Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-frame sequence generator of 4D human body motion

We examine the problem of generating temporally and spatially dense 4D human body motion. On the one hand generative modeling has been extensively studied as a per time-frame static ﬁtting problem for dense 3D models such as mesh representations, where the temporal aspect is left out of the generative model. On the other hand, temporal generative models exist for sparse human models such as marker-based capture representations, but have not to our knowledge been extended to dense 3D shapes. We propose to bridge this gap with a generative auto-encoder-based framework, which encodes morphology, global locomotion including translation and rotation, and multi-frame temporal motion as a single latent space vector. To assess its generalization and factorization abilities, we train our model on a subset of AMASS, leveraging the dense surface models it provides for an extensive set of motion captures. Our results validate the ability of the model to reconstruct 4D sequences of human locomotions within a low error bound, and the meaningfulness of latent space interpolation between latent vectors representing different multi-frame sequences and locomotion types. We also illustrate the beneﬁts of the approach for 4D human motion prediction of future frames from initial human locomotion frames, showing promising abilities of our model to learn realistic spatio-temporal features of human motion. We show that our model allows for data completion of both spatially and temporally sparse data.
Nodal-line semimetal HMTSF-TCNQ: Anomalous orbital diamagnetism and charge density wave

S. Ozaki, ∗ I. Tateishi, 2 H. Matsuura, M. Ogata, 3 and K. Hiraki 5 Department of Physics, University of Tokyo, Bunkyo, Tokyo 113-0033, Japan RIKEN Center for Emergent Matter Science, Wako, Saitama 351-0198, Japan Trans-scale Quantum Science Institute, University of Tokyo, Bunkyo, Tokyo 113-0033, Japan Fukushima Medical University, Fukushima 960-1295, Japan IMR, Tohoku University, Miyagi 980-8577, Japan (Dated: June 9, 2021)
Biocompatibility of 2D silicon nitride: interaction at the nano-bio interface

Determining potential abilities of nanostructures to induce toxicity to biological molecules is still a convoluted challenge in the realm of nanomedicine. Based on the unprecedented achievements of two-dimensional nanomaterials in nearly all areas of applied sciences particularly medicine, we carried out all-atom molecular dynamics simulations to assess the biologically important, yet unmapped issue of biocompatibility of 2D, hexagonal β-Si3N4 nanosheet via investigating its possible cross interactions with both human serum albumin (HSA) and p53 tumor suppressor. Examining the conventional MD indicators in presence and absence of the monolayer revealed that hexagonal Si3N4 nanosheet weakly binds to these two proteins without inducing any important, dramatic change to their secondary structures, revealing accordingly the biological compatibility of the monolayer in case it is released as therapeutics or carriers in vivo. This finding was also broadly supported by the related time-dependent behaviors of the protein-monolayer as well as the protein-water interaction energies.
An Intelligent Hybrid Model for Identity Document Classification

Digitization, i.e., the process of converting information into a digital format, may provide various opportunities (e.g., increase in productivity, disaster recovery, and environmentally friendly solutions) and challenges for businesses. In this context, one of the main challenges would be to accurately classify numerous scanned documents uploaded every day by customers as usual business processes. For example, processes in banking (e.g., applying for loans) or the Government Registry of BDM (Births, Deaths, and Marriages) applications may involve uploading several documents such as a driver's license and passport. There are not many studies available to address the challenge as an application of image classification. Although some studies are available which used various methods, a more accurate model is still required. The current study has proposed a robust fusion model to define the type of identity documents accurately. The proposed approach is based on two different methods in which images are classified based on their visual features and text features. A novel model based on statistics and regression has been proposed to calculate the confidence level for the feature-based classifier. A fuzzy-mean fusion model has been proposed to combine the classifier results based on their confidence score. The proposed approach has been implemented using Python and experimentally validated on synthetic and real-world datasets. The performance of the proposed model is evaluated using the Receiver Operating Characteristic (ROC) curve analysis.
Conditional Deep Inverse Rosenblatt Transports

