Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic derivation of hydrodynamic equations for viscoelastic phase separation

We present a detailed derivation of a simple hydrodynamic two-fluid model, which aims at the description of the phase separation of non-entangled polymer solutions, where viscoelastic effects play a role. It is directly based upon the coarse-graining of a well-defined molecular model, such that all degrees of freedom have a clear and unambiguous molecular interpretation. The considerations are based upon a free-energy functional, and the dynamics is split into a conservative and a dissipative part, where the latter satisfies the Onsager relations and the second law of thermodynamics. The model is therefore fully consistent with both equilibrium and non-equilibrium thermodynamics. The derivation proceeds in two steps: firstly, we derive an extended model comprising two scalar and four vector fields, such that inertial dynamics of the macromolecules and of the relative motion of the two fluids is taken into account. In the second step, we eliminate these inertial contributions and, as a replacement, introduce phenomenological dissipative terms, which can be modeled easily by taking into account the principles of non-equilibrium thermodynamics. The final simplified model comprises the momentum conservation equation, which includes both interfacial and elastic stresses, a convection–diffusion equation where interfacial and elastic contributions occur as well, and a suitably convected relaxation equation for the end-to-end vector field. In contrast to the traditional two-scale description that is used to derive rheological equations of motion, we here treat the hydrodynamic and the macromolecular degrees of freedom on the same basis. Nevertheless, the resulting model is fairly similar, though not fully identical, to models that have been discussed previously. Notably, we find a rheological constitutive equation that differs from the standard Oldroyd-B model. Within the framework of kinetic theory, this difference may be traced back to a different underlying statistical-mechanical ensemble that is used for averaging the stress. To what extent the model is able to reproduce the full phenomenology of viscoelastic phase separation is presently an open question, which shall be investigated in the future.
Rates of convergence for density estimation with GANs

: We undertake a precise study of the non-asymptotic properties of vanilla generative adversarial networks (GANs) and derive theoretical guarantees in the problem of estimating an unknown d -dimensional density p ∗ under a proper choice of the class of generators and discriminators. We prove that the resulting density estimate converges to p ∗ in terms of Jensen-Shannon (JS) divergence at the rate (log n/n ) 2 β/ (2 β + d ) where n is the sample size and β determines the smoothness of p ∗ . This is the ﬁrst result in the literature on density estimation using vanilla GANs with JS rates faster than n − 1 / 2 in the regime β > d/ 2 .
Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays

Most research in the area of automatic essay grading (AEG) is geared towards scoring the essay holistically while there has also been little work done on scoring individual essay traits. In this paper, we describe a way to score essays using a multi-task learning (MTL) approach, where scoring the essay holistically is the primary task, and scoring the essay traits is the auxiliary task. We compare our results with a single-task learning (STL) approach, using both LSTMs and BiLSTMs. To find out which traits work best for different types of essays, we conduct ablation tests for each of the essay traits. We also report the runtime and number of training parameters for each system. We find that MTL-based BiLSTM system gives the best results for scoring the essay holistically, as well as performing well on scoring the essay traits. The MTL systems also give a speed-up of between 2.30 to 3.70 times the speed of the STL system, when it comes to scoring the essay and all the traits.
Predicting a new resonance as charmed-strange baryonic analog of 
Ds0*(2317)

In this paper, we predict $\Omega_{c0}(1P,1/2^-)$, which is as charmed-strange baryonic analogue of $D_{s0}^*(2317)$. By an unquenched quark model, we perform a mass spectrum calculation, and find that the mass of $J^P=1/2^-$ $\Omega_{c}(1P)$ with predominant light degree of freedom $j_\ell=0$ is lowered down to the position below the $\Xi_c\bar{K}$ threshold, with about 90 MeV mass shift. Future experimental search for the predicted charmed-strange baryon is suggested.
OpenMatch: An Open-Source Package for Information Retrieval

Information Retrieval (IR) is an important task and can be used in many applications. Neural IR (Neu-IR) models overcome the vocabulary mismatch problem of sparse retrievers and thrive on the ranking pipeline with semantic matching. Recent progress in IR mainly focuses on Neu-IR models, including efficient dense retrieval, advanced neural architectures and robustly training for few-shot IR that lacks training data. In order to integrate these advantages for researchers and engineers to utilize and develop, OpenMatch provides various functional neural modules based on PyTorch to maintain sufficient extensibility, making it easy to build customized and higher-capacity IR systems. Besides, OpenMatch consists of complicated optimization tricks, various sparse/dense retrieval methods, and advanced few-shot training methods, liber-ating users from surplus labor in baseline reimplementation and neural model finetuning. With OpenMatch, we achieve reasonable performance on various ranking datasets, rank first of the automatic group in TREC COVID (Round 2) and rank top on the MS MARCO Document Ranking leaderboard. The library, experimental methodologies and results of OpenMatch are all publicly available at https://github.com/thunlp/OpenMatch.
Characterization of the Transition-State Structures and Mechanisms for the Isomerization and Cleavage Reactions of Uridine 3‘-m-Nitrobenzyl Phosphate

