Search

An electrostatic hexapole lens is used to manipulate the transverse properties of a beam of barium fluoride molecules from a cryogenic buffer gas source. The spatial distribution of the beam is measured by recording state-selective laser-induced fluorescence on an emccd camera, providing insight into the intensity and transverse position spread of the molecular beam. Although the high mass and unfavorable Stark shift of barium fluoride pose a considerable challenge, the number of molecules in the low-field seeking component of the N=1 state that pass a 4 mm diameter aperture 712 mm behind the source is increased by a factor of 12. Furthermore, it is demonstrated that the molecular beam can be displaced by up to +/-5 mm by moving the hexapole lens. Our measurements agree well with numerical trajectory simulations. We discuss how electrostatic lenses may be used to increase the sensitivity of beam experiments such as the search for the electric dipole moment of the electron.

Recently, we have demonstrated a method to record the longitudinal phase-space distribution of a pulsed cryogenic buffer gas cooled beam of barium fluoride molecules. In this paper, we use this method to determine the influence of various source parameters. Besides the expected dependence on temperature and pressure, the forward velocity of the molecules is strongly correlated with the time they exit the cell, revealing the dynamics of the gas inside the cell. Three observations are particularly noteworthy: (1) The velocity of the barium fluoride molecules increases rapidly as a function of time, reaches a maximum 50-200 $\mu$s after the ablation pulse and then decreases exponentially. We attribute this to the buffer gas being heated up by the plume of hot atoms released from the target by the ablation pulse and subsequently being cooled down via conduction to the cell walls. (2) The time constant associated with the exponentially decreasing temperature increases when the source is used for a longer period of time, which we attribute to the formation of a layer of isolating dust on the walls of the cell. By thoroughly cleaning the cell, the time constant is reset to its initial value. (3) The velocity of the molecules at the trailing end of the molecular pulse depends on the length of the cell. For short cells, the velocity is significantly higher than expected from the sudden freeze model. We attribute this to the target remaining warm over the duration of the molecular pulse giving rise to a temperature gradient within the cell. Our observations will help to optimize the source parameters for producing the most intense molecular beam at the target velocity., Comment: 17 pages, 7 figures

We demonstrate a novel method to determine the longitudinal phase-space distribution of a cryogenic buffer gas beam of barium-fluoride molecules based on a two-step laser excitation scheme. The spatial resolution is achieved by a transversely aligned laser beam that drives molecules from the ground state $X^2\Sigma^+$ to the $A^2\Pi_{1/2}$ state around 860 nm, while the velocity resolution is obtained by a laser beam that is aligned counter-propagating with respect to the molecular beam and that drives the Doppler shifted $A^2\Pi_{1/2}$ to $D^2\Sigma^+$ transition around 797 nm. Molecules in the $D$-state are detected virtually background-free by recording the fluorescence from the $D-X$ transition at 413 nm. As molecules in the ground state do not absorb light at 797 nm, problems due to due to optical pumping are avoided. Furthermore, as the first step uses a narrow transition, this method can also be applied to molecules with hyperfine structure. The measured phase-space distributions, reconstructed at the source exit, show that the average velocity and velocity spread vary significantly over the duration of the molecular beam pulse. Our method gives valuable insight into the dynamics in the source and helps to reduce the velocity and increase the intensity of cryogenic buffer gas beams. In addition, transition frequencies are reported for the $X-A$ and $X-D$ transitions in barium fluoride with an absolute accuracy below 0.3 MHz., Comment: 7 pages, 6 figures

The local field potential (LFP) is as a measure of the combined activity of neurons within a region of brain tissue. While biophysical modeling schemes for LFP in cortical circuits are well established, there is a paramount lack of understanding regarding the LFP properties along the states assumed in cortical circuits over long periods. Here we use a symbolic information approach to determine the statistical complexity based on Jensen disequilibrium measure and Shannon entropy of LFP data recorded from the primary visual cortex (V1) of urethane-anesthetized rats and freely moving mice. Using these information quantifiers, we find consistent relations between LFP recordings and measures of cortical states at the neuronal level. More specifically, we show that LFP's statistical complexity is sensitive to cortical state (characterized by spiking variability), as well as to cortical layer. In addition, we apply these quantifiers to characterize behavioral states of freely moving mice, where we find indirect relations between such states and spiking variability.

Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Frequently used measures for fat estimation are the body mass index (BMI), waist circumference, or the waist-hip-ratio. However, those are rather imprecise measures that do not allow for a discrimination between different types of fat or between fat and muscle tissue. The estimation of visceral (VAT) and abdominal subcutaneous (ASAT) adipose tissue volume has shown to be a more accurate measure for named risk factors. In this work, we show that triangulated body surface meshes can be used to accurately predict VAT and ASAT volumes using graph neural networks. Our methods achieve high performance while reducing training time and required resources compared to state-of-the-art convolutional neural networks in this area. We furthermore envision this method to be applicable to cheaper and easily accessible medical surface scans instead of expensive medical images.

Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly higher, and secondly, the availability of large datasets for pre-training is often limited, impeding training success. This study proposes a simple approach of adapting 2D networks with an intermediate feature representation for processing 3D images. Our method employs attention pooling to learn to assign each slice an importance weight and, by that, obtain a weighted average of all 2D slices. These weights directly quantify the contribution of each slice to the contribution and thus make the model prediction inspectable. We show on all 3D MedMNIST datasets as benchmark and two real-world datasets consisting of several hundred high-resolution CT or MRI scans that our approach performs on par with existing methods. Furthermore, we compare the in-built interpretability of our approach to HiResCam, a state-of-the-art retrospective interpretability approach.

We demonstrate a spin-precession method to observe and analyze multi-level coherence between all hyperfine levels in the $X ^2\Sigma^+,N=0$ ground state of barium monofluoride ($^{138}$Ba$^{19}$F). The signal is sensitive to the state-preparation Rabi frequency and external electric and magnetic fields applied in searches for a permanent electric dipole moment (EDM). In the obtained interference spectrum, the electric field and Rabi frequency become observable simultaneously with the EDM. This method reduces systematic biases and the number of auxiliary measurements for such precision measurements., Comment: 5 pages, 4 figures

Surfaces of planetary bodies can have strong electric fields, subjecting conductive grains to repulsive electrostatic forces. This has been proposed as mechanism to eject grains from the ground. To quantify this process, we study mm-sized basalt aggregates consisting of micrometer constituents exposed to an electric field in drop tower experiments. The dust aggregates acquire high charges on sub-second timescales while sticking to the electrodes according to the field polarity. Charging at the electrodes results in a repulsive (lifting) force and continues until repulsion overcomes adhesion and particles are lifted, moving towards the opposite electrode. Some aggregates remain attached, which is consistent with a maximum charge limit being reached, providing an electrostatic force too small to counteract adhesion. All observations are in agreement with a model of moderately conductive grains with a small but varying number of adhesive contacts to the electrodes. This supports the idea that on planetary surfaces with atmospheres, electrostatic repulsion can significantly contribute to airborne dust and sand, i.e. decrease the threshold wind speed that is required for saltation and increase the particle flux as suggested before.

Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database. More recently, extensions to individual subjects or their attributes, have been introduced. Under the individual/per-instance DP interpretation, we study the connection between the per-subject gradient norm in DP neural networks and individual privacy loss and introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS), which allows one to apportion the subject's privacy loss to their input attributes. We experimentally show how this enables the identification of sensitive attributes and of subjects at high risk of data reconstruction.

The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural networks able to tackle this challenge is impeded by a lack of large datasets and the difficult anatomical localisation of the pancreas. Here, we propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy which is based on the Response Evaluation Criteria in Solid Tumors (RECIST) score, a standardised method for cancer response evaluation by clinicians as well as tumour markers, and clinical evaluation of the patients. We leverage a combination of representation transfer from segmentation to classification, as well as localisation and representation learning. Our approach yields a remarkably data-efficient method able to predict treatment response with a ROC-AUC of 63.7% using only 477 datasets in total.

