Miracle-dz/UniFAR-FTUs huggingface dataset:
2D and 3D Flame Surface Analysis of Flame Acceleration and Deflagration--to--Detonation Transition in Hydrogen--Air Mixtures with Concentration Gradients

Our project aims at improving the understanding of deflagrations and detonations in hydrogen-air mixtures. A special focus lies on the flame acceleration phase and the sudden transition to detonation which is a critical issue for nuclear reactor safety considerations. Whereas in the past homogeneous mixtures have intensely been investigated, concentration gradients perpendicular to the main direction of flame propagation are taken into account here. In case of a core meltdown, this represents a more realistic scenario of hydrogen distribution within a reactor containment dome [1]. Once a reliable prediction in terms of flame speeds and pressure loads is possible, the structural response of the containment can be evaluated.
Periodic Freight Demand Estimation for Large-scale Tactical Planning

Freight carriers rely on tactical planning to design their service network to satisfy demand in a cost-effective way. For computational tractability, deterministic and cyclic Service Network Design (SND) formulations are used to solve large-scale problems. A central input is the periodic demand, that is, the demand expected to repeat in every period in the planning horizon. In practice, demand is predicted by a time series forecasting model and the periodic demand is the average of those forecasts. This is, however, only one of many possible mappings. The problem consisting in selecting this mapping has hitherto been overlooked in the literature. We propose to use the structure of the downstream decision-making problem to select a good mapping. For this purpose, we introduce a multilevel mathematical programming formulation that explicitly links the time series forecasts to the SND problem of interest. The solution is a periodic demand estimate that minimizes costs over the tactical planning horizon. We report results in an extensive empirical study of a large-scale application from the Canadian National Railway Company. They clearly show the importance of the periodic demand estimation problem. Indeed, the planning costs exhibit an important variation over different periodic demand estimates and using an estimate different from the mean forecast can lead to substantial cost reductions. Moreover, the costs associated with the periodic demand estimates based on forecasts were comparable to, or even better than those obtained using the mean of actual demand.
Frequency Effects on Syntactic Rule Learning in Transformers

Pre-trained language models perform well on a variety of linguistic tasks that require symbolic reasoning, raising the question of whether such models implicitly represent abstract symbols and rules. We investigate this question using the case study of BERT’s performance on English subject–verb agreement. Unlike prior work, we train multiple instances of BERT from scratch, allowing us to perform a series of controlled interventions at pre-training time. We show that BERT often generalizes well to subject–verb pairs that never occurred in training, suggesting a degree of rule-governed behavior. We also find, however, that performance is heavily influenced by word frequency, with experiments showing that both the absolute frequency of a verb form, as well as the frequency relative to the alternate inflection, are causally implicated in the predictions BERT makes at inference time. Closer analysis of these frequency effects reveals that BERT’s behavior is consistent with a system that correctly applies the SVA rule in general but struggles to overcome strong training priors and to estimate agreement features (singular vs. plural) on infrequent lexical items.
Where to Find Overmassive Brown Dwarfs: New Benchmark Systems for Binary Evolution

Under the right conditions, brown dwarfs that gain enough mass late in their lives to cross the hydrogen-burning limit will not turn into low-mass stars, but rather remain essentially brown dwarf–like. While these objects, called either beige dwarfs or overmassive brown dwarfs, may exist in principle, it remains unclear exactly how they would form astrophysically. We show that accretion from AGB winds, aided by the wind Roche lobe overflow mechanism, is likely to produce a substantial population of observable overmassive brown dwarfs, though other mechanisms are still plausible. Specifically, we predict that Sun-like stars born with a massive brown dwarf companion on an orbit with a semimajor axis of order 10 au will likely produce overmassive brown dwarfs, which may be found today as companions to the donor star's remnant white dwarf. The identification and characterization of such an object would produce unique constraints on binary evolution, because there is a solid upper limit on the brown dwarf's initial mass.
UV lines as a tracers for the XUV-fluxes of stars and the PLATOspec project

