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This paper presents the successful application of a phase-lock feedback system to maintain the resonance condition of a radio frequency (rf) spin rotator (specifically, an rf Wien filter) with respect to a 120 kHz spin precession in the Cooler Synchrotron (COSY) storage ring. Real-time monitoring of the spin precession and the rf Wien filter signal allows the relative phase between the two to be stabilized at an arbitrary setpoint. The feedback system compensates for deviations in the relative phase by adjusting the frequency and/or phase as needed. With this method, a variation in phase relative to the demand phase with a standard deviation of $\sigma_{\Delta\varphi}\approx 0.2\mathrm{rad}$ could be achieved. The system was implemented in two runs aiming at a first direct measurement of the deuteron electric dipole moment in 2018 and 2021. In addition, the difference between a single-bunch beam affected by the spin rotator and a two-bunch system in which only one bunch is exposed to the spin rotator fields is discussed. Both methods have been used during these beam times. The ability to keep the spin precession and the rf fields synchronized is also crucial for future investigations of electric dipole moments of charged particles using storage rings., Comment: 13 pages, 10 figures

Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate the opportunities of LLMs for automatic regression test generation for programs that take highly structured, human-readable inputs, such as XML parsers or JavaScript interpreters. Concretely, we explore the following regression test generation scenarios for such programs that have so far been difficult to test automatically in the absence of corresponding input grammars: $\bullet$ Bug finding. Given a code change (e.g., a commit or pull request), our LLM-based approach generates a test case with the objective of revealing any bugs that might be introduced if that change is applied. $\bullet$ Patch testing. Given a patch, our LLM-based approach generates a test case that fails before but passes after the patch. This test can be added to the regression test suite to catch similar bugs in the future. We implement Cleverest, a feedback-directed, zero-shot LLM-based regression test generation technique, and evaluate its effectiveness on 22 commits to three subject programs: Mujs, Libxml2, and Poppler. For programs using more human-readable file formats, like XML or JavaScript, we found Cleverest performed very well. It generated easy-to-understand bug-revealing or bug-reproduction test cases for the majority of commits in just under three minutes -- even when only the code diff or commit message (unless it was too vague) was given. For programs with more compact file formats, like PDF, as expected, it struggled to generate effective test cases. However, the LLM-supplied test cases are not very far from becoming effective (e.g., when used as a seed by a greybox fuzzer or as a starting point by the developer)., Comment: 18 pages. This version of the paper was written on Thu, 12 Sep 2024

This paper compares quantum computing simulators running on a single CPU or GPU-based HPC node using the Quantum Volume benchmark commonly proposed for comparing NISQ systems. As simulators do not suffer from noise, the metric used in the comparison is the time required to simulate a set Quantum Volume. The results are important to estimate the feasibility of proof of concept studies and debugging of quantum algorithms on HPC systems. Besides benchmarks of some commonly used simulators, this paper also offers an overview of their main features, a review of the state of quantum computing simulation and quantum computing benchmarking, and some insight into the theory of Quantum Volume., Comment: 11 pages, 2 figures, 2 tables

Protocol implementations are stateful which makes them difficult to test: Sending the same test input message twice might yield a different response every time. Our proposal to consider a sequence of messages as a seed for coverage-directed greybox fuzzing, to associate each message with the corresponding protocol state, and to maximize the coverage of both the state space and the code was first published in 2020 in a short tool demonstration paper. AFLNet was the first code- and state-coverage-guided protocol fuzzer; it used the response code as an indicator of the current protocol state. Over the past five years, the tool paper has gathered hundreds of citations, the code repository was forked almost 200 times and has seen over thirty pull requests from practitioners and researchers, and our initial proposal has been improved upon in many significant ways. In this paper, we first provide an extended discussion and a full empirical evaluation of the technical contributions of AFLNet and then reflect on the impact that our approach and our tool had in the past five years, on both the research and the practice of protocol fuzzing., Comment: 14 pages, 3 tables, 7 figures

Warm dense matter (WDM) plays an important role in astrophysical objects and technological applications, but the rigorous diagnostics of corresponding experiments is notoriously difficult. In this work, we present a model-free analysis of x-ray Thomson scattering (XRTS) measurements at multiple scattering angles. Specifically, we analyze scattering data that have been collected for isochorically heated graphite at the Linac Coherent Light Source (LCLS). Overall, we find good consistency in the extracted temperature between small and large scattering angles, whereas possible signatures of non-equilibrium may be hidden by the source function, and by the available dynamic spectral range. The present proof-of-principle study directly points to improved experimental set-ups for equation-of-state measurements and for the model-free study of relaxation times.