The transition-state structures and mechanisms of the isomerization to the 2‘-isomer and cleavage reactions of uridine 3‘-m-nitrobenzyl phosphate to m-nitrobenzyl alcohol and a 2‘,3‘-cyclic UMP at 86 °C and at pH 2.5, 5.5, and 10.5 have been characterized through kinetic isotope effects. The 18O primary isotope effect of 1.0019 ± 0.0007 and the secondary isotope effect of 0.9904 observed for the cleavage reaction at pH 2.5 are consistent with a neutral phosphorane-like transition-state structure. The cleavage and isomerization reactions at pH 2.5 proceed through a neutral phosphorane intermediate. The 18kbridge and 18knonbridge of unity measured for the pH-independent isomerization reaction at neutral pH support a stepwise mechanism with a monoanionic phosphorane intermediate. The primary and secondary isotope effects of 1.009 ± 0.001 and of 0.9986 ± 0.0004 observed for the pH-independent cleavage reaction are consistent with either a stepwise mechanism through a monoanionic phosphorane intermediate or wi.
Construction of Explicit Symplectic Integrators in General Relativity. I. Schwarzschild Black Holes

Symplectic integrators that preserve the geometric structure of Hamiltonian flows and do not exhibit secular growth in energy errors are suitable for the long-term integration of N-body Hamiltonian systems in the solar system. However, the construction of explicit symplectic integrators is frequently difficult in general relativity because all variables are inseparable. Moreover, even if two analytically integrable splitting parts exist in a relativistic Hamiltonian, all analytical solutions are not explicit functions of proper time. Naturally, implicit symplectic integrators, such as the midpoint rule, are applicable to this case. In general, these integrators are numerically more expensive to solve than same-order explicit symplectic algorithms. To address this issue, we split the Hamiltonian of Schwarzschild spacetime geometry into four integrable parts with analytical solutions as explicit functions of proper time. In this manner, second- and fourth-order explicit symplectic integrators can be easily made available. The new algorithms are also useful for modeling the chaotic motion of charged particles around a black hole with an external magnetic field. They demonstrate excellent long-term performance in maintaining bounded Hamiltonian errors and saving computational cost when appropriate proper time steps are adopted.
Model Adaptation for Image Reconstruction using Generalized Stein's Unbiased Risk Estimator

Deep learning image reconstruction algorithms often suffer from model mismatches when the acquisition scheme differs significantly from the forward model used during training. We introduce a Generalized Stein's Unbiased Risk Estimate (GSURE) loss metric to adapt the network to the measured k-space data and minimize model misfit impact. Unlike current methods that rely on the mean square error in kspace, the proposed metric accounts for noise in the measurements. This makes the approach less vulnerable to overfitting, thus offering improved reconstruction quality compared to schemes that rely on mean-square error. This approach may be useful to rapidly adapt pre-trained models to new acquisition settings (e.g., multi-site) and different contrasts than training data
Deep Reformulated Laplacian Tone Mapping

Wide dynamic range (WDR) images contain more scene details and contrast when compared to common images. However, it requires tone mapping to process the pixel values in order to display properly. The details of WDR images can diminish during the tone mapping process. In this work, we address the problem by combining a novel reformulated Laplacian pyramid and deep learning. The reformulated Laplacian pyramid always decompose a WDR image into two frequency bands where the low-frequency band is global feature-oriented, and the high-frequency band is local feature-oriented. The reformulation preserves the local features in its original resolution and condenses the global features into a low-resolution image. The generated frequency bands are reconstructed and fine-tuned to output the final tone mapped image that can display on the screen with minimum detail and contrast loss. The experimental results demonstrate that the proposed method outperforms state-of-the-art WDR image tone mapping methods. The code is made publicly available at this https URL.
Network-centric Indicators for Fragility in Global Financial Indices

Over the last 2 decades, financial systems have been studied and analyzed from the perspective of complex networks, where the nodes and edges in the network represent the various financial components and the strengths of correlations between them. Here, we adopt a similar network-based approach to analyze the daily closing prices of 69 global financial market indices across 65 countries over a period of 2000–2014. We study the correlations among the indices by constructing threshold networks superimposed over minimum spanning trees at different time frames. We investigate the effect of critical events in financial markets (crashes and bubbles) on the interactions among the indices by performing both static and dynamic analyses of the correlations. We compare and contrast the structures of these networks during periods of crashes and bubbles, with respect to the normal periods in the market. In addition, we study the temporal evolution of traditional market indicators, various global network measures, and the recently developed edge-based curvature measures. We show that network-centric measures can be extremely useful in monitoring the fragility in the global financial market indices.
Stay Alive with Many Options: A Reinforcement Learning Approach for Autonomous Navigation

