Miracle-dz/UniFAR-FTUs huggingface dataset:
Exponentially Many Hypergraph Colourings

The Lovasz Local Lemma is a powerful probabilistic technique for proving the existence of combinatorial objects. It is especially useful for colouring graphs and hypergraphs with bounded maximum degree. This paper presents a general theorem for colouring hypergraphs that in many instances matches or slightly improves upon the bounds obtained using the Lovasz Local Lemma. Moreover, the theorem shows that there are exponentially many colourings. The elementary and self-contained proof is inspired by a recent result for nonrepetitive colourings by Rosenfeld [arXiv:2006.09094]. We apply our general theorem in the setting of proper hypergraph colouring, proper graph colouring, independent transversals, star colouring, nonrepetitive colouring, frugal colouring, Ramsey number lower bounds, and for $k$-SAT.
Attribute-aware Diversification for Sequential Recommendations

Users prefer diverse recommendations over homogeneous ones. However, most previous work on Sequential Recommenders does not consider diversity, and strives for maximum accuracy, resulting in homogeneous recommendations. In this paper, we consider both accuracy and diversity by presenting an Attribute-aware Diversifying Sequential Recommender (ADSR). Specifically, ADSR utilizes available attribute information when modeling a user’s sequential behavior to simultaneously learn the user’s most likely item to interact with, and their preference of attributes. Then, ADSR diversifies the recommended items based on the predicted preference for certain attributes. Experiments on two benchmark datasets demonstrate that ADSR can effectively provide diverse recommendations while maintaining accuracy.
Generating Visually Aligned Sound From Videos

We focus on the task of generating sound from natural videos, and the sound should be both temporally and content-wise aligned with visual signals. This task is extremely challenging because some sounds generated outside a camera can not be inferred from video content. The model may be forced to learn an incorrect mapping between visual content and these irrelevant sounds. To address this challenge, we propose a framework named RegNet. In this framework, we first extract appearance and motion features from video frames to better distinguish the object that emits sound from complex background information. We then introduce an innovative audio forwarding regularizer that directly considers the real sound as input and outputs bottlenecked sound features. Using both visual and bottlenecked sound features for sound prediction during training provides stronger supervision for the sound prediction. The audio forwarding regularizer can control the irrelevant sound component and thus prevent the model from learning an incorrect mapping between video frames and sound emitted by the object that is out of the screen. During testing, the audio forwarding regularizer is removed to ensure that RegNet can produce purely aligned sound only from visual features. Extensive evaluations based on Amazon Mechanical Turk demonstrate that our method significantly improves both temporal and content-wise alignment. Remarkably, our generated sound can fool the human with a 68.12% success rate. Code and pre-trained models are publicly available at https://github.com/PeihaoChen/regnet.
Speaker dependent articulatory-to-acoustic mapping using real-time MRI of the vocal tract

Articulatory-to-acoustic (forward) mapping is a technique to predict speech using various articulatory acquisition techniques (e.g. ultrasound tongue imaging, lip video). Real-time MRI (rtMRI) of the vocal tract has not been used before for this purpose. The advantage of MRI is that it has a high `relative' spatial resolution: it can capture not only lingual, labial and jaw motion, but also the velum and the pharyngeal region, which is typically not possible with other techniques. In the current paper, we train various DNNs (fully connected, convolutional and recurrent neural networks) for articulatory-to-speech conversion, using rtMRI as input, in a speaker-specific way. We use two male and two female speakers of the USC-TIMIT articulatory database, each of them uttering 460 sentences. We evaluate the results with objective (Normalized MSE and MCD) and subjective measures (perceptual test) and show that CNN-LSTM networks are preferred which take multiple images as input, and achieve MCD scores between 2.8-4.5 dB. In the experiments, we find that the predictions of speaker `m1' are significantly weaker than other speakers. We show that this is caused by the fact that 74% of the recordings of speaker `m1' are out of sync.
Hate begets Hate: A Temporal Study of Hate Speech.

