Miracle-dz/UniFAR-FTUs huggingface dataset:
DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement

Disentanglement is a problem in which multiple conversations occur in the same channel simultaneously, and the listener should decide which utterance is part of the conversation he will respond to. We propose a new model, named Dialogue BERT (DialBERT), which integrates local and global semantics in a single stream of messages to disentangle the conversations that mixed together. We employ BERT to capture the matching information in each utterance pair at the utterance-level, and use a BiLSTM to aggregate and incorporate the context-level information. With only a 3% increase in parameters, a 12% improvement has been attained in comparison to BERT, based on the F1-Score. The model achieves a state-of-the-art result on the a new dataset proposed by IBM and surpasses previous work by a substantial margin.
Gamma-ray flux depressions of the Crab Nebula in the high-energy range

The giant gamma-ray flares of the Crab nebula discovered by AGILE and Fermi observatories came as a surprise and have challenged the existing models of pulsar wind nebulae. We have carried out an analysis of 10.5 years of Fermi-LAT observations (Aug 2008 -- Feb 2019) and investigated variability of the Crab nebula in the 100-300 MeV range. Besides the flares, we found several month long depressions of the gamma-ray flux and identified several cases of sharp flux drops, where during one week the flux decreased by an order of magnitude with respect to its average value. No statistically significant variations of the nebula flux in the E>10 GeV range were found in the data. We discuss possible implications of the observed gamma-ray flux depressions on the model of synchrotron emission of the Crab nebula.
A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models

We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time. Specifically, our method is able to produce stable and accurate forecasts 2 days ahead of current time, and uses as inputs (a) official health reports from Chinese Center Disease for Control and Prevention (China CDC), (b) COVID-19-related internet search activity from Baidu, (c) news media activity reported by Media Cloud, and (d) daily forecasts of COVID-19 activity from GLEAM, an agent-based mechanistic model. Our machine-learning methodology uses a clustering technique that enables the exploitation of geo-spatial synchronicities of COVID-19 activity across Chinese provinces, and a data augmentation technique to deal with the small number of historical disease activity observations, characteristic of emerging outbreaks. Our model's predictive power outperforms a collection of baseline models in 27 out of the 32 Chinese provinces, and could be easily extended to other geographies currently affected by the COVID-19 outbreak to help decision makers.
Updated Parameters and a New Transmission Spectrum of HD 97658b

Recent years have seen increasing interest in the characterization of sub-Neptune-sized planets because of their prevalence in the Galaxy, contrasted with their absence in our solar system. HD 97658 is one of the brightest stars hosting a planet of this kind, and we present the transmission spectrum of this planet by combining four Hubble Space Telescope transits, 12 Spitzer/IRAC transits, and eight MOST transits of this system. Our transmission spectrum has a higher signal-to-noise ratio than those from previous works, and the result suggests that the slight increase in transit depth from wavelength 1.1–1.7 μm reported in previous works on the transmission spectrum of this planet is likely systematic. Nonetheless, our atmospheric modeling results are inconclusive, as no model provides an excellent match to our data. Nonetheless, we find that atmospheres with high C/O ratios (C/O ≳ 0.8) and metallicities of ≳100× solar metallicity are favored. We combine the mid-transit times from all of the new Spitzer and MOST observations and obtain an updated orbital period of P = 9.489295 ± 0.000005, with a best-fit transit time center at T0 = 2456361.80690 ± 0.00038 (BJD). No transit timing variations are found in this system. We also present new measurements of the stellar rotation period (34 ± 2 days) and stellar activity cycle (9.6 yr) of the host star HD 97658. Finally, we calculate and rank the Transmission Spectroscopy Metric of all confirmed planets cooler than 1000 K and with sizes between 1 R⊕ and 4 R⊕. We find that at least a third of small planets cooler than 1000 K can be well characterized using James Webb Space Telescope, and of those, HD 97658b is ranked fifth, meaning that it remains a high-priority target for atmospheric characterization.
Long-period eclipsing binaries: towards the true mass-luminosity relation. I. the test sample, observations and data analysis

The mass-luminosity relation is a fundamental law of astrophysics. We have suggested that the currently used mass-luminosity relation is not correct for the M/M⊙ > 2.7 range of mass since it was created utilizing double-lined eclipsing binaries, where the components are synchronized and consequently change each other’s evolutionary path. To exclude this effect, we have started a project to study long-period massive eclipsing binaries in order to construct radial velocity curves and determine masses for the components. We outline our project and present the selected test sample together with the first HRS/SALT spectral observations and the software package, Fitting Binary Stars (fbs), that we developed for the analysis of our spectral data. As the first result, we present the radial velocity curves and best-fit orbital elements for the two components of the FP Car binary system from our test sample.
Are All Good Word Vector Spaces Isomorphic?