Interactions between emitters within an ensemble can give rise to cooperative processes that significantly alter the properties of the emitted light. One such process is superfluorescence (SF), where excited electric dipoles spontaneously couple coherently and effectively radiate as one macroscopic emitter. It requires low energetic disorder, high temporal coherence and oscillator strength, and sub-wavelength volumes of material can be sufficient. Conversely, amplified spontaneous emission (ASE) originates from an avalanche-like stimulated amplification of initially spontaneously emitted photons and does not necessitate temporally coherent interactions among the emitters, but rather requires spatially long enough light propagation within the material to harvest the optical gain. Cesium lead halide perovskite nanocrystals (NCs) are one of the very few materials where both ASE (in disordered films) and SF (in ordered assemblies) were observed, however leaving unclear whether and how these regimes could be connected. Here, we demonstrate that temperature and excitation density can drive the transition between both regimes in a thin film of giant CsPbBr3 perovskite NCs. At temperatures below 45 K, excitonic SF was observed, whereas above a transition range between 45 K and 100 K, ASE prevails, but requires increased optical excitation and emitter density. Our results work out the different collective effects present in lead halide perovskites, providing fundamental insights into cooperative phenomena and important guidance for the development of compact and bright (quantum) light sources.

We derive equations of motion for higher order density response functions using the theory of thermodynamic Green's functions. We also derive expressions for the higher order generalized dielectric functions and polarization functions. Moreover, we relate higher order response functions and higher order collision integrals within the Martin-Schwinger hierarchy. We expect our results to be highly relevant to the study of a variety of quantum many-body systems such as matter under extreme temperatures, densities, and pressures.

As our lives, our businesses, and indeed our world economy become increasingly reliant on the secure operation of many interconnected software systems, the software engineering research community is faced with unprecedented research challenges, but also with exciting new opportunities. In this roadmap paper, we outline our vision of Software Security Analysis for the software systems of the future. Given the recent advances in generative AI, we need new methods to evaluate and maximize the security of code co-written by machines. As our software systems become increasingly heterogeneous, we need practical approaches that work even if some functions are automatically generated, e.g., by deep neural networks. As software systems depend evermore on the software supply chain, we need tools that scale to an entire ecosystem. What kind of vulnerabilities exist in future systems and how do we detect them? When all the shallow bugs are found, how do we discover vulnerabilities hidden deeply in the system? Assuming we cannot find all security flaws, how can we nevertheless protect our system? To answer these questions, we start our research roadmap with a survey of recent advances in software security, then discuss open challenges and opportunities, and conclude with a long-term perspective for the field., Comment: 25 pages single-column, Invited article for ACM TOSEM

X-ray Thomson scattering (XRTS) has emerged as a powerful tool for the diagnostics of matter under extreme conditions. In principle, it gives one access to important system parameters such as the temperature, density, and ionization state, but the interpretation of the measured XRTS intensity usually relies on theoretical models and approximations. In this work, we show that it is possible to extract the Rayleigh weight -- a key property that describes the electronic localization around the ions -- directly from the experimental data without the need for any model calculations or simulations. As a practical application, we consider an experimental measurement of strongly compressed Be at the National Ignition Facility (NIF) [D\"oppner \emph{et al.}, \textit{Nature} \textbf{618}, 270-275 (2023)]. In addition to being interesting in their own right, our results will open up new avenues for diagnostics from \emph{ab initio} simulations, help to further constrain existing chemical models, and constitute a rigorous benchmark for theory and simulations.

