Miracle-dz/UniFAR-FTUs huggingface dataset:
Interplanetary Rapid Transit Missions from Earth to Mars using Directed Laser Energy Driven Light Sails

Interest in the exploration of, and the establishment of a human settlement, on Mars is rapidly growing. To accomplish this, rapid transit will be required to bring important supplies and cargo. Current missions to Mars take at least 150 days, which would be too long in case of emergencies or urgent needs. Therefore, we propose the use of a cutting-edge technology that could allow transit times as short as 20 days: laser energy driven light sails. This propulsion method uses a ground-based laser array to propel a small, lightweight spacecraft attached to a light sail to very high speeds, enabling missions that are much faster than current missions. By utilizing a MATLAB model and a laser propulsion computational tool, we visualize and determine the optimal trajectories and departure windows for these missions. We discuss these trajectories and show that these missions are possible during specific launch windows within a 27-month time period between 2030–2032, but have practical challenges within this period as well. During solar conjunction, such rapid transit missions are limited due to the proximity of the sun, but rapid transits are possible during all orbital phases when the transit time requirement is relaxed. Laser arrays capable of generating up to 13 GW are necessary to enable 20-day missions with a 5 kg spacecraft, capable of carrying valuable lightweight cargo to astronauts, near conjunction, but only 0.55 GW is required around opposition. Required spacecraft velocities always exceed the solar system escape velocity and the trajectories are hyperbolic. A significant challenge for future work involves mechanisms and processes for deceleration and entry, descent, and landing. A ground-based laser array on Mars could address some aspects of this challenge, but orbital geometry limits deceleration potential, implying that payloads would need to be robust to large deceleration and impact g-loads. These 20-day missions to Mars can serve as precursors to more complex, distant missions. Spacecraft mass capabilities can be increased while also decreasing transit times by optimizing the laser array and light sail properties. Multiple spacecraft may also be launched and boosted simultaneously to carry more payload and decrease costs. This work is intended to serve as a proof of concept that lightweight payloads can be transported via such missions. Technologies enabling rapid transit missions can be developed in the next several decades and be applied to deep space missions to other celestial bodies and journeys to interstellar space.
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction

We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction. Our approach allows for more flexible manipulation of text and thus can generate more diverse data while keeping the original event structure unchanged as much as possible. Specifically, it first randomly masks out an adjunct sentence fragment and then infills a variable-length text span with a fine-tuned infilling model. The main advantage lies in that it can replace a fragment of arbitrary length in the text with another fragment of variable length, compared to the existing methods which can only replace a single word or a fixed-length fragment. On trigger and argument extraction tasks, the proposed framework is more effective than baseline methods and it demonstrates particularly strong results in the low-resource setting. Our further analysis shows that it achieves a good balance between diversity and distributional similarity.
Uncertainty quantification in CT pulmonary angiography

Computed tomography (CT) imaging of the thorax is widely used for the detection and monitoring of pulmonary embolism (PE). However, CT images can contain artifacts due to the acquisition or the processes involved in image reconstruction. Radiologists often have to distinguish between such artifacts and actual PEs. We provide a proof of concept in the form of a scalable hypothesis testing method for CT, to enable quantifying uncertainty of possible PEs. In particular, we introduce a Bayesian Framework to quantify the uncertainty of an observed compact structure that can be identified as a PE. We assess the ability of the method to operate under high noise environments and with insufficient data.
Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images

We demonstrate automated segmentation of clear cell renal cell carcinomas (ccRCC), cysts, and surrounding normal kidney parenchyma in patients with von Hippel-Lindau (VHL) syndrome using convolutional neural networks (CNN) on Magnetic Resonance Imaging (MRI). We queried 115 VHL patients and 117 scans (3 patients have two separate scans) with 504 ccRCCs and 1171 cysts from 2015 to 2021. Lesions were manually segmented on T1 excretory phase, co-registered on all contrast-enhanced T1 sequences and used to train 2D and 3D U-Net. The U-Net performance was evaluated on 10 randomized splits of the cohort. The models were evaluated using the dice similarity coefficient (DSC). Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0.88 and DSC scores of 0.78, 0.40, and 0.46 for segmentation of the kidney, cysts, and tumors, respectively. Our 3D U-Net achieved an average ccRCC lesion detection AUC of 0.79 and DSC scores of 0.67, 0.32, and 0.34 for kidney, cysts, and tumors, respectively. We demonstrated good detection and moderate segmentation results using U-Net for ccRCC on MRI. Automatic detection and segmentation of normal renal parenchyma, cysts, and masses may assist radiologists in quantifying the burden of disease in patients with VHL.
Electronic and structural properties of M\"obius boron-nitride and carbon nanobelts

