Miracle-dz/UniFAR-FTUs huggingface dataset:
resolving conflicting comparative effectiveness research in critical care *

Crit Care Med 2012 Vol. 40, No. 11 schools, now teaching ultrasound using a 4-yr curriculum (14). The Society of Ultrasound in Medical Education is championing this cause with the Association of American Medical Colleges and others (15). In the not-too-distant future, medical students graduating from medical school and entering residency will have the POC ultrasound skills required to perform an LE DVT US study, so will residents and fellows. One last morsel of food for thought has to be offered. It seems that if DVT development after major trauma is nearly unavoidable and one has but to look to find thousands around the country, the government may use its power more wisely by encouraging more people to wear seat belts, drive safely, not drive drunk, follow safety rules, and to the extent possible avoid being typically human and doing self-destructive things of all sorts. This will really decrease the number of patients developing DVTs on trauma services, regardless of whether one dares look with ultrasound or not (a task Critical Care Physicians are more than capable of doing).
Aberrant Coronary Artery Origin From the Aorta

To clarify the natural history and management of patients with aberrant origin of a coronary artery from the aorta, we reviewed 18 patients whose right (RCA) or left (LCA) coronary artery arose aberrantly and passed between the aorta and right ventricular infundibulum.Nine patients had aberrant LCA. Three young males died suddenly after exertion, each with proximal focal LCA stenosis. None of the six adults with angina (ages 36-70 years) studied angiographically had proximal LCA stenosis. Unlike the young, "sudden death-prone" patients in whom coronary bypass of proximal stenosis may have prophylactic value, our older patients with aberrant LCA did not have proximal stenosis or sudden death, and therefore LCA bypass for sudden death prophylaxis is not warranted.Nine patients (ages 18-60 years) had aberrant origin and course of the RCA. Seven patients studied angiographically because of angina had no focal proximal RCA stenosis, but two patients had hypoplastic RCA ostia. Although a potential concern in these latter patients, sudden death has not been reported with aberrant RCA, therefore in the absence of syncope, RCA bypass for sudden death prophylaxis is not indicated.
Stochastic PDEs via convex minimization

Abstract We prove the applicability of the Weighted Energy-Dissipation (WED) variational principle to nonlinear parabolic stochastic partial differential equations in abstract form. The WED principle consists in the minimization of a parameter-dependent convex functional on entire trajectories. Its unique minimizers correspond to elliptic-in-time regularizations of the stochastic differential problem. As the regularization parameter tends to zero, solutions of the limiting problem are recovered. This in particular provides a direct approach via convex optimization to the approximation of nonlinear stochastic partial differential equations.
Clustering of Local Group Distances: Publication Bias or Correlated Measurements? VII. A Distance Framework out to 100 Mpc

We consider the body of published distance moduli to the Fornax and Coma galaxy clusters, with specific emphasis on the period since 1990. We have carefully homogenized our final catalogs of distance moduli onto the distance scale established in the previous papers in this series. We assessed systematic differences associated with the use of specific tracers and consequently discarded results based on application of the Tully–Fisher relation and of globular cluster and planetary nebula luminosity functions. We recommend “best” weighted relative distance moduli for the Fornax and Coma clusters with respect to the Virgo cluster benchmark of mag and mag. The set of weighted mean distance moduli (distances) we derived as most representative of the clusters’ distances is m−M0Fornax=31.41±0.15mag(D=19.1−1.2+1.4Mpc)and=31.21±0.28mag(D=17.5−2.2+2.4Mpc),m−M0Coma=34.99±0.38mag(D=99.5−15.9+19.0Mpc)and=34.78±0.27mag(D=90.4−10.6+11.9Mpc), where the first distance modulus for each cluster is the result of our analysis of the direct, absolute distance moduli, while the second modulus is based on distance moduli relative to the Virgo cluster. While the absolute and relative distance moduli for both clusters are mutually consistent within the uncertainties, the relative distance moduli yield shorter absolute distances by ∼1σ. It is unclear what may have caused this small difference for both clusters; lingering uncertainties in the underlying absolute distance scale appear to have given rise to a systematic uncertainty of the order of 0.20 mag.
Learning to Place Objects onto Flat Surfaces in Human-Preferred Orientations

We study the problem of placing a grasped object on an empty flat surface in a human-preferred orientation, such as placing a cup on its bottom rather than on its side. We aim to find the required object rotation such that when the gripper is opened after the object makes a contact with the surface, the object would be stably placed in the desired orientation. We use two neural networks in an iterative fashion. At every iteration, Placement Rotation CNN (PR-CNN) estimates the required object rotation which is executed by the robot, and then Placement Stability CNN (PS-CNN) estimates if the object would be stable if it is placed in its current orientation. In simulation experiments, our approach places objects in human-preferred orientations with a success rate of 86.1% using a dataset of 18 everyday objects. A real-world implementation is presented, which serves as a proof-of-concept for direct sim-to-real transfer. We observe that sometimes it is impossible to place a grasped object in a desired orientation without re-grasping, which motivates future research for grasping with intention to place objects.
Mimicking Evolution with Reinforcement Learning