Neural compression has the potential to revolutionize lossy image compression. Based on generative models, recent schemes achieve unprecedented compression rates at high perceptual quality but compromise semantic fidelity. Details of decompressed images may appear optically flawless but semantically different from the originals, making compression errors difficult or impossible to detect. We explore the problem space and propose a provisional taxonomy of miscompressions. It defines three types of 'what happens' and has a binary 'high impact' flag indicating miscompressions that alter symbols. We discuss how the taxonomy can facilitate risk communication and research into mitigations., Comment: 6 pages, 6 figures

According to our survey of the machine learning for vulnerability detection (ML4VD) literature published in the top Software Engineering conferences, every paper in the past 5 years defines ML4VD as a binary classification problem: Given a function, does it contain a security flaw? In this paper, we ask whether this decision can really be made without further context and study both vulnerable and non-vulnerable functions in the most popular ML4VD datasets. A function is vulnerable if it was involved in a patch of an actual security flaw and confirmed to cause the vulnerability. It is non-vulnerable otherwise. We find that in almost all cases this decision cannot be made without further context. Vulnerable functions are often vulnerable only because a corresponding vulnerability-inducing calling context exists while non-vulnerable functions would often be vulnerable if a corresponding context existed. But why do ML4VD techniques perform so well even though there is demonstrably not enough information in these samples? Spurious correlations: We find that high accuracy can be achieved even when only word counts are available. This shows that these datasets can be exploited to achieve high accuracy without actually detecting any security vulnerabilities. We conclude that the current problem statement of ML4VD is ill-defined and call into question the internal validity of this growing body of work. Constructively, we call for more effective benchmarking methodologies to evaluate the true capabilities of ML4VD, propose alternative problem statements, and examine broader implications for the evaluation of machine learning and programming analysis research.

The exploitation of the strong light-matter coupling regime and exciton-polariton condensates has emerged as a compelling approach to introduce strong interactions and nonlinearities into numerous photonic applications, ranging from low-threshold topological lasers to ultrafast all-optical logic devices. The use of colloidal semiconductor quantum dots with strong three-dimensional confinement as the active material in these microcavities would be highly advantageous due to their versatile structural and compositional tunability and wet-chemical processability, as well as potentially enhanced, confinement-induced polaritonic interactions. Yet, to date, cavity exciton-polariton condensation has neither been achieved with epitaxial nor with colloidal quantum dots. Here, we demonstrate room-temperature polariton condensation in a thin film of monodisperse, colloidal CsPbBr$_3$ quantum dots placed in a tunable optical resonator with a Gaussian-shaped deformation serving as wavelength-scale potential well for the polaritons. The onset of polariton condensation under pulsed optical excitation is manifested in emission by its characteristic superlinear intensity dependence, reduced linewidth, blueshift, and extended temporal coherence. Our results, based on this highly engineerable class of perovskite materials with unique optical properties, pave the way for the development of polaritonic devices for ultrabright coherent light sources and photonic information processing.

How to find memory safety bugs efficiently when navigating a symbolic execution tree that suffers from path explosion? Existing solutions either adopt path search heuristics to maximize coverage rate or chopped symbolic execution to skip uninteresting code (i.e., manually labeled as vulnerability-unrelated) during path exploration. However, most existing search heuristics are not vulnerability-oriented, and manual labeling of irrelevant code-to-be-skipped relies heavily on prior expert knowledge, making it hard to detect vulnerabilities effectively in practice. This paper proposes Vital, a new vulnerability-oriented symbolic execution via type-unsafe pointer-guided Monte Carlo Tree Search (MCTS). A pointer that is type unsafe cannot be statically proven to be safely dereferenced without memory corruption. Our key hypothesis is that a path with more type unsafe pointers is more likely to contain vulnerabilities. Vital drives a guided MCTS to prioritize paths in the symbolic execution tree that contain a larger number of unsafe pointers and to effectively navigate the exploration-exploitation trade-off. We built Vital on top of KLEE and compared it with existing search strategies and chopped symbolic execution. In the former, the results demonstrate that Vital could cover up to 90.03% more unsafe pointers and detect up to 37.50% more unique memory errors. In the latter, the results show that Vital could achieve a speedup of up to 30x execution time and a reduction of up to 20x memory consumption on automatically detecting known vulnerabilities without prior expert knowledge., Comment: 12 pages

