Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the Risk of Infection Spread in Connected Local Regions by a Lattice of SEIR Circuits

The SEIR model, widely used for analyzing and predicting the spread of virus infection, is here extended to considering the movement of people across regions. In contrast to previous models considering the risk due to the travelers from and to other regions, here we consider two factors. First, we consider the movements of susceptible (S), exposed (E), and recovered (R) people as well as infected (I) who can get infected after movement. Second, the infectious contacts between people from different regions are considered and dealt with separately from the first factor. By this, we can reflect the potential risk of future infections that are not out of the range of inspection of infection without over/under-estimating the ratio of E or I among the entire number of people moving. As a result, we show the results of simulation where the influence of traveling across regions, which mean prefectures here, and of the choice of regions to vaccinate with priority, on the risk of infection spread. We find the regions of large population or high effective regeneration number should be vaccinated with priority and that frequent travels tend to cause an influence to enhance the spread especially in the uptrend stage of infections and in regions of smaller populations. Moreover, we show it is not expected that vaccination at a slow pace in Japan will suppress the infection spread sufficiently to extinguish the current wave within a few months.
Shufflecast: An Optical, Data-Rate Agnostic, and Low-Power Multicast Architecture for Next-Generation Compute Clusters

An optical circuit-switched network core has the potential to overcome the inherent challenges of a conventional electrical packet-switched core of today’s compute clusters. As optical circuit switches (OCS) directly handle the photon beams without any optical-electrical-optical (O/E/O) conversion and packet processing, OCS-based network cores have the following desirable properties: a) agnostic to data-rate, b) negligible/zero power consumption, c) no need of transceivers, d) negligible forwarding latency, and e) no need for frequent upgrade. Unfortunately, OCS can only provide point-to-point (unicast) circuits. They do not have built-in support for one-to-many (multicast) communication, yet multicast is fundamental to a plethora of data-intensive applications running on compute clusters nowadays. In this paper, we propose Shufflecast, a novel optical network architecture for next-generation compute clusters that can support high-performance multicast satisfying all the properties of an OCS-based network core. Shufflecast leverages small fanout, inexpensive, passive optical splitters to connect the Top-of-rack (ToR) switch ports, ensuring data-rate agnostic, low-power, physical-layer multicast. We thoroughly analyze Shufflecast’s highly scalable data plane, light-weight control plane, and graceful failure handling. Further, we implement a complete prototype of Shufflecast in our testbed and extensively evaluate the network. Shufflecast is more power-efficient than the state-of-the-art multicast mechanisms. Also, Shufflecast is more cost-efficient than a conventional packet-switched network. By adding Shufflecast alongside an OCS-based unicast network, an all-optical network core with the aforementioned desirable properties supporting both unicast and multicast can be realized.
Adaspeech 2: Adaptive Text to Speech with Untranscribed Data

Text to speech (TTS) is widely used to synthesize personal voice for a target speaker, where a well-trained source TTS model is fine-tuned with few paired adaptation data (speech and its transcripts) on this target speaker. However, in many scenarios, only untranscribed speech data is available for adaptation, which brings challenges to the previous TTS adaptation pipelines (e.g., AdaSpeech). In this paper, we develop AdaSpeech 2, an adaptive TTS system that only leverages untranscribed speech data for adaptation. Specifically, we introduce a mel-spectrogram encoder to a well-trained TTS model to conduct speech reconstruction, and at the same time constrain the output sequence of the mel-spectrogram encoder to be close to that of the original phoneme encoder. In adaptation, we use untranscribed speech data for speech reconstruction and only fine-tune the TTS decoder. AdaSpeech 2 has two advantages: 1) Pluggable: our system can be easily applied to existing trained TTS models without re-training. 2) Effective: our system achieves on-par voice quality with the transcribed TTS adaptation (e.g., AdaSpeech) with the same amount of untranscribed data, and achieves better voice quality than previous untranscribed adaptation methods 1.
Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data

—Many problems in science, engineering, and business require making predictions based on very few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multidimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models ﬁt only one type of observations, such as scalars or images, instead of all available data modalities, which might have been acquired and simulated at great cost. We combine recent developments in deep learning to build more robust predictive models from multimodal data with a recent, novel technique to suppress the bias, and extend it to take into account multiple data modalities. First, an initial, simulation-trained, neural network surrogate model learns important correlations between different data modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to ﬁt the observations. Using fewer than 10 inertial conﬁnement fusion experiments for retraining, we demonstrate that this technique systematically improves simulation predictions while a simple output calibration makes predictions worse. We also offer extensive cross-validation with real and synthetic data to support our ﬁndings. The transfer learning method can be applied to other problems that require transferring knowledge from simulations to the domain of real observations. This paper opens up the path to model calibration using multiple data types, which have traditionally been ignored in predictive models.
NewsEdits: A Dataset of Revision Histories for News Articles (Technical Report: Data Processing)

