Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic design of a robust half-W1 photonic crystal waveguide for interfacing slow light and trapped cold atoms

Novel platforms interfacing trapped cold atoms and guided light in nanoscale waveguides are a promising route to achieve a regime of strong coupling between light and atoms in single pass, with applications to quantum non-linear optics and quantum simulation. A strong challenge for the experimental development of this emerging waveguide-QED field of research is to combine facilitated optical access for atom transport, atom trapping via guided modes and robustness to inherent nanofabrication imperfections. In this endeavor, here we propose to interface Rubidium atoms with a photonic-crystal waveguide based on a large-index GaInP slab. With a specifically tailored half-W1 design, we show that a large chiral coupling to the waveguide can be obtained and guided modes can be used to form two-color dipole traps for atoms at 116~nm from the edge of the structure. This optimized device should greatly improve the level of experimental control and facilitate the atom integration.
A coordinate transformation method for numerical solutions of the electric double layer and electroosmotic flows in a microchannel

The electric double layer (EDL) and electroosmotic flows (EOFs) constitute the theoretical foundations of microfluidics. Numerical solution is one of the effective means of analysis in microfluidics. In general, it is difficult to obtain an accurate numerical solution of complex EOFs because of multiphysical interactions and locally high gradients. In this paper, a new coordinate transformation method is proposed to numerically solve the Poisson–Boltzmann, Navier–Stokes and Nernst–Planck equations to study the EDL and complex EOFs in a microchannel. A series of numerical examples is presented including cases of a homogeneous, discontinous wall electric potential and a locally high wall potential. A systematic comparison of numerical solutions with and without the coordinate transformation is carried out. The numerical results indicate that the coordinate transformation effectively decreases the gradient of the electric potential, ion concentration and electroosmotic velocity in the vicinity of the solid wall, and greatly improves the stability and convergency of the solution. In a transformed coordinate system with a coarse grid, the numerical solutions can be as accurate as those in the original coordinate system with a refined grid. Copyright © 2011 John Wiley & Sons, Ltd.
Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms

Online learning platforms provide diverse questions to gauge the learners' understanding of different concepts. The repository of questions has to be constantly updated to ensure a diverse pool of questions to conduct assessments for learners. However, it is impossible for the academician to manually skim through the large repository of questions to check for duplicates when onboarding new questions from external sources. Hence, we propose a toolQDup in this paper that can surface near-duplicate and semantically related questions without any supervised data. The proposed tool follows an unsupervised hybrid pipeline of statistical and neural approaches for incorporating different nuances in similarity for the task of question duplicate detection. We demonstrate thatQDup can detect near-duplicate questions and also suggest related questions for practice with remarkable accuracy and speed from a large repository of questions. The demo video of the tool can be found at https://www.youtube. com/watch? v=loh0_-7XLW4.
Backward waves in planar insulator–metal–insulator waveguide structures

Existence conditions of backward waves and waves with zero group velocity in insulator–metal–insulator (IMI) structures are obtained and expressions for the group velocity of the surface plasmon polariton (SPP) are derived. We consider the general case when the two insulator half-spaces may be identical or different. The effect of losses on dispersion curves and values of the energy velocity is analyzed. The obtained equations are used for a characterization and a detailed study of backward waves in thin silver film waveguides.
DEA-Net: Single Image Dehazing Based on Detail-Enhanced Convolution and Content-Guided Attention

Single image dehazing is a challenging ill-posed problem which estimates latent haze-free images from observed hazy images. Some existing deep learning based methods are devoted to improving the model performance via increasing the depth or width of convolution. The learning ability of Convolutional Neural Network (CNN) structure is still under-explored. In this paper, a Detail-Enhanced Attention Block (DEAB) consisting of Detail-Enhanced Convolution (DEConv) and Content-Guided Attention (CGA) is proposed to boost the feature learning for improving the dehazing performance. Specifically, the DEConv contains difference convolutions which can integrate prior information to complement the vanilla one and enhance the representation capacity. Then by using the re-parameterization technique, DEConv is equivalently converted into a vanilla convolution to reduce parameters and computational cost. By assigning the unique Spatial Importance Map (SIM) to every channel, CGA can attend more useful information encoded in features. In addition, a CGA-based mixup fusion scheme is presented to effectively fuse the features and aid the gradient flow. By combining above mentioned components, we propose our Detail-Enhanced Attention Network (DEA-Net) for recovering high-quality haze-free images. Extensive experimental results demonstrate the effectiveness of our DEA-Net, outperforming the state-of-the-art (SOTA) methods by boosting the PSNR index over 41 dB with only 3.653 M parameters. (The source code of our DEA-Net is available at https://github.com/cecret3350/DEA-Net.)
Fortifying the Yomdin-Gromov Algebraic Lemma