Interferometric displacement measurements, especially in space interferometry applications, face challenges from thermal expansion. Bonded assemblies of ultra-low thermal expansion glass-ceramics offer a solution; however, transitioning from light transport in fibers to free beam propagation presents a notable challenge. These experiments often need an interface to convert between laser beams propagating through fiber optics into a well-defined free beam and vice versa. These interfaces must also be made of rigid glass pieces that can be bonded to a glass base plate. Current designs for these fiber collimators, often called fiber injector optical sub-assemblies, require multiple glass parts fabricated to very tight tolerances and assembled with special alignment tools. We present a simplified quasi-monolithic fiber collimator that can generate a well-collimated laser beam. The complexity and tolerances of bonding are reduced by combining the alignment of the fiber mode to the imaging lens in one step with active mode control: the welding of the fiber to the glass body. We produce several of these designs and test that the desired light field is achieved, its profile is described as a Gaussian beam, and the beam-pointing stability is acceptable for such a piece. In each case, they perform at least as well as a standard commercial fiber collimator. These Quasi Monolithic Fiber Collimators offer a promising and easy-to-implement solution to convert between free beam and fiber-coupled lasers in experiments sensitive to long term thermal drifts., Comment: TO be Submitted to optics applied

Matching cancer patients to clinical trials is essential for advancing treatment and patient care. However, the inconsistent format of medical free text documents and complex trial eligibility criteria make this process extremely challenging and time-consuming for physicians. We investigated whether the entire trial matching process - from identifying relevant trials among 105,600 oncology-related clinical trials on clinicaltrials.gov to generating criterion-level eligibility matches - could be automated using Large Language Models (LLMs). Using GPT-4o and a set of 51 synthetic Electronic Health Records (EHRs), we demonstrate that our approach identifies relevant candidate trials in 93.3% of cases and achieves a preliminary accuracy of 88.0% when matching patient-level information at the criterion level against a baseline defined by human experts. Utilizing LLM feedback reveals that 39.3% criteria that were initially considered incorrect are either ambiguous or inaccurately annotated, leading to a total model accuracy of 92.7% after refining our human baseline. In summary, we present an end-to-end pipeline for clinical trial matching using LLMs, demonstrating high precision in screening and matching trials to individual patients, even outperforming the performance of qualified medical doctors. Our fully end-to-end pipeline can operate autonomously or with human supervision and is not restricted to oncology, offering a scalable solution for enhancing patient-trial matching in real-world settings., Comment: 149 pages, including Supplements. 3 Main Figures

Photosynthetic microbes have evolved and successfully adapted to the ever-changing environmental conditions in complex microhabitats throughout almost all ecosystems on Earth. In the absence of light, they can sustain their biological functionalities through aerobic respiration, and even in anoxic conditions through anaerobic metabolic activity. For a suspension of photosynthetic microbes in an anaerobic environment, individual cellular motility is directly controlled by its photosynthetic activity, i.e. the intensity of the incident light absorbed by chlorophyll. The effects of the metabolic activity on the collective motility on the population level, however, remain elusive so far. Here, we demonstrate that at high light intensities, a suspension of photosynthetically active microbes exhibits a stable reverse sedimentation profile of the cell density due to the microbes' natural bias to move against gravity. With decreasing photosynthetic activity, and therefore suppressed individual motility, the living suspension becomes unstable giving rise to coherent bioconvective flows. The collective motility is fully reversible and manifests as regular, three-dimensional plume structures, in which flow rates and cell distributions are directly controlled via the light intensity. The coherent flows emerge in the highly unfavourable condition of lacking both light and oxygen and, thus, might help the microbial collective to expand the exploration of their natural habitat in search for better survival conditions.

