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Soft pneumatic actuators have a wide range of applications, including providing haptic feedback embedded in smart garments. Here we investigate actuators fabricated from thermoplastic coated textiles. We measure the effects of fabrication parameters on the robustness and airtightness of small, round pneumatic pouch actuators made from heat-sealed thermoplastic polyurethane-coated nylon, which we call PneuDots. We determine the optimal temperature, time, and pressure for heat-pressing of the textile to create strong bonds and identify the most effective glue to create an airtight seal at the inlet. Compared to elastomeric pneumatic actuators, PneuDots reduce the thickness of the actuator by 96.4% and the mass by 57.2%, increasing their wearability while maintaining a strong force output. We evaluated the force output of the actuators, along with their performance over time. In a blocked force test, PneuDot maximum force transmission was 36.1N, which is 95.3% of the peak force output of an elastomeric pneumatic actuator with the same diameter and pressure. Cyclical testing showed that PneuDots had more stable behavior over time. These results provide best practices for fabrication and indicate the feasibility of textile pneumatic actuators for future wearable applications.

In this study, we propose a novel approach to uncover subgroup-specific and subgroup-common latent factors addressing the challenges posed by the heterogeneity of neurological and mental disorders, which hinder disease understanding, treatment development, and outcome prediction. The proposed approach, sparse Group Factor Analysis (GFA) with regularised horseshoe priors, was implemented with probabilistic programming and can uncover associations (or latent factors) among multiple data modalities differentially expressed in sample subgroups. Synthetic data experiments showed the robustness of our sparse GFA by correctly inferring latent factors and model parameters. When applied to the Genetic Frontotemporal Dementia Initiative (GENFI) dataset, which comprises patients with frontotemporal dementia (FTD) with genetically defined subgroups, the sparse GFA identified latent disease factors differentially expressed across the subgroups, distinguishing between "subgroup-specific" latent factors within homogeneous groups and "subgroup common" latent factors shared across subgroups. The latent disease factors captured associations between brain structure and non-imaging variables (i.e., questionnaires assessing behaviour and disease severity) across the different genetic subgroups, offering insights into disease profiles. Importantly, two latent factors were more pronounced in the two more homogeneous FTD patient subgroups (progranulin (GRN) and microtubule-associated protein tau (MAPT) mutation), showcasing the method's ability to reveal subgroup-specific characteristics. These findings underscore the potential of sparse GFA for integrating multiple data modalities and identifying interpretable latent disease factors that can improve the characterization and stratification of patients with neurological and mental health disorders., Comment: 38 pages, 14 figures

Given a family of rotationally symmetric compact manifolds indexed by the dimension and a weight function, the goal of this paper is to investigate the cut-off phenomenon for the Brownian motions on this family. We provide a class of compact manifolds with non-negative Ricci curvatures for which the cut-off in separation with windows occurs, in high dimension, with different explicit mixing times. We also produce counter-examples, still with non-negative Ricci curvatures, where there are no cut-off in separation. In fact we show a phase transition for the cut-off phenomenon concerning the Brownian motions on a rotationally symmetric compact manifolds. Our proof is based on a previous construction of a sharp strong stationary times by the authors, and some quantitative estimates on the two first moments of the covering time of the dual process. The concentration of measure phenomenon for the above family of manifolds appears to be relevant for the study of the corresponding cut-off.

The eBPF framework enables execution of user-provided code in the Linux kernel. In the last few years, a large ecosystem of cloud services has leveraged eBPF to enhance container security, system observability, and network management. Meanwhile, incessant discoveries of memory safety vulnerabilities have left the systems community with no choice but to disallow unprivileged eBPF programs, which unfortunately limits eBPF use to only privileged users. To improve run-time safety of the framework, we introduce SafeBPF, a general design that isolates eBPF programs from the rest of the kernel to prevent memory safety vulnerabilities from being exploited. We present a pure software implementation using a Software-based Fault Isolation (SFI) approach and a hardware-assisted implementation that leverages ARM's Memory Tagging Extension (MTE). We show that SafeBPF incurs up to 4% overhead on macrobenchmarks while achieving desired security properties., Comment: 15 pages, 9 figures

Malaria remains a significant global health challenge, necessitating rapid and accurate diagnostic methods. While computer-aided diagnosis (CAD) tools utilizing deep learning have shown promise, their generalization to diverse clinical settings remains poorly assessed. This study evaluates the generalization capabilities of a CAD model for malaria diagnosis from thin blood smear images across four sites. We explore strategies to enhance generalization, including fine-tuning and incremental learning. Our results demonstrate that incorporating site-specific data significantly improves model performance, paving the way for broader clinical application., Comment: MICCAI 2024 AMAI Workshop, Accepted for presentation, Submitted Manuscript Version, 10 pages