Using the semiempirical tight binding method as implemented in the xTB program, we characterized M\"obius boron-nitride and carbon-based nanobelts with different sizes and compared them with normal nanobelts. The calculated properties include the infrared spectra, the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), the energy gap, the chemical potential, and the molecular hardness. The agreement between the peaks positions from theoretical infrared spectra compared with experimental ones for all systems, validate the used methodology. Our findings show that for the boron-nitride based nanobelts, the calculated properties have opposite monotonic relationship with the size of the systems whereas, for the carbon-based, the properties show the same monotonic relationship for both types of nanobelts. Also, the torsion presented on the M\"obius nanobelts, in the case of boron-nitride, induced an inhomogeneous surface distribution for the HOMO orbitals. In all cases, the properties vary with the increase in the size of the nanobelts indicating that it is possible to choose the desired values by changing the size and type of the systems.
Machine Fault Classification using Hamiltonian Neural Networks

A new approach is introduced to classify faults in rotating machinery based on the total energy signature estimated from sensor measurements. The overall goal is to go beyond using black-box models and incorporate additional physical constraints that govern the behavior of mechanical systems. Observational data is used to train Hamiltonian neural networks that describe the conserved energy of the system for normal and various abnormal regimes. The estimated total energy function, in the form of the weights of the Hamiltonian neural network, serves as the new feature vector to discriminate between the faults using off-the-shelf classification models. The experimental results are obtained using the MaFaulDa database, where the proposed model yields a promising area under the curve (AUC) of $0.78$ for the binary classification (normal vs abnormal) and $0.84$ for the multi-class problem (normal, and $5$ different abnormal regimes).
MR Elastography With Optimization-Based Phase Unwrapping and Traveling Wave Expansion-Based Neural Network (TWENN)

Magnetic Resonance Elastography (MRE) can characterize biomechanical properties of soft tissue for disease diagnosis and treatment planning. However, complicated wavefields acquired from MRE coupled with noise pose challenges for accurate displacement extraction and modulus estimation. Using optimization-based displacement extraction and Traveling Wave Expansion-based Neural Network (TWENN) modulus estimation, we propose a new pipeline for processing MRE images. An objective function with Dual Data Consistency (Dual-DC) has been used to ensure accurate phase unwrapping and displacement extraction. For the estimation of complex wavenumbers, a complex-valued neural network with displacement covariance as an input has been developed. A model of traveling wave expansion is used to generate training datasets for the network with varying levels of noise. The complex shear modulus map is obtained through fusion of multifrequency and multidirectional data. Validation using brain and liver simulation images demonstrates the practical value of the proposed pipeline, which can estimate the biomechanical properties with minimal root-mean-square errors when compared to state-of-the-art methods. Applications of the proposed method for processing MRE images of phantom, brain, and liver reveal clear anatomical features, robustness to noise, and good generalizability of the pipeline.
A survey on Organoid Image Analysis Platforms

An in-vitro cell culture system is used for biological discoveries and hypothesis-driven research on a particular cell type to understand mechanistic or test pharmaceutical drugs. Conventional in-vitro cultures have been applied to primary cells and immortalised cell lines plated on 2D surfaces. However, they are unreliable in complex physiological environments and can not always predict in-vivo behaviour correctly. Organoids are multicellular spheroids of a primary donor or stem cells that are replaced in vitro cell culture systems and are widely used in biological, biomedical and translational studies. Native heterogeneity, microanatomy, and functionality of an organ or diseased tissue can be represented by three-dimensional in-vitro tissue models such as organoids. Organoids are essential in in-vitro models for drug discovery and personalised drug screening. Many imaging artefacts such as organoid occlusion, overlap, out-of-focus spheroids and considerable heterogeneity in size cause difficulty in conventional image processing. Despite the power of organoid models for biology, their size and shape have mostly not been considered. Drug responses depend on dynamic changes in individual organoid morphology, number and size, which means differences in organoid shape and size, movement through focal planes, and live-cell staining with limited options cause challenges for drug response and growth analysis. In addition, organoids are imaged on a vast range of platforms in large structurally complex phenotypes. Hundreds of organoid cultures generate high-volume images at once, which are challenging to inspect and interpret. Therefore, an automated coding-free, intuitive and scalable image analysis solution is required. This study primarily introduces the importance of the role of the organoid culture system in different disciplines of medical science and various scopes of utilising organoids. Then studies the challenges of operating organoids, followed by reviewing image analysis systems or platforms applied to organoids to address organoid utilising challenges.
Compact Binary Merger Rate in Dark-matter Spikes

Today, the existence of supermassive black holes (SMBHs) in the center of galactic halos is almost confirmed. An extremely dense region referred to as dark-matter spike is expected to form around central SMBHs as they grow and evolve adiabatically. In this work, we calculate the merger rate of compact binaries in dark-matter spikes while considering halo models with spherical and ellipsoidal collapses. Our findings exhibit that ellipsoidal-collapse dark-matter halo models can potentially yield the enhancement of the merger rate of compact binaries. Finally, our results confirm that the merger rate of primordial black hole binaries is consistent with the results estimated by the LIGO-Virgo detectors, while such results cannot be realized for binary neutron stars and primordial black hole-neutron star binaries.
Which Bridges Are Weak Ties? Algebraic Topological Insights on Network Structure and Tie Strength