With the ongoing debate on 'freedom of speech' vs. 'hate speech' there is an urgent need to carefully understand the consequences of the inevitable culmination of the two, i.e., 'freedom of hate speech' over time. An ideal scenario to understand this would be to observe the effects of hate speech in an (almost) unrestricted environment. Hence, we perform the first temporal analysis of hate speech on Gab.com, a social media site with very loose moderation policy. We first generate temporal snapshots of Gab from millions of posts and users. Using these temporal snapshots, we compute an activity vector based on DeGroot model to identify hateful users. The amount of hate speech in Gab is steadily increasing and the new users are becoming hateful at an increased and faster rate. Further, our analysis analysis reveals that the hate users are occupying the prominent positions in the Gab network. Also, the language used by the community as a whole seem to correlate more with that of the hateful users as compared to the non-hateful ones. We discuss how, many crucial design questions in CSCW open up from our work.
Bayesian model averaging for analysis of lattice field theory results

Statistical modeling is a key component in the extraction of physical results from lattice field theory calculations. Although the general models used are often strongly motivated by physics, their precise form is typically ill-determined, and many model variations can be plausibly considered for the same lattice data. Model averaging, which amounts to a probability-weighted average over all model variations, can incorporate systematic errors associated with model choice without being overly conservative. We discuss the framework of model averaging from the perspective of Bayesian statistics, and give useful formulas and approximations for the particular case of least-squares fitting, commonly used in modeling lattice results. In addition, we frame the common problem of data subset selection (e.g. choice of minimum and maximum time separation for fitting a two-point correlation function) as a model selection problem, and study model averaging as a straightforward alternative to manual selection of fit ranges. Numerical examples involving both mock and real lattice data are given.
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning

Despite the significant progress of deep reinforcement learning (RL) in solving sequential decision making problems, RL agents often overfit to training environments and struggle to adapt to new, unseen environments. This prevents robust applications of RL in real world situations, where system dynamics may deviate wildly from the training settings. In this work, our primary contribution is to propose an information theoretic regularization objective and an annealing-based optimization method to achieve better generalization ability in RL agents. We demonstrate the extreme generalization benefits of our approach in different domains ranging from maze navigation to robotic tasks; for the first time, we show that agents can generalize to test parameters more than 10 standard deviations away from the training parameter distribution. This work provides a principled way to improve generalization in RL by gradually removing information that is redundant for task-solving; it opens doors for the systematic study of generalization from training to extremely different testing settings, focusing on the established connections between information theory and machine learning.
Generalized Strichartz estimates for wave and Dirac equations in Aharonov–Bohm magnetic fields

We prove generalized Strichartz estimates for wave and massless Dirac equations in Aharonov-Bohm magnetic fields. Following a well established strategy to deal with scaling critical perturbations of dispersive PDEs, we make use of Hankel transform and rely on some precise estimates on Bessel functions. As a complementary result, we prove a local smoothing estimate for the Klein-Gordon equation in the same magnetic field.
DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement

Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality. Conventional time-frequency (TF) domain methods focus on predicting TF-masks or speech spectrum, via a naive convolution neural network (CNN) or recurrent neural network (RNN). Some recent studies use complex-valued spectrogram as a training target but train in a real-valued network, predicting the magnitude and phase component or real and imaginary part, respectively. Particularly, convolution recurrent network (CRN) integrates a convolutional encoder-decoder (CED) structure and long short-term memory (LSTM), which has been proven to be helpful for complex targets. In order to train the complex target more effectively, in this paper, we design a new network structure simulating the complex-valued operation, called Deep Complex Convolution Recurrent Network (DCCRN), where both CNN and RNN structures can handle complex-valued operation. The proposed DCCRN models are very competitive over other previous networks, either on objective or subjective metric. With only 3.7M parameters, our DCCRN models submitted to the Interspeech 2020 Deep Noise Suppression (DNS) challenge ranked first for the real-time-track and second for the non-real-time track in terms of Mean Opinion Score (MOS).
Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input multiple feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2×, which enables us to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. While our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.
A Simple Equivalent Circuit Approach for Anisotropic Frequency-Selective Surfaces and Metasurfaces

An equivalent circuit (EC) model for the frequency-selective surfaces (FSSs) comprising anisotropic elements is presented. The periodic surface is initially simulated with an arbitrary azimuthal incidence angle, and its surface impedance matrix is derived. The impedance matrix is subsequently rotated by an angle <inline-formula> <tex-math notation="LaTeX">$\varphi ^{rot}$ </tex-math></inline-formula> on the crystal axes <inline-formula> <tex-math notation="LaTeX">$\chi _{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\chi _{2}$ </tex-math></inline-formula>, thus nullifying its extra diagonal terms. The rotation angle <inline-formula> <tex-math notation="LaTeX">$\varphi ^{rot}$ </tex-math></inline-formula> is derived according to the spectral theorem by using the terms of the matrix initially extracted. The diagonal terms of the rotated matrix, that is, the impedances <inline-formula> <tex-math notation="LaTeX">$Z_{\chi _{1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Z_{\chi _{2}}$ </tex-math></inline-formula>, are finally matched with the simple <italic>LC</italic> networks. The circuit model representation of the anisotropic element can be used to analyze the anisotropic FSSs rotated by a generic azimuth angle. The methodology provides a compact description of the generic FSS elements with only five parameters: the lumped parameters of the <italic>LC</italic> network <inline-formula> <tex-math notation="LaTeX">$L_{\chi _{1}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$C_{\chi _{1}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$L_{\chi _{2}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$C_{\chi _{2}}$ </tex-math></inline-formula> and the rotation angle <inline-formula> <tex-math notation="LaTeX">$\varphi ^{rot}$ </tex-math></inline-formula>. The circuit model can consider the presence of the dielectric substrates close to the FSS or a variation in the FSS periodicity without additional computational efforts. The EC model is finally applied to the design of the two transmitting polarization converters based on the anisotropic metasurfaces.
Detection of COVID-19 from Chest X-rays using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0 Software with Open Source Convolutional Neural Networks

