Miracle-dz/UniFAR-FTUs huggingface dataset:
A Kinetic Study of Sunflower Oil Methanolysis Catalyzed by Barium Hydroxide

Methanolysis of sunflower oil was investigated in the presence of barium hydroxide as a catalyst. It was observed that the methanolysis reaction was both heterogeneously and homogeneously catalyzed. The heterogeneously catalyzed reaction is faster than the homogeneously catalyzed one and determines the overall reaction rate. Under optimum reaction conditions, a very high fatty acid methyl ester content was obtained within a few minutes. The barium hydroxide-catalyzed methanolysis is faster than the methanolysis catalyzed by calcium oxide and calcium hydroxide and comparable to the potassium hydroxide-catalyzed methanolysis.
Structures magnétiques de FeGe monoclinique

La structure magnetique de FeGe monoclinique a ete determinee par diffraction des neutrons. Entre TN = 342 K et Tc = 115 K, la structure se caracterise par l'existence de plans a peu pres ferromagnetiques, couples antiferromagnetiquement, alors que les moments des atomes de fer situes entre ces plans ne sont pas ordonnes. Ils s'ordonnent a Tc. Pour T < Tc, la structure presente un arrangement conique a longue periode (≈ 395 A). Les resultats de spectrometrie Mossbauer sont compatibles avec ceux de diffraction neutronique. The magnetic structure of monoclinic FeGe is determined by neutron diffraction. Between TN = 342 K and Tc = 115 K, the magnetic structure is characterized by nearly ferromagnetic planes, antiferromagnetically coupled, while the magnetic moments of iron atoms located between these planes are not ordered. They order magnetically at Tc. For T < Tc, the iron moments form a coned structure with a periodicity of ≈ 395 A. The Mossbauer spectroscopy results are consistent with those of neutron diffraction.
FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders

Pretrained text encoders, such as BERT, have been applied increasingly in various natural language processing (NLP) tasks, and have recently demonstrated significant performance gains. However, recent studies have demonstrated the existence of social bias in these pretrained NLP models. Although prior works have made progress on word-level debiasing, improved sentence-level fairness of pretrained encoders still lacks exploration. In this paper, we proposed the first neural debiasing method for a pretrained sentence encoder, which transforms the pretrained encoder outputs into debiased representations via a fair filter (FairFil) network. To learn the FairFil, we introduce a contrastive learning framework that not only minimizes the correlation between filtered embeddings and bias words but also preserves rich semantic information of the original sentences. On real-world datasets, our FairFil effectively reduces the bias degree of pretrained text encoders, while continuously showing desirable performance on downstream tasks. Moreover, our post-hoc method does not require any retraining of the text encoders, further enlarging FairFil's application space.
Superradiance in string theory

We perform an extensive analysis of the statistics of axion masses and interactions in compactifications of type IIB string theory, and we show that black hole superradiance excludes some regions of Calabi-Yau moduli space. Regardless of the cosmological model, a theory with an axion whose mass falls in a superradiant band can be probed by the measured properties of astrophysical black holes, unless the axion self-interaction is large enough to disrupt formation of a condensate. We study a large ensemble of compactifications on Calabi-Yau hypersurfaces, with 1 ≤ h 1,1 ≤ 491 closed string axions, and determine whether the superradiance conditions on the masses and self-interactions are fulfilled. The axion mass spectrum is largely determined by the Kähler parameters, for mild assumptions about the contributing instantons, and takes a nearly-universal form when h 1,1 ≫ 1. When the Kähler moduli are taken at the tip of the stretched Kähler cone, the fraction of geometries excluded initially grows with h 1,1, to a maximum of ≈ 0.5 at h 1,1 ≈ 160, and then falls for larger h 1,1. Further inside the Kähler cone, the superradiance constraints are far weaker, but for h 1,1 ≫ 100 the decay constants are so small that these geometries may be in tension with astrophysical bounds, depending on the realization of the Standard Model.
CoMoGAN: continuous model-guided image-to-image translation

CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold. To that matter, we introduce a new Functional Instance Normalization layer and residual mechanism, which together disentangle image content from position on target manifold. We rely on naive physics-inspired models to guide the training while allowing private model/translations features. CoMoGAN can be used with any GAN backbone and allows new types of image translation, such as cyclic image translation like timelapse generation, or detached linear translation. On all datasets, it outperforms the literature. Our code is available in this page: https://github.com/cv-rits/CoMoGAN.
Tensor networks and efficient descriptions of classical data