Bridging relationships between individuals situated in different parts of a social network are important conduits for information and resources in social and organizational settings. Dyadic tie strength has often been used as an indicator for whether a relationship is bridging, under the assumption that bridging ties are always weak ties. However, recent empirical evidence suggests that bridging ties are often strong, forcing us to rethink the relationship between social network structure and dyadic tie strength. Here, we provide an analysis based on algebraic topology which clarifies this relationship between network structure and dyadic tie strength. Rather than model the network as a graph, we use a simplicial complex which can explicitly encode group interactions between three or more individuals. First, we show theoretically and empirically that Edge PageRank, an algebraic topological measure originally defined as an extension of the classical PageRank measure, is a valid continuous measure of how well a relationship acts as a bridge. Second, we use the tool of Hodge Decomposition, which allows us to decompose any flow in a simplicial complex into three orthogonal components, to clarify the relationship between dyadic tie strength and network structure. We find that individuals invest less in relationships associated with topological holes in the network, replicating and explaining recent empirical results that bridging relationships spanning short network distances tend to be weak, whereas those spanning longer distances are strong. Our results are validated on 15 large scale datasets and suggest the value of algebraic topological methods in empirical network analysis.
Acoustic detection of UHE neutrinos: ANDIAMO perspectives

A possible detection of ultra-high-energy neutrinos has been attempted since decades through the Askarian radiation and diﬀerent observation techniques. In fact, when such energetic neutrinos interact in a medium are able to produce a thermo-acoustic eﬀect resulting in a bipolar pressure pulse that carries a portion of the energy generated by the particle cascades. This eﬀect can be observed in atmosphere looking for the correlated radio emission and in ice/water searching directly the acoustic pulse. The kilometric attenuation length as well as the well-deﬁned shape of the expected pulse favors a large-area-undersea-array of acoustic sensors as a possible observatory. Previous eﬀorts of taking data with a undersea hydrophones array were obtained thanks to already installed submarine military arrays or acoustic system built to calibrate the positions of Cherenkov light detector units. In this proceeding we propose to use the based but not operative oﬀshore oil rigs powered platforms in the Adriatic sea as the main infrastructure to build an acoustic submarine array of dedicated hydrophones covering a total surface area up to ∼ 10000 Km 2 and a volume up to ∼ 500 Km 3 . A future identiﬁcation of neutrino events at energies greater than 10 18 eV will conﬁrm the presence of powerful accelerators in our Universe able to emit cosmic rays up to ZeV energy range.
Simpler and faster algorithms for detours in planar digraphs

In the directed detour problem one is given a digraph $G$ and a pair of vertices $s$ and~$t$, and the task is to decide whether there is a directed simple path from $s$ to $t$ in $G$ whose length is larger than $\mathsf{dist}_{G}(s,t)$. The more general parameterized variant, directed long detour, asks for a simple $s$-to-$t$ path of length at least $\mathsf{dist}_{G}(s,t)+k$, for a given parameter $k$. Surprisingly, it is still unknown whether directed detour is polynomial-time solvable on general digraphs. However, for planar digraphs, Wu and Wang~[Networks, '15] proposed an $\mathcal{O}(n^3)$-time algorithm for directed detour, while Fomin et al.~[STACS 2022] gave a $2^{\mathcal{O}(k)}\cdot n^{\mathcal{O}(1)}$-time fpt algorithm for directed long detour. The algorithm of Wu and Wang relies on a nontrivial analysis of how short detours may look like in a plane embedding, while the algorithm of Fomin et al.~is based on a reduction to the ${\S}$-disjoint paths problem on planar digraphs. This latter problem is solvable in polynomial time using the algebraic machinery of Schrijver~[SIAM~J.~Comp.,~'94], but the degree of the obtained polynomial factor is huge. In this paper we propose two simple algorithms: we show how to solve, in planar digraphs, directed detour in time $\mathcal{O}(n^2)$ and directed long detour in time $2^{\mathcal{O}(k)}\cdot n^4 \log n$. In both cases, the idea is to reduce to the $2$-disjoint paths problem in a planar digraph, and to observe that the obtained instances of this problem have a certain topological structure that makes them amenable to a direct greedy strategy.
Cox Point Processes for Multi-Altitude LEO Satellite Networks