News article revision histories have the potential to give us novel insights across varied fields of linguistics and social sciences. In this work, we present, to our knowledge, the first publicly available dataset of news article revision histories, or NewsEdits. Our dataset is multilingual; it contains 1,278,804 articles with 4,609,430 versions from over 22 English- and French-language newspaper sources based in three countries. Across version pairs, we count 10.9 million added sentences; 8.9 million changed sentences and 6.8 million removed sentences. Within the changed sentences, we derive 72 million atomic edits. NewsEdits is, to our knowledge, the largest corpus of revision histories of any domain.
Semantic Knowledge Discovery and Discussion Mining of Incel Online Community: Topic modeling

Online forums provide a unique opportunity for online users to share comments and exchange information on a particular topic. Understanding user behaviour is valuable to organizations and has applications for social and security strategies, for instance, identifying user opinions within a community or predicting future behaviour. Discovering the semantic aspects in Incel forums are the main goal of this research; we apply Natural language processing techniques based on topic modeling to latent topic discovery and opinion mining of users from a popular online Incel discussion forum. To prepare the input data for our study, we extracted the comments from Incels.co. The research experiments show that Artificial Intelligence (AI) based on NLP models can be effective for semantic and emotion knowledge discovery and retrieval of useful information from the Incel community. For example, we discovered semantic-related words that describe issues within a large volume of Incel comments, which is difficult with manual methods.
HASFC: A MANO-Compliant Framework for Availability Management of Service Chains

Most softwarized telco services are conveniently framed as service function chains (SFCs). Indeed, being structured as a combination of interconnected nodes, service chains may suffer from the single point of failure problem, meaning that an individual node malfunctioning could compromise the whole chain operation. To guarantee highly available (HA) levels, service providers are required to introduce redundancy strategies to achieve specific availability demands, where cost constraints have to be taken into account as well. Along these lines we propose HASFC, a framework designed to support, through a dedicated REST interface, the MANO infrastructure in deploying SFCs with an optimal availability/cost trade-off. Our framework is equipped with: an availability model builder aimed to construct probabilistic models of the SFC nodes in terms of failure/repair actions; and a chaining and selection module to compose the possible redundant SFCs, and extract the best candidates thereof. Beyond providing architectural details, we demonstrate the functionalities of HASFC through a use case that considers the IP Multimedia Subsystem, an SFC-like structure adopted to manage multimedia contents within 4G/5G networks.
Closing the window on WIMPy inelastic dark matter: journey to the end of the periodic table

Ningqiang Song, 2, 3, ∗ Serge Nagorny, 2, † and Aaron C. Vincent 2, 3, ‡ Department of Physics, Enigneering Physics and Astronomy, Queen’s University, Kingston, ON, K7L 3N6, Canada Arthur B. McDonald Canadian Astroparticle Physics Research Institute, Department of Physics, Engineering Physics and Astronomy, Queen’s University, Kingston ON K7L 3N6, Canada Perimeter Institute for Theoretical Physics, Waterloo ON N2L 2Y5, Canada (Dated: April 21, 2021)
Dynamic and stochastic models of the evolution of aftershocks.

This paper is devoted to the theory of aftershocks. The history of discovery of the Omori law is briefly described, the initial formulation of the law is given in the form of an algebraic formula describing the decrease in the frequency of aftershocks over time. An important generalization of the Oiori formula which is widely used in modern seismology is presented. The generalized law is also expressed by an algebraic formula, but it contains an additional parameter, which makes it possible to more flexibly approximate the observational data. The alternative approach to the theoretical description of aftershocks is to use the differential evolution models. To simulate the averaged dynamics of aftershocks, it is proposed the Verhulst differential equation, also known as the logistic equation. It is shown that the decrease in the frequency of aftershocks with time at the first stage of evolution occurs according to the Omori hyperbola, i.e. in accordance with the original wording of the law. The paper also proposes a stochastic generalization of the equation for the evolution of aftershocks. A random function of time is added to the right-hand side of the logistic equation to simulate noise that affects a dynamic system. As a result, a stochastic Langevin equation was obtained, which can be used to simulate fluctuations in the frequency of aftershocks.
GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing

In recent studies, neural message passing has proved to be an effective way to design graph neural networks (GNNs), which have achieved state-of-the-art performance in many graph-based tasks. However, current neural-message passing architectures typically need to perform an expensive recursive neighborhood expansion in multiple rounds and consequently suffer from a scalability issue. Moreover, most existing neural-message passing schemes are inflexible since they are restricted to fixed-hop neighborhoods and insensitive to the actual demands of different nodes. We circumvent these limitations by a novel feature-message passing framework, called Graph Multi-layer Perceptron (GMLP), which separates the neural update from the message passing. With such separation, GMLP significantly improves the scalability and efficiency by performing the message passing procedure in a pre-compute manner, and is flexible and adaptive in leveraging node feature messages over various levels of localities. We further derive novel variants of scalable GNNs under this framework to achieve the best of both worlds in terms of performance and efficiency. We conduct extensive evaluations on 11 benchmark datasets, including large-scale datasets like ogbn-products and an industrial dataset, demonstrating that GMLP achieves not only the state-of-art performance, but also high training scalability and efficiency. PVLDB Reference Format: Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, and Bin Cui. GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-message Passing. PVLDB, 14(1): XXX-XXX, 2021. doi:XX.XX/XXX.XX
Improving state-of-the-art in Detecting Student Engagement with Resnet and TCN Hybrid Network

Automatic detection of students' engagement in online learning settings is a key element to improve the quality of learning and to deliver personalized learning materials to them. Varying levels of engagement exhibited by students in an online classroom is an affective behavior that takes place over space and time. Therefore, we formulate detecting levels of students' engagement from videos as a spatio-temporal classification problem. In this paper, we present a novel end-to-end Residual Network (ResNet) and Temporal Convolutional Network (TCN) hybrid neural network architecture for students' engagement level detection in videos. The 2D ResNet extracts spatial features from consecutive video frames, and the TCN analyzes the temporal changes in video frames to detect the level of engagement. The spatial and temporal arms of the hybrid network are jointly trained on raw video frames of a large publicly available students' engagement detection dataset, DAiSEE. We compared our method with several competing students' engagement detection methods on this dataset. The ResNet+TCN architecture outperforms all other studied methods, improves the state-of-the-art engagement level detection accuracy, and sets a new baseline for future research.
Prepared for submission to JHEP KIAS-P 21013 T T̄-deformed Fermionic Theories Revisited

We revisit T T̄ deformations of d = 2 theories with fermions with a view toward the quantization. As a simplest illustration, we compute the deformed Dirac bracket for a Majorana doublet and confirm the known eigenvalue flows perturbatively. We mostly consider those T T̄ theories that can be reconstructed from string-like theories upon integrating out the worldsheet metric. After a quick overview of how this works when we add NSR-like or GSlike fermions, we obtain a known non-supersymmetric T T̄ deformation of a N = (1, 1) theory from the latter, based on the Noether energy-momentum. This worldsheet reconstruction implies that the latter is actually a supersymmetric subsector of a d = 3 GS-like model, implying hidden supercharges which we do construct explicitly. This brings us to ask about different T T̄ deformations, such as manifestly supersymmetric T T̄ and also more generally via the symmetric energy-momentum. We show that, for theories with fermions, generic such choices lead us to doubling of degrees of freedom, with potential unitarity issues. We show that the extra sector develops a divergent gap in the “small deformation” limit and decouples in the infrared, although it remains uncertain in what sense these can be considered as a deformation. ar X iv :2 10 4. 09 52 9v 1 [ he pth ] 1 9 A pr 2 02 1
Exploring the Effects on Student Learning and Engagement of COVID-19: an Innovative and Interdisciplinary Approach

Educators are tasked with developing new pedagogical, communication, and assessment strategies to ensure the delivery of quality instruction to students. The nexus that exists between student engagement and teaching methods has been a sustained and distinct focus for higher education institutions. ABSTRACT Educators are tasked with developing new pedagogical, communication, and assessment strategies to ensure the delivery of quality instruction to students. The nexus that exists between student engagement and teaching methods has been a sustained and distinct focus for higher education institutions. However, the emergence of the COVID-19 pandemic has forced institutions to adjust to distinct functionalities. With the shift to fully online models, there exists an opportunity to engage both faculty and students in innovative teaching–learning assessments and processes. Despite the myriad of challenges that COVID-19 introduces, our paper presents effective strategies, namely, the pedagogical community’s focus on opportunities for student ownership in the learning process, the chance to engage students using an innovative social media strategy discussion format, and the formulation of syllabi that incorporate interdisciplinary strategies. Models that actively engage both sides of the educational spectrum will be catalysts for reform. We propose an educational structure that fosters inclusivity in the context of five focused domains: preassessments, prospective socioeconomic gaps, pedagogy, psychological health of students and faculty, and pandemic assessment.
Experiencing literature on the e‐reader: the effects of reading narrative texts on screen

