential study of ionization of the Ne L-shell by Compton scattering of 20 keV photons. We find two physical mechanisms which modify the Compton-electron emission. Firstly, we observe scattering of the Compton electrons at their parent nucleus. Secondly, we find a distinct maximum in the electron momentum distribution close-to-zero momentum which we attribute to a focusing of the electrons by the Coulomb potential., Comment: 5 pages, 4 figures

Short intense laser pulses are routinely used to induce rotational wave packet dynamics of molecules. Ro-vibrational wave packet dynamics has been explored comparatively infrequently, focusing predominantly on extremely light and rigid molecules such as H$_2^+$, H$_2$, and D$_2$. This work presents quantum mechanical calculations that account for the rotational {\em{and}} the vibrational degrees of freedom for a heavier and rather floppy diatomic molecule, namely the neon dimer. For pumping by a strong and short non-resonant pump pulse, we identify several phenomena that depend critically on the vibrational (i.e., radial) degree of freedom. Our calculations show (i) fingerprints of the radial dynamics in the alignment signal; (ii) laser-kick induced dissociative dynamics on very short time scales (ejection of highly structured "jets"); and (iii) tunneling dynamics that signifies the existence of resonance states, which are supported by the effective potential curves for selected finite relative angular momenta. Our theory predictions can be explored by existing state-of-the-art experiments., Comment: 7 figures

In this paper, we present a theoretical and computational workflow for the non-parametric Bayesian inference of drift and diffusion functions of autonomous diffusion processes. We base the inference on the partial differential equations arising from the infinitesimal generator of the underlying process. Following a problem formulation in the infinite-dimensional setting, we discuss optimization- and sampling-based solution methods. As preliminary results, we showcase the inference of a single-scale, as well as a multiscale process from trajectory data.

Tissues of living cells are a prime example of active fluids. There is experimental evidence that tissues generate extensile active stress even though their constituting cells are contractile. Fluctuating forces that could result from cell-substrate interactions have been proposed to be able to induce a transition from contractile to extensile active stress. Through analytic calculations and numerical computations, we show that in isotropic active fluids, nonlinearities and a coupling between fluctuating forces and fluid density are necessary for such a transition to occur. Here, both transitions from extensile to contractile and vice versa are possible., Comment: 10 pages, 2 figures

Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between the predicted and ground-truth explanations. However, this approach fails to consider one crucial aspect of the systems: whether their outputs accurately reflect the users' (post-purchase) sentiments, i.e., whether and why they would like and/or dislike the recommended items. To shed light on this issue, we introduce new datasets and evaluation methods that focus on the users' sentiments. Specifically, we construct the datasets by explicitly extracting users' positive and negative opinions from their post-purchase reviews using an LLM, and propose to evaluate systems based on whether the generated explanations 1) align well with the users' sentiments, and 2) accurately identify both positive and negative opinions of users on the target items. We benchmark several recent models on our datasets and demonstrate that achieving strong performance on existing metrics does not ensure that the generated explanations align well with the users' sentiments. Lastly, we find that existing models can provide more sentiment-aware explanations when the users' (predicted) ratings for the target items are directly fed into the models as input. We will release our code and datasets upon acceptance.

Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challenges have held back adoption of recommender systems in news publishing. To address these challenges, we introduce the Ekstra Bladet News Recommendation Dataset (EB-NeRD). The dataset encompasses data from over a million unique users and more than 37 million impression logs from Ekstra Bladet. It also includes a collection of over 125,000 Danish news articles, complete with titles, abstracts, bodies, and metadata, such as categories. EB-NeRD served as the benchmark dataset for the RecSys '24 Challenge, where it was demonstrated how the dataset can be used to address both technical and normative challenges in designing effective and responsible recommender systems for news publishing. The dataset is available at: https://recsys.eb.dk., Comment: 11 pages, 8 tables, 2 figures, RecSys '24

The RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender systems for news publishing. This paper describes the challenge, including its objectives, problem setting, and the dataset provided by the Danish news publishers Ekstra Bladet and JP/Politikens Media Group ("Ekstra Bladet"). The challenge explores the unique aspects of news recommendation, such as modeling user preferences based on behavior, accounting for the influence of the news agenda on user interests, and managing the rapid decay of news items. Additionally, the challenge embraces normative complexities, investigating the effects of recommender systems on news flow and their alignment with editorial values. We summarize the challenge setup, dataset characteristics, and evaluation metrics. Finally, we announce the winners and highlight their contributions. The dataset is available at: https://recsys.eb.dk., Comment: 5 pages, 3 tables, RecSys' 24