To model existing or future Low Earth Orbit (LEO) satellite networks with multiple constellations, we propose a simple analytical approach to represent the clustering of LEO satellites on orbits, based on a Cox point process. More precisely, we develop a variable-altitude Poisson orbit process that effectively captures the geometric fact that satellites are always positioned on orbits, and these orbits may vary in altitude. Conditioned on the orbit process, satellites situated on these orbits are modeled as Poisson point processes, thereby forming a Cox point process. For this model, we derive useful statistics, including the distribution of the distance to the nearest visible satellite, the outage probability, the Laplace functional of the proposed Cox satellite point process, and the Laplace transform of the interference from the Cox-distributed satellites under general fading. The derived statistics enable the evaluation of the performance of LEO satellite communication systems as functions of network parameters.
Effects of Spatiotemporal Upscaling on Predictions of Reactive Transport in Porous Media

The typical temporal resolution used in modern simulations signiﬁcantly exceeds characteristic time scales at which the system is driven. This is especially so when systems are simu-lated over time-scales that are much longer than the typical temporal scales of forcing factors. We investigate the impact of space-time upscaling on reactive transport in porous media driven by time-dependent boundary conditions whose characteristic time scale is much smaller than that at which transport is studied or observed at the macroscopic level. The focus is on transport of a reactive solute undergoing diﬀusion, advection and heterogeneous reaction on the solid grains boundaries. We ﬁrst introduce a concept of spatiotemporal upscaling in the context of homogenization by multiple-scale expansions, and demonstrate the impact of time-dependent forcings and boundary conditions on macroscopic reactive transport. We then derive the macroscopic equation as well as the corresponding applicability conditions based on the order of magnitude of the Péclet and Damköhler dimensionless numbers. Finally, we demonstrate that the dynamics at the continuum scale is strongly inﬂuenced by the interplay between signal frequency at the boundary and transport processes at the pore level.
Torsion subgroups of small cancellation groups

We prove that torsion subgroups of groups deﬁned by C (6), C (4)– T (4), or C (3)– T (6) small cancellation presentations are ﬁnite cyclic groups. This follows from a more general result on the existence of ﬁxed points for locally elliptic (every element ﬁxes a point) actions of groups on simply connected small cancellation complexes. We present an application concerning automatic continuity. We observe that simply connected C (3)– T (6) complexes may be equipped with a CAT(0) metric. This allows us to get stronger results on locally elliptic actions in that case. It also implies that the Tits Alternative holds for groups acting on simply connected C (3)– T (6) small cancellation complexes with a bound on the order of cell stabilisers.
Length separation studies of single walled carbon nanotube dispersions

In this contribution we present detailed studies of single walled carbon nanotubes (SWNTs) in water surfactant dispersions separated by size exclusion chromatography. We investigated length separated SWNT fractions by AFM, absorption spectroscopy and near‐infrared photoluminescence mapping. The ultrasonic induced scission process can be observed by AFM. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
High-output microwave detector using voltage-induced ferromagnetic resonance

We investigated the voltage-induced ferromagnetic resonance (FMR) with various DC bias voltage and input RF power in magnetic tunnel junctions. We found that the DC bias monotonically increases the homodyne detection voltage due to the nonlinear FMR originating in an asymmetric magnetization-potential in the free layer. In addition, the linear increase of an output voltage to the input RF power in the voltage-induced FMR is more robust than that in spin-torque FMR. These characteristics enable us to obtain an output voltage more than ten times than that of microwave detectors using spin-transfer torque.
Curvature effects in the composite model for low-grazing-angle rough-surface scatter

Abstract The conventional composite surface model used for calculations of radar backscatter treats a large-scale surface component as an ensemble of tilted flat facets. This approach has limitations for low grazing angles. In the paper we develop a new composite model that takes into account the diffraction of an incident field on a curved, large-scale undulating surface. It is shown that, whereas corrections related to curvature effects are small for a steep incidence, those corrections for low-grazing-angle backscatter can be quite essential. Results of numerical simulations of radar backscattering cross sections for two polarizations are presented. Scattering from a surface with one-dimensionally varying roughness is considered. We have studied the role of undulating surface slopes and curvatures in the behaviour of the polarization ratio and found regimes in which the curvature influence on this ratio is more pronounced.
The Gendered Pathways Into Giving and Volunteering: Similar or Different Across Countries?

There has been a steady increase in research studying the role of gender in prosocial behavior, such as charitable giving and volunteering. We provide an extensive review of the interdisciplinary literature and derive hypotheses about three different pathways that lead men and women to differ in their display of giving and volunteering: pathways through social capital, motivations, and resources. We test these hypotheses across 19 countries by analyzing 28,410 individuals, using generalized structural equation models. Our results support previous research, conducted in single countries, that there are distinct different pathways that lead men and women to engage in giving and volunteering: Women report stronger motivations to help others, but men report more of the financial resources that make giving and volunteering possible. The gendered pathways to giving and volunteering that lead through social capital, educational achievement, and financial security vary by country.