We introduce collisions of solids as a new and efficient ionization mechanism for gas in protoplanetary disks, which especially operates in the dense midplane of protoplanetary disks. This idea is sparked by laboratory experiments where we found that charge, which is exchanged by grains in mutual collision (tribocharging), is not tied to their surfaces alone. As kind of collateral effect, charges also become entrained into the gas phase, i.e. collisions ionize the protoplanetary disk. Therefore, solids are not only sinks of charges in disks but also sources. A first estimate shows that ionization rates in the midplane at 1 AU in the range of $10^{-19} ... 10^{-15} \rm \, s^{-1}$ seem feasible depending on the assumption of rather calm or highly turbulent conditions with radial particle pile up., Comment: Accepted for publication by Monthly Notices of the Royal Astronomical Society

The toolbox for material characterization has never been richer than today. Great progress with all kinds of particles and interaction methods provide access to nearly all properties of an object under study. However, a tomographic analysis of the subsurface region remains still a challenge today. In this regard, the Muon-Induced X-ray Emission (MIXE) technique has seen rebirth fueled by the availability of high intensity muon beams. We report here a study conducted at the Paul Scherrer Institute (PSI). It demonstrates that the absence of any beam time-structure leads to low pile-up events and a high signal-to-noise ratio (SNR) with less than one hour acquisition time per sample or data point. This performance creates the perspective to open this technique to a wider audience for the routine investigation of non-destructive and depth-sensitive elemental compositions, for example in rare and precious samples. Using a hetero-structured sample of known elements and thicknesses, we successfully detected the characteristic muonic X-rays, emitted during the capture of a negative muon by an atom, and the gamma-rays resulting from the nuclear capture of the muon, characterizing the capabilities of MIXE at PSI. This sample emphasizes the quality of a continuous beam, and the exceptional SNR at high rates. Such sensitivity will enable totally new statistically intense aspects in the field of MIXE, e.g. elemental 3D-tomography and chemical analysis. Therefore, we are currently advancing our proof-of-concept experiments with the goal of creating a full fledged permanently operated user station to make MIXE available to the wider scientific community as well as industry.

It is a long-standing open question whether electrification of wind-blown sand due to tribocharging - the generation of electric charges on the surface of sand grains by particle-particle collisions - could affect rates of sand transport occurrence on Mars substantially. While previous wind tunnel experiments and numerical simulations addressed how particle trajectories may be affected by external electric fields, the effect of sand electrification remains uncertain. Here we show, by means of wind tunnel simulations under air pressure of 20 mbar, that the presence of electric charges on the particle surface can reduce the minimal threshold wind shear velocity for the initiation of sand transport, u*ft, significantly. In our experiments, we considered different samples, a model system of glass beads as well as a Martian soil analog, and different scenarios of triboelectrification. Furthermore, we present a model to explain the values of u*ft obtained in the wind tunnel that is based on inhomogeneously distributed surface charges. Our results imply that particle transport that subsides, once the wind shear velocity has fallen below the threshold for sustained transport, can more easily be restarted on Mars than previously thought.

In microgravity experiments, we quantified the net charge on systems of two identical, 434 \textmu m diameter glass spheres before and after a collision. We find that charge conservation is significantly violated. Independent of the sign of the total charge, the systems regularly lose some of their net charges, that is, they slightly discharge. This implies that positive as well as negative charge carriers become entrained into the surrounding atmosphere during a collision.

Dust emission mechanisms as one aspect of wind-driven particle motion on planetary surfaces are still poorly understood. The microphysics is important though as it determines dust sizes and morphologies which set sedimentation speeds and optical properties. We consider the effects of tribocharging in this context as grains in wind driven granular matter charge significantly. This leads to large electric fields above the granular bed. Airborne dielectric grains are polarized in these electric fields, which leads to attractive forces between grains. To simulate aggregation under these conditions we carried out drop tower experiments using tracer particles, mimicking the gas coupling behavior of small dust grains in terms of high surface to mass ratios and efficient gas drag. Under microgravity, the particles are released into an observation chamber in which an alternating electric field up to 80 kV/m is applied. Without electric field no aggregation can be observed on timescales of seconds. However, polarization instantly leads to aggregation of particles when the field is switched on and long chains aligned to the electric field form. Scaled to dust entrained into planetary atmospheres, fine and coarse grain fractions might readily form aggregates after being liberated. Under certain natural conditions, aggregates might therefore start chain-like or at least a chain-like appearance is favored. If atmospheric influences on their stability are small, aerodynamic and optical properties might depend on this.

