Miracle-dz/UniFAR-FTUs huggingface dataset:
Hamiltonian zigzag sampler got more momentum than its Markovian counterpart: Equivalence of two zigzags under a momentum refreshment limit

Zigzag and other piecewise deterministic Markov process samplers have attracted significant interest for their non-reversibility and other appealing properties for Bayesian posterior computation. Hamiltonian Monte Carlo is another state-of-the-art sampler, exploiting fictitious momentum to guide Markov chains through complex target distributions. In this article, we uncover a remarkable connection between the zigzag sampler and a variant of Hamiltonian Monte Carlo based on Laplace-distributed momentum. The position and velocity component of the corresponding Hamiltonian dynamics travels along a zigzag path paralleling the Markovian zigzag process; however, the dynamics is non-Markovian in the position-velocity space as the momentum component encodes non-immediate pasts. This information is partially lost during a momentum refreshment step, in which we preserve its direction but re-sample magnitude. In the limit of increasingly frequent momentum refreshments, we prove that Hamiltonian zigzag converges strongly to its Markovian counterpart. This theoretical insight suggests that, when retaining full momentum information, Hamiltonian zigzag can better explore target distributions with highly correlated parameters by suppressing the diffusive behavior of Markovian zigzag. We corroborate this intuition by comparing performance of the two zigzag cousins on high-dimensional truncated multivariate Gaussians, including a 11,235-dimensional target arising from a Bayesian phylogenetic multivariate probit model applied to HIV virus data.
Maritime Just-in-time navigation with Quantum algorithms

Just-in-time arrival in the maritime industry is a key concept for the reduction of Greenhouse gas emissions and cost-cutting, with the aim to reach the industrywide overall climate goals set by the International Maritime Organization (IMO) for 2030. In this note, we propose a mathematical formulation which allows for an implementation on quantum computers.
Quantitative characteristics of cycles and their relations with stretch and spanning tree congestion

The main goal of this article is to introduce new quantitative characteristics of cycles in finite simple connected graphs and to establish relations of these characteristics with the stretch and spanning tree congestion of graphs. The main new parameter is named the support number. We give a polynomial approximation algorithm for the support number with the aid of yet another characteristic we introduce, named the cycle width of the graph.
Robust Open-Vocabulary Translation from Visual Text Representations

Machine translation models have discrete vocabularies and commonly use subword segmentation techniques to achieve an ‘open vocabulary. ’ This approach relies on consistent and correct underlying unicode sequences, and makes models susceptible to degradation from common types of noise and variation. Motivated by the robustness of human language processing, we propose the use of visual text representations, which dispense with a finite set of text embeddings in favor of continuous vocabularies created by processing visually rendered text with sliding windows. We show that models using visual text representations approach or match performance of traditional text models on small and larger datasets. More importantly, models with visual embeddings demonstrate significant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a character permuted German–English task where subword models degrade to 1.9.
Magnetohydrodynamic Winds Driven by the Line Force from the Standard Thin Disk around Supermassive Black Holes. I. The Case of Weak Magnetic Field

Absorption lines with high blueshifted velocities are frequently found in the ultraviolet (UV) and X-ray spectra of luminous active galactic nuclei (AGNs). This implies that high-velocity winds/outflows are common in AGNs. In order to study the formation of high-velocity winds, especially ultrafast outflows (UFOs), we perform two-dimensional magnetohydrodynamic (MHD) simulations. Initially, a magnetic field is set to be weaker than the gas pressure at the disk surface. In our simulations, line force operates on the region like filaments because the X-ray radiation from corona is shielded by dense gas in the inner region at some angle. The location of filaments changes with time and then the line-driven winds are exposed to X-rays and become highly ionized. The line force at the UV bands does not directly drive the highly ionized winds. In the sense of time average, the properties of high-velocity winds meet the formation condition of UFOs. Compared with line force, the function of magnetic field is negligible in directly driving winds. In the MHD model, the region around the rotational axis becomes magnetic-pressure dominated, which prevents gases from spreading to higher latitudes and then enhances the gas column density at middle and low latitudes (20°–70°). Higher column density is helpful to shield X-ray photons, which causes the line force to be more effective in the MHD model than in the hydrodynamic model. Higher-velocity winds with a broader opening angle are produced in the MHD model.
On the Effectiveness of Various Machine Learning Algorithms for THz Channel Estimation