Evolution gave rise to human and animal intelligence here on Earth. We argue that the path to developing artificial human-like-intelligence will pass through mimicking the evolutionary process in a nature-like simulation. In Nature, there are two processes driving the development of the brain: evolution and learning. Evolution acts slowly, across generations, and amongst other things, it defines what agents learn by changing their internal reward function. Learning acts fast, across one's lifetime, and it quickly updates agents' policy to maximise pleasure and minimise pain. The reward function is slowly aligned with the fitness function by evolution, however, as agents evolve the environment and its fitness function also change, increasing the misalignment between reward and fitness. It is extremely computationally expensive to replicate these two processes in simulation. This work proposes Evolution via Evolutionary Reward (EvER) that allows learning to single-handedly drive the search for policies with increasingly evolutionary fitness by ensuring the alignment of the reward function with the fitness function. In this search, EvER makes use of the whole state-action trajectories that agents go through their lifetime. In contrast, current evolutionary algorithms discard this information and consequently limit their potential efficiency at tackling sequential decision problems. We test our algorithm in two simple bio-inspired environments and show its superiority at generating more capable agents at surviving and reproducing their genes when compared with a state-of-the-art evolutionary algorithm.
Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds

3D object detection based on point clouds has become more and more popular. Some methods propose localizing 3D objects directly from raw point clouds to avoid information loss. However, these methods come with complex structures and significant computational overhead, limiting its broader application in real-time scenarios. Some methods choose to transform the point cloud data into compact tensors first and leverage off-the-shelf 2D detectors to propose 3D objects, which is much faster and achieves state-of-the-art results. However, because of the inconsistency between 2D and 3D data, we argue that the performance of compact tensor-based 3D detectors is restricted if we use 2D detectors without corresponding modification. Specifically, the distribution of point clouds is uneven, with most points gather on the boundary of objects, while detectors for 2D data always extract features evenly. Motivated by this observation, we propose DENse Feature Indicator (DENFI), a universal module that helps 3D detectors focus on the densest region of the point clouds in a boundary-aware manner. Moreover, DENFI is lightweight and guarantees real-time speed when applied to 3D object detectors. Experiments on KITTI dataset show that DENFI improves the performance of the baseline single-stage detector remarkably, which achieves new state-of-the-art performance among previous 3D detectors, including both two-stage and multi-sensor fusion methods, in terms of mAP with a 34FPS detection speed.
SoftSMPL: Data‐driven Modeling of Nonlinear Soft‐tissue Dynamics for Parametric Humans

We present SoftSMPL, a learning‐based method to model realistic soft‐tissue dynamics as a function of body shape and motion. Datasets to learn such task are scarce and expensive to generate, which makes training models prone to overfitting. At the core of our method there are three key contributions that enable us to model highly realistic dynamics and better generalization capabilities than state‐of‐the‐art methods, while training on the same data. First, a novel motion descriptor that disentangles the standard pose representation by removing subject‐specific features; second, a neural‐network‐based recurrent regressor that generalizes to unseen shapes and motions; and third, a highly efficient nonlinear deformation subspace capable of representing soft‐tissue deformations of arbitrary shapes. We demonstrate qualitative and quantitative improvements over existing methods and, additionally, we show the robustness of our method on a variety of motion capture databases.
Absolute Dimensions of the Unevolved F-type Eclipsing Binary BT Vulpeculae

We report extensive differential V-band photometry and high-resolution spectroscopy for the 1.14 day, detached, double-lined eclipsing binary BT Vul (F0+F7). Our radial-velocity monitoring and light-curve analysis lead to absolute masses and radii of and for the primary, and and for the secondary. The effective temperatures are 7270 ± 150 K and 6260 ± 180 K, respectively. Both stars are rapid rotators, and the orbit is circular. A comparison with stellar evolution models from the MESA Isochrones and Stellar Tracks series shows excellent agreement with these determinations, for a composition of and an age of 350 Myr. The two components of BT Vul are very near the zero-age main sequence.
Digit Recognition Using Convolution Neural Network

In pattern recognition, digit recognition has always been a very challenging task. This paper aims to extracting a correct feature so that it can achieve better accuracy for recognition of digits. The applications of digit recognition such as in password, bank check process, etc. to recognize the valid user identification. Earlier, several researchers have used various different machine learning algorithms in pattern recognition i.e. KNN, SVM, RFC. The main objective of this work is to obtain highest accuracy 99.15% by using convolution neural network (CNN) to recognize the digit without doing too much pre-processing of dataset.
Stationarity of Time-Series on Graph: A Generalized Approach via Transition Invariance

