Miracle-dz/UniFAR-FTUs huggingface dataset:
Electronic correlation determining correlated plasmons in Sb-doped 
Bi2Se3

Electronic correlation is believed to play an important role in exotic phenomena such as insulator-metal transition, colossal magneto resistance and high temperature superconductivity in correlated electron systems. Recently, it has been shown that electronic correlation may also be responsible for the formation of unconventional plasmons. Herewith, using a combination of angle-dependent spectroscopic ellipsometry, angle resolved photoemission spectroscopy and Hall measurements all as a function of temperature supported by first-principles calculations, the existence of low-loss high-energy correlated plasmons accompanied by spectral weight transfer, a fingerprint of electronic correlation, in topological insulator (Bi$_{0.8}$Sb$_{0.2}$)$_2$Se$_3$ is revealed. Upon cooling, the density of free charge carriers in the surface states decreases whereas those in the bulk states increase, and that the newly-discovered correlated plasmons are key to explaining this phenomenon. Our result shows the importance of electronic correlation in determining new correlated plasmons and opens a new path in engineering plasmonic-based topologically-insulating devices.
Interpretable Cognitive Diagnosis with Neural Network for Intelligent Educational Systems

In intelligent education systems, one key issue is to discover students’ proﬁciency level on speciﬁc knowledge concepts, which called cognitive diagnosis. Existing approaches usually mine the student exercising process by manually designed function, which is usually linear and not sufﬁcient to capture complex relations between students and exercises. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex interactions between student’s and exercise’s factor vectors. The interpretability of factor vectors is guaranteed with the monotonicity assumption borrowed from educational psychology. We provide NeuralCDM model as an implementation example of the framework. Further, we explore the text content for improving NeuralCDM to show the extendability of NeuralCD, and demonstrate the generality of NeuralCD by proving how it covers some traditional diagnostic models. Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability.
A Bayesian Approach for Sequence Tagging with Crowds

Current methods for sequence tagging, a core task in NLP, are data hungry, which motivates the use of crowdsourcing as a cheap way to obtain labelled data. However, annotators are often unreliable and current aggregation methods cannot capture common types of span annotation error. To address this, we propose a Bayesian method for aggregating sequence tags that reduces errors by modelling sequential dependencies between the annotations as well as the ground-truth labels. By taking a Bayesian approach, we account for uncertainty in the model due to both annotator errors and the lack of data for modelling annotators who complete few tasks. We evaluate our model on crowdsourced data for named entity recognition, information extraction and argument mining, showing that our sequential model outperforms the previous state of the art, and that Bayesian approaches outperform non-Bayesian alternatives. We also find that our approach can reduce crowdsourcing costs through more effective active learning, as it better captures uncertainty in the sequence labels when there are few annotations.
Gas velocity structure of the Orion A integral-shaped filament

We present analysis of the gas kinematics of the integral-shaped filament (ISF) in Orion A using four different molecular lines, 12CO (1−0), 13CO (1−0), NH3 (1,1), and N2H+ (1−0). We describe our method to visualize the position–velocity (PV) structure using the intensity-weighted line velocity centroid, which enables us to identify structures that were previously muddled or invisible. We observe a north-to-south velocity gradient in all tracers that terminates in a velocity peak near the centre of the Orion Nebula Cluster (ONC), consistent with the previously reported ‘wave-like’ properties of the ISF. We extract the velocity dispersion profiles and compare the non-thermal line widths to the gas gravitational potential. We find supersonic Mach number profiles, yet the line widths are consistent with the gas being deeply gravitationally bound. We report the presence of two 12CO velocity components along the northern half of the ISF; if interpreted as circular rotation, the angular velocity is $\omega =1.4\, {\rm Myr}^{-1}$. On small scales we report the detection of N2H+ and NH3 ‘twisting and turning’ structures, with short associated time-scales that give the impression of a torsional wave. Neither the nature of these structures nor their relation to the larger scale wave is presently understood.
MIDI-Sandwich2: RNN-based Hierarchical Multi-modal Fusion Generation VAE networks for multi-track symbolic music generation