We provide sharp cylindrical parametrizations of cylindrical cell decompositions by maps with bounded $C^{r}$ norm in the sharply o-minimal setting, thus generalizing and strengthening the Yomdin-Gromov Algebraic Lemma. We introduce forts, geometrical objects encoding the combinatorial structure of cylindrical cell decompositions in o-minimal geometry. Cylindical decompositions, refinements of such decompositions, and cylindrical parametrizations of such decomposition become morphisms in the category of forts. We formulate and prove the above results in the language of forts.
Space environment and magnetospheric Poynting fluxes of the exoplanet Tau Boötis b

Context: The first tentative detection of a magnetic field on the Hot Jupiter type exoplanet $\tau$ Bo\"otis b was recently reported by Turner et al. (2021). The magnetic field was inferred from observations of circularly-polarized radio emission obtained with the LOFAR telescopes. The radio emission is possibly a consequence of the interaction of the surrounding stellar wind with the planet's magnetic field. Methods: We perform magnetohydrodynamic simulations of the space environment around $\tau$ Bo\"otis b and its interaction with the stellar wind using the PLUTO code. We study the magnetospheric energy fluxes and effects of different magnetic field orientations in order to understand the physical processes which cause energy fluxes leading to the observed radio emission given the proposed magnetic field strength in Turner et al. (2021). Furthermore we study the effect of stellar wind density and pressure on magnetospheric energy fluxes given the uncertainty of extrasolar stellar wind predictions. Results: We find in our simulations that the interaction is most likely super--Alfv\'enic and energy fluxes generated by the stellar wind--planet interaction are consistent with the observed radio powers. Magnetospheric Poynting fluxes are of the order of 1-8 $\times10^{18}$ W for open, semi-open and closed magnetospheres. The Poynting fluxes are energetically consistent with the radio powers in Turner et al. (2021) for a magnetospheric Poynting flux-to-radio efficiency $>10^{-3}$ when the magnetic fields of the planet and star are aligned. In case of lower efficiency factors the magnetospheric emission scenario is according to the parameter space modeled in this study not powerful enough. In case of a magnetic polarity reversal of the host star towards an anti-aligned field configuration, expected radio powers in the magnetospheric emission scenario fall below the observable threshold.
Stratification des vari\'et\'es de Hilbert en pr\'esence de ramification

In this paper we study the geometry of the special fiber of Pappas-Rapoport models of Shimura varieties in the Hilbert case. More precisely we prove that the stratification induced by the Hodge polygon is a good stratification, which is false in the Hilbert-Siegel case for $g\geq 2$. Then we use known results on the convolution product of affine Grassmannians to describe the dimension of the strata and obtain that the forgetting morphism to the Kottwitz PEL model is flat in restriction to Hodge strata.
Stop comparing resummation methods

I argue that the consistency of any resummation method can be established if the method follows a power counting derived from a hierarchy of scales. I.e., whether it encodes a top-down effective field theory. This resolves much confusion over which resummation method to use once an approximation scheme is settled on. And if no hierarchy of scales exists, you should be wary about resumming. I give evidence from the study of phase transitions in thermal field theory, where adopting a consistent power-counting scheme and performing a strict perturbative expansion dissolves many common problems of such studies: gauge dependence, strong renormalization scale dependence, the Goldstone boson catastrophe, IR divergences, imaginary potentials, mirages (illusory barriers), perturbative breakdown, and linear terms.
Design and Characterization of Viscoelastic McKibben Actuators with Tunable Force-Velocity Curves

The McKibben pneumatic artificial muscle is a commonly studied soft robotic actuator, and its quasistatic force-length properties have been well characterized and modeled. However, its damping and force-velocity properties are less well studied. Understanding these properties will allow for more robust dynamic modeling of soft robotic systems. The force-velocity response of these actuators is of particular interest because these actuators are often used as hardware models of skeletal muscles for bioinspired robots, and this force-velocity relationship is fundamental to muscle physiology. In this work, we investigated the force-velocity response of McKibben actuators and the ability to tune this response through the use of viscoelastic polymer sheaths. These viscoelastic McKibben actuators (VMAs) were characterized using iso-velocity experiments inspired by skeletal muscle physiology tests. A simplified 1D model of the actuators was developed to connect the shape of the force-velocity curve to the material parameters of the actuator and sheaths. Using these viscoelastic materials, we were able to modulate the shape and magnitude of the actuators' force-velocity curves, and using the developed model, these changes were connected back to the material properties of the sheaths.
Language Cognition and Language Computation - Human and Machine Language Understanding

