Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinearity in large amplitude oscillatory shear (LAOS) of different viscoelastic materials

The present work investigates nonlinear behavior in large amplitude oscillatory shear (LAOS) of four different polymeric materials using simultaneous conventional rheometric measurements and particle-tracking velocimetric observations. In contrast to most studies in the literature that treat nonlinearity in LAOS in steady state, we emphasize by the present four examples that nonlinearity in LAOS often arise in complex fluids due to time-dependent rearrangement of their microstructures in response to LAOS. Consequently, no correlation is obvious between strain dependence of the steady-state stress response and the time-dependent characteristics of the steady-state response. For instance, a highly viscoelastic material made of nano-sized polybutadiene particles exhibits homogeneous deformation and an approximate sinusoidal wave despite strong strain softening. In a second example, a well-entangled polybutadiene solution becomes inhomogeneous over time, and the corresponding nonlinearity (i.e., strain soften.
Mirror instability at finite ion-Larmor radius wavelengths

[1] A fully kinetic theory of the magnetic mirror instability in high-β space plasmas accounting for arbitrary ion-Larmor radius effects is developed. It is shown that incorporation of ion-Larmor radius effects leads to a substantial modification of both the instability growth rate and the instability threshold. For wavelengths of the order of the ion-Larmor radius the effective elasticity of the magnetic field lines is substantially enhanced, yielding an increase in the instability threshold. We derive a compact expression for the growth rate of the fastest-growing mode in the fully kinetic limit. Furthermore, it is shown that in the presence of finite ion-Larmor radius effects a noncoplanar component of the magnetic field perturbations appears. Such a component is usually present in satellite measurements of mirror modes. The relevance of these results in understanding observations of mirror instability–generated signals in space plasmas is outlined.
Dropouts of the outer electron radiation belt in response to solar wind stream interfaces: global positioning system observations

We present a statistical study of relativistic electron counts in the electron radiation belt across a range of drift shells (L*>4) combining data from nine combined X-ray dosimeters (CXD) on the global positioning system (GPS) constellation. The response of the electron counts as functions of time, energy and drift shell are examined statistically for 67 solar wind stream interfaces (SIs); two-dimensional superposed epoch analysis is performed with the CXD data. For these epochs we study the radiation belt dropouts and concurrent variations in key geophysical parameters. At higher L* we observe a tendency for a gradual drop in the electron counts over the day preceding the SI, consistent with outward diffusion and magnetopause shadowing. At all L*, dropouts occur with a median time scale of ≃7 h and median counts fall by 0.4–1.8 orders of magnitude. The central tendencies of radiation belt dropout and recovery depend on both L* and energy. For ≃70 per cent of epochs Sym-H more than −30 nT, yet only three of 67 SIs did not have an associated dropout in the electron data. Statistical maps of electron precipitation suggest that chorus-driven relativistic electron microbursts might be major contributors to radiation belt losses under high-speed stream driving.
A qualitative exploration into the role of illness perceptions in endometriosis-related quality of life

Objectives: Endometriosis is linked to adverse quality of life (QoL) outcomes. In the absence of effective treatment for endometriosis-related symptoms, supporting the QoL of those experiencing endometriosis is crucial. Illness perception (IP) interventions have prompted modest increases in QoL in several chronic conditions, yet IPs have not been comprehensively studied in relation to endometriosis. It is, therefore, necessary to examine the IPs held by individuals experiencing endometriosis to establish whether IP-based interventions might be useful in supporting QoL in this population. This research aims to gain an understanding of the IPs held by people experiencing endometriosis and their impact on QoL. Design: Qualitative using one-to-one online semi-structured interviews. Methods: Thirty individuals with endometriosis participated. Interviews sought to gain an understanding of participant experiences and perceptions in relation to living with endometriosis. Reflexive thematic analysis was used to develop themes. Results: Three major themes were developed: (1) a life disrupted; (2) lost and fragmented sense of self; and (3) complex emotional responses. Largely negative IPs were held by individuals living with the condition which, along with endometriosis-specific symptoms and reduced functioning, fuelled fears for the future and reduced QoL. Conclusions: Endometriosis-specific symptoms fuelled adverse QoL outcomes directly, and indirectly through moulding IPs. The disruption to the life trajectory associated with experiencing the condition as well as perceptions of control had a major impact on participant well-being, self-concept, and the varied emotional responses associated with experiencing endometriosis. IP-based interventions may support the well-being of those experiencing endometriosis whilst effective treatment is sought.
Evaluation of Young’s modulus of MgB2 filaments in composite wires for the superconducting links for the high-luminosity LHC upgrade