High-dimensional quantum computing has generated significant interest due to its potential to address scalability and error correction challenges faced by traditional qubit-based systems. This paper investigates the Average Gate Fidelity (AGF) of single qudit systems under Markovian noise in the Lindblad formalism, extending previous work by developing a comprehensive theoretical framework for the calculation of higher-order correction terms. We derive general expressions for the perturbative expansion of the Average Gate Infidelity (AGI) in terms of the environmental coupling coefficient and validate these with extensive numerical simulations, emphasizing the transition from linear to nonlinear behaviour in the strong coupling regime. Our findings highlight the dependence of AGI on qudit dimensionality, quantum gate choice, and noise strength, providing critical insights for optimising quantum gate design and error correction protocols. Additionally, we utilise our framework to identify universal bounds for the AGI in the strong coupling regime and explore the practical implications for enhancing the performance of near-term qudit architectures. This study offers a robust foundation for future research and development in high-dimensional quantum computing, contributing to the advancement of robust, high-fidelity quantum operations.

Scanning Tunneling Spectroscopy (STS) is a unique technique to probe the local density of states (LDOS) at the atomic scale by measuring the tunneling conductance between a sharp tip and a sample surface. However, the technique suffers of well-known limitations, the so-called set-point effect, which can potentially introduce artifacts in the measurements. We compare several STS imaging schemes applied to the LDOS modulations of the charge density wave state on atomically flat surfaces, and demonstrate that only constant-height STS is capable of mapping the intrinsic LDOS. In the constant-current STS, commonly used and easier-to-implement, the tip-sample distance variations imposed by the feedback loop result in set-point-dependent STS images and possibly mislead the identification of the CDW gap edges., Comment: 7 pages, 6 figures

In this paper, we map out the landscape of options available to visual artists for creating personal artworks, including crafting, adapting and navigating deep generative models. Following that, we argue for revisiting model crafting, defined as the design and manipulation of generative models for creative goals, and motivate studying and designing for model crafting as a creative activity in its own right., Comment: Accepted to CHI 2024 workshop on Generative AI and HCI

Monolithic operating systems, where all kernel functionality resides in a single, shared address space, are the foundation of most mainstream computer systems. However, a single flaw, even in a non-essential part of the kernel (e.g., device drivers), can cause the entire operating system to fall under an attacker's control. Kernel hardening techniques might prevent certain types of vulnerabilities, but they fail to address a fundamental weakness: the lack of intra-kernel security that safely isolates different parts of the kernel. We survey kernel compartmentalization techniques that define and enforce intra-kernel boundaries and propose a taxonomy that allows the community to compare and discuss future work. We also identify factors that complicate comparisons among compartmentalized systems, suggest new ways to compare future approaches with existing work meaningfully, and discuss emerging research directions., Comment: 24 pages, 7 figures

This chapter investigates the complexities surrounding the iron biogeochemical cycle from the Archean to present, with a focus on assessing the balance between iron sources and sinks during long periods of Earth's history with relatively invariable redox conditions, when steady state can be safely assumed. Currently, the residence time of iron in the ocean may be as short as approximately 5 years. The input flux of iron is highly sensitive to redox cycling in sediments, while its removal primarily occurs through dispersed processes of oxidation and precipitation. In the Archean, we find a significant imbalance between continental and hydrothermal inputs, which collectively contribute between 61,500 to 263,000 Gg/yr of dissolved iron to the oceans, and the most obvious sinks such as iron formations (IFs), which sequester up to ~43,000 Gg/yr of iron. A possible solution to this imbalance involves the dispersed abiotic precipitation and removal of iron as silicates, sulfides, and carbonates in marine basins. Additionally, we calculate the residence time of dissolved iron in the Archean oceans to be between 6 kyr and 3 Myr, which is significantly longer than the ocean mixing timescale. Our estimates indicate that under the anoxic Archean atmosphere, the iron cycle was more protracted than today, and the isotopic compositions and concentrations of dissolved iron were likely more uniform. Distinct water bodies were likely confined to limited areas or specific, dynamic systems with intense iron turnover, such as regions where deep-sea upwelling currents brought hydrothermal iron to photic zones rich in biotic or abiotic oxidants., Comment: In press, Treatise on Geochemistry, 3rd Edition. 23954 words, 18 figures, 3 tables