Language understanding is a key scientific issue in the fields of cognitive and computer science. However, the two disciplines differ substantially in the specific research questions. Cognitive science focuses on analyzing the specific mechanism of the brain and investigating the brain's response to language; few studies have examined the brain's language system as a whole. By contrast, computer scientists focus on the efficiency of practical applications when choosing research questions but may ignore the most essential laws of language. Given these differences, can a combination of the disciplines offer new insights for building intelligent language models and studying language cognitive mechanisms? In the following text, we first review the research questions, history, and methods of language understanding in cognitive and computer science, focusing on the current progress and challenges. We then compare and contrast the research of language understanding in cognitive and computer sciences. Finally, we review existing work that combines insights from language cognition and language computation and offer prospects for future development trends.
Simultaneous superadditivity of the direct and complementary channel capacities

Quantum communication channels differ from their classical counterparts because their capacities can be superadditive. The principle of monogamy of entanglement suggests that superadditive improvements in the transmission capacity of a channel should reduce the amount of information loss to the environment. We challenge this intuition by demonstrating that the coherent and private information of a channel and its complement can be simultaneously superadditive for arbitrarily many channel uses. To quantify the limits of this effect, we consider the notion of max (resp. total) private information of a channel, which represents the maximum (resp. sum) of the private information of the channel itself and its complement, and study its relationship with the coherent information of the individual direct and complementary channels. For a varying number of channel uses, we show that these quantities can obey different interleaving sequences of inequalities.
Semiconductor Thermal and Electrical Properties Decoupled by Localized Phonon Resonances

Thermoelectric materials convert heat into electricity through thermally driven charge transport in solids or vice versa for cooling. To compete with conventional energy‐conversion technologies, a thermoelectric material must possess the properties of both an electrical conductor and a thermal insulator. However, these properties are normally mutually exclusive because of the interconnection between scattering mechanisms for charge carriers and phonons. Recent theoretical investigations on sub‐device scales have revealed that nanopillars attached to a membrane exhibit a multitude of local phonon resonances, spanning the full spectrum, that couple with the heat‐carrying phonons in the membrane and cause a reduction in the in‐plane thermal conductivity, with no expected change in the electrical properties because the nanopillars are outside the pathway of voltage generation and charge transport. Here this effect is demonstrated experimentally for the first time by investigating device‐scale suspended silicon membranes with GaN nanopillars grown on the surface. The nanopillars cause up to 21% reduction in the thermal conductivity while the power factor remains unaffected, thus demonstrating an unprecedented decoupling in the semiconductor's thermoelectric properties. The measured thermal conductivity behavior for coalesced nanopillars and corresponding lattice‐dynamics calculations provide evidence that the reductions are mechanistically tied to the phonon resonances. This finding paves the way for high‐efficiency solid‐state energy recovery and cooling.
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks

Long short-term memory (LSTM) is one of the robust recurrent neural network architectures for learning sequential data. However, it requires considerable computational power to learn and implement both software and hardware aspects. This paper proposed a novel LiteLSTM architecture based on reducing the LSTM computation components via the weights sharing concept to reduce the overall architecture computation cost and maintain the architecture performance. The proposed LiteLSTM can be signiﬁcant for processing large data where time-consuming is crucial while hardware resources are limited, such as the security of IoT devices and medical data processing. The proposed model was evaluated and tested empirically on three diﬀerent datasets from the computer vision, cybersecurity, speech emotion recognition domains. The proposed LiteLSTM has comparable accuracy to the other state-of-the-art recurrent architecture while using a smaller computation budget.
Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study

Most approaches to cross-modal retrieval (CMR) focus either on object-centric datasets, meaning that each document depicts or describes a single object, or on scene-centric datasets, meaning that each image depicts or describes a complex scene that involves multiple objects and relations between them. We posit that a robust CMR model should generalize well across both dataset types. Despite recent advances in CMR, the reproducibility of the results and their generalizability across different dataset types has not been studied before. We address this gap and focus on the reproducibility of the state-of-the-art CMR results when evaluated on object-centric and scene-centric datasets. We select two state-of-the-art CMR models with different architectures: (i) CLIP; and (ii) X-VLM. Additionally, we select two scene-centric datasets, and three object-centric datasets, and determine the relative performance of the selected models on these datasets. We focus on reproducibility, replicability, and generalizability of the outcomes of previously published CMR experiments. We discover that the experiments are not fully reproducible and replicable. Besides, the relative performance results partially generalize across object-centric and scene-centric datasets. On top of that, the scores obtained on object-centric datasets are much lower than the scores obtained on scene-centric datasets. For reproducibility and transparency we make our source code and the trained models publicly available.
On the Structural Generalization in Text-to-SQL