Observations in the UV-regime are very important for exoplanet research, because many diagnostically important lines for studying stellar activity are in this regime. Studying stellar activity is not only important because of its negative effects on the determination planetary parameters, but also because the XUV-radiation from the host stars affects the photochemistry and the erosion of planetary atmospheres . Unfortunately, the XUV-region is only accessible from space. However, since the XUV-radiation is correlated with the Ca II H&K-lines, we can use these lines to study the XUV radiation indirectly. The Ca II H&K-lines for relatively bright stars can be observed with PLATOspec, a new high-resolution echelle spectrograph in development for the ESO 1.5m telescope at La Silla. One advantage compared to instruments on larger telescopes will be that large programs can be carried out. There will be two modes for obtaining precise RV-measurements. In the future, the CUBES instrument on the VLT will be able to study the same lines to probe the XUV-radiation in much fainter targets.
Critical weaknesses in shielding strategies for COVID-19

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has led to a wide range of non-pharmaceutical interventions being implemented around the world to curb transmission. However, the economic and social costs of some of these measures, especially lockdowns, has been high. An alternative and widely discussed public health strategy for the COVID-19 pandemic would have been to ‘shield’ those most vulnerable to COVID-19 (minimising their contacts with others), while allowing infection to spread among lower risk individuals with the aim of reaching herd immunity. Here we retrospectively explore the effectiveness of this strategy using a stochastic SEIR framework, showing that even under the unrealistic assumption of perfect shielding, hospitals would have been rapidly overwhelmed with many avoidable deaths among lower risk individuals. Crucially, even a small (20%) reduction in the effectiveness of shielding would have likely led to a large increase (>150%) in the number of deaths compared to perfect shielding. Our findings demonstrate that shielding the vulnerable while allowing infections to spread among the wider population would not have been a viable public health strategy for COVID-19 and is unlikely to be effective for future pandemics.
Embedding Node Structural Role Identity Using Stress Majorization

Nodes in networks may have one or more functions that determine their role in the system. As opposed to local proximity, which captures the local context of nodes, the role identity captures the functional "role" that nodes play in a network, such as being the center of a group, or the bridge between two groups. This means that nodes far apart in a network can have similar structural role identities. Several recent works have explored methods for embedding the roles of nodes in networks. However, these methods all rely on either approximating or indirect modeling of structural equivalence. In this paper, we present a novel and flexible framework using stress majorization, to transform the high-dimensional role identities in networks directly (without approximation or indirect modeling) to a low-dimensional embedding space. Our method is also flexible, in that it does not rely on specific structural similarity definitions. We evaluated our method on the tasks of node classification, clustering, and visualization, using three real-world and five synthetic networks. Our experiments show that our framework achieves superior results than existing methods in learning node role representations.
Spin-current instability at a magnetic domain wall in a ferromagnetic superfluid: A generation mechanism of eccentric fractional skyrmions

Spinful superfluids of ultracold atoms are ideal for investigating the intrinsic properties of spin current and texture because they are realized in an isolated, nondissipative system free from impurities, dislocations, and thermal fluctuations. This study theoretically reveals the impact of spin current on a magnetic domain wall in spinful superfluids. An exact wall solution is obtained in the ferromagnetic phase of a spin-1 Bose–Einstein condensate with easy-axis anisotropy at zero temperature. The bosonic-quasiparticle mechanics analytically show that the spin current along the wall becomes unstable if the velocity exceeds the critical spin-current velocities, leading to complicated situations because of the competition between transverse magnons and ripplons. Our direct numerical simulation reveals that this system has a mechanism to generate an eccentric fractional skyrmion, which has a fractional topological charge, but its texture is not similar to that of a meron. This mechanism is in contrast to the generation of conventional skyrmions in easy-axis magnets. The theoretical findings can be examined in the same situation as in a recent experiment on ultracold atoms. In terms of the universality of spontaneous symmetry breaking, unexplored similar phenomena are expected in different physical systems with the same broken symmetry.
Multiwavelength Analysis of A1240, the Double Radio-relic Merging Galaxy Cluster Embedded in an ∼80 Mpc-long Cosmic Filament

