Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Quantifiable Dialogue Coherence Evaluation

Automatic dialogue coherence evaluation has attracted increasing attention and is crucial for developing promising dialogue systems. However, existing metrics have two major limitations: (a) they are mostly trained in a simplified two-level setting (coherent vs. incoherent), while humans give Likert-type multi-level coherence scores, dubbed as “quantifiable”; (b) their predicted coherence scores cannot align with the actual human rating standards due to the absence of human guidance during training. To address these limitations, we propose Quantifiable Dialogue Coherence Evaluation (QuantiDCE), a novel framework aiming to train a quantifiable dialogue coherence metric that can reflect the actual human rating standards. Specifically, QuantiDCE includes two training stages, Multi-Level Ranking (MLR) pre-training and Knowledge Distillation (KD) fine-tuning. During MLR pre-training, a new MLR loss is proposed for enabling the model to learn the coarse judgement of coherence degrees. Then, during KD fine-tuning, the pretrained model is further finetuned to learn the actual human rating standards with only very few human-annotated data. To advocate the generalizability even with limited fine-tuning data, a novel KD regularization is introduced to retain the knowledge learned at the pre-training stage. Experimental results show that the model trained by QuantiDCE presents stronger correlations with human judgements than the other state-of-the-art metrics.
Investigating the delay between dust radiation and star-formation in local and distant quenching galaxies

We investigate the timescale over which the infrared (IR) luminosity decreases after a complete and rapid quenching of star formation using observations of local and high-redshift galaxies. From spectral energy distribution modelling, we derive the time since quenching of a subsample of 14 galaxies from the Herschel Reference Survey that suffer from ram-pressure stripping due to the environment of the Virgo cluster and of a subsample of 7 rapidly quenched COSMOS galaxies selected through a state-of-the-art statistical method already tested on the determination of galaxy star formation history (SFH). Three out of the seven COSMOS galaxies have an optical spectrum with no emission line, confirming their quenched nature. We obtained the present physical properties of the combined sample (local plus high-redshift) from the long-term SFH properties, as well as the past LIR of these galaxies just before their quenching. We show that this past LIR is consistent with the LIR of reference samples of normally star-forming galaxies with same stellar mass and redshift as each of our quenched galaxies. We put constraints on the present to past IR luminosity ratio as a function of quenching time. The two samples probe different dynamical ranges in terms of quenching age with the HRS galaxies exhibiting longer timescales (0.2–3 Gyr) compared to the COSMOS ones (< 100 Myr). Assuming an exponential decrease in the LIR after quenching, the COSMOS quenched galaxies are consistent with short e-folding times of less than a couple of hundred million years, while the properties of the HRS quenched galaxies are compatible with larger timescales of several hundred million years. For the HRS sample, this result is consistent with the known quenching mechanism that affected them, namely ram pressure stripping due to the environment. For the COSMOS sample, different quenching processes are acting on short to intermediate timescales. Processes such as galaxy mergers, disk instabilities, and environmental effects can produce such strong star formation variability.
Detection of Preventable Fetal Distress During Labor From Scanned Cardiotocogram Tracings Using Deep Learning

Despite broad application during labor and delivery, there remains considerable debate about the value of electronic fetal monitoring (EFM). EFM includes the surveillance of fetal heart rate (FHR) patterns in conjunction with the mother's uterine contractions, providing a wealth of data about fetal behavior and the threat of diminished oxygenation and cerebral perfusion. Adverse outcomes universally associate a fetal injury with the failure to timely respond to FHR pattern information. Historically, the EFM data, stored digitally, are available only as rasterized pdf images for contemporary or historical discussion and examination. In reality, however, they are rarely reviewed systematically or purposefully. Using a unique archive of EFM collected over 50 years of practice in conjunction with adverse outcomes, we present a deep learning framework for training and detection of incipient or past fetal injury. We report 94% accuracy in identifying early, preventable fetal injury intrapartum. This framework is suited for automating an early warning and decision support system for maintaining fetal well-being during the stresses of labor. Ultimately, such a system could enable obstetrical care providers to timely respond during labor and prevent both urgent intervention and adverse outcomes. When adverse outcomes cannot be avoided, they can provide guidance to the early neuroprotective treatment of the newborn.
CoRank: A clustering cum graph ranking approach for extractive summarization