—Terahertz communication is one of the most promising wireless communication technologies, due to its capability to provide high bitrates. THz frequencies suffer however from high signal attenuation and signal degradation, which makes the THz channel modeling and estimation fundamentally hard. On the other hand, channel estimation of THz transmission system is critical for THz systems to be practically adopted in wireless communications. We consider the problem of channel modeling with deterministic channel propagation and the related physical characteristics of THz bands, and study the effectiveness of various machine learning algorithms to estimate the channel. We apply different machine learning algorithms for channel estimation, including neural networks (NN), logistic regression (LR), and projected gradient ascent (PGA). Numerical results show that PGA algorithm yields the most promising performance at SNR=0 dB with NMSE of -12.8 dB.
Semantic Image Matting

Natural image matting separates the foreground from background in fractional occupancy which can be caused by highly transparent objects, complex foreground (e.g., net or tree), and/or objects containing very fine details (e.g., hairs). Although conventional matting formulation can be applied to all of the above cases, no previous work has attempted to reason the underlying causes of matting due to various foreground semantics.We show how to obtain better alpha mattes by incorporating into our framework semantic classification of matting regions. Specifically, we consider and learn 20 classes of matting patterns, and propose to extend the conventional trimap to semantic trimap. The proposed semantic trimap can be obtained automatically through patch structure analysis within trimap regions. Meanwhile, we learn a multi-class discriminator to regularize the alpha prediction at semantic level, and content-sensitive weights to balance different regularization losses. Experiments on multiple benchmarks show that our method outperforms other methods and has achieved the most competitive state-of-the-art performance. Finally, we contribute a large-scale Semantic Image Matting Dataset with careful consideration of data balancing across different semantic classes. Code and dataset are available at https://github.com/nowsyn/SIM.
Microscopic description of quadrupole-octupole coupling in neutron-rich actinides and superheavy nuclei with the Gogny-D1M energy density functional

The interplay between quadrupole and octupole degrees of freedom is discussed in a series of neutron-rich actinides and superheavy nuclei with 92 ≤ Z ≤ 110 and 186 ≤ N ≤ 202. In addition to the static Hartree-Fock-Bogoliubov approach, dynamical beyond-mean-field correlations are taken into account via both parity restoration and symmetry-conserving Generator Coordinate Method calculations based on the Gogny-D1M energy density functional. Physical properties such as correlation energies, negative-parity excitation energies as well as reduced transition probabilities B(E1) and B(E3) are discussed in detail. It is shown that, for the studied nuclei, the quadrupole-octupole coupling is weak and to a large extent the properties of negative parity states can be reasonably well described in terms of the octupole degree of freedom alone.
VGNMN: Video-grounded Neural Module Network to Video-Grounded Language Tasks

Neural module networks (NMN) have achieved success in image-grounded tasks such as Visual Question Answering (VQA) on synthetic images. However, very limited work on NMN has been studied in the video-grounded language tasks. These tasks extend the complexity of traditional visual tasks with the additional visual temporal variance. Motivated by recent NMN approaches on image-grounded tasks, we introduce Video-grounded Neural Module Network (VGNMN) to model the information retrieval process in video-grounded language tasks as a pipeline of neural modules. VGNMN ﬁrst decomposes all language components to explicitly resolve any entity references and detect corresponding action-based inputs from the question. The detected entities and actions are used as parameters to instantiate neural module networks and extract visual cues from the video. Our experiments show that VGNMN can achieve promising performance on two video-grounded language tasks: video QA and video-grounded dialogues.
The role of aerothermochemistry in double cone and double wedge flows

In this work, hypervelocity flows over double cone and double wedge geometries are studied. The flow configurations established over the double cone/double wedge models are extremely sensitive to thermochemistry, and thus serve as ideal benchmarks for validating chemical models. The goals of this research are: i) to investigate the coupling between the fluid mechanics and thermochemistry in these flow fields by varying freestream flow composition and enthalpy, ii) to implement a diagnostic suite for time-resolved surface and freestream measurements, iii) to investigate the nature of flow field unsteadiness across various test conditions, and lastly iv) to extend the experimental database for shock wave boundary/layer interactions. An expansion tube is used to generate flows with enthalpies ranging from 2.2-8.0 MJ/kg (2-4 km/s) and Mach numbers from 4-7. The expansion tube is a novel impulse facility for accelerating a test gas to these velocities, while maintaining a minimally dissociated freestream. Additionally, the facility allows variation of the freestream composition (between nitrogen and air), while maintaining freestream test parameters (Mach number, density, enthalpy) to within 0.5%. Two models are used: a 25◦-55◦ double cone model and a 30◦-55◦ double wedge. There are four diagnostic components to this research which aim to enable a better understanding of these canonical flow fields. Single frame, high resolution schlieren photography is used to visualize various flow features including: the separation zone formed in the corner, the triple point interaction, and a supersonic shear layer. From these images, a separation zone length scaling parameter is determined. This parameter, derived for wedge geometries, is successfully applied to conical geometries by using a judicious choice of flow properties for scaling. In the wedge image series, nitrogen test conditions exhibit a distinct increase in bow shock standoff distance. Additionally, aft wedge
Fabrication of SnO2 Nanopaticles/BiOI n–p Heterostructure for Wider Spectrum Visible-Light Photocatalytic Degradation of Antibiotic Oxytetracycline Hydrochloride

