Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-grained Geolocation Prediction of Tweets with Human Machine Collaboration

Twitter is a useful resource to analyze peoples' opinions on various topics. Often these topics are correlated or associated with locations from where these Tweet posts are made. For example, restaurant owners may need to know where their target customers eat with respect to the sentiment of the posts made related to food, policy planners may need to analyze citizens' opinion on relevant issues such as crime, safety, congestion, etc. with respect to specific parts of the city, or county or state. As promising as this is, less than $1\%$ of the crawled Tweet posts come with geolocation tags. That makes accurate prediction of Tweet posts for the non geo-tagged tweets very critical to analyze data in various domains. In this research, we utilized millions of Twitter posts and end-users domain expertise to build a set of deep neural network models using natural language processing (NLP) techniques, that predicts the geolocation of non geo-tagged Tweet posts at various level of granularities such as neighborhood, zipcode, and longitude with latitudes. With multiple neural architecture experiments, and a collaborative human-machine workflow design, our ongoing work on geolocation detection shows promising results that empower end-users to correlate relationship between variables of choice with the location information.
Multitask Learning for Citation Purpose Classification

We present our entry into the 2021 3C Shared Task Citation Context Classification based on Purpose competition. The goal of the competition is to classify a citation in a scientific article based on its purpose. This task is important because it could potentially lead to more comprehensive ways of summarizing the purpose and uses of scientific articles, but it is also difficult, mainly due to the limited amount of available training data in which the purposes of each citation have been hand-labeled, along with the subjectivity of these labels. Our entry in the competition is a multi-task model that combines multiple modules designed to handle the problem from different perspectives, including hand-generated linguistic features, TF-IDF features, and an LSTM-with-attention model. We also provide an ablation study and feature analysis whose insights could lead to future work.
Partially Fake it Till you Make It: Mixing Real and Fake Thermal Images for Improved Object Detection

In this paper we propose a novel data augmentation approach for visual content domains that have scarce training datasets, compositing synthetic 3D objects within real scenes. We show the performance of the proposed system in the context of object detection in thermal videos, a domain where i) training datasets are very limited compared to visible spectrum datasets and ii) creating full realistic synthetic scenes is extremely cumbersome and expensive due to the difficulty in modeling the thermal properties of the materials of the scene. We compare different augmentation strategies, including state of the art approaches obtained through RL techniques, the injection of simulated data and the employment of a generative model, and study how to best combine our proposed augmentation with these other techniques. Experimental results demonstrate the effectiveness of our approach, and our single-modality detector achieves state-of-the-art results on the FLIR ADAS dataset.
Shape registration in the time of transformers

In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds. The proposed approach is data-driven and adopts for the first time the transformer architecture in the registration task. Our method is general and applies to different settings. Given a fixed template with some desired properties (e.g. skinning weights or other animation cues), we can register raw acquired data to it, thereby transferring all the template properties to the input geometry. Alternatively, given a pair of shapes, our method can register the first onto the second (or vice-versa), obtaining a high-quality dense correspondence between the two. In both contexts, the quality of our results enables us to target real applications such as texture transfer and shape interpolation. Furthermore, we also show that including an estimation of the underlying density of the surface eases the learning process. By exploiting the potential of this architecture, we can train our model requiring only a sparse set of ground truth correspondences ($10\sim20\%$ of the total points). The proposed model and the analysis that we perform pave the way for future exploration of transformer-based architectures for registration and matching applications. Qualitative and quantitative evaluations demonstrate that our pipeline outperforms state-of-the-art methods for deformable and unordered 3D data registration on different datasets and scenarios.
Searching For Transiting Planets Around Halo Stars. ii. Constraining the Occurrence Rate of Hot Jupiters

Jovian planet formation has been shown to be strongly correlated with host-star metallicity, which is thought to be a proxy for disk solids. Observationally, previous works have indicated that Jovian planets preferentially form around stars with solar and supersolar metallicities. Given these findings, it is challenging to form planets within metal-poor environments, particularly for hot Jupiters that are thought to form via metallicity-dependent core accretion. Although previous studies have conducted planet searches for hot Jupiters around metal-poor stars, they have been limited due to small sample sizes, which are a result of a lack of high-quality data making hot-Jupiter occurrence within the metal-poor regime difficult to constrain until now. We use a large sample of halo stars observed by TESS to constrain the upper limit of hot-Jupiter occurrence within the metal-poor regime (−2.0 ≤ [Fe/H] ≤ −0.6). Placing the most stringent upper limit on hot-Jupiter occurrence, we find the mean 1σ upper limit to be 0.18% for radii 0.8–2 R Jupiter and periods 0.5–10 days. This result is consistent with previous predictions indicating that there exists a certain metallicity below which no planets can form.
Tensor-based framework for training flexible neural networks