Stationarity is a cornerstone in classical signal processing (CSP) for modeling and characterizing various stochastic signals for the ensuing analysis. However, in many complex real world scenarios, where the stochastic process lies over an irregular graph structure, the CSP is incapable of handling such signals. It is essential to establish a new framework to analyze the high-dimensional graph structured stochastic signals by taking the underlying structure into account. To this end, looking through the lens of operator theory, we first propose a new bivariate joint transition operator (JTO) consistent with the abstract form of translation operators in other signal domains. Moreover, we characterize time-vertex filtering based on the proposed JTO. Thereupon, we put forth a new definition of stationarity in time-vertex domain using the proposed JTO together with its spectral characterization. Then a new joint power spectral density (JPSD) estimator, called generalized Welch method (GWM), is presented. Simulation results are provided to show the efficacy of this JPSD estimator. Then, by modeling the brain Electroencephalography (EEG) signals as time-varying graph signals, we use JPSD as the feature for the challenging task of emotion recognition. Experimental results demonstrate that JPSD yields superior emotion recognition accuracy in comparison with the classical power spectral density (PSD) as the feature set. Eventually, we provide some concluding remarks.
Large Dispersive Interaction between a CMOS Double Quantum Dot and Microwave Photons

We report a large coupling rate, $g_0/(2\pi)=183$ MHz, between the charge state of a double quantum dot in a CMOS split-gate silicon nanowire transistor and microwave photons in a lumped-element resonator formed by hybrid integration with a superconducting inductor. We enhance the coupling by exploiting the large interdot lever arm of an asymmetric split-gate device, $\alpha=0.72$, and by inductively coupling to the resonator to increase its impedance, $Z_\text{r}=560$ $\Omega$. In the dispersive regime, the large coupling strength at the DQD hybridisation point produces a frequency shift comparable to the resonator linewidth, the optimal setting for maximum state visibility. We exploit this regime to demonstrate rapid gate-based readout of the charge degree of freedom, with an SNR of 3.3 in 50 ns. In the resonant regime, the fast charge decoherence rate precludes reaching the strong coupling regime, but we show a clear route to spin-photon circuit quantum electrodynamics using hybrid CMOS systems.
Video to Events: Recycling Video Datasets for Event Cameras

Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with respect to conventional cameras: high dynamic range (HDR), high temporal resolution, and no motion blur. Recently, novel learning approaches operating on event data have achieved impressive results. Yet, these methods require a large amount of event data for training, which is hardly available due the novelty of event sensors in computer vision research. In this paper, we present a method that addresses these needs by converting any existing video dataset recorded with conventional cameras to synthetic event data. This unlocks the use of a virtually unlimited number of existing video datasets for training networks designed for real event data. We evaluate our method on two relevant vision tasks, i.e., object recognition and semantic segmentation, and show that models trained on synthetic events have several benefits: (i) they generalize well to real event data, even in scenarios where standard-camera images are blurry or overexposed, by inheriting the outstanding properties of event cameras; (ii) they can be used for fine-tuning on real data to improve over state-of-the-art for both classification and semantic segmentation.
A classification of the dynamics of three-dimensional stochastic ecological systems

The classification of the long-term behavior of dynamical systems is a fundamental problem in mathematics. For both deterministic and stochastic dynamics specific classes of models verify Palis' conjecture: the long-term behavior is determined by a finite number of stationary distributions. In this paper we consider the classification problem for stochastic models of interacting species. For a large class of three-species, stochastic differential equation models, we prove a variant of Palis' conjecture: the long-term statistical behavior is determined by a finite number of stationary distributions and, generically, three general types of behavior are possible: 1) convergence to a unique stationary distribution that supports all species, 2) convergence to one of a finite number of stationary distributions supporting two or fewer species, 3) convergence to convex combinations of single species, stationary distributions due to a rock-paper-scissors type of dynamic. Moreover, we prove that the classification reduces to computing Lyapunov exponents (external Lyapunov exponents) that correspond to the average per-capita growth rate of species when rare. Our results stand in contrast to the deterministic setting where the classification is incomplete even for three-dimensional, competitive Lotka--Volterra systems. For these SDE models, our results also provide a rigorous foundation for ecology's modern coexistence theory (MCT) which assumes the external Lyapunov exponents determine long-term ecological outcomes.
Improved Source Counting and Separation for Monaural Mixture