In this paper, we investigate a nonlocal traffic flow model based on a scalar conservation law, where a stochastic velocity function is assumed. In addition to the modeling, theoretical properties of the stochastic nonlocal model are provided, also addressing the question of well-posedness. A detailed numerical analysis offers insights how the stochasticity affects the evolution of densities. Finally, numerical examples illustrate the mean behavior of solutions and the influence of parameters for a large number of realizations., Comment: 36 pages, 7 figures, 1 table

The orientation in which a source image is captured can affect the resulting security in downstream applications. One reason for this is that many state-of-the-art methods in media security assume that image statistics are similar in the horizontal and vertical directions, allowing them to reduce the number of features (or trainable weights) by merging coefficients. We show that this artificial symmetrization tends to suppress important properties of natural images and common processing operations, causing a loss of performance. We also observe the opposite problem, where unaddressed directionality causes learning-based methods to overfit to a single orientation. These are vulnerable to manipulation if an adversary chooses inputs with the less common orientation. This paper takes a comprehensive approach, identifies and systematizes causes of directionality at several stages of a typical acquisition pipeline, measures their effect, and demonstrates for three selected security applications (steganalysis, forensic source identification, and the detection of synthetic images) how the performance of state-of-the-art methods can be improved by properly accounting for directionality.

The two-phase horizontally periodic quasistationary Stokes flow in $\mathbb{R}^2$, describing the motion of two immiscible fluids with equal viscosities that are separated by a sharp interface, which is parameterized as the graph of a function $f=f(t)$, is considered in the general case when both gravity and surface tension effects are included. Using potential theory, the moving boundary problem is formulated as a fully nonlinear and nonlocal parabolic problem for the function $f$. Based on abstract parabolic theory, it is proven that the problem is well-posed in all subcritical spaces $\mathrm{H}^r(\mathbb{S})$, $r\in(3/2,2)$. Moreover, the stability properties of the flat equilibria are analyzed in dependence on the physical properties of the fluids., Comment: 39 pages

Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g. planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extreme pressures and temperatures typically involves additional model assumptions, which makes it difficult to assess the accuracy of the experimental data. rigorously. On the other hand, theory and modeling have produced extensive collections of data. They originate from a very large variety of models and simulations including path integral Monte Carlo (PIMC) simulations, density functional theory (DFT), chemical models, machine-learned models, and combinations thereof. At the same time, each of these methods has fundamental limitations (fermion sign problem in PIMC, approximate exchange-correlation functionals of DFT, inconsistent interaction energy contributions in chemical models, etc.), so for some parameter ranges accurate predictions are difficult. Recently, a number of breakthroughs in first principle PIMC and DFT simulations were achieved which are discussed in this review. Here we use these results to benchmark different simulation methods. We present an update of the hydrogen phase diagram at high pressures, the expected phase transitions, and thermodynamic properties including the equation of state and momentum distribution. Furthermore, we discuss available dynamic results for warm dense hydrogen, including the conductivity, dynamic structure factor, plasmon dispersion, imaginary-time structure, and density response functions. We conclude by outlining strategies to combine different simulations to achieve accurate theoretical predictions.

Multicellular tumor spheroids (MCTS) are advanced cell culture systems for assessing the impact of combinatorial radio(chemo)therapy. They exhibit therapeutically relevant in-vivo-like characteristics from 3D cell-cell and cell-matrix interactions to radial pathophysiological gradients related to proliferative activity and nutrient/oxygen supply, altering cellular radioresponse. State-of-the-art assays quantify long-term curative endpoints based on collected brightfield image time series from large treated spheroid populations per irradiation dose and treatment arm. Here, spheroid control probabilities are documented analogous to in-vivo tumor control probabilities based on Kaplan-Meier curves. This analyses require laborious spheroid segmentation of up to 100.000 images per treatment arm to extract relevant structural information from the images, e.g., diameter, area, volume and circularity. While several image analysis algorithms are available for spheroid segmentation, they all focus on compact MCTS with clearly distinguishable outer rim throughout growth. However, treated MCTS may partly be detached and destroyed and are usually obscured by dead cell debris. We successfully train two Fully Convolutional Networks, UNet and HRNet, and optimize their hyperparameters to develop an automatic segmentation for both untreated and treated MCTS. We systematically validate the automatic segmentation on larger, independent data sets of spheroids derived from two human head-and-neck cancer cell lines. We find an excellent overlap between manual and automatic segmentation for most images, quantified by Jaccard indices at around 90%. For images with smaller overlap of the segmentations, we demonstrate that this error is comparable to the variations across segmentations from different biological experts, suggesting that these images represent biologically unclear or ambiguous cases., Comment: 30 pages, 23 figures