Activation functions (AFs) are an important part of the design of neural networks (NNs), and their choice plays a predominant role in the performance of a NN. In this work, we are particularly interested in the estimation of flexible activation functions using tensor-based solutions, where the AFs are expressed as a weighted sum of predefined basis functions. To do so, we propose a new learning algorithm which solves a constrained coupled matrix-tensor factorization (CMTF) problem. This technique fuses the first and zeroth order information of the NN, where the first-order information is contained in a Jacobian tensor, following a constrained canonical polyadic decomposition (CPD). The proposed algorithm can handle different decomposition bases. The goal of this method is to compress large pretrained NN models, by replacing subnetworks, {\em i.e.,} one or multiple layers of the original network, by a new flexible layer. The approach is applied to a pretrained convolutional neural network (CNN) used for character classification.
Game theory and scholarly publishing: premises for an agreement around open access

Actors in research and scientific publishing are gradually joining the Open-Access (OA) movement, which is gaining momentum to become nowadays at the heart of scientific policies in high-income countries. The rise of OA generates profound changes in the chain of production and dissemination of knowledge. Free access to peer-reviewed research methods and results has contributed to the dynamics of science observed in recent years. The modes of publication and access have also evolved; the classic model, based on journal subscriptions is gradually giving way to new economic models that have appeared with the arrival of OA. The objective of this article is twofold. First, propose a model for the publishing market based on the literature as well as on changes in open science policies. Second, analyze publishing strategies of publishers and institutions. To do so, we relied on game theory in economics. Results show that in the short term, the publisher's equilibrium strategy is to adopt a hybridpublishing model, while the institutions' equilibrium strategy is to publish in OA. This equilibrium is not stable and that in the medium/long term, the two players will converge on an OA publishing strategy. The analysis of the equilibrium in mixed-strategies confirms this result.
Intergenerational Risk Sharing in a Collective Defined-Contribution Pension System: A Simulation Study with Bayesian Optimization

Pension reform is a crucial societal problem in many countries, and traditional pension schemes, such as Pay-As-You-Go and Defined-Benefit schemes, are being replaced by more sustainable ones. One challenge for a public pension system is the management of a systematic risk that affects all individuals in one generation (e.g., that caused by a worse economic situation). Such a risk cannot be diversified within one generation, but may be reduced by sharing with other (younger and/or older) generations, i.e., by intergenerational risk sharing (IRS). In this work, we investigate IRS in a Collective Defined-Contribution (CDC) pension system. We consider a CDC pension model with overlapping multiple generations, in which a funding-ratio-liked declaration rate is used as a means of IRS. We perform an extensive simulation study to investigate the mechanism of IRS. One of our main findings is that the IRS works particularly effectively for protecting pension participants in the worst scenarios of a tough financial market. Apart from these economic contributions, we make a simulation-methodological contribution for pension studies by employing Bayesian optimization, a modern machine learning approach to black-box optimization, in systematically searching for optimal parameters in our pension model. *Institute of Insurance Science, Ulm University, Germany, an.chen@uni-ulm.de. †Data Science Department, EURECOM, France, motonobu.kanagawa@eurecom.fr. ‡Institute of Insurance Science, Ulm University, Germany. (Corresponding: zhang.fangyuan@uniulm.de) 1 ar X iv :2 10 6. 13 64 4v 1 [ ec on . G N ] 2 5 Ju n 20 21
Clustering in massive neutrino cosmologies via Eulerian Perturbation Theory

We introduce an Eulerian Perturbation Theory to study the clustering of tracers for cosmologies in the presence of massive neutrinos. Our approach is based on mapping recently-obtained Lagrangian Perturbation Theory results to the Eulerian framework. We add Effective Field Theory counterterms, IR-resummations and a biasing scheme to compute the one-loop redshift-space power spectrum. To assess our predictions, we compare the power spectrum multipoles against synthetic halo catalogues from the QUIJOTE simulations, finding excellent agreement on scales k ≲ 0.25 h Mpc-1. One can obtain the same fitting accuracy using higher wave-numbers, but then the theory fails to give a correct estimation of the linear bias parameter. We further discuss the implications for the tree-level bispectrum. Finally, calculating loop corrections is computationally costly, hence we derive an accurate approximation wherein we retain only the main features of the kernels, as produced by changes to the growth rate. As a result, we show how FFTLog methods can be used to further accelerate the loop computations with these reduced kernels.
CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning

The ability to perform causal and counterfactual reasoning are central properties of human intelligence. Decision-making systems that can perform these types of reasoning have the potential to be more generalizable and interpretable. Simulations have helped advance the state-of-the-art in this domain, by providing the ability to systematically vary parameters (e.g., confounders) and generate examples of the outcomes in the case of counterfactual scenarios. However, simulating complex temporal causal events in multi-agent scenarios, such as those that exist in driving and vehicle navigation, is challenging. To help address this, we present a high-fidelity simulation environment that is designed for developing algorithms for causal discovery and counterfactual reasoning in the safety-critical context. A core component of our work is to introduce \textit{agency}, such that it is simple to define and create complex scenarios using high-level definitions. The vehicles then operate with agency to complete these objectives, meaning low-level behaviors need only be controlled if necessary. We perform experiments with three state-of-the-art methods to create baselines and highlight the affordances of this environment. Finally, we highlight challenges and opportunities for future work.
Secure communication over generalised quantum multiple access channels

We investigate the security of generalized quantum multiple-access channels. We provide the formula for the achievable rate region of secure communication in the scenario of two senders and a single receiver. We explicitly specify a protocol for secure communication in this scenario, which employs superdense coding. The protocol is based on the distribution of a tripartite GHZ state. It allows for both symmetric and asymmetric key distribution whereby one of the senders can have twice the capacity of the other sender. We prove the security of the protocol against general quantum attacks, analyze different strategies of the eavesdropper and compute the key rate for a range of noisy quantum channels.
Finite elements for divdiv-conforming symmetric tensors in arbitrary dimension

Several div-conforming and divdiv-conforming finite elements for symmetric tensors on simplexes in arbitrary dimension are constructed in this work. The shape function space is first split as the trace space and the bubble space. The later is further decomposed into the null space of the differential operator and its orthogonal complement. Instead of characterization of these subspaces of the shape function space, characterization of the duals spaces are provided. Vector div-conforming finite elements are firstly constructed as an introductory example. Then new symmetric div-conforming finite elements are constructed. The dual subspaces are then used as build blocks to construct divdiv conforming finite elements.
CEG_A_285573 19..25

1Department of Medicine, BronxCare Health System, Affiliated with Icahn School of Medicine at Mount Sinai, Bronx, NY 10457, USA; 2Division of Gastroenterology, BronxCare Health System, Affiliated with Icahn School of Medicine at Mount Sinai, Bronx, NY 10457, USA Background: Colonoscopy, although a low-risk procedure, is not without associated adverse events. The rates of major adverse events such as perforation and bleeding after a colonoscopy are well reported. The rates of minor incidents following a colonoscopy, however, are less well examined. Recently the Centers for Medicare and Medicaid Services (CMS) started public reporting on the quality of outpatient endoscopy facilities by using a measure of risk-standardized rates of unplanned hospital visits within 7 days of colonoscopy. Aim: We intended to record and present the characteristics of our patient population who had an unplanned hospital visit within 7 days after undergoing colonoscopy in an outpatient setting. Methods: This is a retrospective single-center observational study. During the study period of July 2018 to December 2019, we reviewed charts of all patients who returned to the emergency room within a week of undergoing an outpatient colonoscopy. Patient demographics, clinical data and details of colonoscopy were collected and analyzed. Results: Of the 5344 outpatient colonoscopies performed, our post-colonoscopy emergency room visit rate was 1.05% (n=56). The mean age of the participants was 58 years and 55% were male; 32% of our patients reported gastrointestinal symptoms such as abdominal pain or gastrointestinal bleeding. Patients with gastrointestinal symptoms had a higher rate of polypectomies performed (36.4% vs 11.8%, P = 0.04) and reported higher illicit drug use (31.9% vs 5.9%, P = 0.02) compared with those with non-gastrointestinal complaints. After colonoscopy, 41% of the patients reported reasons for emergency room visits that were entirely unrelated to the procedure. Conclusion: Our study highlights that unplanned visits within 7 days of colonoscopy are not necessarily related to the procedure, and those that are, tend to be due to unavoidable patient factors. Hence the CMS measure may not be an accurate determinant of the quality of procedure or facility care delivered.
Supplemental File to “Temporal Modulation Network for Controllable Space-Time Video Super-Resolution”

Here, we illustrate the detailed network architecture of our proposed TMNet in Figure 1. We first extract the corresponding initial features F = {F 2i−1}i=1 via five residual blocks. Each residual block contains a sequence of “Conv-ReLU-Conv” operations with a skip connection. The Controllable Feature Interpolation (CFI) is performed by the Pyramid, Cascading and Deformable (PCD) module [9] modulated by our proposed Temporal Modulation Block (TMB), which is illustrated in Figure 2 of our main paper. The detailed structure of our TMB block is shown in 2 (right). The proposed Locallytemporal Feature Comparison (LFC) module is presented in Figure 2 (left). The BDConvLSTM part is directly implemented by employing the Bi-directional Deformable ConvLSTM network in [11]. The Upsampling part contains operations of two “Convolutions (Conv), Pixel-Shuffle, and LeakyReLU”, and one “Conv-LeakyReLU-Conv”.
A sociological perspective on the phenomenon of solo-living in Eastern Europe: An attempt at conceptualization

