Miracle-dz/UniFAR-FTUs huggingface dataset:
FDMT: A Benchmark Dataset for Fine-grained Domain Adaptation in Machine Translation

Previous domain adaptation research usually neglect the diversity in translation within a same domain, which is a core problem for adapting a general neural machine translation (NMT) model into a speciﬁc domain in real-world scenarios. One representative of such challenging scenarios is to deploy a translation system for a conference with a speciﬁc topic, e.g. computer networks or natural language processing, where there is usually extremely less resources due to the limited time schedule. To motivate a wide investigation in such settings, we present a real-world ﬁne-grained domain adaptation task in machine translation (FDMT). The FDMT dataset (Zh-En) consists of four sub-domains of information technology: autonomous vehicles, AI education, real-time networks and smart phone. To be closer to reality, FDMT does not employ any in-domain bilingual training data. Instead, each sub-domain is equipped with monolingual data, bilingual dictionary and knowledge base, to encourage in-depth exploration of these available resources. Corresponding development set and test set are provided for evaluation purpose. We make quantitative experiments and deep analyses in this new setting, which benchmarks the ﬁne-grained domain adaptation task and reveals several challenging problems that need to be addressed.
Contour Integral Methods for Nonlinear Eigenvalue Problems: A Systems Theoretic Approach

Contour integral methods for nonlinear eigenvalue problems seek to compute a subset of the spectrum in a bounded region of the complex plane. We briefly survey this class of algorithms, establishing a relationship to system realization techniques in control theory. This connection motivates a new general framework for contour integral methods (for linear and nonlinear eigenvalue problems), building on recent developments in multi-point rational interpolation of dynamical systems. These new techniques, which replace the usual Hankel matrices with Loewner matrix pencils, incorporate general interpolation schemes and permit ready recovery of eigenvectors. Because the main computations (the solution of linear systems associated with contour integration) are identical for these Loewner methods and the traditional Hankel approach, a variety of new eigenvalue approximations can be explored with modest additional work. Numerical examples illustrate the potential of this approach. We also discuss how the concept of filter functions can be employed in this new framework, and show how contour methods enable a data-driven modal truncation method for model reduction.
Measurement of an excitonic wave function

Excitons, Coulomb-bound electron-hole pairs, are the fundamental excitations governing optoelectronic properties of semiconductors. While optical signatures of excitons have been studied extensively, experimental access to the excitonic wave function itself has been elusive. Using multidimensional photoemission spectroscopy, we present a momentum-, energyand timeresolved perspective on excitons in the layered semiconductor WSe2. By tuning the excitation wavelength, we determine the energy-momentum signature of bright exciton formation and its difference from conventional single-particle excited states. The multidimensional data allows to retrieve fundamental exciton properties like the binding energy and the exciton-lattice coupling and to reconstruct the real-space excitonic wave function via Fourier transform. All quantities are in excellent agreement with microscopic calculations. Our approach provides a full characterization of the exciton wave function and is applicable to bright and dark excitons in semiconducting materials, heterostructures and devices. Excitons, bound electron-hole quasi-particles carrying energy and momentum but no net charge, are fundamental excitations of semiconductors and insulators arising from light-matter interaction.1 An initial excitonic polarization induced by a light field (often referred to as coherent excitons and e.g. detected by optical absorption spectroscopy) rapidly loses coherence with the driving field and dephases into a population of incoherent excitonic states.2, 3 The generated excitons propagate in solid-state materials through diffusion4, 5 and eventually release their energy e.g. in the form of luminescence (photon), lattice excitation (phonon) or dissociation into single charged quasi-particles.6–9 Understanding exciton physics is of capital importance for advanced photonic and optoelectronic applications including photovoltaics. Layered transition metal dichalcogenide (TMDC) semiconductors exhibit rich exciton physics even at room temperature due to strong Coulomb interaction.10 Excitons in TMDCs feature large oscilla-
LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding

Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents. We propose LayoutLMv2 architecture with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. Specifically, with a two-stream multi-modal Transformer encoder, LayoutLMv2 uses not only the existing masked visual-language modeling task but also the new text-image alignment and text-image matching tasks, which make it better capture the cross-modality interaction in the pre-training stage. Meanwhile, it also integrates a spatial-aware self-attention mechanism into the Transformer architecture so that the model can fully understand the relative positional relationship among different text blocks. Experiment results show that LayoutLMv2 outperforms LayoutLM by a large margin and achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks, including FUNSD (0.7895 to 0.8420), CORD (0.9493 to 0.9601), SROIE (0.9524 to 0.9781), Kleister-NDA (0.8340 to 0.8520), RVL-CDIP (0.9443 to 0.9564), and DocVQA (0.7295 to 0.8672).
Impacts of sampling dissolved organic matter with passive capillary wicks versus aqueous soil extraction