MgB2 wire is a promising superconductor for the superconducting links for the high-luminosity upgrade of the large Hadron collider at CERN. The mechanical properties of MgB2 must be fully quantified for the cable design, and in this study, we evaluate the Young’s modulus of MgB2 filaments in wires with a practical level of critical current. The Young’s moduli of MgB2 filaments by two different processes, in situ and ex situ, were compared. Two different evaluation methods were applied to an in situ MgB2 wire, a single-fiber tensile test and a tensile test after removing Monel. In addition, the Young’s modulus of the few-micron-thick Nb–Ni reaction layer in an ex situ processed wire was evaluated using a nanoindentation testing technique to improve the accuracy of analysis based on the rule of mixtures. The Young’s moduli of the in situ and ex situ MgB2 wires were in the range of 76–97 GPa and no distinct difference depending on the fabrication process was found.
Dimensional Measurement Data Analysis: Part I — A Zone Fitting Algorithm

Measured data taken from Coordinate Measuring Machines are Cartesian coordinates of points from the surface of a geometric solid. These points need to be placed in the same coordinate reference frame as the design model to yield deviations from target (design) geometry. Currently, data fitting techniques such as least squares fit or min-max fit are employed to the localize the measured points with respect to the design model. Tolerance verification is accomplished by determining whether the deviations of the measured points are within tolerance limits. If the tolerance is represented by tolerance zones, such data fitting techniques may not fulfill the desired verification objective. This paper describes a zone fitting algorithm that determines whether a set of points fits into a given tolerance zone. If the point set fits into the zone, it returns a rigid body transformation that places the points inside the zone. If the criterion of a pass/fail decision is to verify whether the measured points fit into the tolerance zone, an approach employing the proposed zone fit yields a more accurate verification result than least squares or min-max approaches as shown in a number of examples in this paper. A subsequent paper (part II) addresses the determination of a minimum zone that extends inspection from a pass fail mode to a quality analysis operation.
Four‐node mixed Hu–Washizu shell element with drilling rotation

In this paper, enhanced four‐node shell elements with six DOFs/node based on the Hu–Washizu (HW) functional are developed for Green strain. The drilling rotation is included through the drilling rotation constraint equation. The key features of the approach are as follows. The shell HW functional is derived from the shell potential energy functional, which is an alternative to the derivation from the three‐dimensional HW functional. This method is more versatile as it enables the derivation of the so‐called partial HW functionals, with different treatment of the bending/twisting part and the transverse shear part of strain energy. For the membrane part of HW shell elements, a seven‐parameter stress, a nine‐parameter strain and a two‐parameter enhanced assumed displacement gradient enhancement are selected as optimal. The assumed representations of stress and strain are defined in skew coordinates in the natural basis at the element's center. This improves accuracy and has positive theoretical consequences. The drilling rotation constraint equation is treated by the perturbed Lagrange method. The faulty term resulting from the equal‐order approximations of displacements and the drilling rotation is eliminated, and one spurious mode is stabilized using the gamma method. The proposed formulation is insensitive to the element's distortions and yields a large radius of convergence in the examples involving in‐plane bending.
The transition from oxygen-rich to carbon stars

Evolutionary tracks for the transition from oxygen-rich to carbon-rich stars are calculated, using the model of Willems and de Jong (1988). It is found that model spectra produce excellent fits to the observed energy distributions of carbon stars. The mass-loss rate and the time since shell detachment can be determined for each individual object by comparing observed data with the models. 76 references.
An adaptation of InfoMap to absorbing random walks using absorption-scaled graphs

InfoMap is a popular approach to detect densely connected"communities"of nodes in networks. To detect such communities, InfoMap uses random walks and ideas from information theory. Motivated by the dynamics of disease spread on networks, whose nodes can have heterogeneous disease-removal rates, we adapt InfoMap to absorbing random walks. To do this, we use absorption-scaled graphs (in which edge weights are scaled according to absorption rates) and Markov time sweeping. One of our adaptations of InfoMap converges to the standard version of InfoMap in the limit in which the node-absorption rates approach $0$. We demonstrate that the community structure that one obtains using our adaptations of InfoMap can differ markedly from the community structure that one detects using methods that do not account for node-absorption rates. We also illustrate that the community structure that is induced by heterogeneous absorption rates can have important implications for susceptible-infected-recovered (SIR) dynamics on ring-lattice networks. For example, in some situations, the outbreak duration is maximized when a moderate number of nodes have large node-absorption rates.
A Bayesian theory of market impact