Currently, almost all the multi-track music generation models use the Convolutional Neural Network (CNN) to build the generative model, while the Recurrent Neural Network (RNN) based models can not be applied in this task. In view of the above problem, this paper proposes a RNN-based Hierarchical Multi-modal Fusion Generation Variational Autoencoder (VAE) network, MIDI-Sandwich2, for multi-track symbolic music generation. Inspired by VQ-VAE2, MIDI-Sandwich2 expands the dimension of the original hierarchical model by using multiple independent Binary Variational Autoencoder (BVAE) models without sharing weights to process the information of each track. Then, with multi-modal fusion technology, the upper layer named Multi-modal Fusion Generation VAE (MFG-VAE) combines the latent space vectors generated by the respective tracks, and uses the decoder to perform the ascending dimension reconstruction to simulate the inverse operation of multi-modal fusion, multi-modal generation, so as to realize the RNN-based multi-track symbolic music generation. For the multi-track format pianoroll, we also improve the output binarization method of MuseGAN, which solves the problem that the refinement step of the original scheme is difficult to differentiate and the gradient is hard to descent, making the generated song more expressive. The model is validated on the Lakh Pianoroll Dataset (LPD) multi-track dataset. Compared to the MuseGAN, MIDI-Sandwich2 can not only generate harmonious multi-track music, the generation quality is also close to the state of the art level. At the same time, by using the VAE to restore songs, the semi-generated songs reproduced by the MIDI-Sandwich2 are more beautiful than the pure autogeneration music generated by MuseGAN. Both the code and the audition audio samples are open source on this https URL.
Probing High-Energy Hadronic Interactions with Extensive Air Showers

In this article, I will review how observations performed with extensive air showers are being used to probe hadronic interactions at high energy. I will briefly overview the new studies exploring the connection between the dynamics of air showers and multiparticle production, and how this knowledge can be translated into constraints on high energy hadronic models. I will also overview direct measurements, complementary to, and beyond the reach of, accelerator experiments.
Understanding Assessment: Guidelines for Utilizing a Qualitative Approach.

The background and purpose of course assessment are examined. The paper includes suggestions for classroom instructors in setting up formative assessment plans based on qualitative research. Focus is on the interview technique. It is discussed as a practical source for evaluating what actually takes place during a course, including the appraisal of the course itself, instructional procedures, and content. Steps for conducting student interviews are intended to promote student involvement and to aid instructors in determining ability levels and course focus. In the drive toward academic excellence, formative assessment assists students and instructors in reaching the goal of successful learning in the classroom. There is a 24-item list of references. (SLD) *********************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * *********************************************************************** U.11. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL CENT RESOUER R(ERIC) CES INFORMATION This document has been reproduced as recetved from the person or organtration orogmating C Minor changes have been made to improve rpfoductton duality Points of view or opinions staled in thus document do not necessarily represent official OERI position or policy . Assessment 1 "PERMISSION TO REPRODUCE THIS MATERIAL HAS BEEN GRANTED BY /ta,7), NOa4A-725 /WI* TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC). " Understanding Assessment: Guidelines for Utilizing a Qualitative Approach Linda Hollandsworta, Ph.D. University of South Carolin2JCoastal Carolina College P. O. Box 1954 Conway, South Carolina 29526 (803) 448-1481, ext. 2439 (Office) (803) 449-3897 (Home) Running Head: Assesment Total Number of Pages: 12 4
METHOD OF DISTRIBUTIONS FOR SYSTEMS WITH STOCHASTIC FORCING

The method of distributions is developed for systems that are governed by hyperbolic conservation laws with stochastic forcing. The method yields a deterministic equation for the cumulative density distribution (CDF) of a system state, e.g., for flow velocity governed by an inviscid Burgers' equation with random source coefficients. This is achieved without recourse to any closure approximation. The CDF model is verified against MC simulations using spectral numerical approximations. It is shown that the CDF model accurately predicts the mean and standard deviation for Gaussian, normal and beta distributions of the random coefficients.
On the Optimality of Gaussian Kernel Based Nonparametric Tests against Smooth Alternatives

Nonparametric tests via kernel embedding of distributions have witnessed a great deal of practical successes in recent years. However, statistical properties of these tests are largely unknown beyond consistency against a fixed alternative. To fill in this void, we study here the asymptotic properties of goodness-of-fit, homogeneity and independence tests using Gaussian kernels, arguably the most popular and successful among such tests. Our results provide theoretical justifications for this common practice by showing that tests using Gaussian kernel with an appropriately chosen scaling parameter are minimax optimal against smooth alternatives in all three settings. In addition, our analysis also pinpoints the importance of choosing a diverging scaling parameter when using Gaussian kernels and suggests a data-driven choice of the scaling parameter that yields tests optimal, up to an iterated logarithmic factor, over a wide range of smooth alternatives. Numerical experiments are also presented to further demonstrate the practical merits of the methodology.
FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, non-autoregressive seq2seq models generate all tokens in one pass, which leads to increased efficiency through parallel processing on hardware such as GPUs. However, directly modeling the joint distribution of all tokens simultaneously is challenging, and even with increasingly complex model structures accuracy lags significantly behind autoregressive models. In this paper, we propose a simple, efficient, and effective model for non-autoregressive sequence generation using latent variable models. Specifically, we turn to generative flow, an elegant technique to model complex distributions using neural networks, and design several layers of flow tailored for modeling the conditional density of sequential latent variables. We evaluate this model on three neural machine translation (NMT) benchmark datasets, achieving comparable performance with state-of-the-art non-autoregressive NMT models and almost constant decoding time w.r. t the sequence length.
Radio and X-ray monitoring of the accreting millisecond X-ray pulsar IGR J17591−2342 in outburst