In this work, n–p heterostructure SnO2/BiOI photocatalyst was successfully fabricated through a facile chemical bath method. The photocatalysts was applied to minimize antibiotic oxytetracycline hydrochloride (OTTCH) and methyl orange (MO) under visible light irradiation. SnO2/BiOI composite exhibited excellent photocatalytic performance for the refractory pollutant OTTCH and MO decomposition. The sample of 30 wt % SnO2/BiOI possessed the best photocatalytic performance in all the obtained catalysts. Several reaction parameters affecting OTTCH degradation such as initial concentration, ion species, and concentration were investigated systematically. The optical and electrical properties of materials demonstrate that the transfer rate of electron–hole pairs dramatically improve though forming an n–p junction in SnO2/BiOI hybrid. Moreover, the energy band alignments of SnO2/BiOI junction were confirmed via combining DRS and XPS analysis, which provided strong support for the proposed mechanism. This work co.
Rebound and scattering of motile Chlamydomonas algae in confined chambers

Motivated by recent experiments demonstrating that motile algae get trapped in draining foams, we study the trajectories of microorganisms confined in model foam channels (section of a Plateau border). We track single Chlamydomonas reinhardtii cells confined in a thin three-circle microfluidic chamber and show that their spatial distribution exhibits strong corner accumulation. Using empirical scattering laws observed in previous experiments (scattering with a constant scattering angle), we next develop a two-dimension geometrical model and compute the phase space of trapped and periodic trajectories of swimmers inside a three-circles billiard. We find that the majority of cell trajectories end up in a corner, providing a geometrical mechanism for corner accumulation. Incorporating the distribution of scattering angles observed in our experiments and including hydrodynamic interactions between the cells and the surfaces into the geometrical model enables us to reproduce the experimental probability density function of micro-swimmers in microfluidic chambers. Both our experiments and models demonstrate therefore that motility leads generically to trapping in complex geometries.
Explicit derivation of the Fraunhofer diffraction formula for oblique incidence

We have analytically explored the Rayleigh–Sommerfeld scalar diffraction for oblique incidence. We have explicitly derived the Fraunhofer diffraction formulae for oblique incidence of a plane scalar wave on various apertures, such as single-slit, circular aperture and diffraction grating. Such derivations in the background of Rayleigh–Sommerfeld scalar diffraction theory would be important for an undergraduate course on optics.
TeachText: CrossModal Generalized Distillation for Text-Video Retrieval

In recent years, considerable progress on the task of text-video retrieval has been achieved by leveraging large-scale pretraining on visual and audio datasets to construct powerful video encoders. By contrast, despite the natural symmetry, the design of effective algorithms for exploiting large-scale language pretraining remains under-explored. In this work, we are the first to investigate the design of such algorithms and propose a novel generalized distillation method, TeachText, which leverages complementary cues from multiple text encoders to provide an enhanced supervisory signal to the retrieval model. Moreover, we extend our method to video side modalities and show that we can effectively reduce the number of used modalities at test time without compromising performance. Our approach advances the state of the art on several video retrieval benchmarks by a significant margin and adds no computational overhead at test time. Last but not least, we show an effective application of our method for eliminating noise from retrieval datasets. Code and data can be found at https://www.robots. ox.ac. uk/˜vgg/research/teachtext/.
Noise-Assisted Discord-Like Correlations in Light-Harvesting Photosynthetic Complexes

Transport phenomena in photosynthetic systems have attracted a great deal of attention due to their potential role in devising novel photovoltaic materials. In particular, energy transport in light-harvesting complexes is considered quite efficient due to the balance between coherent quantum evolution and decoherence, a phenomenon coined Environment-Assisted Quantum Transport (ENAQT). Although this effect has been extensively studied, its behavior is typically described in terms of the decoherence’s strength, namely weak, moderate or strong. Here, we study the ENAQT in terms of quantum correlations that go beyond entanglement. Using a subsystem of the Fenna–Matthews–Olson complex, we find that discord-like correlations maximize when the subsystem’s transport efficiency increases, while the entanglement between sites vanishes. Our results suggest that quantum discord is a manifestation of the ENAQT and highlight the importance of beyond-entanglement correlations in photosynthetic energy transport processes.
Point-to-point Coulomb effects in high brightness photoelectron beam lines for ultrafast electron diffraction