Exploring the generalization of a text-to-SQL parser is essential for a system to automatically adapt the real-world databases. Previous works provided investigations focusing on lexical diversity, including the influence of the synonym and perturbations in both natural language questions and databases. However, research on the structure variety of database schema~(DS) is deficient. Specifically, confronted with the same input question, the target SQL is probably represented in different ways when the DS comes to a different structure. In this work, we provide in-deep discussions about the structural generalization of text-to-SQL tasks. We observe that current datasets are too templated to study structural generalization. To collect eligible test data, we propose a framework to generate novel text-to-SQL data via automatic and synchronous (DS, SQL) pair altering. In the experiments, significant performance reduction when evaluating well-trained text-to-SQL models on the synthetic samples demonstrates the limitation of current research regarding structural generalization. According to comprehensive analysis, we suggest the practical reason is the overfitting of (NL, SQL) patterns.
Energetics of point defects in yttrium aluminum garnet doped with Mg and Si

An influence of Mg and Si dopants on the formation energies and the concentration of point defects in yttrium aluminum garnet (YAG) is studied using the density functional approach. The formation energies of Mg and Si substitutional and interstitial defects, native point defects and defect complexes versus the oxygen chemical potential are obtained. It is shown that in YAG doped with Mg, negatively charged Mg substitutional defects are compensated by free carriers (holes) and positively charged oxygen vacancies, whereas interstitial Mg ions play a minor role. The concentration of oxygen vacancies increases under an increase in the concentration of Mg ions. In YAG doped with Si, positively charged Si substitutional defects are compensated by negatively changed isolated cation vacancies and complexes of Si ions and cation vacancies. Under an increase in the concentration of Si ions most of Al and Y vacancies bind in complexes with Si ions. As a result, the concentration of isolated cation vacancies depends nonmonotonically on the concentration of Si ions. The maximum of the concentration of isolated cation vacancies is reached at $0.02 - 0.04$ at. % of Si, depending on sintering conditions. Mg - Si complexes have very low formation energies. Due to formation of such complexes, Si and Mg increases the solubility of each other in YAG. At the same time Mg - Si complexes do not influence the concentration of anion and cation vacancies. The overall concentration of vacancies in YAG codoped with Mg and Si in equal atomic concentrations is low. At an excess concentration of Si or Mg the concentration of vacancies increases by orders of magnitude.
Random vectors on the spin configuration of a Curie-Weiss model on Erdos-Renyi random graphs

This article is concerned with the asymptotic behaviour of random vectors in a diluted ferromagnetic model. We consider a model introduced by Bovier&Gayrard (1993) with ferromagnetic interactions on a directed Erd\H{o}s-R\'enyi random graph. Here, directed connections between graph nodes are uniformly drawn at random with a probability p that depends on the number of nodes N and is allowed to go to zero in the limit. If $Np\longrightarrow\infty$ in this model, Bovier&Gayrard (1993) proved a law of large numbers almost surely, and Kabluchko et al. (2020) proved central limit theorems in probability. Here, we generalise these results for $\beta<1$ in the regime $Np\longrightarrow\infty$. We show that all those random vectors on the spin configuration that have a limiting distribution under the Curie-Weiss model converge weakly towards the same distribution under the diluted model, in probability on graph realisations. This generalises various results from the Curie-Weiss model to the diluted model. As a special case, we derive a law of large numbers and central limit theorem for two disjoint groups of spins.
Mephisto: A Framework for Portable, Reproducible, and Iterative Crowdsourcing

We introduce Mephisto, a framework to make crowdsourcing for research more reproducible, transparent, and collaborative. Mephisto provides abstractions that cover a broad set of task designs and data collection workflows, and provides a simple user experience to make best-practices easy defaults. In this whitepaper we discuss the current state of data collection and annotation in ML research, establish the motivation for building a shared framework to enable researchers to create and open-source data collection and annotation tools as part of their publication, and outline a set of suggested requirements for a system to facilitate these goals. We then step through our resolution in Mephisto, explaining the abstractions we use, our design decisions around the user experience, and share implementation details and where they align with the original motivations. We also discuss current limitations, as well as future work towards continuing to deliver on the framework's initial goals. Mephisto is available as an open source project, and its documentation can be found at www.mephisto. ai.