Differential privacy (DP) allows the quantification of privacy loss when the data of individuals is subjected to algorithmic processing such as machine learning, as well as the provision of objective privacy guarantees. However, while techniques such as individual R\'enyi DP (RDP) allow for granular, per-person privacy accounting, few works have investigated the impact of each input feature on the individual's privacy loss. Here we extend the view of individual RDP by introducing a new concept we call partial sensitivity, which leverages symbolic automatic differentiation to determine the influence of each input feature on the gradient norm of a function. We experimentally evaluate our approach on queries over private databases, where we obtain a feature-level contribution of private attributes to the DP guarantee of individuals. Furthermore, we explore our findings in the context of neural network training on synthetic data by investigating the partial sensitivity of input pixels on an image classification task.

The Gaussian mechanism (GM) represents a universally employed tool for achieving differential privacy (DP), and a large body of work has been devoted to its analysis. We argue that the three prevailing interpretations of the GM, namely $(\varepsilon, \delta)$-DP, f-DP and R\'enyi DP can be expressed by using a single parameter $\psi$, which we term the sensitivity index. $\psi$ uniquely characterises the GM and its properties by encapsulating its two fundamental quantities: the sensitivity of the query and the magnitude of the noise perturbation. With strong links to the ROC curve and the hypothesis-testing interpretation of DP, $\psi$ offers the practitioner a powerful method for interpreting, comparing and communicating the privacy guarantees of Gaussian mechanisms., Comment: Under review at PETS 2022

Context. Particles in protoplanetary disks go through a number of phases that are dominated by collisions. In each of these events, grains exchange electrical charge via triboelectric effects. This enhances the stability of particle aggregates. Aim. Dielectric grains are easily charged by collisions. Here, we investigate whether a charge is capable of inducing an aggregation of particles and we consider how collision properties, such as ticking velocities and collisional cross-sections, are altered. Methods. We explored aggregation in microgravity experiments based on the observation of the motion of submillimeter (submm) grains following many collisions. In the process, grains attract each other, collide, stick, and ultimately form small aggregates. Results. We observed a bottom-up formation of irregular aggregates from submm grains. While some of the observed trajectories during the approach of grains reflect the presence of a pure Coulomb potential, the motion is not always in agreement with pure Kepler motion. Higher-order potentials of multipole charge distributions stand as a plausible explanation for this behavior. An immediate consequence of charging is that the particles continue to stick to each other at velocities of $\sim 10 \, \rm cm/s,$ while surface forces of neutral grains are only expected to allow sticking below $\sim 1 \, \rm mm/s$. No bouncing collision was observed among hundreds of collisions in the given parameter range. Applied to early phases of planet formation, the forming aggregates are therefore the first steps in a new growth phase beyond the traditional bouncing barrier in planet formation.

The specificity and simplicity of the Watson-Crick base pair interactions make DNA one of the most versatile construction materials for creating nanoscale structures and devices. Among several DNA-based approaches, the DNA origami technique excels in programmable self-assembly of complex, arbitrary shaped structures with dimensions of hundreds of nanometers. Importantly, DNA origami can be used as templates for assembly of functional nanoscale components into three-dimensional structures with high precision and controlled stoichiometry. This is often beyond the reach of other nanofabrication techniques. In this Perspective, we highlight the capability of the DNA origami technique for realization of novel nanophotonic systems. First, we introduce the basic principles of designing and fabrication of DNA origami structures. Subsequently, we review recent advances of the DNA origami applications in nanoplasmonics, single-molecule and super-resolution fluorescent imaging, as well as hybrid photonic systems. We conclude by outlining the future prospects of the DNA origami technique for advanced nanophotonic systems with tailored functionalities.