Hierarchical reinforcement learning approaches learn policies based on hierarchical decision structures. However, training such methods in practice may lead to poor generalization, with either sub-policies executing actions for too few time steps or devolving into a single policy altogether. In our work, we introduce an alternative approach to sequentially learn such skills without using an overarching hierarchical policy, in the context of environments in which an objective of the agent is to prolong the episode for as long as possible, or in other words ‘stay alive’. We demonstrate the utility of our approach in a simulated 3D navigation environment which we have built. We show that our method outperforms prior methods such as Soft Actor Critic and Soft Option Critic on our environment, as well as the Atari River Raid environment.
Can Small and Synthetic Benchmarks Drive Modeling Innovation? A Retrospective Study of Question Answering Modeling Approaches

Datasets are not only resources for training accurate, deployable systems, but are also benchmarks for developing new modeling approaches . While large, natural datasets are necessary for training accurate systems, are they necessary for driving modeling innovation? For example, while the popular SQuAD question answering benchmark has driven the development of new modeling approaches, could synthetic or smaller benchmarks have led to similar innovations? This counterfactual question is impossible to answer, but we can study a necessary condition: the ability for a benchmark to recapitulate ﬁndings made on SQuAD. We conduct a retrospective study of 20 SQuAD modeling approaches, investigating how well 32 existing and synthesized benchmarks concur with SQuAD—i.e., do they rank the approaches similarly? We carefully construct small, targeted synthetic benchmarks that do not re-semble natural language, yet have high concurrence with SQuAD, demonstrating that naturalness and size are not necessary for reﬂecting historical modeling improvements on SQuAD. Our results raise the intriguing possibility that small and carefully designed synthetic benchmarks may be useful for driving the development of new modeling approaches.
The Flare and Warp of the Young Stellar Disk Traced with LAMOST DR5 OB-type Stars

We present an analysis of the spatial density structure for the outer disk from 8–14 kpc with the LAMOST DR5 13,534 OB-type stars and observe similar flaring on the north and south sides of the disk, implying that the flaring structure is symmetrical about the Galactic plane, for which the scale height at different Galactocentric distances is from 0.14 to 0.5 kpc. By using the average slope to characterize the flaring strength, we find that the thickness of the OB stellar disk is similar but that flaring is slightly stronger compared to the thin disk as traced by red giant branch stars, possibly implying that secular evolution is not the main contributor to the flaring but rather perturbation scenarios such as interactions with passing dwarf galaxies could be possible. When comparing the scale height of the OB stellar disk on the north and south sides with the gas disk, the former one is slightly thicker than the latter one by ≈33 and 9 pc, meaning that one could tentatively use young OB-type stars to trace the gas properties. Meanwhile, we determine that the radial scale length of the young OB stellar disk is 1.17 ± 0.05 kpc, which is shorter than that of the gas disk, confirming that the gas disk is more extended than the stellar disk. What is more, by considering the midplane displacements (Z 0) in our density model we find that almost all values of Z 0 are within 100 pc, with an increasing trend as Galactocentric distance increases.
GraphEBM: Molecular Graph Generation with Energy-Based Models

Molecular graph generation is an emerging area of research with numerous applications. This problem remains challenging as molecular graphs are discrete, irregular, and permutation invariant to node order. Notably, most existing approaches fail to guarantee the intrinsic property of permutation invariance, resulting in unexpected bias in generative models. In this work, we propose GraphEBM to generate molecular graphs using energy-based models. In particular, we parameterize the energy function in a permutation invariant manner, thus making GraphEBM permutation invariant. We apply Langevin dynamics to train the energy function by approximately maximizing likelihood and generate samples with low energies. Furthermore, to generate molecules with a specific desirable property, we propose a simple yet effective strategy, which pushes down energies with flexible degrees according to the properties of corresponding molecules. Finally, we explore the use of GraphEBM for generating molecules with multiple objectives in a compositional manner. Comprehensive experimental results on random, goal-directed, and compositional generation tasks demonstrate the effectiveness of our proposed method.
Electroluminescence yield in pure krypton