We investigate the potential of tensor network based machine learning methods to scale to large image and text data sets. For that, we study how the mutual information between a subregion and its complement scales with the subsystem size L, similarly to how it is done in quantum many-body physics. We find that for text, the mutual information scales as a power law L with a close to volume law exponent, indicating that text cannot be efficiently described by 1D tensor networks. For images, the scaling is close to an area law, hinting at 2D tensor networks such as PEPS could have an adequate expressibility. For the numerical analysis, we introduce a mutual information estimator based on autoregressive networks, and we also use convolutional neural networks in a neural estimator method.
Majority Voting with Bidirectional Pre-translation Improves Bitext Retrieval

Obtaining high-quality parallel corpora is of paramount importance for training NMT systems. However, as many language pairs lack adequate gold-standard training data, a popular approach has been to mine so-called ”pseudo-parallel” sentences from paired documents in two languages. In this paper, we outline some problems with current methods, propose computationally economical solutions to those problems, and demonstrate success with novel methods on the Tatoeba similarity search benchmark and on a downstream task, namely NMT. We uncover the effect of resource-related factors (i.e. how much mono-lingual/bilingual data is available for a given language) on the optimal choice of bitext mining approach, and echo problems with the oft-used BUCC dataset that have been observed by others. We make the code and data used for our experiments publicly available. 1
Inference for Generative Capsule Models

Capsule networks (see e.g. Hinton et al., 2018) aim to encode knowledge and reason about the relationship between an object and its parts. In this paper we specify a \emph{generative} model for such data, and derive a variational algorithm for inferring the transformation of each object and the assignments of observed parts to the objects. We apply this model to (i) data generated from multiple geometric objects like squares and triangles ("constellations"), and (ii) data from a parts-based model of faces. Recent work by Kosiorek et al. [2019] has used amortized inference via stacked capsule autoencoders (SCAEs) to tackle this problem -- our results show that we significantly outperform them where we can make comparisons (on the constellations data).
Knowledge Graph Question Answering using Graph-Pattern Isomorphism

Knowledge Graph Question Answering (KGQA) systems are often based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.
On naturality of the Ozsváth–Szabó contact
invariant

We discuss functoriality properties of the Ozsvath-Szabo contact invariant, and expose a number of results which seemed destined for folklore. We clarify the (in)dependence of the invariant on the basepoint, prove that it is functorial with respect to contactomorphisms, and show that it is strongly functorial under Stein cobordisms.
Robust gapless superconductivity in 
4Hb−TaS2

David Dentelski, 2 Ezra Day-Roberts, Turan Birol, Rafael M. Fernandes, and Jonathan Ruhman 2 Department of Physics, Bar-Ilan University, 52900, Ramat Gan, Israel Center for Quantum Entanglement Science and Technology, Bar-Ilan University, 52900, Ramat Gan Israel School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, 55455 MN (Dated: March 26, 2021)
Relative depolarization of the black hole photon ring in GRMHD models of Sgr A* and M87*

Using general relativistic magnetohydrodynamic simulations of accreting black holes, we show that a suitable subtraction of the linear polarization per pixel from total intensity images can enhance the photon ring feature. We find that the photon ring is typically a factor of ≃2 less polarized than the rest of the image. This is due to a combination of plasma and general relativistic effects, as well as magnetic turbulence. When there are no other persistently depolarized image features, adding the subtracted residuals over time results in a sharp image of the photon ring. We show that the method works well for sample, viable GRMHD models of Sgr A* and M87*, where measurements of the photon ring properties would provide new measurements of black hole mass and spin, and potentially allow for tests of the ‘no-hair’ theorem of general relativity.
On a free boundary model for three-dimensional MEMS with a hinged top plate: Stationary case

A stationary free boundary problem modeling a three-dimensional electrostatic MEMS device is investigated. The device is made of a rigid ground plate and an elastic top plate which is hinged at its boundary, the plates being held at different voltages. The model couples a nonlocal fourth-order equation for the deformation of the top plate to a Laplace equation for the electrostatic potential in the free domain between the two plates. The strength of the coupling is tuned by a parameter λ which is proportional to the square of the applied voltage difference. Existence of a stable stationary solution is established for small values of λ. Nonexistence of stationary solutions is obtained when λ is large enough.
Effects of lightning strokes on underground gravitational waves observatories

For ground-based gravitational wave (GW) detectors, lightning strokes in the atmosphere are sources of environmental noise. Some GW detectors are built or planned in underground facilities, and knowledge of how lightning strokes affect them is of interest. In this paper, the lightning detection system in KAGRA is introduced, and the properties of the magnetic field measured inside and outside the KAGRA tunnel are shown. One lightning-induced event in the GW channel of the KAGRA main interferometer is also showed. Finally, possible applications of lightning events for the GW experiments are discussed.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training

Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction between image-text pairs, by assuming that there exists strong semantic correlation between the text and image modalities. Since this strong assumption is often invalid in real-world scenarios, we choose to implicitly model the cross-modal correlation for large-scale multi-modal pre-training, which is the focus of the Chinese project `WenLan' led by our team. Specifically, with the weak correlation assumption over image-text pairs, we propose a two-tower pre-training model called BriVL within the cross-modal contrastive learning framework. Unlike OpenAI CLIP that adopts a simple contrastive learning method, we devise a more advanced algorithm by adapting the latest method MoCo into the cross-modal scenario. By building a large queue-based dictionary, our BriVL can incorporate more negative samples in limited GPU resources. We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model. Extensive experiments demonstrate that the pre-trained BriVL model outperforms both UNITER and OpenAI CLIP on various downstream tasks.
Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions

A plethora of observational data obtained over the last couple of decades has allowed cosmology to enter into a precision era and has led to the foundation of the standard cosmological constant and cold dark matter paradigm, known as the ΛCDM model. Given the many possible extensions of this concordance model, we present here several novel consistency tests which could be used to probe for deviations from ΛCDM. First, we derive a joint consistency test for the spatial curvature Ω k, 0 and the matter density Ω m,0 parameters, constructed using only the Hubble rate H ( z ), which can be determined directly from observations. Second, we present a new test of possible deviations from homogeneity using the combination of two datasets, either the baryon acoustic oscillation (BAO) and H ( z ) data or the transversal and radial BAO data, while we also introduce two consistency tests for ΛCDM which could be reconstructed via the transversal and radial BAO data. We then reconstruct the aforementioned tests using the currently available data in a model independent manner using a particular machine learning approach, namely the Genetic Algorithms. Finally, we also report on a ∼ 4 σ tension on the transition redshift as determined by the H ( z ) and radial BAO data.
A Search for Cloud Cores Affected by Shocked Carbon Chain Chemistry in L1251

We searched for shocked carbon chain chemistry (SCCC) sources with C3S abundances surpassing those of HC5N toward the dark cloud L1251, using the Effelsberg telescope at the K band (18–26 GHz). L1251-1 and L1251-3 are identified as the most promising SCCC sources. The two sources harbor young stellar objects. We conducted mapping observations toward L1251-A, the western tail of L1251, at λ ∼ 3 mm with the Purple Mountain Observatory 13.7 m and the Nobeyama Radio Observatory 45 m telescopes in lines of C2H, N2H+, CS, HCO+, SO, HC3N, and C18O as well as in CO 3–2 using the James Clerk Maxwell Telescope (JCMT). The spectral data were combined with archival data including Spitzer and Herschel continuum maps for further analysis. Filamentary substructures labeled as F1–F6 were extracted in L1251, with F1 being associated with L1251-A hosting L1251-1. The peak positions of dense gas traced by HCO+ are misaligned relative to those of the dust clumps. Episodic outflows are common in this region. The twisted morphology of F1 and velocity distribution along L1251-A may originate from stellar feedback. SCCC in L1251-1 may have been caused by outflow activities originated from the infrared source IRS1. The signposts of ongoing SCCC and the broadened line widths of C3S and C4H in L1251-1 as well as the distribution of HC3N are also related to outflow activities in this region. L1251-1 (IRS1) together with the previously identified SCCC source IRS3 demonstrate that L1251-A is an excellent region to study SCCC.
Revealing three-dimensional quantum criticality by Sr substitution in Han purple

Classical and quantum phase transitions (QPTs), with their accompanying concepts of criticality and universality, are a cornerstone of statistical thermodynamics. An excellent example of a controlled QPT is the ﬁeld-induced ordering of a gapped quantum magnet. Although numerous “quasi-one-dimensional” coupled spin-chain and -ladder materials are known whose ordering transition is three-dimensional (3D), quasi-two-dimensional (2D) systems are special for multiple reasons. Motivated by the ancient pigment Han purple (BaCuSi 2 O 6 ), a quasi-2D material displaying anomalous critical properties, we present a complete analysis of Ba 0 . 9 Sr 0 . 1 CuSi 2 O 6 . We measure the zero-ﬁeld magnetic excitations by neutron spectroscopy and deduce the spin Hamiltonian. We probe the ﬁeld-induced transition by combining magnetization, speciﬁc-heat, torque, and magnetocalorimetric measurements with nuclear magnetic resonance studies near the QPT. With a Bayesian statistical analysis and large-scale Quantum Monte Carlo simulations, we demonstrate unambiguously that observable 3D quantum critical scaling is restored by the structural simpliﬁcation arising from light Sr substitution in Han purple.
A conditional, a fuzzy and a probabilistic interpretation of self-organising maps