The COVID-19 pandemic has been having a severe and catastrophic effect on humankind and is being considered the most crucial health calamity of the century. One of the best methods of detecting COVID-19 is from radiological images, namely X-rays and Computed Tomography or CT scan images. Many companies and educational organizations have come together during this crisis and created various Deep Learning models for the effective diagnosis of COVID-19 from chest radiography images. For example, the University of Waterloo, along with Darwin AI, has designed its Deep Learning model COVID-Net and created a dataset called COVIDx, consisting of 13,975 images. In this study, COGNEXs Deep Learning Software-VisionPro Deep Learning is used to classify these Chest X-rays from the COVIDx dataset. The results are compared with the results of COVID-Net and various other state of the art Deep Learning models from the open-source community. Deep Learning tools are often referred to as black boxes because humans cannot interpret how or why a model is classifying an image into a particular class. This problem is addressed by testing VisionPro Deep Learning with two settings, firstly by selecting the entire image, that is, selecting the entire image as the Region of Interest-ROI, and secondly by segmenting the lungs in the first step, and then doing the classification step on the segmented lungs only, instead of using the entire image. VisionPro Deep Learning results-on the entire image as the ROI it achieves an overall F-score of 94.0 percent, and on the segmented lungs, it gets an F-score of 95.3 percent, which is at par or better than COVID-Net and other state of the art open-source Deep Learning models.
Extracellular cellulolytic enzyme profiles of five lignicolous mangrove fungi

Five lignicolous mangrove fungi (Hypoxlon oceanicum, Julella avicenniae, Lignincola laevis, Savoryella lignicola and Trematosphaeria mangrovei) produced extracellular endoglucanase, cellobiohydrolase and β-glucosidase when cultivated under static conditions in a defined liquid growth medium. Agitation of cultures completely repressed enzyme production. Cellulase production by H. oceanicum was 2–5 fold higher compared to other strains. Increasing salinity generally resulted in reduced enzyme production although responses varied, but J. avicenniae appeared relatively unaffected by salinity within the range 0–3.4%.
Multi-Scale Deep Compressive Imaging

Recently, deep learning-based compressive imaging (DCI) has surpassed conventional compressive imaging in reconstruction quality and running speed. While multi-scale sampling has shown superior performance over single-scale, research in DCI has been limited to single-scale sampling. Despite training with single-scale images, DCI tends to favor low-frequency components similar to conventional multi-scale sampling, especially at low subrates. From this perspective, it would be easier for the network to learn multi-scale features with a multi-scale sampling architecture. In this work, we propose a multi-scale deep compressive imaging (MS-DCI) framework which jointly learns to decompose, sample, and reconstruct images at multi-scale. A three-phase end-to-end training scheme is introduced with an initial and two enhanced reconstruction phases to demonstrate the efficiency of multi-scale sampling and further improve the reconstruction performance. We analyze the decomposition methods (including pyramid, wavelet, and scale-space), sampling matrices, and measurements and show the empirical benefit of MS-DCI, which consistently outperforms both conventional and deep learning-based approaches.
Multilingual Translation with Extensible Multilingual Pretraining and Finetuning