Single-channel speech separation in time domain and frequency domain has been widely studied for voice-driven applications over the past few years. Most of previous works assume known number of speakers in advance, however, which is not easily accessible through monaural mixture in practice. In this paper, we propose a novel model of single-channel multi-speaker separation by jointly learning the time-frequency feature and the unknown number of speakers. Specifically, our model integrates the time-domain convolution encoded feature map and the frequency-domain spectrogram by attention mechanism, and the integrated features are projected into high-dimensional embedding vectors which are then clustered with deep attractor network to modify the encoded feature. Meanwhile, the number of speakers is counted by computing the Gerschgorin disks of the embedding vectors which are orthogonal for different speakers. Finally, the modified encoded feature is inverted to the sound waveform using a linear decoder. Experimental evaluation on the GRID dataset shows that the proposed method with a single model can accurately estimate the number of speakers with 96.7 % probability of success, while achieving the state-of-the-art separation results on multi-speaker mixtures in terms of scale-invariant signal-to-noise ratio improvement (SI-SNRi) and signal-to-distortion ratio improvement (SDRi).
Revisiting Few-shot Activity Detection with Class Similarity Control

Many interesting events in the real world are rare making preannotated machine learning ready videos a rarity in consequence. Thus, temporal activity detection models that are able to learn from a few examples are desirable. In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection based on proposal regression which detects the start and end time of the activities in untrimmed videos. Our model is end-to-end trainable, takes into account the frame rate differences between few-shot activities and untrimmed test videos, and can benefit from additional few-shot examples. We experiment on three large scale benchmarks for temporal activity detection (ActivityNet1.2, ActivityNet1.3 and THUMOS14 datasets) in a few-shot setting. We also study the effect on performance of different amount of overlap with activities used to pretrain the video classification backbone and propose corrective measures for future works in this domain. Our code will be made available.
Spatio-Temporal Action Detection with Multi-Object Interaction

Spatio-temporal action detection in videos requires localizing the action both spatially and temporally in the form of an "action tube". Nowadays, most spatio-temporal action detection datasets (e.g. UCF101-24, AVA, DALY) are annotated with action tubes that contain a single person performing the action, thus the predominant action detection models simply employ a person detection and tracking pipeline for localization. However, when the action is defined as an interaction between multiple objects, such methods may fail since each bounding box in the action tube contains multiple objects instead of one person. In this paper, we study the spatio-temporal action detection problem with multi-object interaction. We introduce a new dataset that is annotated with action tubes containing multi-object interactions. Moreover, we propose an end-to-end spatio-temporal action detection model that performs both spatial and temporal regression simultaneously. Our spatial regression may enclose multiple objects participating in the action. During test time, we simply connect the regressed bounding boxes within the predicted temporal duration using a simple heuristic. We report the baseline results of our proposed model on this new dataset, and also show competitive results on the standard benchmark UCF101-24 using only RGB input.
Demonstrating Immersive Media Delivery on 5G Broadcast and Multicast Testing Networks

This work presents eight demonstrators and one showcase developed within the 5G-Xcast project. They experimentally demonstrate and validate key technical enablers for the future of media delivery, associated with multicast and broadcast communication capabilities in 5th Generation (5G). In 5G-Xcast, three existing testbeds: IRT in Munich (Germany), 5GIC in Surrey (U.K.), and TUAS in Turku (Finland), have been developed into 5G broadcast and multicast testing networks, which enables us to demonstrate our vision of a converged 5G infrastructure with fixed and mobile accesses and terrestrial broadcast, delivering immersive audio-visual media content. Built upon the improved testing networks, the demonstrators and showcase developed in 5G-Xcast show the impact of the technology developed in the project. Our demonstrations predominantly cover use cases belonging to two verticals: Media & Entertainment and Public Warning, which are future 5G scenarios relevant to multicast and broadcast delivery. In this paper, we present the development of these demonstrators, the showcase, and the testbeds. We also provide key findings from the experiments and demonstrations, which not only validate the technical solutions developed in the project, but also illustrate the potential technical impact of these solutions for broadcasters, content providers, operators, and other industries interested in the future immersive media delivery.
"Trust me, I have a Ph.D.": A Propensity Score Analysis on the Halo Effect of Disclosing One's Offline Social Status in Online Communities

Online communities adopt various reputation schemes to measure content quality. This study analyzes the effect of a new reputation scheme that exposes one's offline social status, such as an education degree, within an online community. We study two Reddit communities that adopted this scheme, whereby posts include tags identifying education status referred to as flairs, and we examine how the “transferred” social status affects the interactions among the users. We computed propensity scores to test whether flairs give ad-hoc authority to the adopters while minimizing the effects of confounding variables such as topics of content. The results show that exposing academic degrees is likely to lead to higher audience votes as well as larger discussion size, compared to the users without the disclosed identities, in a community that covers peer-reviewed scientific articles. In another community with a focus on casual science topics, exposing mere academic degrees did not obtain such benefits. Still, the users with the highest degree (e.g., Ph.D. or M.D.) were likely to receive more feedback from the audience. These findings suggest that reputation schemes that link the offline and online worlds could induce halo effects on feedback behaviors differently depending upon the community culture. We discuss the implications of this research for the design of future reputation mechanisms.