The available liquidity at any time in financial markets falls largely short of the typical size of the orders that institutional investors would trade. In order to reduce the impact on prices due to the execution of large orders, traders in financial markets split large orders into a series of smaller ones, which are executed sequentially. The resulting sequence of trades is called a meta-order. Empirical studies have revealed a non-trivial set of statistical laws on how meta-orders affect prices, which include i) the square-root behaviour of the expected price variation with the total volume traded, ii) its crossover to a linear regime for small volumes, and iii) a reversion of average prices towards its initial value, after the sequence of trades is over. Here we recover this phenomenology within a minimal theoretical framework where the market sets prices by incorporating all information on the direction and speed of trade of the meta-order in a Bayesian manner. The simplicity of this derivation lends further support to the robustness and universality of market impact laws. In particular, it suggests that the square-root impact law originates from the over-estimation of order flows originating from meta-orders.
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning

Value-decomposition methods, which reduce the difficulty of a multi-agent system by decomposing the joint state-action space into local observation-action spaces, have become popular in cooperative multi-agent reinforcement learning (MARL). However, value-decomposition methods still have the problems of tremendous sample consumption for training and lack of active exploration. In this paper, we propose a scalable value-decomposition exploration (SVDE) method, which includes a scalable training mechanism, intrinsic reward design, and explorative experience replay. The scalable training mechanism asynchronously decouples strategy learning with environmental interaction, so as to accelerate sample generation in a MapReduce manner. For the problem of lack of exploration, an intrinsic reward design and explorative experience replay are proposed, so as to enhance exploration to produce diverse samples and filter non-novel samples, respectively. Empirically, our method achieves the best performance on almost all maps compared to other popular algorithms in a set of StarCraft II micromanagement games. A data-efficiency experiment also shows the acceleration of SVDE for sample collection and policy convergence, and we demonstrate the effectiveness of factors in SVDE through a set of ablation experiments.
Large Selective Kernel Network for Remote Sensing Object Detection

Recent research on remote sensing object detection has largely focused on improving the representation of oriented bounding boxes but has overlooked the unique prior knowledge presented in remote sensing scenarios. Such prior knowledge can be useful because tiny remote sensing objects may be mistakenly detected without referencing a sufficiently long-range context, which can vary for different objects. This paper considers these priors and proposes the lightweight Large Selective Kernel Network (LSKNet). LSKNet can dynamically adjust its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios. To our knowledge, large and selective kernel mechanisms have not been previously explored in remote sensing object detection. Without bells and whistles, our lightweight LSKNet sets new state-of-the-art scores on standard benchmarks, i.e., HRSC2016 (98.46% mAP), DOTA-v1.0 (81.85% mAP), and FAIR1M-v1.0 (47.87% mAP).
Resilient Design in Nuclear Energy: Critical Lessons from a Cross-Disciplinary Review of the Fukushima Dai-ichi Nuclear Accident

Nuclear energy has been gaining momentum recently as one of the solutions to tackle climate change. However, significant environmental and health-risk concerns remain associated with potential accidents. Despite significant preventive efforts, we must acknowledge that accidents may happen and, therefore, develop strategies and technologies for mitigating their consequences. In this paper, we review the Fukushima Dai-ichi Nuclear Power Plant accident, synthesize the time series and accident progressions across relevant disciplines, including in-plant physics and engineering systems, operators' actions, emergency responses, meteorology, radionuclide release and transport, land contamination, and health impacts. In light of the latest observations and simulation studies, we identify three key factors that exacerbated the consequences of the accident: (1) the failure of Unit 2 containment venting, (2) the insufficient integration of radiation measurements and meteorology data in the evacuation strategy, and (3) the limited risk assessment and emergency preparedness. We propose new research and development directions to improve the resilience of nuclear power plants, including (1) meteorology-informed proactive venting, (2) machine learning-enabled adaptive evacuation zones, and (3) comprehensive risk-informed emergency planning while leveraging the experience from responses to other disasters.
Influence of the boreal spring Southern Annular Mode on summer surface air temperature over northeast China