IGR J17591−2342 is a new accreting millisecond X-ray pulsar that was recently discovered in outburst in 2018. Early observations revealed that the source’s radio emission is brighter than that of any other known neutron star low-mass X-ray binary (NS–LMXB) at comparable X-ray luminosity, and assuming its likely ≳6 kpc distance. It is comparably radio bright to black hole LMXBs at similar X-ray luminosities. In this work, we present the results of our extensive radio and X-ray monitoring campaign of the 2018 outburst of IGR J17591−2342. In total, we collected 10 quasi-simultaneous radio (VLA, ATCA) and X-ray (Swift–XRT) observations, which make IGR J17591−2342 one of the best-sampled NS–LMXBs. We use these to fit a power-law correlation index $\beta = 0.37^{+0.42}_{-0.40}$ between observed radio and X-ray luminosities (LR ∝ LXβ). However, our monitoring revealed a large scatter in IGR J17591−2342’s radio luminosity (at a similar X-ray luminosity, LX ∼1036 erg s−1, and spectral state), with LR ∼ 4 × 1029 erg s−1 during the first three reported observations, and up to a factor of 4 lower LR during later radio observations. None the less, the average radio luminosity of IGR J17591−2342 is still one of the highest among NS–LMXBs, and we discuss possible reasons for the wide range of radio luminosities observed in such systems during outburst. We found no evidence for radio pulsations from IGR J17591−2342 in our Green Bank Telescope observations performed shortly after the source returned to quiescence. None the less, we cannot rule out that IGR J17591−2342 becomes a radio millisecond pulsar during quiescence.
Better PAC-Bayes Bounds for Deep Neural Networks using the Loss Curvature

We investigate whether it's possible to tighten PAC-Bayes bounds for deep neural networks by utilizing the Hessian of the training loss at the minimum. For the case of Gaussian priors and posteriors we introduce a Hessian-based method to obtain tighter PAC-Bayes bounds that relies on closed form solutions of layerwise subproblems. We thus avoid commonly used variational inference techniques which can be difficult to implement and time consuming for modern deep architectures. Through careful experiments we analyze the influence of the prior mean, prior covariance, posterior mean and posterior covariance on obtaining tighter bounds. We discuss several limitations in further improving PAC-Bayes bounds through more informative priors.
Modelling the evolution of PAH abundance in galaxies

We investigate the evolution of polycyclic aromatic hydrocarbon (PAH) abundance in a galaxy, which is a crucial step to understand the evolution of bright emission features in the mid-infrared range. We calculate the evolution of dust grain size distribution in a manner consistent with the physical conditions of the interstellar medium by post-processing our previous hydrodynamical simulation of an isolated disc galaxy. We also differentiate between aromatic and non-aromatic grains for carbonaceous dust species and explicitly considered the aromatization process. As a consequence, our model explains the metallicity dependence of PAH abundances in nearby galaxies well. The PAH abundance increase is driven particularly by the interplay between shattering and accretion (dust growth). The fast aromatization guarantees that the small carbonaceous grains trace PAHs very well. Since shattering and accretion are sensitive to the dust abundance, we predict that the PAH-to-dust abundance ratio increases as the metallicity increases. This is consistent with the observation data of nearby galaxies.
Dynamic Time-Frequency Division Duplex

In this paper, we introduce dynamic time-frequency-division duplex (D-TFDD), which is a novel duplexing scheme that combines time-division duplex (TDD) and frequency-division duplex (FDD). In D-TFDD, a user receives from the base station (BS) on the downlink in one frequency band and transmits to the BS on the uplink in another frequency band, as in FDD. Next, the user shares its uplink transmission (downlink reception) on the corresponding frequency band with the uplink transmission or the downlink reception of another user in a D-TDD fashion. Hence, in a given frequency band, the BS communicates with user 1 (U1) and user 2 (U2) in a D-TDD fashion. The proposed D-TFDD scheme does not require inter-cell interference (ICI) knowledge and only requires channel state information (CSI) of the local BS-U1 and BS-U2 channels. Thereby, it is practical for implementation. The proposed D-TFDD scheme increases the throughput region between the BS and the two users in a given frequency band, and significantly decreases the outage probabilities on the corresponding BS-U1 and BS-U2 channels. Most importantly, the proposed D-TFDD scheme doubles the diversity gain on both the corresponding BS-U1 and the BS-U2 channels compared to the diversity gain of existing duplexing schemes, which results in very large performance gains.
Pressure-induced topological phase transition in noncentrosymmetric elemental tellurium

