Miracle-dz/UniFAR-FTUs huggingface dataset:
Empathosphere: Promoting Constructive Communication in Ad-hoc Virtual Teams through Perspective-taking Spaces

When members of ad-hoc virtual teams need to collectively ideate or deliberate, they often fail to engage with each others' perspectives in a constructive manner. At best, this leads to sub-optimal outcomes, and, at worst, it can cause conflicts that lead to teams not wanting to continue working together. Prior work has attempted to facilitate constructive communication by highlighting problematic communication patterns and nudging teams to alter their interaction norms. However, these approaches achieve limited success because they fail to acknowledge two social barriers: (1) it is hard to reset team norms mid-interaction, and (2) corrective nudges have limited utility unless team members believe it is safe to voice their opinion and that their opinion will be heard. This paper introduces Empathosphere, a chat-embedded intervention to mitigate these barriers and foster constructive communication in teams. To mitigate the first barrier, Empathosphere leverages the known benefits of "experimental spaces" in dampening existing norms and creating a climate conducive to change. Empathosphere instantiates this "space'' as a separate communication channel in a team's workspace. To mitigate the second barrier, Empathosphere harnesses the benefits of perspective-taking to cultivate a group climate that promotes a norm of members speaking up and engaging with each other. Empathosphere achieves this by orchestrating authentic socio-emotional exchanges designed to induce perspective-taking. A controlled study ($N=110$) compared Empathosphere to an alternate intervention strategy of prompting teams to reflect on their team experience. We found that Empathosphere led to higher work satisfaction, encouraged more open communication and feedback within teams, and boosted teams' desire to continue working together. This work demonstrates that "experimental spaces," particularly those that integrate methods of encouraging perspective-taking, can be a powerful means of improving communication in virtual teams.
Shape dynamics of a red blood cell in Poiseuille flow

We use numerical simulations to study the dynamics of red blood cells (RBCs) in unconfined and confined Poiseuille flow. Previous numerical studies with 3D vesicles have indicated that the slipper shape observed in experiments at high capillary number can be attributed to the bistability due to the interplay of wall push and outward migration tendency at higher viscosity contrasts. In this paper, we study this outward migration and bistability using numerical simulations for 3D capsules and provide phase diagrams of RBC dynamics with and without viscosity contrast. We observe a bistability of slipper and croissants in confined Poiseuille flow with viscosity contrast as observed in experiments.
A Practical Contrastive Learning Framework for Single Image Super-Resolution

Contrastive learning has achieved remarkable success on various high-level tasks, but there are fewer contrastive learning-based methods proposed for low-level tasks. It is challenging to adopt vanilla contrastive learning technologies proposed for high-level visual tasks to low-level image restoration problems straightly. Because the acquired high-level global visual representations are insufficient for low-level tasks requiring rich texture and context information. In this article, we investigate the contrastive learning-based single-image super-resolution (SISR) from two perspectives: positive and negative sample construction and feature embedding. The existing methods take naive sample construction approaches (e.g., considering the low-quality input as a negative sample and the ground truth as a positive sample) and adopt a prior model (e.g., pretrained very deep convolutional networks proposed by visual geometry group (VGG) model) to obtain the feature embedding. To this end, we propose a practical contrastive learning framework for SISR (PCL-SR). We involve the generation of many informative positive and hard negative samples in frequency space. Instead of utilizing an additional pretrained network, we design a simple but effective embedding network inherited from the discriminator network, which is more task-friendly. Compared with the existing benchmark methods, we retrain them by our proposed PCL-SR framework and achieve superior performance. Extensive experiments have been conducted to show the effectiveness and technical contributions of our proposed PCL-SR thorough ablation studies. The code and resulting models will be released via https://github.com/Aitical/PCL-SISR.
Gravitational Wave Sources in Our Galactic Backyard: Predictions for BHBH, BHNS, and NSNS Binaries Detectable with LISA

Future searches for gravitational waves from space will be sensitive to double compact objects in our Milky Way. We present new simulations of the populations of double black holes (BHBHs), BH neutron stars (BHNSs), and double neutron stars (NSNSs) that will be detectable by the planned space-based gravitational-wave detector called Laser Interferometer Space Antenna (LISA). For our estimates, we use an empirically informed model of the metallicity-dependent star formation history of the Milky Way. We populate it using an extensive suite of binary population-synthesis predictions for varying assumptions relating to mass transfer, common-envelope, supernova kicks, remnant masses, and wind mass-loss physics. For a 4(10) yr LISA mission, we predict between 30–370(50–550) detections over these variations, out of which 6–154 (9–238) are BHBHs, 2–198 (3–289) are BHNSs, and 3–35 (4–57) are NSNSs. We expect that about 50% (60%) can be distinguished from double white dwarf sources based on their mass or eccentricity and localization. Specifically, for about 10% (15%), we expect to be able to determine chirp masses better than 10%. For 13% (13%), we expect sky-localizations better than 1°. We discuss how the variations in the physics assumptions alter the distribution of properties of the detectable systems, even when the detection rates are unchanged. We further discuss the possibility of multimessenger observations of pulsar populations with the Square Kilometre Array and assess the benefits of extending the LISA mission.
The Fixed-b Limiting Distribution and the ERP of HAR Tests Under Nonstationarity