We present a multiwavelength study of the double radio-relic cluster A1240 at z = 0.195. Our Subaru-based weak-lensing analysis detects three mass clumps forming an ∼4 Mpc filamentary structure elongated in a north–south orientation. The northern ( M200=2.61−0.60+0.51×1014M⊙ ) and middle ( M200=1.09−0.43+0.34×1014M⊙ ) mass clumps separated by ∼1.3 Mpc are associated with A1240 and colocated with the X-ray peaks and cluster galaxy overdensities revealed by Chandra and MMT/Hectospec observations, respectively. The southern mass clump ( M200=1.78−0.55+0.44×1014M⊙ ), ∼1.5 Mpc to the south of the middle clump, coincides with the galaxy overdensity in A1237, the A1240 companion cluster at z = 0.194. Considering the positions, orientations, and polarization fractions of the double radio relics measured by the LOFAR study, we suggest that A1240 is a postmerger binary system in the returning phase with a time since collision of ∼1.7 Gyr. With the SDSS DR16 data analysis, we also find that A1240 is embedded in the much larger scale (∼80 Mpc) filamentary structure whose orientation is in remarkable agreement with the hypothesized merger axis of A1240.
Can Edge Probing Tests Reveal Linguistic Knowledge in QA Models?

There have been many efforts to try to understand what grammatical knowledge (e.g., ability to understand the part of speech of a token) is encoded in large pre-trained language models (LM). This is done through ‘Edge Probing’ (EP) tests: supervised classification tasks to predict the grammatical properties of a span (whether it has a particular part of speech) using only the token representations coming from the LM encoder. However, most NLP applications fine-tune these LM encoders for specific tasks. Here, we ask: if an LM is fine-tuned, does the encoding of linguistic information in it change, as measured by EP tests? Specifically, we focus on the task of Question Answering (QA) and conduct experiments on multiple datasets. We find that EP test results do not change significantly when the fine-tuned model performs well or in adversarial situations where the model is forced to learn wrong correlations. From a similar finding, some recent papers conclude that fine-tuning does not change linguistic knowledge in encoders but they do not provide an explanation. We find that EP models are susceptible to exploiting spurious correlations in the EP datasets. When this dataset bias is corrected, we do see an improvement in the EP test results as expected.
Experimental Demonstration of a New Oscillator Concept Based on Degenerate Band Edge in Microstrip Circuit

The first experimental demonstration of an oscillator based on a periodic, resonant microstrip circuit with a degenerate band edge (DBE) is presented. The DBE is a fourth-order exceptional degeneracy of the eigenmodes in a lossless periodic waveguide that is visible in the wavenumber-frequency dispersion diagram, and the periodic microstrip behaves as a frequency selective medium. The presence of the DBE condition and the associated DBE resonance allow for a stable, single-mode oscillation as well as stability with varying the load resistance.
AliMe MKG: A Multi-modal Knowledge Graph for Live-streaming E-commerce

Live streaming is becoming an increasingly popular trend of sales in E-commerce. The core of live-streaming sales is to encourage customers to purchase in an online broadcasting room. To enable customers to better understand a product without jumping out, we propose AliMe MKG, a multi-modal knowledge graph that aims at providing a cognitive profile for products, through which customers are able to seek information about and understand a product. Based on the MKG, we build an online live assistant that highlights product search, product exhibition and question answering, allowing customers to skim over item list, view item details, and ask item-related questions. Our system has been launched online in the Taobao app, and currently serves hundreds of thousands of customers per day.
Structure formation in the effective field theory of holographic dark energy