Recent work demonstrates the potential of multilingual pretraining of creating one model that can be used for various tasks in different languages. Previous work in multilingual pretraining has demonstrated that machine translation systems can be created by finetuning on bitext. In this work, we show that multilingual translation models can be created through multilingual finetuning. Instead of finetuning on one direction, a pretrained model is finetuned on many directions at the same time. Compared to multilingual models trained from scratch, starting from pretrained models incorporates the benefits of large quantities of unlabeled monolingual data, which is particularly important for low resource languages where bitext is not available. We demonstrate that pretrained models can be extended to incorporate additional languages without loss of performance. We double the number of languages in mBART to support multilingual machine translation models of 50 languages. Finally, we create the ML50 benchmark, covering low, mid, and high resource languages, to facilitate reproducible research by standardizing training and evaluation data. On ML50, we demonstrate that multilingual finetuning improves on average 1 BLEU over the strongest baselines (being either multilingual from scratch or bilingual finetuning) while improving 9.3 BLEU on average over bilingual baselines from scratch.
Impact of SARS-CoV-2 pandemic on pulmonary hypertension out-patient clinics in Germany: a multi-centre study

Pulmonary hypertension is frequently underdiagnosed, and referral is delayed with subsequent impact on outcomes. During the SARS-CoV-2 pandemic, restrictions on daily life and changes in hospitals' daily routine care were introduced in Germany. This multi-centre study provides evidence for a negative influence of these restrictions on patient care in pulmonary hypertension expert referral centres.
E-PASS Scoring System May Be Useful for Prediction of Postoperative Complications in Super Elderly Colorectal Cancer Surgery Patients

Objectives: Japan is facing an aging society. Elderly individuals are generally more prone to comorbidities and have weaker immune defenses, with ominous prognostic implications if postoperative complications arise. The aim of this study was to explore scoring systems for predicting postoperative morbidity risk in super elderly patients (≥85 years old) after colorectal surgery for cancer. Methods: A population of elderly patients (n = 145) surgically treated for primary colorectal cancer within our department between April 2007 and December 2018 was examined retrospectively, assessing the capacities of various indices, such as Estimation of Physiologic Ability and Surgical Stress (E-PASS), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), prognostic nutritional index (PNI), and modified Glasgow Prognostic Score (mGPS), to predict postoperative complications. Results: NLR, PLR, and mGPS did not differ significantly in the presence or absence of complications, whereas PNI tended to be lower if complications developed. The E-PASS system showed no group-wise differences in preoperative risk score (PRS), but the surgical stress score (SSS) and the comprehensive risk score (CRS; a composite of PRS and SSS) was significantly higher in patients with complications. Based on the cutoff value calculated from the Receiver operating curve (ROC) for the E-PASS CRS (−0.0580), patients were stratified into low-scoring and high-scoring (HSG) groups. Although not significantly different, the overall survival in the HSG tended to be lower by comparison. Conclusions: The E-PASS scoring system may be a useful predictor of postoperative complications in super elderly patients requiring colorectal cancer surgery.
Craniofacial characteristics of Syrian adolescents with Class II division 1 malocclusion: a retrospective study

Background Malocclusion characteristics vary across different ethnic groups and populations. Limited data are available regarding the characteristics of Syrian adolescents with Class II division 1 (Class II-1) malocclusion, and the recent inflow of Syrian refugees and immigrants into Europe and many areas worldwide demonstrate the need for updated studies to discover the craniofacial characteristics of these new immigrants. Objectives The present compound cephalometric and tooth-size study sought to assess the dentofacial morphology, upper-airway dimensions, and tooth-size characteristics of Syrian adolescents with Class II-1 malocclusion and compare the results with established Syrian population norms. Materials and Methods The study sample consisted of 43 Syrian patients including 24 females and 19 males with Class II-1 malocclusion (age: 14.3 (±1.5) years, mean (±SD)). Cephalometric radiographs and orthodontic casts were analyzed using special orthodontic software (OnyxCeph3™) and a universal digital caliper, respectively. Statistics were calculated using the SPSS software. Results In Syrian adolescents with Class II-1 malocclusion, the position of the mandible relative to the nasion perpendicular (mean (95% confidence interval)) was −11.01 (−12.45, −9.57) mm. Facial axis angle showed a negative value: −6.25 (−7.65, −4.85) degrees. An obtuse nasolabial angle was observed: 104.05 (101.77, 106.33) degrees. The average width of the upper pharynx was 11.50 (10.53, 12.47) mm; however, there was no prevalence of an upper-pharyngeal width of 5 mm or less. The average value of the anterior tooth-size ratio was 80.69 (79.85, 81.53) percent. In total, 39.5% of the investigated subjects had anterior ratios outside two standard deviations from Bolton’s norm, while 25.6% of the investigated subjects had anterior ratios outside two standard deviations from Syrian population norm. Conclusions In this study, the inter-maxillary discrepancy of Class II-1 Syrian adolescents was a consequence of their hyperdivergent facial pattern. The observed small pharyngeal widths were not clinically significant, while the anterior tooth-size discrepancy might be clinically relevant.