Soil Sci. Soc. Am. J. 76:2019–2030 doi:10.2136/sssaj2012.0061 Received 21 Feb. 2012. *Corresponding author (jnperdri@email.arizona.edu). © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Impacts of Sampling Dissolved Organic Matter with Passive Capillary Wicks Versus Aqueous Soil Extraction Soil Chemistry
A Versatile Keyframe-Based Structureless Filter for Visual Inertial Odometry

Motion estimation by fusing data from at least a camera and an Inertial Measurement Unit (IMU) enables many applications in robotics. However, among the multitude of Visual Inertial Odometry (VIO) methods, few efficiently estimate device motion with consistent covariance, and calibrate sensor parameters online for handling data from consumer sensors. This paper addresses the gap with a Keyframe-based Structureless Filter (KSF). For efficiency, landmarks are not included in the filter's state vector. For robustness, KSF associates feature observations and manages state variables using the concept of keyframes. For flexibility, KSF supports anytime calibration of IMU systematic errors, as well as extrinsic, intrinsic, and temporal parameters of each camera. Estimator consistency and observability of sensor parameters were analyzed by simulation. Sensitivity to design options, e.g., feature matching method and camera count was studied with the EuRoC benchmark. Sensor parameter estimation was evaluated on raw TUM VI sequences and smartphone data. Moreover, pose estimation accuracy was evaluated on EuRoC and TUM VI sequences versus recent VIO methods. These tests confirm that KSF reliably calibrates sensor parameters when the data contain adequate motion, and consistently estimate motion with accuracy rivaling recent VIO methods. Our implementation runs at 42 Hz with stereo camera images on a consumer laptop.
With False Friends Like These, Who Can Have Self-Knowledge?

Adversarial examples arise from excessive sensitivity of a model. Commonly studied adversarial examples are malicious inputs, crafted by an adversary from correctly classified examples, to induce misclassification. This paper studies an intriguing, yet far overlooked consequence of the excessive sensitivity, that is, a misclassified example can be easily perturbed to help the model to produce correct output. Such perturbed examples look harmless, but actually can be maliciously utilized by a false friend to make the model self-satisfied. Thus we name them hypocritical examples. With false friends like these, a poorly performed model could behave like a state-of-the-art one. Once a deployer trusts the hypocritical performance and uses the “well-performed” model in real-world applications, potential security concerns appear even in benign environments. In this paper, we formalize the hypocritical risk for the first time and propose a defense method specialized for hypocritical examples by minimizing the tradeoff between natural risk and an upper bound of hypocritical risk. Moreover, our theoretical analysis reveals connections between adversarial risk and hypocritical risk. Extensive experiments verify the theoretical results and the effectiveness of our proposed methods.
From Hertzsprung’s problem to pattern-rewriting systems

Drawing on a problem posed by Hertzsprung in 1887, we say that a given permutation $\pi\in\mathcal{S}_n$ contains the Hertzsprung pattern $\sigma\in\mathcal{S}_k$ if there is factor $\pi(d+1)\pi(d+2)\cdots\pi(d+k)$ of $\pi$ such that $\pi(d+1)-\sigma(1) =\cdots = \pi(d+k)-\sigma(k)$. Using a combination of the Goulden-Jackson cluster method and the transfer-matrix method we determine the joint distribution of occurrences of any set of (incomparable) Hertzsprung patterns, thus substantially generalizing earlier results by Jackson~\etal\ on the distribution of ascending and descending runs in permutations. We apply our results to the problem of counting permutations up to pattern-replacement equivalences, and using pattern-rewriting systems -- a new formalism similar to the much studied string-rewriting systems -- we solve a couple of open problems raised by Linton et al. in 2012.
Black Holes and Other Clues to the Quantum Structure of Gravity

Bringing gravity into a quantum-mechanical framework is likely the most profound remaining problem in fundamental physics. The “unitarity crisis” for black hole evolution appears to be a key facet of this problem, whose resolution will provide important clues. Investigating this raises the important structural question of how to think about subsystems and localization of information in quantum gravity. Paralleling field theory, the answer to this is expected to be an important ingredient in the mathematical structure of the theory. Perturbative gravity results indicate a structure different from that of quantum field theory, but suggest an avenue to defining subsystems. If black holes do behave similarly to familiar subsystems, unitarity demands new interactions that transfer entanglement from them. Such interactions can be parameterized in an effective approach, without directly addressing the question of the fundamental dynamics, whether that is associated with quantum spacetime, wormholes, or something else. Since such interactions need to extend outside the horizon, that raises the question of whether they can be constrained, or might be observed, by new electromagnetic or gravitational wave observations of strong gravity regions. This note overviews and provides connections between these developments.
Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging

