Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud property trends in hot and ultra-hot giant gas planets (WASP-43b, WASP-103b, WASP-121b, HAT-P-7b, and WASP-18b)

Ultra-hot Jupiters are the hottest exoplanets discovered so far. Observations begin to provide insight into the composition of their extended atmospheres and their chemical day/night asymmetries. Both are strongly affected by cloud formation. We explore trends in cloud properties for a sample of five giant gas planets: WASP-43b, WASP-18b, HAT-P-7b, WASP-103b, and WASP-121b. This provides a reference frame for cloud properties for the JWST targets WASP-43b and WASP-121b. We further explore chemically inert tracers to observe geometrical asymmetries, and if the location of inner boundary of a 3D GCM matters for the clouds that form. The large day/night temperature differences of ultra-hot Jupiters cause large chemical asymmetries: cloud-free days but cloudy nights, atomic vs. molecular gases and respectively different mean molecular weights, deep thermal ionospheres vs. low-ionised atmospheres, undepleted vs enhanced C/O. WASP-18b, as the heaviest planet in the sample, has the lowest global C/O. The global climate may be considered as similar amongst ultra-hot Jupiters, but different to that of hot gas giants. The local weather, however, is individual for each planet since the local thermodynamic conditions, and hence the local cloud and gas properties, differ. The morning and the evening terminator of ultra-hot Jupiters will carry signatures of their strong chemical asymmetry such that ingress/egress asymmetries can be expected. An increased C/O ratio is a clear sign of cloud formation, making cloud modelling a necessity when utilizing C/O (or other mineral ratios) as tracer for planet formation. The changing geometrical extension of the atmosphere from the day to the nightside may be probed through chemically inert species like helium. Ultra-hot Jupiters are likely to develop deep atmospheric ionospheres which may impact the atmosphere dynamics through MHD processes.
Critical Current Distributions of Recent Bi-2212 Round Wires

Bi<sub>2</sub>Sr<sub>2</sub>CaCu<sub>2</sub>O<inline-formula><tex-math notation="LaTeX">$_{8+x} $</tex-math></inline-formula> (Bi-2212) is the only high-field, high-temperature superconductor (HTS) capable of reaching a critical current density <italic>J</italic><sub>c</sub>(16 T, 4.2 K) of 6500 A·mm<sup>−2</sup> in the highly desirable round wire (RW) form. However, state-of-the-art Bi-2212 conductors still have a critical current density (<italic>J</italic><sub>c</sub>) to depairing current density (<italic>J</italic><sub>d</sub>) ratio around 20 to 30 times lower than that of state-of-the-art Nb–Ti or REBCO. Previously, we have shown that recent improvements in Bi-2212 RW <italic>J</italic><sub>c</sub> are due to improved connectivity associated with optimization of the heat treatment process, and most recently due to a transition to a finer and more uniform powder manufactured by Engi-Mat. One quantitative measure of connectivity may be the critical current (<italic>I</italic><sub>c</sub>) distribution, since the local <italic>I</italic><sub>c</sub> in a wire can vary along the length due to variable vortex-microstructure interactions and to factors such as filament shape variations, grain-to-grain connectivity variations and blocking secondary phase distributions. Modeling the experimental <italic>V-I</italic> transition measured on a low resistance shunt as a complex sum of voltage contributions of individual filament and wire sub-sections allows a numerical extraction of the <italic>I</italic><sub>c</sub> distribution from the <italic>d<sup>2</sup>V/dI<sup>2</sup></italic> treatment of the <italic>V-I</italic> curves. Here we compare ∼ 0.1 m length <italic>I</italic><sub>c</sub> distributions of Bi-2212 RWs with recent state-of-the-art very high-<italic>J</italic><sub>c</sub> Engi-Mat powder and lower <italic>J</italic><sub>c</sub> and older Nexans granulate powder. We do find that the <italic>I</italic><sub>c</sub> spread for Bi-2212 wires is about twice the relative standard of high-<italic>J</italic><sub>c</sub> Nb–Ti well below <italic>H</italic><sub>irr</sub>. We do not yet see any obvious contribution of the Bi-2212 anisotropy to the <italic>I</italic><sub>c</sub> distribution and are rather encouraged that these Bi-2212 round wires show relative <italic>I</italic><sub>c</sub> distributions not too far from high-<italic>J</italic><sub>c</sub> Nb–Ti wires.
Thermoresponsivity of Poly(N-isopropylacrylamide) microgels in water-trehalose mixture: an experimental and simulation study