Picking diverse objects in the real world is a fundamental robotics skill. However, many objects in such settings are bulky, heavy, or irregularly shaped, making them ungraspable by conventional end effectors like suction grippers and parallel jaw grippers (PJGs). In this paper, we expand the range of pickable items without hardware modifications using bimanual nonprehensile manipulation. We focus on a grocery shopping scenario, where a bimanual mobile manipulator equipped with a suction gripper and a PJG is tasked with retrieving ungraspable items from tightly packed grocery shelves. From visual observations, our method first identifies optimal grasp points based on force closure and friction constraints. If the grasp points are occluded, a series of nonprehensile nudging motions are performed to clear the obstruction. A bimanual grasp utilizing contacts on the side of the end effectors is then executed to grasp the target item. In our replica grocery store, we achieved a 90% success rate over 102 trials in uncluttered scenes, and a 67% success rate over 45 trials in cluttered scenes. We also deployed our system to a real-world grocery store and successfully picked previously unseen items. Our results highlight the potential of bimanual nonprehensile manipulation for in-the-wild robotic picking tasks. A video summarizing this work can be found at youtu.be/g0hOrDuK8jM

The INTENTAS project aims to develop an atomic sensor utilizing entangled Bose-Einstein condensates (BECs) in a microgravity environment. This key achievement is necessary to advance the capability for measurements that benefit from both entanglement-enhanced sensitivities and extended interrogation times. The project addresses significant challenges related to size, weight, and power management (SWaP) specific to the experimental platform at the Einstein-Elevator in Hannover. The design ensures a low-noise environment essential for the creation and detection of entanglement. Additionally, the apparatus features an innovative approach to the all-optical creation of BECs, providing a flexible system for various configurations and meeting the requirements for rapid turnaround times. Successful demonstration of this technology in the Einstein-Elevator will pave the way for a future deployment in space, where its potential applications will unlock high-precision quantum sensing.

We consider the two-dimensional water-wave problem with a general non-zero vorticity field in a fluid volume with a flat bed and a free surface. The nonlinear equations of motion for the chosen surface and volume variables are expressed with the aid of the Dirichlet-Neumann operator and the Green function of the Laplace operator in the fluid domain. Moreover, we provide new explicit expressions for both objects. The field of a point vortex and its interaction with the free surface is studied as an example. In the small-amplitude long-wave Boussinesq and KdV regimes, we obtain appropriate systems of coupled equations for the dynamics of the point vortex and the time evolution of the free surface variables., Comment: 36 pages, 4 figures

Augmented Reality (AR) is evolving to become the next frontier in social media, merging physical and virtual reality into a living metaverse, a Social MediARverse. With this transition, we must understand how different contexts (public, semi-public, and private) affect user engagement with AR content. We address this gap in current research by conducting an online survey with 110 participants, showcasing 36 AR videos, and polling them about the content's fit and appropriateness. Specifically, we manipulated these three spaces, two forms of dynamism (dynamic vs. static), and two dimensionalities (2D vs. 3D). Our findings reveal that dynamic AR content is generally more favorably received than static content. Additionally, users find sharing and engaging with AR content in private settings more comfortable than in others. By this, the study offers valuable insights for designing and implementing future Social MediARverses and guides industry and academia on content visualization and contextual considerations.

Coupled angular momentum eigenstates are widely used in atomic and nuclear physics calculations, and are building blocks for spin networks and the Schur transform. To combine two angular momenta $\mathbf{J}_1$ and $\mathbf{J}_2$, forming eigenstates of their total angular momentum $\mathbf{J}=\mathbf{J}_1+\mathbf{J}_2$, we develop a quantum-walk scheme that does not require inputting $O(j^3)$ nonzero Clebsch-Gordan (CG) coefficients classically. In fact, our scheme may be regarded as a unitary method for computing CG coefficients on quantum computers with a typical complexity of $O(j)$ and a worst-case complexity of $O(j^3)$. Equivalently, our scheme provides decompositions of the dense CG unitary into sparser unitary operations. Our scheme prepares angular momentum eigenstates using a sequence of Hamiltonians to move an initial state deterministically to desired final states, which are usually highly entangled states in the computational basis. In contrast to usual quantum walks, whose Hamiltonians are prescribed, we engineer the Hamiltonians in $\mathfrak{su}(2)\times \mathfrak{su}(2)$, which are inspired by, but different from, Hamiltonians that govern magnetic resonances and dipole interactions. To achieve a deterministic preparation of both ket and bra states, we use projection and destructive interference to double pinch the quantum walks, such that each step is a unit-probability population transfer within a two-level system. We test our state preparation scheme on classical computers, reproducing CG coefficients. We also implement small test problems on current quantum hardware., Comment: 26 pages total, 17 pages main text, 8 figures