Coded aperture snapshot spectral imaging (CASSI) is a promising technique to capture the three-dimensional hyperspectral image (HSI) using a single coded two-dimensional (2D) measurement, in which algorithms are used to perform the inverse problem. Due to the ill-posed nature, various regularizers have been exploited to reconstruct the 3D data from the 2D measurement. Unfortunately, the accuracy and computational complexity are unsatisfied. One feasible solution is to utilize additional information such as the RGB measurement in CASSI. Considering the combined CASSI and RGB measurement, in this paper, we propose a new fusion model for the HSI reconstruction. We investigate the spectral low-rank property of HSI composed of a spectral basis and spatial coefficients. Specifically, the RGB measurement is utilized to estimate the coefficients, meanwhile the CASSI measurement is adopted to provide the orthogonal spectral basis. We further propose a patch processing strategy to enhance the spectral low-rank property of HSI. The proposed model neither requires non-local processing or iteration, nor the spectral sensing matrix of the RGB detector. Extensive experiments on both simulated and real HSI dataset demonstrate that our proposed method outperforms previous state-of-the-art not only in quality but also speeds up the reconstruction more than 5000 times.
A Maximal Correlation Approach to Imposing Fairness in Machine Learning

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information-theoretic view. The maximal correlation framework is introduced for expressing fairness constraints and shown to be capable of deriving regularizers that enforce independence and separation-based fairness criteria, which admit optimization algorithms that are more computationally efficient than existing algorithms. We show that these algorithms provide smooth performance-fairness tradeoff curves and perform competitively with state-of-the-art methods on the Communities and Crimes dataset.
Object sorting using faster R-CNN

In a factory production line, different industry parts need to be quickly differentiated and sorted for further process. Parts can be of different colors and shapes. It is tedious for humans to differentiate and sort these objects in appropriate categories. Automating this process would save more time and cost. In the automation process, choosing an appropriate model to detect and classify different objects based on specific features is more challenging. In this paper, three different neural network models are compared to the object sorting system. They are namely CNN, Fast R-CNN, and Faster R-CNN. These models are tested, and their performance is analyzed. Moreover, for the object sorting system, an Arduino-controlled 5 DoF (degree of freedom) robot arm is programmed to grab and drop symmetrical objects to the targeted zone. Objects are categorized into classes based on color, defective and non-defective objects.
Colonoscopy Polyp Detection: Domain Adaptation From Medical Report Images to Real-time Videos

Automatic colorectal polyp detection in colonoscopy video is a fundamental task, which has received a lot of attention. Manually annotating polyp region in a large scale video dataset is time-consuming and expensive, which limits the development of deep learning techniques. A compromise is to train the target model by using labeled images and infer on colonoscopy videos. However, there are several issues between the image-based training and video-based inference, including domain differences, lack of positive samples, and temporal smoothness. To address these issues, we propose an Image-video-joint polyp detection network (Ivy-Net) to address the domain gap between colonoscopy images from historical medical reports and real-time videos. In our Ivy-Net, a modified mixup is utilized to generate training data by combining the positive images and negative video frames at the pixel level, which could learn the domain adaptive representations and augment the positive samples. Simultaneously, a temporal coherence regularization (TCR) is proposed to introduce the smooth constraint on feature-level in adjacent frames and improve polyp detection by unlabeled colonoscopy videos. For evaluation, a new large colonoscopy polyp dataset is collected, which contains 3056 images from historical medical reports of 889 positive patients and 7.5-hour videos of 69 patients (28 positive). The experiments on the collected dataset demonstrate that our Ivy-Net achieves the state-of-the-art result on colonoscopy video.
Random Planted Forest: a directly interpretable tree ensemble

We introduce a novel interpretable tree based algorithm for prediction in a regression setting. Our motivation is to estimate the unknown regression function from a functional decomposition perspective in which the functional components correspond to lower order interaction terms. The idea is to modify the random forest algorithm by keeping certain leaves after they are split instead of deleting them. This leads to non-binary trees which we refer to as planted trees. An extension to a forest leads to our random planted forest algorithm. Additionally, the maximum number of covariates which can interact within a leaf can be bounded. If we set this interaction bound to one, the resulting estimator is a sum of one-dimensional functions. In the other extreme case, if we do not set a limit, the resulting estimator and corresponding model place no restrictions on the form of the regression function. In a simulation study we find encouraging prediction and visualisation properties of our random planted forest method. We also develop theory for an idealized version of random planted forests in cases where the interaction bound is low. We show that if it is smaller than three, the idealized version achieves asymptotically optimal convergence rates up to a logarithmic factor. Code is available on GitHub https://github.com/PlantedML/randomPlantedForest.
Astrophysical and Structural Parameters, And Dynamical Evolution Of The Open Clusters NGC 1245 And NGC 2099