We present a novel ofﬂine-online method to mitigate the computational burden of the characterization of conditional beliefs in statistical learning. In the ofﬂine phase, the proposed method learns the joint law of the belief random variables and the observational random variables in the tensor-train (TT) format. In the online phase, it utilizes the resulting order-preserving conditional transport map to issue real-time characterization of the conditional beliefs given new observed information. Compared with the state-of-the-art normalizing ﬂows techniques, the proposed method relies on function approximation and is equipped with thorough performance analysis. This also allows us to further extend the capability of transport maps in challenging problems with high-dimensional observations and high-dimensional belief variables. On the one hand, we present novel heuristics to reorder and/or reparametrize the variables to enhance the approximation power of TT. On the other, we integrate the TT-based transport maps and the parameter reordering/reparametrization into layered compositions to further improve the performance of the resulting transport maps. We demonstrate the efﬁciency of the proposed method on various statistical learning tasks in ordinary differential equations (ODEs) and partial differential equations (PDEs).
A likelihood-based sensitivity analysis for publication bias on the summary ROC in meta-analysis of diagnostic test accuracy

In meta-analysis of diagnostic test accuracy, the summary receiver operating characteristic (SROC) curve is a recommended method to summarize the diagnostic capacity of a medical test in the presence of study-specific cutoff values. The SROC curve can be estimated by bivariate modeling of pairs of sensitivity and specificity across multiple diagnostic studies, and the area under the SROC curve (SAUC) gives the aggregate estimate of diagnostic test accuracy. However, publication bias is a major threat to the validity of the estimates. To make inference of the impact of publication bias on the SROC curve or the SAUC, we propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis of Copas. In the proposed method, the SROC curve or the SAUC are estimated by maximizing the likelihood constrained by different values of the marginal probability of selective publication under different mechanisms of selective publication. A cutoff-dependent selection function is developed to model the selective publication mechanism via the t-type statistics or p-value of the linear combination of the logit-transformed sensitivity and specificity from the published studies. It allows us to model selective publication suggested by the funnel plots of sensitivity, specificity, or diagnostic odds ratio, which are often observed in practice. A real meta-analysis of diagnostic test accuracy is re-analyzed to illustrate the proposed method, and simulation studies are conducted to evaluate its performance.
Toward Social Role-Based Interruptibility Management

Pervasive and ubiquitous computing facilitates immediate access to information in the sense of always-on. Information, such as news, messages, or reminders, can significantly enhance our daily routines but are rendered useless or disturbing when not being aligned with our intrinsic interruptibility preferences. Attention management systems use machine learning to identify short-term opportune moments, so that information delivery leads to fewer interruptions. Humans’ intrinsic interruptibility preferences—established for and across social roles and life domains—would complement short-term attention and interruption management approaches. In this article, we present our comprehensive results toward social role-based attention and interruptibility management. Our approach combines on-device sensing and machine learning with theories from social science to form a personalized two-stage classification model. Finally, we discuss the challenges of the current and future AI-driven attention management systems concerning privacy, ethical issues, and future directions.
Electric-field noise in a high-temperature superconducting surface ion trap

Scaling up trapped-ion quantum computers requires new trap materials to be explored. Here, we present experiments with a surface ion trap made from the high-temperature superconductor YBCO, a promising material for future trap designs. We show that voltage noise from superconducting electrode leads is negligible within the sensitivity $S_V=9\times 10^{-20}\,\mathrm{V}^2\mathrm{Hz}^{-1}$ of our setup, and for lead dimensions typical for advanced trap designs. Furthermore, we investigate the frequency and temperature dependence of electric field noise above a YBCO surface. We find a $1/f$ spectral dependence of the noise and a non-trivial temperature dependence, with a plateau in the noise stretching over roughly $60\,\mathrm{K}$. The onset of the plateau coincides with the superconducting transition, indicating a connection between the dominant noise and the YBCO trap material. We exclude the YBCO bulk as origin of the noise and suggest further experiments to decide between the two remaining options explaining the observed temperature dependence: noise screening within the superconducting phase, or surface noise activated by the YBCO bulk through some unknown mechanism.
Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces

Decision making under uncertainty can be framed as a partially observable Markov decision process (POMDP). Finding exact solutions of POMDPs is generally computationally intractable, but the solution can be approximated by sampling-based approaches. These sampling-based POMDP solvers rely on multi-armed bandit (MAB) heuristics, which assume the outcomes of different actions to be uncorrelated. In some applications, like motion planning in continuous spaces, similar actions yield similar outcomes. In this paper, we utilize variants of MAB heuristics that make Lipschitz continuity assumptions on the outcomes of actions to improve the efficiency of sampling-based planning approaches. We demonstrate the effectiveness of this approach in the context of motion planning for automated driving.
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition

Deep neural networks have been shown as a class of useful tools for addressing signal recognition issues in recent years, especially for identifying the nonlinear feature structures of signals. However, this power of most deep learning techniques heavily relies on an abundant amount of training data, so the performance of classic neural nets decreases sharply when the number of training data samples is small or unseen data are presented in the testing phase. This calls for an advanced strategy, i.e., model-agnostic meta-learning (MAML), which is able to capture the invariant representation of the data samples or signals. In this paper, inspired by the special structure of the signal, i.e., real and imaginary parts consisted in practical time-series signals, we propose a Complex-valued Attentional MEta Learner (CAMEL) for the problem of few-shot signal recognition by leveraging attention and meta-learning in the complex domain. To the best of our knowledge, this is also the first complex-valued MAML that can find the first-order stationary points of general nonconvex problems with theoretical convergence guarantees. Extensive experiments results showcase the superiority of the proposed CAMEL compared with the state-of-the-art methods.
Discovery and origins of giant optical nebulae surrounding quasar PKS 0454−22

We report optical integral-field spectroscopy in the field of one of the most luminous quasars in the z < 1 Universe, PKS 0454−22, with the Multi-Unit Spectroscopic Explorer. These data enable the discovery of three large ionized nebulae emitting in [O II], Hβ , and [O III] with projected areas of 1720, 1520, and 130 pkpc2, which we refer to as N1, N2, and N3, respectively. N1 spatially and kinematically surrounds the quasar host and five nearby galaxies. The morphology and kinematics of N1 are most consistent with stripped interstellar medium resulting from ongoing interactions. Its ionization properties can be explained by quasar photoionization. N2 spatially and kinematically surrounds two galaxies which are at projected distances of d ≈ 90 pkpc and line-of-sight velocities of ∆v ≈+1410 km s−1 from the quasar. The morphology and kinematics of N2 are also consistent with stripped interstellar medium. However, its ionization state requires additional ionization sources beyond the quasar, likely from fast shocks as it moves through the hot halo associated with a galaxy over-density around the quasar. N3 is not coincident with any galaxies with secure redshifts, and may arise from a cool gas structure in the intragroup medium or a dwarf galaxy. These large ionized nebulae demonstrate that interactions can produce cool gas structures on halo scales, while also possibly facilitating quasar fueling. The growing availability of wide-area integral field spectroscopic data will continue to reveal the morphologies, kinematics, and conditions of the gas flows, which may fuel galaxy and black hole growth.
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future

In real-time forecasting in public health, data collection is a non-trivial and demanding task. Often after initially released, it undergoes several revisions later (maybe due to human or technical constraints) - as a result, it may take weeks until the data reaches to a stable value. This so-called 'backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. In this paper, we introduce the multi-variate backfill problem using COVID-19 as the motivating example. We construct a detailed dataset composed of relevant signals over the past year of the pandemic. We then systematically characterize several patterns in backfill dynamics and leverage our observations for formulating a novel problem and neural framework Back2Future that aims to refines a given model's predictions in real-time. Our extensive experiments demonstrate that our method refines the performance of top models for COVID-19 forecasting, in contrast to non-trivial baselines, yielding 18% improvement over baselines, enabling us obtain a new SOTA performance. In addition, we show that our model improves model evaluation too;hence policy-makers can better understand the true accuracy of forecasting models in real-time.
How to Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild

Successful active speaker detection requires a three-stage pipeline: (i) audio-visual encoding for all speakers in the clip, (ii) inter-speaker relation modeling between a reference speaker and the background speakers within each frame, and (iii) temporal modeling for the reference speaker. Each stage of this pipeline plays an important role for the final performance of the created architecture. Based on a series of controlled experiments, this work presents several practical guidelines for audio-visual active speaker detection. Correspondingly, we present a new architecture called ASDNet, which achieves a new state-of-the-art on the AVA-ActiveSpeaker dataset with a mAP of 93.5% outperforming the second best with a large margin of 4.7%. Our code and pretrained models are publicly available 1.
A Falta de Pan, Buenas Son Tortas: The Efficacy of Predicted UPOS Tags for Low Resource UD Parsing

We evaluate the efficacy of predicted UPOS tags as input features for dependency parsers in lower resource settings to evaluate how treebank size affects the impact tagging accuracy has on parsing performance. We do this for real low resource universal dependency treebanks, artificially low resource data with varying treebank sizes, and for very small treebanks with varying amounts of augmented data. We find that predicted UPOS tags are somewhat helpful for low resource treebanks, especially when fewer fully-annotated trees are available. We also find that this positive impact diminishes as the amount of data increases.
Time-series Imputation of Temporally-occluded Multiagent Trajectories