Alkaline-earth-like elements play pivotal roles in advanced quantum sensing technologies, notably optical clocks, with unprecedented precision achieved in recent years. Despite remarkable progress, current optical lattice clocks still face challenges in meeting the demanding size, weight, and power consumption constraints essential for space applications. Conventional atom sources, such as ovens or dispensers, require substantial heating power, making up a significant fraction of the system's overall power consumption. Addressing this challenge, we present a novel microstructured atomic oven based on fused silica, designed for miniaturization and low-power operation. We characterize the oven by loading a magneto-optical trap with Yb evaporated from the oven and demonstrate operation with a loading rate above $10^8$ $\mathrm{atoms}/\mathrm{s}$ for heating powers below $250$ $\mathrm{mW}$., Comment: 7 pages, 10 figures

We study maximal regularity with respect to continuous functions for strongly continuous semigroups on locally convex spaces as well as its relation to the notion of admissible operators. This extends several results for classical strongly continuous semigroups on Banach spaces. In particular, we show that Travis' characterization of $\mathrm{C}$-maximal regularity using the notion of bounded semivariation carries over to the general case. Under some topological assumptions, we further show the equivalence between maximal regularity and admissibility in this context.

Stress generation by the actin cytoskeleton shapes cells and tissues. Despite impressive progress in live imaging and quantitative physical descriptions of cytoskeletal network dynamics, the connection between processes at molecular scales and cell-scale spatio-temporal patterns is still unclear. Here we review studies reporting acto-myosin clusters of micrometer size and with lifetimes of several minutes in a large number of organisms ranging from fission yeast to humans. Such structures have also been found in reconstituted systems in vitro and in theoretical analysis of cytoskeletal dynamics. We propose that tracking these clusters can serve as a simple readout for characterising living matter. Spatio-temporal patterns of clusters could serve as determinants of morphogenetic processes that play similar roles in diverse organisms.

Large language models (LLMs) bear great potential for automating tedious development tasks such as creating and maintaining code documentation. However, it is unclear to what extent developers can effectively prompt LLMs to create concise and useful documentation. We report on a controlled experiment with 20 professionals and 30 computer science students tasked with code documentation generation for two Python functions. The experimental group freely entered ad-hoc prompts in a ChatGPT-like extension of Visual Studio Code, while the control group executed a predefined few-shot prompt. Our results reveal that professionals and students were unaware of or unable to apply prompt engineering techniques. Especially students perceived the documentation produced from ad-hoc prompts as significantly less readable, less concise, and less helpful than documentation from prepared prompts. Some professionals produced higher quality documentation by just including the keyword Docstring in their ad-hoc prompts. While students desired more support in formulating prompts, professionals appreciated the flexibility of ad-hoc prompting. Participants in both groups rarely assessed the output as perfect. Instead, they understood the tools as support to iteratively refine the documentation. Further research is needed to understand which prompting skills and preferences developers have and which support they need for certain tasks., Comment: Accepted at the 40th IEEE International Conference on Software Maintenance and Evolution (ICSME)

We identify $240$ short-period ($P \lesssim 10$ days) binary systems in the TESS data, $180$ of which are heartbeat binaries (HB). The sample is mostly a mix of A and B-type stars and primarily includes eclipsing systems, where over $30\%$ of the sources with primary and secondary eclipses show a secular change in their inter-eclipse timings and relative eclipse depths over a multi-year timescale, likely due to orbital precession. The orbital parameters of the population are estimated by fitting a heartbeat model to their phase curves and Gaia magnitudes, where the model accounts for ellipsoidal variability, Doppler beaming, reflection effects, and eclipses. We construct the sample's period-eccentricity distribution and find an eccentricity cutoff (where $e \rightarrow 0$) at a period $1.7$ days. Additionally, we measure the periastron advance rate for the $12$ of the precessing sources and find that they all exhibit prograde apsidal precession, which is as high as $9^{\circ}$ yr$^{-1}$ for one of the systems. Using the inferred stellar parameters, we estimate the general relativistic precession rate of the argument of periastron for the population and expect over $30$ systems to show a precession in excess of $0.3^{\circ}$ yr$^{-1}$, Comment: Accepted for publication in ApJ