We report on the electrostatic trapping of neutral SrF molecules. The molecules are captured from a cryogenic buffer-gas beam source into the moving traps of a 4.5 m long traveling-wave Stark decelerator. The SrF molecules in $X^2\Sigma^+(v=0, N=1)$ state are brought to rest as the velocity of the moving traps is gradually reduced from 190 m/s to zero. The molecules are held for up to 50 ms in multiple electric traps of the decelerator. The trapped packets have a volume (FWHM) of 1 mm$^{3}$ and a velocity spread of 5(1) m/s which corresponds to a temperature of $60(20)$ mK. Our result demonstrates a factor 3 increase in the molecular mass that has been Stark-decelerated and trapped. Heavy molecules (mass$>$100 amu) offer a highly increased sensitivity to probe physics beyond the Standard Model. This work significantly extends the species of neutral molecules of which slow beams can be created for collision studies, precision measurement and trapping experiments.

In protoplanetary disks, zones of dense particle configuration promote planet formation. Solid particles in dense clouds alter their motion through collective effects and back reaction to the gas. The effect of particle-gas feedback with ambient solid-to-gas ratios $\epsilon > 1$ on the stopping time of particles is investigated. In experiments on board the International Space Station we studied the evolution of a dense granular gas while interacting with air. We observed diffusion of clusters released at the onset of an experiment but also the formation of new dynamical clusters. The solid-to-gas mass ratio outside the cluster varied in the range of about $\epsilon_{\rm avg} \sim 2.5 - 60$. We find that the concept of gas drag in a viscous medium still holds, even if the medium is strongly dominated in mass by solids. However, a collective factor has to be used, depending on $\epsilon_{\rm avg} $, i.e. the drag force is reduced by a factor 18 at the highest mass ratios. Therefore, flocks of grains in protoplanetary disks move faster and collide faster than their constituents might suggest.

Collisions electrically charge grains which promotes growth by coagulation. We present aggregation experiments with three large ensembles of basalt beads ($150\,\mu\mathrm{m} - 180\,\mu\mathrm{m})$, two of which are charged, while one remains almost neutral as control system. In microgravity experiments, free collisions within these samples are induced with moderate collision velocities ($0 - 0.2 \,\mathrm{m\,s}^{-1}$). In the control system, coagulation stops at (sub-)mm size while the charged grains continue to grow. A maximum agglomerate size of 5\,cm is reached, limited only by bead depletion in the free volume. For the first time, charge-driven growth well into the centimeter range is directly proven by experiments. In protoplanetary disks, this agglomerate size is well beyond the critical size needed for hydrodynamic particle concentration as, e.g., by the streaming instabilities.

In (pre)-planetary environments, dust and sand grains regularly collide. They electrically charge and discharge during these events. In this work, we study if cosmic radiation has any influence on the equilibrium charge state on timescales of minutes. We developed an experiment that was carried out during the ascent of a stratospheric balloon. With increasing altitude, the radiation activity increases by a factor of 54. However, we found only a very minor decrease in grain charges of up to 30%. This implies that charge-moderated processes from thunderstorms on Earth, over early phases of planet formation to particle motion on the Martian surface on short timescales essentially proceed unhindered from a direct influence of cosmic radiation.

For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial. For example, existent segmentation algorithms based on natural images are neither efficient in their parameter use nor optimized for medical imaging. Here we present MoNet, a highly optimized neural-network-based pancreatic segmentation algorithm focused on achieving high performance by efficient multi-scale image feature utilization.

The hyperfine splitting of the 5g -> 4f transitions in muonic 185,187-Re has been measured using high resolution HPGe detectors and compared to state-of-the-art atomic theoretical predictions. The spectroscopic quadrupole moment has been extracted using modern fitting procedures and compared to the values available in literature obtained from muonic X rays of natural rhenium. The extracted values of the nuclear spectroscopic quadrupole moment are 2.07(5) barn and 1.94(5) barn, respectively for 185-Re and 187-Re. This work is part of a larger effort at the Paul Scherrer Institut towards the measurement of the nuclear charge radii of radioactive elements., Comment: 15 pages, 8 figures, 8 tables; accepted for publication in Phys. Rev. C