In this work we use a multi-technique approach to gain new insights into the effects of trehalose on the volume phase transition of poly(N-isopropylacrylamide) (PNIPAM) microgels, a model synthetic biomimetic system. By dynamic light scattering the temperature-dependent microgel hydrodynamic volume is monitored, while Raman spectroscopy and molecular dynamics simulations explore at the molecular scale changes of solvation and dynamics across the transition. At 0.72 M trehalose concentration the phase transition temperature is lowered by ≈ 10 K, with a concomitant decrease of transition enthalpy not compensated by a corresponding reduction in transition entropy. In water-trehalose solution the microgel particles are ≈ 20% more expanded than in pure water, keeping unchanged their thermal contraction, swelling capacity, and the amount of absorbed water. Strongly hydrated trehalose molecules mainly develop water-mediated interactions with PNIPAM, thus preserving the polymer hydration state both before and after the transition. However, the presence of trehalose deeply impacts on the dynamics of the system, with a drastic slowing down of PNIPAM motions and of its hydration shell. Our investigation reveals a coherent picture in which trehalose sustains hydration and favors, both thermodynamically and kinetically, the collapsed conformation of the microgel, by inhibiting local motions of polymer residues and water.
Inflation, gravity mediated supersymmetry breaking, and de Sitter vacua in supergravity with a Kähler-invariant Fayet-Iliopoulos term

We use a new mechanism for generating a Fayet-Iliopoulos term in supergravity, which is not associated to an R symmetry, to construct a semi-realistic theory of slow-roll inﬂation for a theory with the same K¨ahler potential and superpotential as the KKLT string background (without anti-D3 branes). In our model, supersymmetry must be broken at a high scale in a hidden sector to ensure that the cutoﬀ of the eﬀective ﬁeld theory is above the Hubble scale of inﬂation. The gravitino has a super-EeV mass and supersymmetry breaking is communicated to the observable sector through gravity mediation. Some mass scales of the supersymmetry-breaking soft terms in the observable sector can be parametrically smaller than the SUSY breaking scale. If a string realization of the new FI term were found, our model could be the basis for a low energy eﬀective supergravity description of realistic superstring models of inﬂation.
Supernova neutrino signals based on long-term axisymmetric simulations

We study theoretical neutrino signals from core-collapse supernova (CCSN) computed using axisymmetric CCSN simulations that cover the post-bounce phase up to ∼ 4 s. We provide basic quantities of the neutrino signals such as event rates, energy spectra, and cumulative number of events at some terrestrial neutrino detectors, and then discuss some new features in the late phase that emerge in our models. Contrary to popular belief, neutrino emissions in the late phase are not always steady, but rather have temporal fluctuations, the vigor of which hinges on the CCSN model and neutrino flavor. We find that such temporal variations are not primarily driven by proto-neutron star (PNS) convection, but by fallback accretion in exploding models. We assess the detectability of these temporal variations, and find that IceCube is the most promising detector with which to resolve them. We also update fitting formulae first proposed in our previous paper for which the total neutrino energy (TONE) emitted at the CCSN source is estimated from the cumulative number of events in each detector. This will be a powerful technique with which to analyze real observations, particularly for low-statistics data.
Moral Decision-Making in Medical Hybrid Intelligent Systems: A Team Design Patterns Approach to the Bias Mitigation and Data Sharing Design Problems