The evolution of the cosmic dust content and the cycle between metals and dust in the interstellar medium (ISM) play a fundamental role in galaxy evolution. The chemical enrichment of the Universe can be traced through the evolution of the dust-to-metals ratio (DTM) and the dust-to-gas ratio (DTG) with metallicity. We use a novel method to determine mass estimates of the DTM, DTG and dust composition based on our previous measurements of the depletion of metals in different environments (the Milky Way, the Magellanic Clouds, and damped Lyman-$\alpha$ absorbers, DLAs, toward quasars and towards gamma-ray bursts, GRBs), which were calculated from the relative abundances of metals in the ISM through absorption-line spectroscopy column densities observed mainly from VLT/UVES and X-shooter, and HST/STIS. We derive the dust extinction from the estimated dust depletion ($A_{V, \rm depl}$) and compare with the $A_{V}$ from extinction. We find that the DTM and DTG ratios increase with metallicity and with the dust tracer [Zn/Fe]. This suggests that grain growth in the ISM is a dominant process of dust production. The increasing trend of the DTM and DTG with metallicity is in good agreement with a dust production and evolution model. Our data suggest that the stellar dust yield is much lower than the metal yield and thus that the overall amount of dust in the warm neutral medium that is produced by stars is much lower. We find that $A_{V,\rm depl}$ is overall lower than $A_{V, \rm ext}$ for the Milky Way and a few Magellanic Clouds lines of sight, a discrepancy that is likely related to the presence of carbonaceous dust. We show that the main elements that contribute to the dust composition are, O, Fe, Si, Mg, C, S, Ni and Al for all the environments. Abundances at low dust regimes suggest the presence of pyroxene and metallic iron in dust., Comment: Accepted for publication in A&A. Abstract abridged

The PAW method has been used to compute the iron L$_{2,3}$ edges of x-ray absorption spectra (XAS) and x-ray magnetic circular dichroism (XMCD) of the spin-crossover Fe(phen)$_{2}$(NCS)$_{2}$ molecule when adsorbed on Cu(001) surface and in the gas phase, for both the high spin (HS) and low spin (LS) states. It is found that the calculated XAS and XMCD with the static core hole or the Slater transition state half hole are in less good agreement with experiment than those using the so called initial state. This disagreement is due to the reduction of the iron spin magnetic moment caused by the static screening of the core hole by the photo-electron. The L$_{2,3}$ XAS formula is found to be directly related to the unoccupied $3d$ density of states (DOS), and hence the symmetry broken $e_g$ and the $t_{2g}$ iron DOS are used to explain the XAS and XMCD results. It is demonstrated that the dependence of the HS XMCD on the direction of incident x-ray circularly polarized light with respect to the magnetization direction can be used to determine the iron octahedron deformation, while the XMCD for various magnetization directions is directly related to the anisotropy of the orbital magnetic moment and the magneto-crystalline energy. It is also shown that the magnetic dipole moment $T_z$ is very large due to the strong distortion of the iron octahedron and is necessary for an accurate determination of the sum rule computed spin magnetic moment., Comment: 39 pages, 5 figures

The applicability of DFT+$U$ to the star-of-David phase of $1T$-TaS$_2$ is examined. The viability of a Mott phase as a ground state for the bulk of this material is briefly discussed in light of the available experimental evidence.

Provenance graphs are structured audit logs that describe the history of a system's execution. Recent studies have explored a variety of techniques to analyze provenance graphs for automated host intrusion detection, focusing particularly on advanced persistent threats. Sifting through their design documents, we identify four common dimensions that drive the development of provenance-based intrusion detection systems (PIDSes): scope (can PIDSes detect modern attacks that infiltrate across application boundaries?), attack agnosticity (can PIDSes detect novel attacks without a priori knowledge of attack characteristics?), timeliness (can PIDSes efficiently monitor host systems as they run?), and attack reconstruction (can PIDSes distill attack activity from large provenance graphs so that sysadmins can easily understand and quickly respond to system intrusion?). We present KAIROS, the first PIDS that simultaneously satisfies the desiderata in all four dimensions, whereas existing approaches sacrifice at least one and struggle to achieve comparable detection performance. Kairos leverages a novel graph neural network-based encoder-decoder architecture that learns the temporal evolution of a provenance graph's structural changes to quantify the degree of anomalousness for each system event. Then, based on this fine-grained information, Kairos reconstructs attack footprints, generating compact summary graphs that accurately describe malicious activity over a stream of system audit logs. Using state-of-the-art benchmark datasets, we demonstrate that Kairos outperforms previous approaches., Comment: 24 pages, 16 figures, to appear in the 45th IEEE Symposium on Security and Privacy (S&P'24)