Background The digitalisation of literature is proliferating, and the increasing spread of digital reading devices and the availability of digital texts is likely to make books on screen a lasting phenomenon, but little attention has been paid to the consequences of digitalisation for the experience of narrative fiction. While on the one hand, reading literature on a digital reading device might trigger a superficial processing of the text, and problems regarding orientation within the narrative, the awareness of reading a literary text might, on the other hand, lead to more in‐depth and complex processing, independent of reading medium. This study examines whether the reading performance and the emotional and cognitive experiences of the reception of a literary text vary between reading a printed book or an e‐reader. Methods Using a between‐subjects experimental design, 207 participants read the beginning of a novel either in a printed book or on an e‐reader. They then completed a reading comprehension test and questionnaires about their cognitive and emotional experiences. Results Overall, the results do not suggest the clear superiority of either of the two reading media. Neither reading speed nor reading comprehension differed significantly between the two groups. Even though a broad range of reading experiences was measured, neither cognitive nor emotional reading experiences differed significantly between the groups. Conclusion An e‐reader does not affect either reading performance or cognitive and emotional experience of reading a narrative text, compared with a printed book.
Mutations in the SARS-CoV-2 spike protein modulate the virus affinity to the human ACE2 receptor, an in silico analysis

The increasing number of SARS-CoV-2 variants associated with highly transmissible phenotypes is a health-public concern in the current pandemic scenario. Herein, we developed a comprehensive in silico analysis of the changes occurring upon mutations in the viral spike. We focused on mutants located in the receptor-binding domain of the viral spike protein and analyzed whether these mutants modulate the interaction with the human host receptor angiotensin-converting enzyme II (ACE2). Thirty-two highly prevalent mutants were retrieved from the GISAID database, and their structural models were built using the SWISS-Model server. The stabilization effect for each mutation was assessed by the DUET and DeepDGG software. By applying molecular docking using both Z-Dock and Haddock software we found that multiple mutations, including A475V, V455E, V445L, and V445I, resulted in the higher binding free energy as compared to the wild type (WT) spike protein, thus had a destabilizing effect on the binding to ACE2. On the other hand, several mutants, including the most prevalent N501Y and B.1. 1.7 variants, as well as the K444R, L455F, Q493R, and Y505W variants exhibited lower binding free energy as compared to the WT spike. These mutants showed an increased number of electrostatic interactions with ACE2 than the WT spike protein, and they changed the interaction pattern of the neighboring residues. Together, the results presented in this study contribute to a better understanding of the changes in the interaction between SARS-CoV-2 and the human host ACE2 receptor associated with point mutations in the viral spike protein.
Patient Perspectives on the Benefits and Risks of Percutaneous Coronary Interventions: A Qualitative Study

Background Growing evidence for coronary stents in patients with stable coronary artery disease (CAD) suggests that the benefits of stents are uncertain. The goal of this study was to assess patients’ informational needs and how patients react to information about the uncertain benefit of stents to CAD patients. Methods Semi-structured qualitative interviews (N=20) were conducted with patients with stable CAD who received a recent stent. Data were coded and analyzed using a mixed inductive-deductive approach. Results Some patients mistakenly believed that the purpose of their stent was to prevent a future heart attack, and few were previously aware of the uncertain benefit. Nearly all patients perceived positive outcomes from their procedure, even if their symptoms persisted. Some patients had difficulty accepting evidence that stents may not reduce the risk of heart attack or reliably improve symptoms. Nonetheless, patients still expressed a desire to receive new information about the uncertain benefits of stents and wanted to have received this information early in their care. Conclusion Many patients with stable CAD do not understand the intended benefit of coronary stents and want to be informed of the evidence of uncertain benefit of coronary stents, even if this would not change their decision. Improved communication and patient education tools are needed to better inform patients. An intervention providing patients with this information early has the potential to solve these problems.
Validation of red cell distribution width as a COVID-19 severity screening tool

Aim: The aim of this study is the predictive validation of red cell distribution width (RDW) in COVID-19 patients. Method: In total, 331 COVID-19 patients were classified as ‘severe’ and ‘nonsevere’ groups based on the WHO standard criteria. The levels of RDW standard deviation (SD) were evaluated as both continuous and categorical variables. Multivariate statistical analyses were used. Results: RDW-SD ≤43 and ≤47 fl thresholds showed high specificity (90.1–91.4%) for diagnosing nonsevere illness and no risk of death. RDW-SD >47 indicated severe illness and a high mortality risk while 43<RDW-SD≤47 indicated severe illness with low risk of death. Conclusion: RDW-SD levels may be a potent independent predictor of the infection severity and mortality probability in COVID-19 patients.