Online information has increased tremendously in today's age of Internet. As a result, the need has arose to extract relevant content from the plethora of available information. Researchers are widely using automatic text summarization techniques for extracting useful and relevant information from voluminous available information, it also enables users to obtain valuable knowledge in a limited period of time with minimal effort. The summary obtained from the automatic text summarization often faces the issues of diversity and information coverage. Promising results are obtained for automatic text summarization by the introduction of new techniques based on graph ranking of sentences, clustering, and optimization. This research work proposes CoRank, a two-stage sentence selection model involving clustering and then ranking of sentences. The initial stage involves clustering of sentences using a novel clustering algorithm, and later selection of salient sentences using CoRank algorithm. The approach aims to cover two objectives: maximum coverage and diversity, which is achieved by the extraction of main topics and sub-topics from the original text. The performance of the CoRank is validated on DUC2001 and DUC 2002 data sets.
Consistent Two-Flow Network for Tele-Registration of Point Clouds

Rigid registration of partial observations is a fundamental problem in various applied fields. In computer graphics, special attention has been given to the registration between two partial point clouds generated by scanning devices. State-of-the-art registration techniques still struggle when the overlap region between the two point clouds is small, and completely fail if there is no overlap between the scan pairs. In this article, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration. Our technique is based on a novel neural network design that learns a prior of a class of shapes and can complete a partial shape. The key idea is combining the registration and completion tasks in a way that reinforces each other. In particular, we simultaneously train the registration network and completion network using two coupled flows, one that register-and-complete, and one that complete-and-register, and encourage the two flows to produce a consistent result. We show that, compared with each separate flow, this two-flow training leads to robust and reliable tele-registration, and hence to a better point cloud prediction that completes the registered scans. It is also worth mentioning that each of the components in our neural network outperforms state-of-the-art methods in both completion and registration. We further analyze our network with several ablation studies and demonstrate its performance on a large number of partial point clouds, both synthetic and real-world, that have only small or no overlap.
Multiple field-induced phases in the frustrated triangular magnet 
Cs3Fe2Br9

D. Brüning, T. Fröhlich, D. Gorkov, 2 I. Ćısařová, Y. Skourski, L. Rossi, B. Bryant, S. Wiedmann, M. Meven, 7 A. Ushakov, S.V. Streltsov, 9 D. Khomskii, P. Becker, L. Bohatý, M. Braden, and T. Lorenz II. Physikalisches Institut, Universität zu Köln, Zülpicher Straße 77, D-50937 Köln, Germany Heinz Maier-Leibnitz Zentrum (MLZ), Technische Universität München, Lichtenbergstr. 1, 85748 Garching, Germany Department of Inorganic Chemistry, Charles University in Prague, Hlavova 2030/8, 128 43 Prague 2, Czech Republic Hochfeld-Magnetlabor Dresden (HLD-EMFL), Helmholtz-Zentrum Dresden-Rossendorf, 01314 Dresden, Germany High Field Magnet Laboratory (HFML-EMFL) and Institute for Molecules and Materials, Radboud University, 6525 ED Nijmegen, The Netherlands RWTH Aachen University, Institut für Kristallographie, 52056 Aachen, Germany Jülich Centre for Neutron Science JCNS at Heinz Maier-Leibnitz Zentrum (MLZ) M.N. Mikheev Institute of Metal Physics, Ural Branch, Russian Academy of Sciences, 620137 Ekaterinburg, Russia Ural Federal University, 620002 Ekaterinburg, Russia Abteilung Kristallographie, Institut für Geologie und Mineralogie, Universität zu Köln, Zülpicher Straße 49b, 50674 Köln, Germany (Dated: June 2, 2021)
Nora: The Well-Being Coach

The current pandemic has forced people globally to remain in isolation and practice social distancing, which creates the need for a system to combat the resulting loneliness and negative emotions. In this paper we propose Nora, a virtual coaching platform designed to utilize natural language understanding in its dialogue system and suggest other recommendations based on user interactions. It is intended to provide assistance and companionship to people undergoing self-quarantine or work-from-home routines. Nora helps users gauge their well-being by detecting and recording the user's emotion, sentiment, and stress. Nora also recommends various workout, meditation, or yoga exercises to users in support of developing a healthy daily routine. In addition, we provide a social community inside Nora, where users can connect and share their experiences with others undergoing a similar isolation procedure. Nora can be accessed from anywhere via a web link and has support for both English and Mandarin.
Iterative reconstruction excursions for Baryon Acoustic Oscillations and beyond

The density field reconstruction technique has been widely used for recovering the Baryon Acoustic Oscillation (BAO) feature in galaxy surveys that has been degraded due to nonlinearities. Recent studies advocated adopting iterative steps to improve the recovery much beyond that of the standard technique. In this paper, we investigate the performance of a few selected iterative reconstruction techniques focusing on the BAO and the broadband-shape of the two-point clustering. We include redshift-space distortions, halo bias, and shot noise and inspect the components of the reconstructed field in Fourier space and in configuration space using both density field-based reconstruction and displacement field-based reconstruction. We find that the displacement field reconstruction becomes quickly challenging in the presence of non-negligible shot noise and therefore present surrogate methods that can be practically applied to a much more sparse field such as galaxies. For a galaxy field, implementing a debiasing step to remove the Lagrangian bias appears crucial for the displacement field reconstruction. We show that the iterative reconstruction does not substantially improve the BAO feature beyond an aggressively optimized standard reconstruction with a small smoothing kernel. However, we find taking iterative steps allows us to use a small smoothing kernel more ‘stably’, i.e. without causing a substantial deviation from the linear power spectrum on large scales. In one specific example we studied, we find that a deviation of 13 per cent in P(k ∼ 0.1 h Mpc−1) with an aggressive standard reconstruction can reduce to 3-4 per cent with iterative steps.
Understanding peacefulness through the world news