Increasing automation in the healthcare sector calls for a Hybrid Intelligence (HI) approach to closely study and design the collaboration of humans and autonomous machines. Ensuring that medical HI systems' decision-making is ethical is key. The use of Team Design Patterns (TDPs) can advance this goal by describing successful and reusable configurations of design problems in which decisions have a moral component, as well as through facilitating communication in multidisciplinary teams designing HI systems. For this research, TDPs were developed to describe a set of solutions for two design problems in a medical HI system: (1) mitigating harmful biases in machine learning algorithms and (2) sharing health and behavioral patient data with healthcare professionals and system developers. The Socio-Cognitive Engineering methodology was employed, integrating operational demands, human factors knowledge, and a technological analysis into a set of TDPs. A survey was created to assess the usability of the patterns on their understandability, effectiveness, and generalizability. The results showed that TDPs are a useful method to unambiguously describe solutions for diverse HI design problems with a moral component on varying abstraction levels, that are usable by a heterogeneous group of multidisciplinary researchers. Additionally, results indicated that the SCE approach and the developed questionnaire are suitable methods for creating and assessing TDPs. The study concludes with a set of proposed improvements to TDPs, including their integration with Interaction Design Patterns, the inclusion of several additional concepts, and a number of methodological improvements. Finally, the thesis recommends directions for future research.
ROAD: The Road Event Awareness Dataset for Autonomous Driving

Humans drive in a holistic fashion which entails, in particular, understanding dynamic road events and their evolution. Injecting these capabilities in autonomous vehicles can thus take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle’s ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event. We benchmark various detection tasks, proposing as a baseline a new incremental algorithm for online road event awareness termed 3D-RetinaNet. We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the winners of the ICCV2021 ROAD challenge, which highlight the challenges faced by situation awareness in autonomous driving. ROAD is designed to allow scholars to investigate exciting tasks such as complex (road) activity detection, future event anticipation and continual learning. The dataset is available at https://github.com/gurkirt/road-dataset; the baseline can be found at https://github.com/gurkirt/3D-RetinaNet.
Anomalous Hall instability in the Chern-Simons magnetohydrodynamics

The Chern-Simons magnetohydrodynamics (CSMHD) is introduced using a Maxwell-Chern-Simons (MCS) Lagrangian including an axion-like field $\Theta$. The MCS equation of motion derived from this Lagrangian consists of a modified current, including a chiral magnetic (CM) and an anomalous Hall (AH) current, in addition to the ordinary Ohm current of resistive magnetohydrodynamics (MHD). The former consists of an axial chemical potential, which is given in terms of the temporal comoving derivative of $\Theta$, and the latter arises from the spatial gradient of $\Theta$. As it turns out, the existence of the axial chemical potential is a nonequilibrium effect that plays no role in the linear stability analysis, whereas the AH current arises as in the first-order linear perturbation of the thermal equilibrium. We analyze the linear stability and causality of the CSMHD in a resistive and chiral medium. We show that the Alfven modes propagating sufficiently close to the direction of the magnetic field are unstable but causal. They are also accompanied by a genuine nonhydro mode. A stable mode in a particular direction can correspond to an unstable mode propagating in the exact opposite direction. The AH instability is a manifestation of a breakdown of the parity. A numerical analysis of the phase velocity confirms these results.
Deep Deformation Detail Synthesis for Thin Shell Models