We derive astrophysical and structural parameters of open clusters NGC 1245 and NGC 2099 from 2MASS JHKs and Gaia DR2 photometric / astrometric data bases. Their likely members have been determined from Gaia DR2 proper motion data. Our E(B – V) values (2MASS) are slightly smaller than the literature values, whereas our E(B – V) values (Gaia DR2) agree with the literature within the uncertainties. Their distance moduli/distances and ages are in a good agreement with the early studies. NGC 1245 has steep negative core mass function slope (MFs) (χcore = −1.24). Its halo (χhalo = +0.78) and overall (χoverall = −0.95) MFs indicate small-scale mass segregation from its core to the outer regions, due to its [trlx(overall), τoverall] = [302 Myr, 5]. The MFs of NGC 2099 is very negative steep (χcore = −2.67) in the core, and quite positive steep (χhalo = +1.41) in the halo. This kind of MF slope steeping from the core to the outskirts indicates that lowmass stars in the core are transferred to the cluster's outskirts, while massive stars sink in the core, because of mass segregation. NGC 2099's flat overall MFs (χoverall = +0.91) and its τoverall = 8 indicate mass segregation. These OCs with the relatively large masses 8700 M (NGC 1245) and 5660 M (NGC 2099), which locate at RGC > 9 kpc, expose to external perturbations such as tidal effects and shock waves, and they lose their stars in low-proportions.
Biodisponibilidade de formas de fósforo acumuladas em solo sob sistema plantio direto

The addition of fertilizers to the soil increases P contents in many forms of different bond energies with soil colloids. This has been observed in the soil surface layer under no tillage system, however there is little information about the availability of these P forms to plants. The study aimed to evaluate the bioavailability of P forms of a soil under no tillage with P applications. Samples were collected (0-10 cm) in a Rhodic Hapludox that had been fertilized with 0, 180, 360, 540, and 720 kg ha-1 of P2O5 over six years of cultivation prior to this study. The samples were dried, ground, filled in 2 kg pots and subjected to 15 successive crops without replacement of absorbed P. After every three successive crops, soil samples were collected for analysis. Phosphorus fractionation was analyzed by Hedley's method. Results showed that inorganic P extracted by NaHCO3 0.5 mol L-1 has a buffering capacity similar to P available by anion exchange resin, and can be considered labile P as well. In the long term, all soil P forms act in the soil solution buffer. In fertilized soils, the inorganic and organic P forms act in a similar way buffering the absorbed P by plants. In soils with little or no fertilization, the organic forms are the main P source of plants.
Results and Prospects of Radiative and Electroweak Penguin Decays at Belle II

Radiative and electroweak penguin mediated decays of $B$ mesons are a great probe for physics beyond the Standard Model of particle physics. Furthermore, recently anomalies on exclusive $b \rightarrow s \ell^{+} \ell^{-}$ processes, which may imply lepton flavor universality violation, have been observed. Belle II experiment sheds light on the anomalies with the measurement of an inclusive analysis in this mode. Thanks to smaller hadronic uncertainties compared with exclusive modes, complementary information can be provided. Belle II is also a unique experiment to search for processes involving neutrinos such as $B \rightarrow K^{(*)} \nu \overline{\nu}$. We report the results and prospects of an inclusive $B \rightarrow X_s \ell^{+} \ell^{-}$ analysis and a search for $B \to K^{(*)} \nu \overline{\nu}$.
Jet collimation in NGC 315 and other nearby AGN

Aims. The collimation of relativistic jets in galaxies is a poorly understood process. Detailed radio studies of the jet collimation region have been performed so far in a few individual objects, providing important constraints for jet formation models. However, the extent of the collimation zone as well as the nature of the external medium possibly confining the jet are still debated. Methods. In this article, we present a multifrequency and multiscale analysis of the radio galaxy NGC 315, including the use of mm-VLBI data up to 86 GHz, aimed at revealing the evolution of the jet collimation profile. We then consider results from the literature to compare the jet expansion profile in a sample of 27 low-redshift sources, mainly comprising radio galaxies and BL Lacs, which were classified based on the accretion properties as low-excitation (LEG) and high-excitation (HEG) galaxies. Results. We propose that the jet collimation in NGC 315 is completed on sub-parsec scales. A transition from a parabolic to conical jet shape is detected at zt = 0.58 ± 0.28 parsecs or ∼5 × 103 Schwarzschild radii (RS) from the central engine, a distance which is much smaller than the Bondi radius, rB ∼ 92 pc, estimated based on X-ray data. The jet in this and in a few other LEG in our sample may be initially confined by a thick disk extending out to ∼103 − 104RS. A comparison between the mass-scaled jet expansion profiles of all sources indicates that jets in HEG are surrounded by thicker disk-launched sheaths and collimate on larger scales with respect to jets in LEG. These results suggest that disk winds play an important role in the jet collimation mechanism, particularly in high-luminosity sources. The impact of winds on the origin of the FRI and FRII dichotomy in radio galaxies is also discussed.