Previous evidence has showed that the boreal spring Southern Annular Mode (SAM) regulates summer monsoon precipitation in East Asian subtropics through the Northern Indian Ocean sea surface temperature (NIO SST). This paper further demonstrates how a positive (negative) boreal spring SAM is usually followed by a warmer (cooler) summer over northeast China (NEC), suggesting impacts of the SAM even extend to the northern mid‐latitudes. A positive spring SAM usually leads to anomalous anticyclonic sinking motion over NEC during summer through the NIO SST, which results in cloud reduction and changes in radiation, and thus surface warming.
Identifiability Results for Multimodal Contrastive Learning

Contrastive learning is a cornerstone underlying recent progress in multi-view and multimodal learning, e.g., in representation learning with image/caption pairs. While its effectiveness is not yet fully understood, a line of recent work reveals that contrastive learning can invert the data generating process and recover ground truth latent factors shared between views. In this work, we present new identifiability results for multimodal contrastive learning, showing that it is possible to recover shared factors in a more general setup than the multi-view setting studied previously. Specifically, we distinguish between the multi-view setting with one generative mechanism (e.g., multiple cameras of the same type) and the multimodal setting that is characterized by distinct mechanisms (e.g., cameras and microphones). Our work generalizes previous identifiability results by redefining the generative process in terms of distinct mechanisms with modality-specific latent variables. We prove that contrastive learning can block-identify latent factors shared between modalities, even when there are nontrivial dependencies between factors. We empirically verify our identifiability results with numerical simulations and corroborate our findings on a complex multimodal dataset of image/text pairs. Zooming out, our work provides a theoretical basis for multimodal representation learning and explains in which settings multimodal contrastive learning can be effective in practice.
A novel dual skip connection mechanism in U-Nets for building footprint extraction

—The importance of building footprints and their inventory has been recognised as an enabler for multiple societal problems. Extracting urban building footprint is complex and requires semantic segmentation of very high-resolution (VHR) earth observation (EO) images. U-Net is a common deep learning architecture for such segmentation. It has seen several re-incarnation including U-Net++ and U-Net3+ with a focus on multi-scale feature aggregation with re-designed skip connections. However, the exploitation of multi-scale information is still evolving. In this paper, we propose a dual skip connection mechanism (DSCM) for U-Net and a dual full-scale skip connection mechanism (DFSCM) for U-Net3+. The DSCM in U-Net doubles the features in the encoder and passes them to the decoder for precise localisation. Similarly, the DFSCM incorporates increased low-level context information with high-level semantics from feature maps in different scales. The DSCM is further tested in ResUnet and different scales of U-Net. The proposed mechanisms, therefore, produce several novel networks that are evaluated in a benchmark WHU building dataset and a multi-resolution dataset that we develop for the City of Melbourne. The results on the benchmark dataset demonstrate 17.7% and 18.4% gain in F1 score and Intersection over Union (IoU) compared to the state-of-the-art vanilla U-Net3+. In the same experimental setup, DSCM on U-Net and ResUnet provides a gain in ﬁve accuracy measures against the original networks. The codes will be available in a GitHub link after peer review.
Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning

We present a physics-informed machine learning (PIML) scheme for the feedback linearization of nonlinear discrete-time dynamical systems. The PIML finds the nonlinear transformation law, thus ensuring stability via pole placement, in one step. In order to facilitate convergence in the presence of steep gradients in the nonlinear transformation law, we address a greedy-wise training procedure. We assess the performance of the proposed PIML approach via a benchmark nonlinear discrete map for which the feedback linearization transformation law can be derived analytically; the example is characterized by steep gradients, due to the presence of singularities, in the domain of interest. We show that the proposed PIML outperforms, in terms of numerical approximation accuracy, the traditional numerical implementation, which involves the construction--and the solution in terms of the coefficients of a power-series expansion--of a system of homological equations as well as the implementation of the PIML in the entire domain, thus highlighting the importance of continuation techniques in the training procedure of PIML.
Unifying flavors of fault tolerance with the ZX calculus

There are several models of quantum computation which exhibit shared fundamental fault-tolerance properties. This article makes commonalities explicit by presenting these different models in a unifying framework based on the ZX calculus. We focus on models of topological fault tolerance - specifically surface codes - including circuit-based, measurement-based and fusion-based quantum computation, as well as the recently introduced model of Floquet codes. We find that all of these models can be viewed as different flavors of the same underlying stabilizer fault-tolerance structure, and sustain this through a set of local equivalence transformations which allow mapping between flavors. We anticipate that this unifying perspective will pave the way to transferring progress among the different views of stabilizer fault-tolerance and help researchers familiar with one model easily understand others.