In this paper we establish a link between fuzzy and preferential semantics for description logics and Self-Organising Maps, which have been proposed as possible candidates to explain the psychological mechanisms underlying category generalisation. In particular, we show that the input/output behavior of a Self-Organising Map after training can be described by a fuzzy description logic interpretation as well as by a preferential interpretation, based on a concept-wise multipreference semantics, which takes into account preferences with respect to different concepts and has been recently proposed for ranked and for weighted defeasible description logics. Properties of the network can be proven by model checking on the fuzzy or on the preferential interpretation. Starting from the fuzzy interpretation, we also provide a probabilistic account for this neural network model.
Learning-Based Vulnerability Analysis of Cyber-Physical Systems

This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS). Specifically, we consider a control architecture widely used in CPS, where the low-level control is based on a feedback controller and an observer (e.g., the extended Kalman filter (EKF)), while also employing an anomaly detector. To facilitate analyzing the impact potential sensing attacks could have on systems with general nonlinear dynamics, we develop learning-enabled attack generators capable of designing stealthy attacks that maximally degrade system operation. We show how such problem can be cast within a learning-based grey-box framework where only parts of the runtime information are known to the attacker. We then introduce two methods for generating effective stealthy attacks, based on feed-forward neural networks (FNN) and recurrent neural networks (RNN). Both types of attack-generator models are trained offline, using a cost function that combines the attack impact on the estimation error (and thus control) and the residual signal used for anomaly detection; this enables the trained models to recursively generate effective yet stealthy sensor attacks in real-time while requiring different levels of system information at runtime. The effectiveness of the proposed methods is demonstrated on several case studies with varying levels of complexity and nonlinearity: inverted pendulum, autonomous driving vehicles (ADV), and unmanned areal vehicles (UAVs).
Observational Signatures of Cosmic-Ray Interactions in Molecular Clouds

We investigate ionization and heating of gas in the dense, shielded clumps/cores of molecular clouds bathed by an influx of energetic, charged cosmic rays (CRs). These molecular clouds have complex structures, with substantial variation in their physical properties over a wide range of length scales. The propagation and distribution of CRs is thus regulated accordingly, in particular, by the magnetic fields threaded through the clouds and into the dense regions within. We have found that a specific heating rate reaching 10−26 erg cm−3 s−1 can be sustained in the dense clumps/cores for Galactic environments, and this rate increases with CR energy density. The propagation of CRs and heating rates in some star-forming filaments identified in IC 5146 are calculated, with the CR diffusion coefficients in these structures determined from magnetic field fluctuations inferred from optical and near-infrared polarizations of starlight, which is presumably a magnetic field tracer. Our calculations indicate that CR heating can vary by nearly three orders of magnitude between different filaments within a cloud due to different levels of CR penetration. The CR ionization rate among these filaments is similar. The equilibrium temperature that could be maintained by CR heating alone is of order 1 K in a Galactic environment, but this value would be higher in strongly star-forming environments, thus causing an increase in the Jeans mass of their molecular clouds.
Spurious Antenna Modes of the Transmon Qubit

Superconducting qubits are resonant absorbers of pair-breaking radiation. The metal pads that form the qubit capacitance support standing wave modes at frequencies of order 100~GHz; these modes are strongly coupled to free-space impedance through their electric dipole moment. While the antenna mode of the 3D transmon qubit is easily seen to be a resonant dipole, other 2D qubit types can be understood as the aperture duals of wire loop antennas or folded dipoles. For typical Josephson junction parameters, the junction provides a reasonable conjugate match to the fundamental antenna mode. We calculate the contribution to quasiparticle poisoning from resonant absorption of blackbody radiation. We extend our analysis to dissipation at the qubit frequency, where radiative losses provide an ultimate limit to qubit energy relaxation time. A clear understanding of the spurious antenna modes of qubits will allow designs that are insensitive to pair-breaking radiation and that display reduced radiative losses at the qubit frequency.
Weak integrability breaking and level spacing distribution

Recently it was suggested that certain perturbations of integrable spin chains lead to a weak breaking of integrability in the sense that integrability is preserved at the first order in the coupling. Here we examine this claim using level spacing distribution. We find that the volume dependent crossover between integrable and chaotic level spacing statistics {which marks the onset of quantum chaotic behaviour, is markedly different for weak vs. strong breaking of integrability. In particular}, for the gapless case we find that the crossover coupling as a function of the volume LL scales with a 1/L^{2}1/L2 law for weak breaking as opposed to the 1/L^{3}1/L3 law previously found for the strong case.