For safety reasons, unprivileged users today have only limited ways to customize the kernel through the extended Berkeley Packet Filter (eBPF). This is unfortunate, especially since the eBPF framework itself has seen an increase in scope over the years. We propose SandBPF, a software-based kernel isolation technique that dynamically sandboxes eBPF programs to allow unprivileged users to safely extend the kernel, unleashing eBPF's full potential. Our early proof-of-concept shows that SandBPF can effectively prevent exploits missed by eBPF's native safety mechanism (i.e., static verification) while incurring 0%-10% overhead on web server benchmarks., Comment: 8 pages, 5 figures, to appear in the 1st SIGCOMM Workshop on eBPF and Kernel Extensions

Inflated-beam soft robots, such as tip-everting vine robots, can control curvature by contracting one beam side via pneumatic actuation. This work develops a general finite element modeling approach to characterize their bending. The model is validated across four pneumatic actuator types (series, compression, embedded, and fabric pneumatic artificial muscles), and can be extended to other designs. These actuators employ two bending mechanisms: geometry-based contraction and material-based contraction. The model accounts for intricate nonlinear effects of buckling and anisotropy. Experimental validation includes three working pressures (10, 20, and 30 kPa) for each actuator type. Geometry-based contraction yields significant deformation (92.1% accuracy) once the buckling pattern forms, reducing slightly to 80.7% accuracy at lower pressures due to stress singularities during buckling. Material-based contraction achieves smaller bending angles but remains at least 96.7% accurate. The open source models available at http://www.vinerobots.org support designing inflated-beam robots like tip-everting vine robots, contributing to waste reduction by optimizing designs based on material properties and stress distribution for effective bending and stress management.

Soft pneumatic actuators are used to steer soft growing "vine" robots while being flexible enough to undergo the tip eversion required for growth. In this study, we compared the performance of three types of pneumatic actuators in terms of their ability to perform eversion, quasi-static bending, dynamic motion, and force output: the pouch motor, the cylindrical pneumatic artificial muscle (cPAM), and the fabric pneumatic artificial muscle (fPAM). The pouch motor is advantageous for prototyping due to its simple manufacturing process. The cPAM exhibits superior bending behavior and produces the highest forces, while the fPAM actuates fastest and everts at the lowest pressure. We evaluated a range of dimensions for each actuator type. Larger actuators can produce more significant deformations and forces, but smaller actuators inflate faster and can evert at a lower pressure. Because vine robots are lightweight, the effect of gravity on the functionality of different actuators is minimal. We developed a new analytical model that predicts the pressure-to-bending behavior of vine robot actuators. Using the actuator results, we designed and demonstrated a 4.8 m long vine robot equipped with highly maneuverable 60x60 mm cPAMs in a three-dimensional obstacle course. The vine robot was able to move around sharp turns, travel through a passage smaller than its diameter, and lift itself against gravity., Comment: 13 pages, 8 figures, 3 tables

We investigate renormalized curvature flow (RCF) and stochastic renormalized curvature flow (SRCF) for convex sets in the plane.RCF is the gradient descent flow for logarithm of $\sigma/\lambda^2$ where $\sigma$ is the perimeter and $\lambda$ is the volume. SRCF is RCF perturbated by a Brownian noise and has the remarkable property that it can be intertwined with the Brownian motion, yielding a generalization of Pitman "2M-X" theorem. We prove that along RCF, entropy $\mathcal{E}_t$ for curvature as well as $h_t:=\sigma_t/\lambda_t$ are non-increasing. We deduce infinite lifetime and convergence to a disk after normalization.For SRCF the situation is more complicated. The process $(h_t)_t$ is always a supermartingale. For $(\mathcal{E}_t)_t$ to be a supermartingale, we need that the starting set is invariant by the isometry group $G_n$ generated by the reflection with respect to the vertical line and the rotation of angle $2\pi/n$ with $n\ge 3$. But for proving infinite lifetime, we need invariance of the starting set by $G_n$ with $n\ge 7$. We provide the first SRCF with infinite lifetime which cannot be reduced to a finite dimensional flow. Gage inequality plays a major role in our study of the regularity of flows, as well as a careful investigation of morphological skeletons. We characterize symmetric convex sets with star shaped skeletons in terms of properties of their Gauss map. Finally, we establish a new isoperimetric estimate for these sets, of order $1/n^4$ where $n$ is the number of branches of the skeleton.