In lead halide perovskites (APbX3), the effect of the A-site cation on optical and electronic properties has initially been thought to be marginal. Yet, evidence of beneficial effects on solar cell performance and light emission is accumulating. Here, we report that the A-cation in soft APbBr3 colloidal quantum dots (QDs) controls the phonon-induced localization of the exciton wavefunction. Insights from ab initio molecular dynamics and single-particle fluorescence spectroscopy demonstrate that anharmonic lattice vibrations and the resulting polymorphism act as an additional confinement potential. Avoiding the trade-off between single-photon purity and optical stability faced by downsizing conventional QDs into the strong confinement regime, dynamical phonon-induced confinement in large organic-inorganic perovskite QDs enables bright (10^6 photons/s), stable (> 1h), and pure (> 95%) single-photon emission in a widely tuneable spectral range (495-745 nm). Strong electron-phonon interaction in soft perovskite QDs provides an unconventional route toward the development of scalable room-temperature quantum light sources.

We present quasi-exact ab initio path integral Monte Carlo (PIMC) results for the partial static density responses and local field factors of hydrogen in the warm dense matter regime, from solid density conditions to the strongly compressed case. The full dynamic treatment of electrons and protons on the same footing allows us to rigorously quantify both electronic and ionic exchange--correlation effects in the system, and to compare with earlier incomplete models such as the archetypal uniform electron gas [Phys. Rev. Lett. 125, 235001 (2020)] or electrons in a fixed ion snapshot potential [Phys. Rev. Lett. 129, 066402 (2022)] that do not take into account the interplay between the two constituents. The full electronic density response is highly sensitive to electronic localization around the ions, and our results constitute unambiguous predictions for upcoming X-ray Thomson scattering (XRTS) experiments with hydrogen jets and fusion plasmas. All PIMC results are made freely available and can directly be used for a gamut of applications, including inertial confinement fusion calculations and the modelling of dense astrophysical objects. Moreover, they constitute invaluable benchmark data for approximate but computationally less demanding approaches such as density functional theory or PIMC within the fixed-node approximation.

Regulation of cell proliferation is a crucial aspect of tissue development and homeostasis and plays a major role in morphogenesis, wound healing, and tumor invasion. A phenomenon of such regulation is contact inhibition, which describes the dramatic slowing of proliferation, cell migration and individual cell growth when multiple cells are in contact with each other. While many physiological, molecular and genetic factors are known, the mechanism of contact inhibition is still not fully understood. In particular, the relevance of cellular signaling due to interfacial contact for contact inhibition is still debated. Cellular automata (CA) have been employed in the past as numerically efficient mathematical models to study the dynamics of cell ensembles, but they are not suitable to explore the origins of contact inhibition as such agent-based models assume fixed cell sizes. We develop a minimal, data-driven model to simulate the dynamics of planar cell cultures by extending a probabilistic CA to incorporate size changes of individual cells during growth and cell division. We successfully apply this model to previous in-vitro experiments on contact inhibition in epithelial tissue: After a systematic calibration of the model parameters to measurements of single-cell dynamics, our CA model quantitatively reproduces independent measurements of emergent, culture-wide features, like colony size, cell density and collective cell migration. In particular, the dynamics of the CA model also exhibit the transition from a low-density confluent regime to a stationary postconfluent regime with a rapid decrease in cell size and motion. This implies that the volume exclusion principle, a mechanical constraint which is the only inter-cellular interaction incorporated in the model, paired with a size-dependent proliferation rate is sufficient to generate the observed contact inhibition., Comment: 15 pages, 17 figures

We present extensive new \emph{ab initio} path integral Monte Carlo (PIMC) results for a variety of structural properties of warm dense hydrogen and beryllium. To deal with the fermion sign problem -- an exponential computational bottleneck due to the antisymmetry of the electronic thermal density matrix -- we employ the recently proposed [\textit{J.~Chem.~Phys.}~\textbf{157}, 094112 (2022); \textbf{159}, 164113 (2023)] $\xi$-extrapolation method and find excellent agreement with exact direct PIMC reference data where available. This opens up the intriguing possibility to study a gamut of properties of light elements and potentially material mixtures over a substantial part of the warm dense matter regime, with direct relevance for astrophysics, material science, and inertial confinement fusion research.