Peacefulness is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peacefulness through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country's profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peacefulness.
Transformation Models for Flexible Posteriors in Variational Bayes

The main challenge in Bayesian models is to determine the posterior for the model parameters. Already, in models with only one or few parameters, the analytical posterior can only be determined in special settings. In Bayesian neural networks, variational inference is widely used to approximate difficult-to-compute posteriors by variational distributions. Usually, Gaussians are used as variational distributions (Gaussian-VI) which limits the quality of the approximation due to their limited flexibility. Transformation models on the other hand are flexible enough to fit any distribution. Here we present transformation model-based variational inference (TM-VI) and demonstrate that it allows to accurately approximate complex posteriors in models with one parameter and also works in a mean-field fashion for multi-parameter models like neural networks.
Rapid expansion of red giant stars during core helium flash by waves propagation to the envelope and implications to exoplanets

We assume that the strong convection during core helium flash of low mass red giant branch (RBG) stars excite waves that propagate to the envelope, and find that the energy that these waves deposit in the envelope causes envelope expansion and brightening. We base our assumption and the estimate of the waves’ energy on studies that explored such a process due to the vigorous core convection of massive stars just before they experience a core collapse supernova explosion. Using the stellar evolutionary code mesa we find that the waves’ energy causes an expansion within few years by tens to hundreds solar radii. Despite the large brightening, we expect the increase in radius and luminosity to substantially enhance mass loss rate and dust formation. The dust shifts the star to become much redder (to the infrared), and the star might actually become fainter in the visible. The overall appearance is of a faint red transient event that lasts for months to few years. We suggest that in some cases envelope expansion might lead stars that are about to leave the RGB to engulf exoplanets. The extended envelope has a smaller binding energy to a degree that allows planets of several Jupiter masses or more and brown dwarfs to survive the common envelope evolution. We suggest this scenario to account for the planet orbiting the white dwarf (WD) WD 1856+534 (TIC 267574918) and for the WD brown dwarf binary system ZTFJ003855.0+203025.5.
Flat-band ferromagnetism and spin waves in the Haldane-Hubbard model

We study the ﬂat-band ferromagnetic phase of the Haldane-Hubbard model on a honeycomb lattice within a bosonization scheme for ﬂat-band Chern insulators, focusing on the calculation of the spin-wave excitation spectrum. We consider the Haldane-Hubbard model with the noninteracting lower bands in a nearly-ﬂat band limit, previously determined for the spinless model, and at 1 / 4-ﬁlling of its corresponding noninteracting limit. Within the bosonization scheme, the Haldane-Hubbard model is mapped into an eﬀective interacting boson model, whose quadratic term allows us to determine the spin-wave spectrum at the harmonic approximation. We show that the excitation spectrum has two branches with a Goldstone mode and Dirac points at center and at the K and K (cid:48) points of the ﬁrst Brillouin zone, respectively. We also consider the eﬀects on the spin-wave spectrum due to an energy oﬀset in the on-site Hubbard repulsion energies and due to the presence of an staggered on-site energy term, both quantities associated with the two triangular sublattices. In both cases, we ﬁnd that an energy gap opens at the K and K (cid:48) points. Moreover, we also ﬁnd some evidences for an instability of the ﬂat-band ferromagnetic phase in the presence of the staggered on-site energy term. We provide some additional results for the square lattice topological Hubbard model previous studied within the bosonization formalism and comment on the diﬀerences between the bosonization scheme implementation for the correlated Chern insulators on both square and honeycomb lattices.
Holographic entanglement entropy of two disjoint intervals in AdS$_3$/CFT$_2$

The Ryu-Takayanagi conjecture predicts a holographic dual of the entanglement entropy of a CFT. It proposes that the entanglement entropy is given by the area of the minimal surface in the dual spacetime. In the semi-classical limit, this conjecture is supported by the saddle point approximation. If there are multiple classical solutions, it is assumed that only the minimal action contributes to the entanglement entropy. However, we will point out that these saddles equally contribute to the entanglement entropy in some cases. Therefore, the derivation of the conjecture is incomplete if there are multiple extremal surfaces that extend from a sub-system on the AdS boundary. We will consider two disjoint intervals in CFT$_{1+1}$ as the simplest but non-trivial example, and propose another candidate for a holographic dual of the entanglement entropy of this system, which is the sum of all the signed areas of extremal surfaces in the dual spacetime. After that, we will derive it from the CFT calculations and propose the corresponding gravity side action.
Approximate polymorphisms