We investigate the structure formation in the effective field theory (EFT) of the holographic dark energy. The equation of motion for the energy contrast δ m of the cold dark matter is the same as the one in the general relativity up to the leading order in the small scale limit, provided the equation of state is quintessence-like. Our EFT breaks down while the equation of state becomes phantom-like. By imposing additional constraints by hand at the Hamiltonian level the unstable scalar graviton can be eliminated solving the instability problem without changing the background evolution. In this case depending on the form of the constraint the effective gravitational constant can be modified and the dust component can obtain a non-vanishing sound speed.
On a class of nonlocal obstacle type problems related to the distributional Riesz fractional derivative

In this work, we consider the nonlocal obstacle problem with a given obstacle $\psi$ in a bounded Lipschitz domain $\Omega$ in $\mathbb{R}^{d}$, such that $\mathbb{K}_\psi^s=\{v\in H^s_0(\Omega):v\geq\psi \text{ a.e. in }\Omega\}\neq\emptyset$, given by \[u\in\mathbb{K}_\psi^s:\langle\mathcal{L}_au,v-u\rangle\geq\langle F,v-u\rangle\quad\forall v\in\mathbb{K}^s_\psi,\] for $F\in H^{-s}(\Omega)$, the dual space of $H^s_0(\Omega)$, $0<s<1$. The nonlocal operator $\mathcal{L}_a:H^s_0(\Omega)\to H^{-s}(\Omega)$ is defined with a measurable, bounded, strictly positive singular kernel $a(x,y)$, possibly not symmetric, by \[\langle\mathcal{L}_au,v\rangle=P.V. \int_{\mathbb{R}^d}\int_{\mathbb{R}^d}v(x)(u(x)-u(y))a(x,y)dydx=\mathcal{E}_a(u,v),\] with $\mathcal{E}_a$ being a Dirichlet form. Also, the fractional operator $\tilde{\mathcal{L}}_A=-D^s\cdot AD^s$ defined with the distributional Riesz $s$-fractional derivative and a bounded matrix $A(x)$ gives a well defined integral singular kernel. The corresponding $s$-fractional obstacle problem converges as $s\nearrow1$ to the obstacle problem in $H^1_0(\Omega)$ with the operator $-D\cdot AD$ given with the gradient $D$. We mainly consider problems involving the bilinear form $\mathcal{E}_a$ with one or two obstacles, and the N-membranes problem, deriving a weak maximum principle, comparison properties, approximation by bounded penalization, and the Lewy-Stampacchia inequalities. This provides regularity of the solutions, including a global estimate in $L^\infty(\Omega)$, local H\"older regularity when $a$ is symmetric, and local regularity in $W^{2s,p}_{loc}(\Omega)$ and $C^1(\Omega)$ for fractional $s$-Laplacian obstacle-type problems. These novel results are complemented with the extension of the Lewy-Stampacchia inequalities to the order dual of $H^s_0(\Omega)$ and some remarks on the associated $s$-capacity for general $\mathcal{L}_a$.
Interrogating COVID-19 Vaccine Hesitancy in the Philippines with a Nationwide Open-Access Online Survey

To mitigate the unprecedented health, social, and economic damage of COVID-19, the Philippines is undertaking a nationwide vaccination program to mitigate the effects of the global pandemic. In this study, we interrogated COVID-19 vaccine hesitancy in the country by deploying a nationwide open-access online survey, two months before the rollout of the national vaccination program. The Health Belief Model (HBM) posits that people are likely to adopt disease prevention behaviors and to accept medical interventions like vaccines if there is sufficient motivation and cues to action. A majority of our 7,193 respondents (62.5%) indicated that they were willing to be vaccinated against COVID-19. Moreover, multivariable analysis revealed that HBM constructs were associated with vaccination intention in the Philippines. Perceptions of high susceptibility, high severity, and significant benefits were all good predictors for vaccination intent. We also found that external cues to action were important. Large majorities of our respondents would only receive the COVID-19 vaccines after many others had received it (72.8%) or after politicians had received it (68.2%). Finally, our study revealed that most (21%) were willing to pay an amount of PHP1,000 [USD20] for the COVID-19 vaccines with an average willing-to-pay amount of PHP1,892 [USD38].
Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography

Invasive coronary angiography is a primary imaging modality that visualizes the lumen area of coronary arteries for the diagnosis of coronary artery diseases and guidance for interventional devices. In the current practice of quantitative coronary analysis (QCA), semi-automatic segmentation tools require labor-intensive and time-consuming manual correction; this limits their application in the catheterization room. For a more automated QCA, it is necessary to minimize operator intervention through robust segmentation methods with improved predictability. In this study, we introduced two selective ensemble methods that integrated the weighted ensemble approach with per-image quality estimation. In our selective ensemble methods, the segmentation outcomes from five base models with different loss functions were ranked by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranking. The ranking criteria based on mask morphology were determined empirically to avoid frequent types of segmentation errors, whereas the estimation of DSCs was performed by comparing the pseudo-ground truth generated from a meta-learner. In the assessment with 7,426 frames from 2,924 patients, the selective ensemble methods improved segmentation performance with DSCs of up to 93.11% and provided a better delineation of lumen boundaries near the coronary lesion with local DSCs of up to 94.04%, outperforming all individual models and hard voting ensembles. The probability of mask disconnection at the most narrowed region could be minimized to <1%. The robustness of the proposed methods was evident in the external validation. Inference time for major vessel segmentation was approximately one-third, indicating that our selective ensemble methods may allow the real-time application of QCA-based diagnostic methods in routine clinical settings.
Proposed Specifiers for Conduct Disorder (PSCD): Factor Structure and Validation of the Self-Report Version in Community and Forensic Samples of Portuguese Youth

The Proposed Specifiers for Conduct Disorder (PSCD) was developed as a measure to assess the multifaceted model of psychopathic traits in children/youth (i.e., grandiose–manipulative [GM], callous–unemotional [CU], and daring–impulsive [DI] traits) in addition to Conduct Disorder (CD) symptoms. This study aims to test the psychometric properties of the PSCD-self-report version across community (n = 648; 52.9% female) and forensic male youth (n = 258) from the Portuguese population. Results supported a general factor and four specific factors (GM, CU, DI, CD), which was invariant across gender and sample type. Evidence for reliability, construct, and temporal validity were also found. Overall, the PSCD appears to be a promising measure for assessing psychopathic traits in youth from both community and forensic settings, which may contribute to the discussion around the conceptualization, assessment, predictive value, and clinical usefulness of the multifaceted model of psychopathy in youthful populations, particularly in its association with CD.
Fission yeast Stn1 maintains stability of repetitive DNA at subtelomere and ribosomal DNA regions

Abstract Telomere binding protein Stn1 forms the CST (Cdc13/CTC1-STN1-TEN1) complex in budding yeast and mammals. Likewise, fission yeast Stn1 and Ten1 form a complex indispensable for telomere protection. We have previously reported that stn1-1, a high-temperature sensitive mutant, rapidly loses telomere DNA at the restrictive temperature due to frequent failure of replication fork progression at telomeres and subtelomeres, both containing repetitive sequences. It is unclear, however, whether Stn1 is required for maintaining other repetitive DNAs such as ribosomal DNA. In this study, we have demonstrated that stn1-1 cells, even when grown at the permissive temperature, exhibited dynamic rearrangements in the telomere-proximal regions of subtelomere and ribosomal DNA repeats. Furthermore, Rad52 and γH2A accumulation was observed at ribosomal DNA repeats in the stn1-1 mutant. The phenotypes exhibited by the stn1-1 allele were largely suppressed in the absence of Reb1, a replication fork barrier-forming protein, suggesting that Stn1 is involved in the maintenance of the arrested replication forks. Collectively, we propose that Stn1 maintains the stability of repetitive DNAs at subtelomeres and rDNA regions.