Existing algorithms for aligning cross-lingual word vector spaces assume that vector spaces are approximately isomorphic. As a result, they perform poorly or fail completely on non-isomorphic spaces. Such non-isomorphism has been hypothesised to result almost exclusively from typological differences between languages. In this work, we ask whether non-isomorphism is also crucially a sign of degenerate word vector spaces. We present a series of experiments across diverse languages which show that, besides inherent typological differences, variance in performance across language pairs can largely be attributed to the size of the monolingual resources available, and to the properties and duration of monolingual training (e.g. "under-training").
A Graph Convolutional Network Composition Framework for Semi-supervised Classification

Graph convolutional networks (GCNs) have gained popularity due to high performance achievable on several downstream tasks including node classification. Several architectural variants of these networks have been proposed and investigated with experimental studies in the literature. Motivated by a recent work on simplifying GCNs, we study the problem of designing other variants and propose a framework to compose networks using building blocks of GCN. The framework offers flexibility to compose and evaluate different networks using feature and/or label propagation networks, linear or non-linear networks, with each composition having different computational complexity. We conduct a detailed experimental study on several benchmark datasets with many variants and present observations from our evaluation. Our empirical experimental results suggest that several newly composed variants are useful alternatives to consider because they are as competitive as, or better than the original GCN.
Weakly Supervised Semantic Point Cloud Segmentation: Towards 10× Fewer Labels

Point cloud analysis has received much attention recently; and segmentation is one of the most important tasks. The success of existing approaches is attributed to deep network design and large amount of labelled training data, where the latter is assumed to be always available. However, obtaining 3d point cloud segmentation labels is often very costly in practice. In this work, we propose a weakly supervised point cloud segmentation approach which requires only a tiny fraction of points to be labelled in the training stage. This is made possible by learning gradient approximation and exploitation of additional spatial and color smoothness constraints. Experiments are done on three public datasets with different degrees of weak supervision. In particular, our proposed method can produce results that are close to and sometimes even better than its fully supervised counterpart with 10X fewer labels.
Locality Preserving Loss to Align Vector Spaces

We present a locality preserving loss (LPL)that improves the alignment between vector space representations (i.e., word or sentence embeddings) while separating (increasing distance between) uncorrelated representations as compared to the standard method that minimizes the mean squared error (MSE) only. The locality preserving loss optimizes the projection by maintaining the local neighborhood of embeddings that are found in the source, in the target domain as well. This reduces the overall size of the dataset required to the train model. We argue that vector space alignment (with MSE and LPL losses) acts as a regularizer in certain language-based classification tasks, leading to better accuracy than the base-line, especially when the size of the training set is small. We validate the effectiveness ofLPL on a cross-lingual word alignment task, a natural language inference task, and a multi-lingual inference task.
Resource-Optimized Fermionic Local-Hamiltonian Simulation on Quantum Computer for Quantum Chemistry

The ability to simulate a fermionic system on a quantum computer is expected to revolutionize chemical engineering, materials design, nuclear physics, to name a few. Thus, optimizing the simulation circuits is of significance in harnessing the power of quantum computers. Here, we address this problem in two aspects. In the fault-tolerant regime, we optimize the Rz and T gate counts along with the ancilla qubit counts required, assuming the use of a product-formula algorithm for implementation. We obtain a savings ratio of two in the gate counts and a savings ratio of eleven in the number of ancilla qubits required over the state of the art. In the pre-fault tolerant regime, we optimize the two-qubit gate counts, assuming the use of the variational quantum eigensolver (VQE) approach. Specific to the latter, we present a framework that enables bootstrapping the VQE progression towards the convergence of the ground-state energy of the fermionic system. This framework, based on perturbation theory, is capable of improving the energy estimate at each cycle of the VQE progression, by about a factor of three closer to the known ground-state energy compared to the standard VQE approach in the test-bed, classically-accessible system of the water molecule. The improved energy estimate in turn results in a commensurate level of savings of quantum resources, such as the number of qubits and quantum gates, required to be within a pre-specified tolerance from the known ground-state energy. We also explore a suite of generalized transformations of fermion to qubit operators and show that resource-requirement savings of up to more than 20%, in small instances, is possible.
Soft-bubble grippers for robust and perceptive manipulation