Significance Topological Weyl semimetal, which is a gapless semimetallic phase protected by symmetry, generally appears by band gap closing in noncentrosymmetric semiconductors. So far, there have been only a limited number of reports of such a topological phase transition so that many basic aspects have remained unexplored. Here we report potential signatures of the topological phase transition under pressure in elemental tellurium with noncentrosymmetric chiral crystal structure. Our result represents that elemental tellurium can be one of the model systems for the topological phase transition from a semiconductor to a Weyl semimetal, opening the avenue for studying the topological phase transition by the external field and resultant topological properties. Recent progress in understanding the electronic band topology and emergent topological properties encourage us to reconsider the band structure of well-known materials including elemental substances. Controlling such a band topology by external field is of particular interest from both fundamental and technological viewpoints. Here we report possible signatures of the pressure-induced topological phase transition from a semiconductor to a Weyl semimetal in elemental tellurium probed by transport measurements. Pressure variation of the periods of Shubnikov–de Haas oscillations, as well as oscillation phases, shows an anomaly around the pressure theoretically predicted for topological phase transition. This behavior is consistent with the pressure-induced band deformation and resultant band-crossing effect. Moreover, effective cyclotron mass is reduced toward the critical pressure, potentially reflecting the emergence of massless linear dispersion. The present result paves the way for studying the electronic band topology in well-known compounds and topological phase transition by the external field.
Global dynamics of the interstellar medium in magnetized disc galaxies

Magnetic fields are an elemental part of the interstellar medium in galaxies. However, their impact on gas dynamics and star formation in galaxies remains controversial. We use a suite of global magnetohydrodynamic simulations of isolated disc galaxies to study the influence of magnetic fields on the diffuse and dense gas in the discs. We find that the magnetic field acts in multiple ways. Stronger magnetized discs fragment earlier due to the shorter growth time of the Parker instability. Due to the Parker instability in the magnetized discs, we also find cold ($T \lt 50\, \mathrm{K}$) and dense ($n\sim 10^3 {--}10^4\, \mathrm{cm}^{-3}$) gas several hundred pc above/below the mid-plane without any form of stellar feedback. In addition, magnetic fields change the fragmentation pattern. While in the hydrodynamic case, the disc breaks up into ring-like structures, magnetized discs show the formation of filamentary entities that extent both in the azimuthal and radial direction. These kpc scale filaments become magnetically (super-)critical very quickly and allow for the rapid formation of massive giant molecular clouds. Our simulations suggest that major differences in the behaviour of star formation – due to a varying magnetization – in galaxies could arise.
Data Interpretation over Plots

Reasoning over plots by question answering (QA) is a challenging machine learning task at the intersection of vision, language processing, and reasoning. Existing synthetic datasets (FigureQA, DVQA) do not model variability in data labels, real-valued data, or complex reasoning questions. Consequently, proposed models for these datasets do not fully address the challenge of reasoning over plots. We propose PlotQA with 8.1 million question-answer pairs over 220,000 plots with data from real-world sources and questions based on crowd-sourced question templates. 26% of the questions in PlotQA have answers that are not in a fixed vocabulary, requiring reasoning capabilities. Analysis of existing models on PlotQA reveals that a hybrid model is required: Specific questions are answered better by choosing the answer from a fixed vocabulary or by extracting it from a predicted bounding box in the plot, while other questions are answered with a table question-answering engine which is fed with a structured table extracted by visual element detection. For the latter, we propose the VOES pipeline and combine it with SAN-VQA to form a hybrid model SAN-VOES. On the DVQA dataset, SAN-VOES model has an accuracy of 58%, significantly improving on highest reported accuracy of 46%. On the PlotQA dataset, SAN-VOES has an accuracy of 54%, which is the highest amongst all the models we trained. Analysis of each module in the VOES pipeline reveals that further improvement in accuracy requires more accurate visual element detection.
Not even Khovanov homology

We construct a supercategory that can be seen as a skew version of (thickened) KLR algebras for the type $A$ quiver. We use our supercategory to construct homological invariants of tangles and show that for every link our invariant gives a link homology theory supercategorifying the Jones polynomial. Our homology is distinct from even Khovanov homology and we present evidence supporting the conjecture that it is isomorphic to odd Khovanov homology. We also show that cyclotomic quotients of our supercategory give supercategorifications of irreducible finite-dimensional representations of $\mathfrak{gl}_n$ of level 2.