We show that the limiting distribution of HAR test statistics under fixed-b asymptotics is not pivotal when the data are nonstationarity. It takes the form of a complicated function of Gaussian processes and depends on the second moments of the relevant series (e.g., of the regressors and errors for the case of the linear regression model). Hence, existing fixed-b inference methods based on stationarity are not theoretically valid in general. The nuisance parameters entering the fixed-b limiting distribution can be consistently estimated under small-b asymptotics but only with nonparametric rate of convergence. We show that the error in rejection probability (ERP) is an order of magnitude larger than that under stationarity and is also larger than that of HAR tests based on HAC estimators under conventional asymptotics. These theoretical results reconcile with recent finite-sample evidence showing that fixed-b HAR tests can perform poorly when the data are nonstationary. They can be conservative under the null hypothesis and have non-monotonic power under the alternative hypothesis irrespective of how large the sample size is. JEL Classification: C12, C13, C18, C22, C32, C51
Partial regularity for minima of higher-order quasiconvex integrands with natural Orlicz growth

A partial regularity theorem is presented for minimisers of korder functionals subject to a quasiconvexity and general growth condition. We will assume a natural growth condition governed by an N-function satisfying the ∆2 and ∇2 conditions, assuming no quantitative estimates on the second derivative of the integrand; this is new even in the k = 1 case. These results will also be extended to the case of strong local minimisers.
Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks

We consider the problem of graph-matching on a network of 3D shapes with uncertainty quantification. We assume that the pairwise shape correspondences are efficiently represented as \emph{functional maps}, that match real-valued functions defined over pairs of shapes. By modeling functional maps between nearly isometric shapes as elements of the Lie group $SO(n)$, we employ \emph{synchronization} to enforce cycle consistency of the collection of functional maps over the graph, hereby enhancing the accuracy of the individual maps. We further introduce a tempered Bayesian probabilistic inference framework on $SO(n)$. Our framework enables: (i) synchronization of functional maps as maximum-a-posteriori estimation on the Riemannian manifold of functional maps, (ii) sampling the solution space in our energy based model so as to quantify uncertainty in the synchronization problem. We dub the latter \emph{Riemannian Langevin Functional Map (RLFM) Sampler}. Our experiments demonstrate that constraining the synchronization on the Riemannian manifold $SO(n)$ improves the estimation of the functional maps, while our RLFM sampler provides for the first time an uncertainty quantification of the results.
3D High-Quality Magnetic Resonance Image Restoration in Clinics Using Deep Learning

Shortening acquisition time and reducing the motion artifacts are two of the most essential concerns in magnetic resonance imaging. As a promising solution, deep learning-based high-quality MR image restoration has been investigated to generate highly-resolved and motion artifact-free MR images from lower resolution images acquired with shortened acquisition time or motion artifact-corrupted images. However, numerous problems still exist to prevent deep learning approaches from becoming practical in the clinic environment. Specifically, most of the prior works focus solely on the network but ignore the impact of various down-sampling strategies on the acquisition time. Besides, the long inference time and high GPU consumption are also the bottlenecks to deploy most of the prior works in clinics. Furthermore, prior studies employ random movement in retrospective motion artifact generation, resulting in uncontrollable severity of motion artifact. More importantly, doctors are unsure whether the generated MR images are trustworthy, making diagnosis difficult. To overcome all these problems, we adopted a unified framework of 2D deep learning neural network for both 3D MRI super-resolution and motion artifact reduction, demonstrating such a framework can achieve better performance in 3D MRI restoration tasks compared to other state-of-the-art methods and remain the GPU consumption and inference time significantly low, thus easier to deploy. We also analyzed several down-sampling strategies based on the acceleration factor, including multiple combinations of in-plane and through-plane down-sampling, and developed a controllable and quantifiable motion artifact generation method. At last, the pixel-wise uncertainty was calculated and used to estimate the accuracy of the generated image, providing additional information for a reliable diagnosis.
Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets

In this paper, we study the generalization performance of global minima for implementing empirical risk minimization (ERM) on over-parameterized deep ReLU nets. Using a novel deepening scheme for deep ReLU nets, we rigorously prove that there exist perfect global minima achieving almost optimal generalization error bounds for numerous types of data under mild conditions. Since over-parameterization is crucial to guarantee that the global minima of ERM on deep ReLU nets can be realized by the widely used stochastic gradient descent (SGD) algorithm, our results indeed fill a gap between optimization and generalization.
Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity

DETR is the first end-to-end object detector using a transformer encoder-decoder architecture and demonstrates competitive performance but low computational efficiency on high resolution feature maps. The subsequent work, Deformable DETR, enhances the efficiency of DETR by replacing dense attention with deformable attention, which achieves 10x faster convergence and improved performance. Deformable DETR uses the multiscale feature to ameliorate performance, however, the number of encoder tokens increases by 20x compared to DETR, and the computation cost of the encoder attention remains a bottleneck. In our preliminary experiment, we observe that the detection performance hardly deteriorates even if only a part of the encoder token is updated. Inspired by this observation, we propose Sparse DETR that selectively updates only the tokens expected to be referenced by the decoder, thus help the model effectively detect objects. In addition, we show that applying an auxiliary detection loss on the selected tokens in the encoder improves the performance while minimizing computational overhead. We validate that Sparse DETR achieves better performance than Deformable DETR even with only 10% encoder tokens on the COCO dataset. Albeit only the encoder tokens are sparsified, the total computation cost decreases by 38% and the frames per second (FPS) increases by 42% compared to Deformable DETR. Code is available at https://github.com/kakaobrain/sparse-detr
Video Content Classification using Deep Learning

Video content classification is an important research content in computer vision, which is widely used in many fields, such as image and video retrieval, computer vision. This paper presents a model that is a combination of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) which develops, trains, and optimizes a deep learning network that can identify the type of video content and classify them into categories such as"Animation, Gaming, natural content, flat content, etc". To enhance the performance of the model novel keyframe extraction method is included to classify only the keyframes, thereby reducing the overall processing time without sacrificing any significant performance.
Transient adverse events after REGN-CoV2 administration for mild COVID-19 patients and their potential predictive factors: a single center analysis

Background The efficacy of REGN-COV2 in preventing severe COVID-19 has been proven, and its use in outpatient and home settings is expanding. Adverse events such as fever and decreased oxygen saturation, which are often seen after REGN-COV2 administration, are generally transient, but predicting these events is useful in developing a monitoring plan for patients. Methods We performed a retrospective analysis of 76 patients who received REGN-CoV2 between August and September 2021. The clinical course of the patients' fever and oxygen administration was collected from their medical records, and the patients were divided into two groups based on the presence or absence of these adverse events, and the underlying pathology and blood sampling data were compared. Parameters that showed significant differences were further examined by Fisher's exact probability test to see if the use of appropriate thresholds would significantly correlate with the occurrence of adverse events. Findings Of the 76 patients, 47 had fever of 38.5{degrees}C or higher within 24 hours after administration, and 27 of these patients had a body temperature of 37.5{degrees}C or lower before administration. Oxygen was required in 14 cases, 5 of which required oxygen more than 24 hours after administration of REGN-COV2, and additional treatment such as dexamethasone was given as a transition to moderate disease. Among the parameters analyzed, except for fever before administration, lymphocyte count and IFN{lambda}3 showed significant differences between the fever and non-fever groups. There was also a significant difference in ferritin and CRP between the oxygen required and non-required groups. This was also the case in the comparison excluding patients who had fever before administration. In addition to IFN{lambda}3, ferritin, and CRP, there was a significant difference in LDH between the group that required additional treatment and the group that did not. Fisher's exact test was used to examine the prediction threshold for fever and non-fever groups. The sensitivity and specificity were 55% and 79%, respectively with odds ratio 4.746 (95% CI: 1.666 to 14.12) when lymphocytes counts <950/L was used (p=0.004). Similarly, when IFN{lambda}3>5.0 was used as the cutoff, sensitivity 72%, specificity 76%, odds ratio 8.220 (2.857 to 22.22; p<0.0001). Interpretations Fever and decreased oxygen saturation after administration of REGN-Cov2 were found to correlate with the severity factors of COVID-19 itself. Evaluation of these items at the time of administration is useful not only for predicting the severity of illness but also for the development of adverse events in patients.
Towards Understanding the Impact of Model Size on Differential Private Classification