This article examines the phenomenon of solitary living as a consequence of the processes of individualization that are characteristic of the entire modern world. The urgency of this problem arises from the increase in the number of individuals who prefer to live alone. Although this trend is typical for Eastern Europe, as it is for the rest of the world, it is not widely considered in scientific circles. Whilst analyzing scientific literature in different spheres (philosophy, economics, psychology, sociology) and statistical data on solitary living, the author has come to the conclusion that to describe the phenomenon in a sociological vein, it is most appropriate to consider solitary living as a “solo-living” lifestyle. The article also examines the main studies of solitary living in Eastern Europe, and through such work, it was concluded that there is not enough research: current research mainly considers single living among the older generation, despite the fact that the number of young people who prefer this lifestyle continues to grow. In the course of the study, the author was faced with the problem of layering related concepts: solos, singles, solo-living, single-person households, and others. To avoid this problem, the author distinguishes between these concepts and makes an attempt to conceptualize the concept of “solo-living”, offers the author’s definition, and outlines further research prospects.
Fractional order PID controller adaptation for PMSM drive using hybrid grey wolf optimization

Received Feb 10, 2021 Revised Mar 16, 2021 Accepted Apr 14, 2021 In this paper, the closed loop speed controller parameters are optimized for the permanent magnet synchronous motor (PMSM) drive on the basis of the indirect field-oriented control (IFOC) technique. In this derive system under study, the speed and current controllers are implemented using the fractional order proportional, integral, and derivative (FOPID) controlling technique. FOPID is considered as efficient techniques for ripple minimization. The hybrid grey wolf optimizer (HGWO) is applied to obtain the optimal controllers in case of implementing conventional PID as well as FOPID controllers in the derive system. The optimal controller parameters tend to enhance the drive response as ripple content in speed and current, either during steady state time or transient time. The drive system is modeled and tested under various operating condition of load torque and speed. Finally, the performance for PID and FOPID are evaluated and compared within MATLAB/Simulink environment. The results attain the efficacy of the operating performance with the FOPID controller. The result shows a fast response and reduction of ripples in the torque and the current.
Mismatch Repair: From Preserving Genome Stability to Enabling Mutation Studies in Real-Time Single Cells

Mismatch Repair (MMR) is an important and conserved keeper of the maintenance of genetic information. Miroslav Radman’s contributions to the field of MMR are multiple and tremendous. One of the most notable was to provide, along with Bob Wagner and Matthew Meselson, the first direct evidence for the existence of the methyl-directed MMR. The purpose of this review is to outline several aspects and biological implications of MMR that his work has helped unveil, including the role of MMR during replication and recombination editing, and the current understanding of its mechanism. The review also summarizes recent discoveries related to the visualization of MMR components and discusses how it has helped shape our understanding of the coupling of mismatch recognition to replication. Finally, the author explains how visualization of MMR components has paved the way to the study of spontaneous mutations in living cells in real time.
Systematic Scoring of Tubular Injury Patterns Reveals Interplay between Distinct Tubular and Glomerular Lesions in ANCA-Associated Glomerulonephritis

Background: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a small vessel vasculitis, most frequently presenting as microscopic polyangiitis (MPA) or granulomatosis with polyangiitis (GPA). Acute tubular injury with the presence of tubulitis was previously reported to be of prognostic value in ANCA glomerulonephritis (GN). In particular, distinct tubular injury lesions were associated with the deterioration of kidney function at AAV disease onset, as well as renal resistance to treatment, and higher risk of progression to composite outcome in patients with AAV. To expand our knowledge regarding distinct tubular lesions in AAV, we aimed to describe acute tubular injury patterns in association with glomerular lesions in ANCA GN by systematic histological scoring. Methods: A total number of 48 renal biopsies with confirmed renal involvement of AAV admitted to the University Medical Center Göttingen from 2015 to 2020 were retrospectively examined. By systematic scoring of tubular injury lesions, the association between clinical parameters, laboratory markers, and histopathological findings was explored. Results: We have shown that cellular casts in renal biopsies were frequently observed in the majority of cases with ANCA GN. Furthermore, we showed that tubular epithelial simplification with dilatation correlated with MPA and MPO subtypes, C3c hypocomplementemia, severe renal involvement, and uACR. Red blood cell (RBC) casts were associated with increased levels of C-reactive protein (CRP), leukocyturia, and hematuria. Finally, we found that hyaline casts were associated with an increased fraction of glomeruli with global glomerular sclerosis. Conclusions: Acute tubular injury patterns were correlated with active ANCA GN, whereas tubular injury lesions reflecting the later stages of kidney disease correlated with chronic glomerular lesions. These results suggest an interplay between different renal compartments.