In an effort to increase spatial and temporal resolution of ultrafast electron diffraction and microscopy, ultra-high brightness photocathodes are actively sought to improve electron beam quality. Beam dynamics codes often approximate the Coulomb interaction with mean-field space charge, which is a good approximation in traditional beams. However, point-to-point Coulomb effects, such as disorder induced heating and the Boersch effect, can not be neglected in cold, dense beams produced by such photocathodes. In this paper, we introduce two new numerical methods to calculate the important effects of the photocathode image charge when using a point-to-point interaction model. Equipped with an accurate model of the image charge, we calculate the effects of point-to-point interactions on two high brightness photoemission beamlines for ultrafast diffraction. The first beamline uses a 200 keV gun, whereas the second uses a 5 MeV gun, each operating in the single-shot diffraction regime with $10^5$ electrons/pulse. Assuming a zero photoemission temperature, it is shown that including stochastic Coulomb effects increases the final emittance of these beamlines by over a factor of 2 and decreases the peak transverse phase space density by over a factor of 3 as compared to mean-field simulations. We then introduce a method to compute the energy released by disorder induced heating using the pair correlation function. This disorder induced heating energy was found to scale very near the theoretical result for stationary ultracold plasmas, and it accounts for over half of the emittance growth above mean-field simulations.
Intense Optical Parametric Amplification in Dispersion-Engineered Nanophotonic Lithium Niobate Waveguides

Luis Ledezma1,2,∗, Ryoto Sekine1,∗, Qiushi Guo1,∗, Rajveer Nehra1, Saman Jahani1 and Alireza Marandi1,† 1Department of Electrical Engineering, California Institute of Technology, Pasadena, California 91125, USA. 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA. ∗These authors contributed equally to this work. †Email: marandi@caltech.edu (Dated: May 5, 2021)
Hierarchical Topic Presence Models

Topic models analyze text from a set of documents. Documents are modeled as a mixture of topics, with topics defined as probability distributions on words. Inferences of interest include the most probable topics and characterization of a topic by inspecting the topic's highest probability words. Motivated by a data set of web pages (documents) nested in web sites, we extend the Poisson factor analysis topic model to hierarchical topic presence models for analyzing text from documents nested in known groups. We incorporate an unknown binary topic presence parameter for each topic at the web site and/or the web page level to allow web sites and/or web pages to be sparse mixtures of topics and we propose logistic regression modeling of topic presence conditional on web site covariates. We introduce local topics into the Poisson factor analysis framework, where each web site has a local topic not found in other web sites. Two data augmentation methods, the Chinese table distribution and P\'{o}lya-Gamma augmentation, aid in constructing our sampler. We analyze text from web pages nested in United States local public health department web sites to abstract topical information and understand national patterns in topic presence.
Electronic properties of bismuth nanostructures

The passivation of thin Bi(1 1 1) ﬁlms with hydrogen and oxide capping layers is investigated from ﬁrst principles. Considering termination-related changes of the crystal structure, we show how the bands and density of states are aﬀected. In the context of the much discussed semimetal-to-semiconductor transition and the band topology of the bulk material, we consider the eﬀects of conﬁnement in the whole Brillouin zone and go beyond standard density functional theory by including many-body interactions via the G 0 W 0 approximation. The conductivity of unterminated ﬁlms is calculated via the Boltzmann transport equation using the simple constant relaxation time approximation and compared to experimental observations that have suggested a two-channel model. This In this supplemental we show some more details regarding the quasiparticle corrections in the thin ﬁlms. It is often assumed that the G 0 W 0 corrected band structure can be approximated with a DFT band structure, where the conduction bands are shifted uniformly to higher energies (scissors operator). Here, the corrections resemble a step-like function of the DFT energy or band index quite well, in particular for the ﬁve bilayer thick slab. However, the states at Γ do not shift to higher energies by the same amount which smears out the sharp transition from the valence to the conduction bands.
Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data

Going beyond correlations, the understanding and identification of causal relationships in observational time series, an important subfield of Causal Discovery, poses a major challenge. The lack of access to a well-defined ground truth for real-world data creates the need to rely on synthetic data for the evaluation of these methods. Existing benchmarks are limited in their scope, as they either are restricted to a"static"selection of data sets, or do not allow for a granular assessment of the methods' performance when commonly made assumptions are violated. We propose a flexible and simple to use framework for generating time series data, which is aimed at developing, evaluating, and benchmarking time series causal discovery methods. In particular, the framework can be used to fine tune novel methods on vast amounts of data, without"overfitting"them to a benchmark, but rather so they perform well in real-world use cases. Using our framework, we evaluate prominent time series causal discovery methods and demonstrate a notable degradation in performance when their assumptions are invalidated and their sensitivity to choice of hyperparameters. Finally, we propose future research directions and how our framework can support both researchers and practitioners.