The krypton electroluminescence yield was studied, at room temperature, as a function of electric field in the gas scintillation gap. A large area avalanche photodiode has been used to allow the simultaneous detection of the electroluminescence pulses as well as the direct interaction of x-rays, the latter being used as a reference for the calculation of the number of charge carriers produced by the electroluminescence pulses and, thus, the determination of the number of photons impinging the photodiode. An amplification parameter of 113 photons per kV per drifting electron and a scintillation threshold of 2.7 Td ( 0.7 kV cm−1 bar−1 at 293 K ) was obtained, in good agreement with the simulation data reported in the literature. On the other hand, the ionisation threshold in krypton was found to be around 13.5 Td (3.4 kV cm−1 bar−1), less than what had been obtained by the most recent simulation work-package. The krypton amplification parameter is about 80% and 140% of those measured for xenon and argon, respectively. The electroluminescence yield in krypton is of great importance for modeling krypton-based double-phase or high-pressure gas detectors, which may be used in future rare event detection experiments. Corresponding author. ar X iv :2 10 2. 00 03 2v 1 [ ph ys ic s. in sde t] 2 9 Ja n 20 21
Smith–Purcell radiation of a vortex electron

A wide variety of emission processes by electron wave packets with an orbital angular momentum ℓℏ, called the vortex electrons, can be influenced by a nonparaxial contribution due to their intrinsic electric quadrupole moment. We study Smith–Purcell radiation from a conducting grating generated by a vortex electron, described as a generalized Laguerre–Gaussian packet, which has an intrinsic magnetic dipole moment and an electric quadrupole moment. By using a multipole expansion of the electromagnetic field of such an electron, we employ a generalized surface-current method, applicable for a wide range of parameters. The radiated energy contains contributions from the charge, from the magnetic moment, and from the electric quadrupole moment, as well as from their interference. The quadrupole contribution grows as the packet spreads while propagating, and it is enhanced for large ℓ. In contrast to the linear growth of the radiation intensity from the charge with a number of strips N, the quadrupole contribution reveals an N 3 dependence, which puts a limit on the maximal grating length for which the radiation losses stay small. We study spectral-angular distributions of the Smith–Purcell radiation both analytically and numerically and demonstrate that the electron’s vorticity can give rise to detectable effects for non-relativistic and moderately relativistic electrons. On a practical side, preparing the incoming electron’s state in a form of a non-Gaussian packet with a quadrupole moment—such as the vortex electron, an Airy beam, a Schrödinger cat state, and so on—one can achieve quantum enhancement of the radiation power compared to the classical linear regime. Such an enhancement would be a hallmark of a previously unexplored quantum regime of radiation, in which non-Gaussianity of the packet influences the radiation properties much stronger than the quantum recoil.
Parameter-free Stochastic Optimization of Variationally Coherent Functions

We design and analyze an algorithm for first-order stochastic optimization of a large class of functions on R. In particular, we consider the variationally coherent functions which can be convex or non-convex. The iterates of our algorithm on variationally coherent functions converge almost surely to the global minimizer x∗. Additionally, the very same algorithm with the same hyperparameters, after T iterations guarantees on convex functions that the expected suboptimality gap is bounded by Õ(‖x∗ − x0‖T) for any ǫ > 0. It is the first algorithm to achieve both these properties at the same time. Also, the rate for convex functions essentially matches the performance of parameter-free algorithms. Our algorithm is an instance of the Follow The Regularized Leader algorithm with the added twist of using rescaled gradients and time-varying linearithmic regularizers.
Quantifying Visual Image Quality: A Bayesian View

Image quality assessment (IQA) models aim to establish a quantitative relationship between visual images and their quality as perceived by human observers. IQA modeling plays a special bridging role between vision science and engineering practice, both as a test-bed for vision theories and computational biovision models and as a powerful tool that could potentially have a profound impact on a broad range of image processing, computer vision, and computer graphics applications for design, optimization, and evaluation purposes. The growth of IQA research has accelerated over the past two decades. In this review, we present an overview of IQA methods from a Bayesian perspective, with the goals of unifying a wide spectrum of IQA approaches under a common framework and providing useful references to fundamental concepts accessible to vision scientists and image processing practitioners. We discuss the implications of the successes and limitations of modern IQA methods for biological vision and the prospect for vision science to inform the design of future artificial vision systems. (The detailed model taxonomy can be found at http://ivc.uwaterloo.ca/research/bayesianIQA/.) Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
A linearized framework and a new benchmark for model selection for fine-tuning

Fine-tuning from a collection of models pre-trained on different domains (a"model zoo") is emerging as a technique to improve test accuracy in the low-data regime. However, model selection, i.e. how to pre-select the right model to fine-tune from a model zoo without performing any training, remains an open topic. We use a linearized framework to approximate fine-tuning, and introduce two new baselines for model selection -- Label-Gradient and Label-Feature Correlation. Since all model selection algorithms in the literature have been tested on different use-cases and never compared directly, we introduce a new comprehensive benchmark for model selection comprising of: i) A model zoo of single and multi-domain models, and ii) Many target tasks. Our benchmark highlights accuracy gain with model zoo compared to fine-tuning Imagenet models. We show our model selection baseline can select optimal models to fine-tune in few selections and has the highest ranking correlation to fine-tuning accuracy compared to existing algorithms.