Manipulation in cluttered environments like homes requires stable grasps, precise placement and robustness against external contact. Towards addressing these challenges, we present the Soft-bubble gripper system that combines highly compliant gripping surfaces with dense-geometry visuotactile sensing and facilitates multiple kinds of tactile perception. We first present several mechanical design advances on the Soft-bubble sensors including a fabrication technique to deposit custom patterns to the internal surface of the sensor membrane that enables tracking of shear-induced displacement of the grasped object. The depth maps output by the internal imaging sensor are used in an in-hand proximity pose estimation framework - the method better captures distances to corners or edges on the object geometry. We also extend our previous work on tactile classification and integrate the system within a robust manipulation pipeline for cluttered home environments. The capabilities of the proposed system are demonstrated through robust execution of multiple real-world manipulation tasks.
Attentive Normalization for Conditional Image Generation

Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain. It is still not sufficient for categories with complicated structures. In this paper, we characterize long-range dependence with attentive normalization (AN), which is an extension to traditional instance normalization. Specifically, the input feature map is softly divided into several regions based on its internal semantic similarity, which are respectively normalized. It enhances consistency between distant regions with semantic correspondence. Compared with self-attention GAN, our attentive normalization does not need to measure the correlation of all locations, and thus can be directly applied to large-size feature maps without much computational burden. Extensive experiments on class-conditional image generation and semantic inpainting verify the efficacy of our proposed module.
"Go eat a bat, Chang!": An Early Look on the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19

The outbreak of the COVID-19 pandemic has changed our lives in unprecedented ways. In the face of the projected catastrophic consequences, many countries have enacted social distancing measures in an attempt to limit the spread of the virus. Under these conditions, the Web has become an indispensable medium for information acquisition, communication, and entertainment. At the same time, unfortunately, the Web is being exploited for the dissemination of potentially harmful and disturbing content, such as the spread of conspiracy theories and hateful speech towards specific ethnic groups, in particular towards Chinese people since COVID-19 is believed to have originated from China. In this paper, we make a first attempt to study the emergence of Sinophobic behavior on the Web during the outbreak of the COVID-19 pandemic. We collect two large-scale datasets from Twitter and 4chan's Politically Incorrect board (/pol/) over a time period of approximately five months and analyze them to investigate whether there is a rise or important differences with regard to the dissemination of Sinophobic content. We find that COVID-19 indeed drives the rise of Sinophobia on the Web and that the dissemination of Sinophobic content is a cross-platform phenomenon: it exists on fringe Web communities like \dspol, and to a lesser extent on mainstream ones like Twitter. Also, using word embeddings over time, we characterize the evolution and emergence of new Sinophobic slurs on both Twitter and /pol/. Finally, we find interesting differences in the context in which words related to Chinese people are used on the Web before and after the COVID-19 outbreak: on Twitter we observe a shift towards blaming China for the situation, while on /pol/ we find a shift towards using more (and new) Sinophobic slurs.
Clinicopathological Spectrum of Xanthogranulomatous Pyelonephritis

Xanthogranulomatous pyelonephritis (XGP) is an uncommon and distinct type of chronic infective pyelonephritis causing destruction of the kidney, severely affecting the renal function. The perinephric adipose tissue and peritoneum is not uncommonly involved. The study was undertaken to decipher the clinicopathologic spectrum of XGP. Forty cases of XGP were diagnosed on histopathology over a period of 13 years (2005–2017). Relevant clinical details and radiological findings were recorded from the case files. Out of a total of 40 cases, 26 were female and 14 were male with a mean age of 39.5 ± 13.6 years. Flank pain was the most common presenting symptom. All the patients had unilateral disease and underwent nephrectomy for a nonfunctional kidney. Gross examination showed enlarged kidney with replacement of cortico-medullary tissue by yellow nodular areas of fatty tissue and dilatation of the pelvicalyceal system. Thirty-six (90%) cases had nephrolithiasis. Histologically, the characteristic feature was the existence of lipid-laden foamy macrophages. Renal parenchymal involvement was diffuse in majority (31, 77.5%). Two (5.0%) of the patients had coexisting carcinoma in the same kidney. Histopathologic examination gives the definitive diagnosis of XGP which relies on the characteristic morphology. Surgical intervention in the form of nephrectomy is the treatment of choice and offers good treatment outcomes.
Structure-Level Knowledge Distillation For Multilingual Sequence Labeling

Multilingual sequence labeling is a task of predicting label sequences using a single unified model for multiple languages. Compared with relying on multiple monolingual models, using a multilingual model has the benefit of a smaller model size, easier in online serving, and generalizability to low-resource languages. However, current multilingual models still underperform individual monolingual models significantly due to model capacity limitations. In this paper, we propose to reduce the gap between monolingual models and the unified multilingual model by distilling the structural knowledge of several monolingual models (teachers) to the unified multilingual model (student). We propose two novel KD methods based on structure-level information: (1) approximately minimizes the distance between the student’s and the teachers’ structure-level probability distributions, (2) aggregates the structure-level knowledge to local distributions and minimizes the distance between two local probability distributions. Our experiments on 4 multilingual tasks with 25 datasets show that our approaches outperform several strong baselines and have stronger zero-shot generalizability than both the baseline model and teacher models.
Multiparton Interactions in pp collisions from Machine Learning