In physics‐based cloth animation, rich folds and detailed wrinkles are achieved at the cost of expensive computational resources and huge labor tuning. Data‐driven techniques make efforts to reduce the computation significantly by utilizing a preprocessed database. One type of methods relies on human poses to synthesize fitted garments, but these methods cannot be applied to general cloth animations. Another type of methods adds details to the coarse meshes obtained through simulation, which does not have such restrictions. However, existing works usually utilize coordinate‐based representations which cannot cope with large‐scale deformation, and requires dense vertex correspondences between coarse and fine meshes. Moreover, as such methods only add details, they require coarse meshes to be sufficiently close to fine meshes, which can be either impossible, or require unrealistic constraints to be applied when generating fine meshes. To address these challenges, we develop a temporally and spatially as‐consistent‐as‐possible deformation representation (named TS‐ACAP) and design a DeformTransformer network to learn the mapping from low‐resolution meshes to ones with fine details. This TS‐ACAP representation is designed to ensure both spatial and temporal consistency for sequential large‐scale deformations from cloth animations. With this TS‐ACAP representation, our DeformTransformer network first utilizes two mesh‐based encoders to extract the coarse and fine features using shared convolutional kernels, respectively. To transduct the coarse features to the fine ones, we leverage the spatial and temporal Transformer network that consists of vertex‐level and frame‐level attention mechanisms to ensure detail enhancement and temporal coherence of the prediction. Experimental results show that our method is able to produce reliable and realistic animations in various datasets at high frame rates with superior detail synthesis abilities compared to existing methods.
A Novel Deep Learning Method for Textual Sentiment Analysis

Sentiment analysis is known as one of the most crucial tasks in the field of natural language processing and Convolutional Neural Network (CNN) is one of those prominent models that is commonly used for this aim. Although convolutional neural networks have obtained remarkable results in recent years, they are still confronted with some limitations. Firstly, they consider that all words in a sentence have equal contributions in the sentence meaning representation and are not able to extract informative words. Secondly, they require a large number of training data to obtain considerable results while they have many parameters that must be accurately adjusted. To this end, a convolutional neural network integrated with a hierarchical attention layer is proposed which is able to extract informative words and assign them higher weight. Moreover, the effect of transfer learning that transfers knowledge learned in the source domain to the target domain with the aim of improving the performance is also explored. Based on the empirical results, the proposed model not only has higher classification accuracy and can extract informative words but also applying incremental transfer learning can significantly enhance the classification performance.
Evidence for microscopic kurtosis in neural tissue revealed by correlation tensor MRI

The impact of microscopic diffusional kurtosis (µK), arising from restricted diffusion and/or structural disorder, remains a controversial issue in contemporary diffusion MRI (dMRI). Recently, correlation tensor imaging (CTI) was introduced to disentangle the sources contributing to diffusional kurtosis, without relying on a‐priori multi‐gaussian component (MGC) or other microstructural assumptions. Here, we investigated µK in in vivo rat brains and assessed its impact on state‐of‐the‐art methods ignoring µK.
Effect of pH on dimensional stability of rat tail tendon collagen fiber

The organized molecular structure of collagen is related to its dimensional stability. The dimensional stability of collagen arises from the interplay of various intermolecular forces such as covalent, hydrogen bonding, electrostatic interactions, hydrophobic interactions, London or van der Waals forces, and weak interactions. A structure–function relationship exists in collagen. Electrostatic interactions play an important role in dimensional stabilization. The dimensional stability of rat tail tendon (RTT) collagen fiber is affected by the change in the net fixed charge on the molecule as a function of pH. Thermal and mechanical properties are dependent on molecular and lattice orders. The pH dependence of thermal shrinkage, isometric tension, differential scanning calorimetry, swelling behavior, tensile strength, and percent extension and stress relaxation behavior are studied in 0.02M Tris-maleate buffer at pH 4–8. The observed experimental results provide compelling evidence that electrostatic interactions play an important role in the dimensional stability of RTT collagen. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 75: 1577–1584, 2000
HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving

Humans learn compositional and causal abstraction, \ie, knowledge, in response to the structure of naturalistic tasks. When presented with a problem-solving task involving some objects, toddlers would first interact with these objects to reckon what they are and what can be done with them. Leveraging these concepts, they could understand the internal structure of this task, without seeing all of the problem instances. Remarkably, they further build cognitively executable strategies to \emph{rapidly} solve novel problems. To empower a learning agent with similar capability, we argue there shall be three levels of generalization in how an agent represents its knowledge: perceptual, conceptual, and algorithmic. In this paper, we devise the very first systematic benchmark that offers joint evaluation covering all three levels. This benchmark is centered around a novel task domain, HALMA, for visual concept development and rapid problem-solving. Uniquely, HALMA has a minimum yet complete concept space, upon which we introduce a novel paradigm to rigorously diagnose and dissect learning agents' capability in understanding and generalizing complex and structural concepts. We conduct extensive experiments on reinforcement learning agents with various inductive biases and carefully report their proficiency and weakness.
Enhanced Modality Transition for Image Captioning

Image captioning model is a cross-modality knowledge discovery task, which targets at automatically describing an image with an informative and coherent sentence. To generate the captions, the previous encoder-decoder frameworks directly forward the visual vectors to the recurrent language model, forcing the recurrent units to generate a sentence based on the visual features. Although these sentences are generally readable, they still suffer from the lack of details and highlights, due to the fact that the substantial gap between the image and text modalities is not sufficiently addressed. In this work, we explicitly build a Modality Transition Module (MTM) to transfer visual features into semantic representations before forwarding them to the language model. During the training phase, the modality transition network is optimised by the proposed modality loss, which compares the generated preliminary textual encodings with the target sentence vectors from a pre-trained text auto-encoder. In this way, the visual vectors are transited into the textual subspace for more contextual and precise language generation. The novel MTM can be incorporated into most of the existing methods. Extensive experiments have been conducted on the MS-COCO dataset demonstrating the effectiveness of the proposed framework, improving the performance by 3.4% comparing to the state-of-the-arts.
Highly uniform GaSb quantum dots with indirect-direct bandgap crossover at telecom range

We demonstrate a new quantum-confined semiconductor material based on GaSb quantum dots (QDs) embedded in single-crystalline AlGaSb matrix by filling droplet-etched nanoholes. The droplet-mediated growth mechanism allows formation of low QD densities required for non-classical single-QD light sources. The photoluminescence (PL) experiments reveal that the GaSb QDs have an indirect-direct bandgap crossover at telecom wavelengths. This is due to the alignment of the Γ and L valleys in the conduction band as a result of quantum confinement controlled by dimensions of the nanostructure. We show that in the direct bandgap regime close to 1.5 μm wavelength, the GaSb QDs have a type I band alignment and exhibit excitonic emission with narrow spectral lines and very low inhomogeneous broadening of PL emission owing to the high material quality and dimensional uniformity. These properties are extremely promising in terms of applications in infrared quantum optics and quantum photonic integration.
Ionoluminescence of zircon: rare earth emissions and radiation damage

The luminescence of synthetic rare earth (RE)-doped zircons and three natural zircons has been studied by ion beam excitation (ion beam luminescence or ionoluminescence (IL)). Luminescence results from electronic transitions between energy levels of the RE ions, giving emissions with characteristic energies. Studies of co-doped Ho : Dy zircon show that electronic cascades from Ho3+ to Dy3+ ions result in intense Dy3+ luminescence even when Ho is far more abundant. Dy3+ ions have the greatest luminescence cross-section of common RE3+ ions. Implantation of heavier N+ ions into zircon, chosen to mimic accelerated radiation-induced structural modification, causes significant sample degradation. Frenkel-type defects and groups in the zircon are associated with a broad luminescence band of ~600 nm, and these defects modify energy cascades between RE3+ ions, thereby reducing the efficiency of energy deposition via the Dy3+ luminescence decay pathway. The broad band of ~600 nm created by implantation is common in natural zircons and is attributed primarily to intrinsic Frenkel defects caused by natural radiation. Dy3+ luminescence is apparent from natural zircons even though Dy is one of the least abundant RE ions in zircon, and this behaviour is explained by the high luminescence cross-section of Dy3+ in zircon. Radiation-induced structural damage reduces the efficiency of energy transport between different RE3+ ions. IL provides key insights into both the interactions between defects and the nature of luminescence centres in zircon.