For a function g∶{0,1}m→{0,1}, a function f∶ {0,1}n→{0,1} is called a g-polymorphism if their actions commute: f(g(row1(Z)),…,g(rown(Z))) = g(f(col1(Z)),…,f(colm(Z))) for all Z∈{0,1}n× m. The function f is called an approximate g-polymorphism if this equality holds with probability close to 1, when Z is sampled uniformly. A pair of functions f0,f1∶ {0,1}n → {0,1} are called a skew g-polymorphism if f0(g(row1(Z)),…,g(rown(Z))) = g(f1(col1(Z)),…,f1(colm(Z))) for all Z∈{0,1}n× m. We study the structure of exact polymorphisms as well as approximate polymorphisms. Our results include a proof that an approximate polymorphism f must be close to an exact skew polymorphism, and a characterization of exact skew polymorphisms, which shows that besides trivial cases, only the functions AND, XOR, OR, NAND, NOR, XNOR admit non-trivial exact skew polymorphisms. We also study the approximate polymorphism problem in the list-decoding regime (i.e., when the probability equality holds is not close to 1, but is bounded away from some value). We show that if f(x ∧ y) = f(x) ∧ f(y) with probability larger than s∧≈ 0.815 then f correlates with some junta, and s∧ is the optimal threshold for this property. Our result generalize the classical linearity testing result of Blum, Luby and Rubinfeld, that in this language showed that the approximate polymorphisms of g = XOR are close to XOR’s, as well as a recent result of Filmus, Lifshitz, Minzer and Mossel, showing that the approximate polymorphisms of AND can only be close to AND functions.
Blowup of Solutions to a Damped Euler Equation with Homogeneous Three-Point Boundary Condition

It is known that solutions to the inviscid Proudman-Johnson equation subject to a homogeneous three-point boundary condition can develop singularities in finite time. Given the regularizing effect that damping can have, in this paper we consider the possibility of finite-time singularity formation in solutions of the generalized inviscid Proudman-Johnson equation with damping subject to the same homogeneous three-point boundary condition. In particular, we will derive criteria the initial condition must satisfy in order for solutions to blowup in finite time under the effect of a smooth time-dependent damping term.
Dynamic-Deep: ECG Task-Aware Compression

—Monitoring medical data, e.g., Electrocardiogram (ECG) signals, is a common application of Internet of Things (IoT) devices. Compression methods are often applied on the massive amounts of sensor data generated before sending it to the Cloud to reduce storage and delivery costs. A lossy compression provides high compression gain (CG) but may reduce the performance of an ECG application (downstream task) due to information loss. Previous works on ECG monitoring focus either on optimizing the signal reconstruction or the task’s performance. Instead, we advocate a lossy compression solution that allows conﬁguring a desired performance level on the downstream tasks while maintaining an optimized CG. We propose Dynamic-Deep, a task-aware compression that uses convolutional autoencoders. The compression level is dynamically selected to yield an optimized compression without violating tasks’ performance requirements. We conduct an extensive evaluation of our approach on common ECG datasets using two popular ECG applications, which includes heart rate (HR) arrhythmia classiﬁcation. We demonstrate that Dynamic-Deep improves HR classiﬁcation F1-score by a factor of 3 and increases CG by up to 83% compared to the previous state-of-the-art (autoencoder-based) compressor. Additionally, Dynamic-Deep has a 67% lower memory footprint. Analyzing Dynamic-Deep on the Google Cloud Platform, we observe a 97% reduction in cloud costs compared to a no compression solution. To the best of our knowledge, Dynamic-Deep is the ﬁrst proposal to focus on balancing the need for high performance of cloud-based downstream tasks and the desire to achieve optimized compression in IoT ECG monitoring settings.
Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are trained on clean images and are not robust when handling noisy ones, even if a restoration preprocessing step is applied. While novel methods address this problem, they rely on modified feature extractors and thus necessitate retraining. We instead propose a method that can be applied on a pretrained classifier. Our method exploits a fidelity map estimate that is fused into the internal representations of the feature extractor, thereby guiding the attention of the network and making it more robust to noisy data. We improve the noisy-image classification (NIC) results by significantly large margins, especially at high noise levels, and come close to the fully retrained approaches. Furthermore, as proof of concept, we show that when using our oracle fidelity map we even outperform the fully retrained methods, whether trained on noisy or restored images.