Differential privacy (DP) is an essential technique for privacy-preserving. It was found that a large model trained for privacy preserving performs worse than a smaller model (e.g. ResNet50 performs worse than ResNet18). To better understand this phenomenon, we study high dimensional DP learning from the viewpoint of generalization. Theoretically, we show that for the simple Gaussian model with even small DP noise, if the dimension is large enough, then the classification error can be as bad as the random guessing. Then we propose a feature selection method to reduce the size of the model, based on a new metric which trades off the classification accuracy and privacy preserving. Experiments on real data support our theoretical results and demonstrate the advantage of the proposed method.
A Census of the Stellar Populations in the Sco-Cen Complex

I have used high-precision photometry and astrometry from the early installment of the third data release of Gaia (EDR3) to perform a survey for members of the stellar populations within the Sco-Cen complex, which consist of Upper Sco, UCL/LCC, the V1062 Sco group, Ophiuchus, and Lupus. Among Gaia sources with σ π < 1 mas, I have identified 10,509 candidate members of those populations. I have compiled previous measurements of spectral types, Li equivalent widths, and radial velocities for the candidates, which are available for 3169, 1420, and 1740 objects, respectively. In a subset of candidates selected to minimize field star contamination, I estimate that the contamination is ≲1% and the completeness is ∼90% at spectral types of ≲M6–M7 for the populations with low extinction (Upper Sco, V1062 Sco, UCL/LCC). I have used that cleaner sample to characterize the stellar populations in Sco-Cen in terms of their initial mass functions, ages, and space velocities. For instance, all of the populations in Sco-Cen have histograms of spectral types that peak near M4–M5, which indicates that they share similar characteristic masses for their initial mass functions (∼0.15–0.2 M ⊙). After accounting for incompleteness, I estimate that the Sco-Cen complex contains nearly 10,000 members with masses above ∼0.01 M ⊙. Finally, I also present new estimates for the intrinsic colors of young stars and brown dwarfs (≲20 Myr) in bands from Gaia EDR3, the Two Micron All Sky Survey, the Wide-field Infrared Survey Explorer, and the Spitzer Space Telescope.
SwiftSRGAN - Rethinking Super-Resolution for Efficient and Real-time Inference

In recent years, there have been several advancements in the task of image super-resolution using the state of the art Deep Learning-based architectures. Many super-resolution-based techniques previously published, require high-end and top-of-the-line Graphics Processing Unit (GPUs) to perform image super-resolution. With the increasing advancements in Deep Learning approaches, neural networks have become more and more compute hungry. We took a step back and, focused on creating a real-time efficient solution. We present an architecture that is faster and smaller in terms of its memory footprint. The proposed architecture uses Depth-wise Separable Convolutions to extract features and, it performs on-par with other super-resolution GANs (Generative Adversarial Networks) while maintaining real-time inference and a low memory footprint. A real-time super-resolution enables streaming high resolution media content even under poor bandwidth conditions. We need a real-time and efficient solution for tasks like cloud gaming and media streaming but also not at the cost of using a high-end top-of-the-line GPU. While maintaining an efficient trade-off between the accuracy and latency, we are able to produce a comparable performance model which is one-eighth (1/8) the size of super-resolution GANs and computes 74 times faster than super-resolution GANs. This significant reduction in inference time enables us to perform super-resolution in real-time.
Efficient Sampling Policy for Selecting a Subset With the Best

In this article, we study the problem of selecting a subset with the best of a finite number of alternatives under a fixed simulation budget. Our work aims to maximize the posterior probability of correctly selecting such a subset. We formulate the dynamic sampling decision as a stochastic control problem in a Bayesian setting. In an approximate dynamic programming paradigm, we propose a sequential sampling policy based on value function approximation. We analyze the asymptotic property of the proposed sampling policy. Numerical experiments demonstrate the efficiency of the proposed procedure.
A neural ordinary differential equation framework for modeling inelastic stress response via internal state variables

We propose a neural network framework to preclude the need to define or observe incompletely or inaccurately defined states of a material in order to describe its response. The neural network design is based on the classical Coleman-Gurtin internal state variable theory. In the proposed framework the states of the material are inferred from observable deformation and stress. A neural network describes the flow of internal states and another represents the map from internal state and strain to stress. We investigate tensor basis, component, and potential-based formulations of the stress model. Violations of the second law of thermodynamics are prevented by a constraint on the weights of the neural network. We extend this framework to homogenization of materials with microstructure with a graph-based convolutional neural network that preprocesses the initial microstructure into salient features. The modeling framework is tested on large datasets spanning inelastic material classes to demonstrate its general applicability.