In multiagent environments, several decision-making individuals interact while adhering to the dynamics constraints imposed by the environment. These interactions, combined with the potential stochasticity of the agents' decision-making processes, make such systems complex and interesting to study from a dynamical perspective. Significant research has been conducted on learning models for forward-direction estimation of agent behaviors, for example, pedestrian predictions used for collision-avoidance in self-driving cars. However, in many settings, only sporadic observations of agents may be available in a given trajectory sequence. For instance, in football, subsets of players may come in and out of view of broadcast video footage, while unobserved players continue to interact off-screen. In this paper, we study the problem of multiagent time-series imputation, where available past and future observations of subsets of agents are used to estimate missing observations for other agents. Our approach, called the Graph Imputer, uses forward- and backward-information in combination with graph networks and variational autoencoders to enable learning of a distribution of imputed trajectories. We evaluate our approach on a dataset of football matches, using a projective camera module to train and evaluate our model for the off-screen player state estimation setting. We illustrate that our method outperforms several state-of-the-art approaches, including those hand-crafted for football.
A Perspective on Time towards Wireless 6G

With the advent of 5G technology, the notion of latency got a prominent role in wireless connectivity, serving as a proxy term for addressing the requirements for real-time communication. As wireless systems evolve towards 6G, the ambition to immerse the digital into the physical reality will increase. Besides making the real-time requirements more stringent, this immersion will bring the notions of time, simultaneity, presence, and causality to a new level of complexity. A growing body of research points out that latency is insufficient to parameterize all real-time requirements. Notably, one such requirement that received a significant attention is information freshness, defined through the Age of Information (AoI) and its derivatives. The objective of this article is to investigate the general notion of timing in wireless communication systems and networks and its relation to effective information generation, processing, transmission, and reconstruction at the senders and receivers. We establish a general statistical framework of timing requirements in wireless communication systems, which subsumes both latency and AoI. The framework is made by associating a timing component with the two basic statistical operations, decision and estimation. We first use the framework to present a representative sample of the existing works that deal with timing in wireless communication. Next, it is shown how the framework can be used with different communication models of increasing complexity, starting from the basic Shannon one-way communication model and arriving to communication models for consensus, distributed learning, and inference. Overall, this paper fills an important gap in the literature by providing a systematic treatment of various timing measures in wireless communication and sets the basis for design and optimization for the next-generation real-time systems.
Surveillance of COVID-19 pandemic using social media: a reddit study in North Carolina

Coronavirus disease (COVID-19) pandemic has changed various aspects of people's lives and behaviors. At this stage, there are no other ways to control the natural progression of the disease than adopting mitigation strategies such as wearing masks, watching distance, and washing hands. Moreover, at this time of social distancing, social media plays a key role in connecting people and providing a platform for expressing their feelings. In this study, we tap into social media to surveil the uptake of mitigation and detection strategies, and capture issues and concerns about the pandemic. In particular, we explore the research question, "how much can be learned regarding the public uptake of mitigation strategies and concerns about COVID-19 pandemic by using natural language processing on Reddit posts? " After extracting COVID-related posts from the four largest subreddit communities of North Carolina over six months, we performed NLP-based preprocessing to clean the noisy data. We employed a custom Named-entity Recognition (NER) system and a Latent Dirichlet Allocation (LDA) method for topic modeling on a Reddit corpus. We observed that mask, flu, and testing are the most prevalent named-entities for "Personal Protective Equipment", "symptoms", and "testing" categories, respectively. We also observed that the most discussed topics are related to testing, masks, and employment. The mitigation measures are the most prevalent theme of discussion across all subreddits.
Generalized framework of weak-value amplification in path interference of polarized light for the enhancement of all possible polarization anisotropy effects

Using the profound interferometric philosophy of weak value amplification, we propose a simple, general and a robust polarization method for the amplification and quantification of small magnitudes of all possible polarization anisotropy effects in a single experimental embodiment.The approach is experimentally realized by introducing a weak coupling between the polarization degree of freedom of light and the path degree of freedom in a Mach-Zehnder interferometer in the presence of a weak anisotropy effect. Real and imaginary weak value amplifications of different polarization anisotropy effects are manifested as characteristic changes in relevant Stokes vector elements at the exit port of the interferometer, which follow orthogonal trajectories in the Poincare sphere. The proof of concept experiment demonstrates that using this scheme, one can faithfully extract and quantify anisotropy parameter that is smaller than the typical sensitivity of measurement of a given Stokes parameter of a traditional polarimeter by a large weak value amplification factor. This opens up possibility of a sample measuring weak value polarimeter for studying rich variety of fundamental optical effects and for materials characterization and precision metrology.