Over the last years, Machine Learning (ML) tools have been successfully applied to a wealth of problems in high-energy physics. In this work, we discuss the extraction of the average number of Multiparton Interactions ($\langle N_{\rm mpi} \rangle$) from minimum-bias pp data at LHC energies using ML methods. Using the available ALICE data on transverse momentum spectra as a function of multiplicity, we report that for minimum-bias pp collisions at $\sqrt{s} =$ 7 TeV the average $N_{\rm mpi}$ is 3.98 $\pm$ 1.01, which complements our previous results for pp collisions at $\sqrt{s} =$ 5.02 and 13 TeV. The comparisons indicate a modest energy dependence of $\langle N_{\rm mpi} \rangle$. We also report the multiplicity dependence of $N_{\rm mpi}$ for the three center-of-mass energies. These results are qualitatively consistent with the existing ALICE measurements sensitives to MPI, therefore they provide additional experimental evidence of the presence of MPI in pp collisions.
Activity patterns of neurons in the caudal medial medulla are modulated during swallow in the cat

Neurons in the medial reticular formation, caudal to obex, have activity patterns that are modulated during cough and participate in assemblies with raphe and ventral respiratory column neurons. We speculated that neurons in this area would also participate in swallow. Extracellular activity of neurons in the caudal medial medulla was recorded with multiple electrode arrays in 5 spontaneously breathing, anesthetized cats. Electromyograms were recorded from the geniohyoid, thyropharyngeus, thyroarytenoid, posterior criciarytenoid, upper esophageal sphincter, transversus abdominis, and parasternal muscles as indicators of swallow and other airway protective behaviors. Swallow was produced by mechanical stimulation of the pharynx or infusion of water into this region of the airway. Less than 10% of the neurons were modulated during breathing. The activities of 54% of neurons were altered during swallow. Response patterns included increases or decreases in discharge of spontaneously active neurons and recruitment of silent neurons. In some cases, neurons also were excited by mechanical stimulation of the trachea. The results are consistent with a swallow medullary network that is more widespread than previously appreciated. Neurons in this area also participate in other airway protective behaviors, such as cough. Supported by NIH R33 89104.
The Covid-19 pandemic in Italy

In this note I use data from the website of Dipartimento della Protezione Civile (DPC), COVID19 Italia, to describe and summarize some evidence on the pandemic in Italy with no attempt at structural modeling. The DPC data represent the main source of quantitative information on the Covid-19 pandemic in Italy and are described in more detail in Section 2. They are updated daily, so plan to update my note (almost) daily, and post it to EIEF’s Covid-19 Forum shortly after the DPC data have been released. I hope that the stylized facts presented here will help in sorting out possible interpretations of the process and provide a temporal perspective. To this purpose, I will also use a simple forecasting model to predict the date when the number of confirmed new cases will approximately be zero. I would like to stress three points regarding this prediction exercise. First, since it is not based on a structural model of the contagion but on pure extrapolation of the trends observed in the DPC data, understanding the precise nature of these data is important. In particular, the number of confirmed cases is not equal to the unobserved number of people who are or have been infected but only to the total number of people who tested positive, which is likely to be an order of magnitude smaller (see, e.g., Li et al., 2020). Further, the unknown ratio between confirmed and infected cases should not be taken as approximately constant, as the criteria and the intensity of the testing process is known to vary both over time and across regions of the country. Since the number of infected cases is unobserved, and is not in a fixed relation with the observed number of confirmed cases, my statistical description and projections need not apply to the latent process governing the pandemic. Second, my prediction exercise is based on the assumption that the underlying process governing the pandemic does not change. This is quite a strong assumption in the present circumstances, as we ∗ E-mail address: fp211@georgetown.edu. I thank Luca Anderlini, Francesco Corielli, Giuseppe De Luca, Enrico di Salle, Carlo Favero, Andrea Fazari, Raffaela Giordano, Paolo Giudici, Jose Luis Guerrero Cusumano, Luigi Guiso, Ioannis Laliotis, Edoardo Marconi, Fabrizio Mazzonna, Caterina Peracchi, Marta Peracchi, Aldo Rustichini, Pietro Suppa, and Daniele Terlizzese for comments and helpful discussions. I also thank again Paolo Giudici for providing the data for China