Non-technical end-users are silent and invisible users of the state-of-the-art explainable artificial intelligence (XAI) technologies. Their demands and requirements for AI explainability are not incorporated into the design and evaluation of XAI techniques, which are developed to explain the rationales of AI decisions to end-users and assist their critical decisions. This makes XAI techniques ineffective or even harmful in high-stakes applications, such as healthcare, criminal justice, finance, and autonomous driving systems. To systematically understand end-users' requirements to support the technical development of XAI, we conducted the EUCA user study with 32 layperson participants in four AI-assisted critical tasks. The study identified comprehensive user requirements for feature-, example-, and rule-based XAI techniques (manifested by the end-user-friendly explanation forms) and XAI evaluation objectives (manifested by the explanation goals), which were shown to be helpful to directly inspire the proposal of new XAI algorithms and evaluation metrics. The EUCA study findings, the identified explanation forms and goals for technical specification, and the EUCA study dataset support the design and evaluation of end-user-centered XAI techniques for accessible, safe, and accountable AI., Comment: EUCA dataset, code, and appendix are at: https://weinajin.github.io/euca. An earlier longer version of the EUCA study (with EUCA prototyping framework) is at: arXiv:2102.02437. The followup paper applying the EUCA study findings is at: arXiv:2208.08739

We report the detection of stimulated hydrogen radio recombination line (RRL) emission from ionized gas in a $z=0.89$ galaxy using 580--1670 MHz observations from the MeerKAT Absorption Line Survey (MALS). The RRL emission originates in a galaxy that intercepts and strongly lenses the radio blazar PKS 1830-211 ($z=2.5$). This is the second detection of RRLs outside of the local universe and the first clearly associated with hydrogen. We detect effective H144$\alpha$ (and H163$\alpha$) transitions at observed frequencies of 1156 (798) MHz by stacking 17 (27) RRLs with 21$\sigma$ (14$\sigma$) significance. The RRL emission contains two main velocity components and is coincident in velocity with HI 21 cm and OH 18 cm absorption. We use the RRL spectral line energy distribution and a Bayesian analysis to constrain the density ($n_e$) and the volume-averaged pathlength ($\ell$) of the ionized gas. We determine $\log( n_e ) = 2.0_{-0.7}^{+1.0}$ cm$^{-3}$ and $\log( \ell ) = -0.7_{-1.1}^{+1.1}$ pc towards the north east (NE) lensed image, likely tracing the diffuse thermal phase of the ionized ISM in a thin disk. Towards the south west (SW) lensed image, we determine $\log( n_e ) = 3.2_{-1.0}^{+0.4}$ cm$^{-3}$ and $\log( \ell ) = -2.7_{-0.2}^{+1.8}$ pc, tracing gas that is more reminiscent of H II regions. We estimate a star formation (surface density) rate of $\Sigma_{\mathrm{SFR}} \sim 0.6$ M$_{\odot}$ yr$^{-1}$ kpc$^{-2}$ or SFR $\sim 50$ M$_{\odot}$ yr$^{-1}$, consistent with a star-forming main sequence galaxy of $M_{\star} \sim 10^{11}$ M$_{\odot}$. The discovery presented here opens up the possibility of studying ionized gas at high redshifts using RRL observations from current and future (e.g., SKA and ngVLA) radio facilities., Comment: 17 pages, 10 figures, 2 tables, accepted in ApJ

Let X and Y be horospherical Mori fibre spaces which are birational equivariantly with respect to the group action. Then, there is a horospherical Sarkisov program from X/S to Y /T .

Security research has concentrated on converting operating system audit logs into suitable graphs, such as provenance graphs, for analysis. However, provenance graphs can grow very large requiring significant computational resources beyond what is necessary for many security tasks and are not feasible for resource constrained environments, such as edge devices. To address this problem, we present the \textit{resource-interaction graph} that is built directly from the audit log. We show that the resource-interaction graph's storage requirements are significantly lower than provenance graphs using an open-source data set with two container escape attacks captured from an edge device. We use a graph autoencoder and graph clustering technique to evaluate the representation for an anomaly detection task. Both approaches are unsupervised and are thus suitable for detecting zero-day attacks. The approaches can achieve f1 scores typically over 80\% and in some cases over 90\% for the selected data set and attacks., Comment: 15 pages, 11 figures, 6 tables, for dataset see https://github.com/jpope8/container-escape-dataset, for code see https://github.com/jpope8/container-escape-analysis