Miracle-dz/UniFAR-FTUs huggingface dataset:
Informed collaboration in health care: An embedded‐cases study in geriatric telepsychiatry

Telemedicine, the delivery of medical consultation and services, is enabled by information and communications technologies. Participants in telemedicine consultation services (ie, clinicians, patients, and other parties) use video conferencing, special‐purpose software and/or hardware, and data and notes from electronic medical records and other systems to formulate effective treatment plans. Telemedicine is a particular mode of informed collaboration—information and communications technology–supported collaborative activity in which collaborators rely (wholly or in part) on digitally stored information retrieved from various sources to pursue their collective objectives. We report on an embedded‐cases study of a long‐running geriatric telepsychiatry clinic in the north‐eastern United States. Our analysis draws on the theory of distributed cognition (DCog) to explicate the ways in which information and cognitive effort are distributed across individuals and artefacts to support psychiatric consultation from a distance and how these processes play out over time in routines. On the basis of this case study, we offer a refinement of DCog theory, focusing on four interrelated dynamic processes that, we propose, act as critical enablers of informed collaboration: cognitive offloading, visibility of action, intersubjective understanding, and knowledge redundancy. We discuss practical implications for designing telemedicine services and tools, and we offer suggestions for further research on informed collaboration.
Machine-Learning Ocean Dynamics from Lagrangian Drifter Trajectories

Lagrangian ocean drifters provide highly accurate approximations of ocean surface currents but are sparsely located across the globe. As drifters passively follow ocean currents, there is minimal control on where they will be making measurements, providing limited temporal coverage for a given region. Complementary Eulerian velocity data is available with global coverage, but is itself limited by the spatial and temporal resolution possible with satellite altimetry measurements. Additionally, altimetry measurements approximate geostrophic components of ocean currents but neglect smaller sub-mesoscale motions and require smoothing and interpolation from raw satellite track measurements. In an effort to harness the rich dynamics available in ocean drifter datasets, we have trained a recurrent neural network on the time history of drifter motion to minimize the error in a reduced-order Maxey-Riley drifter model. This approach relies on a slow-manifold approximation to determine the most mathematically-relevant variables with which to train, subsequently improving the temporal and spatial resolution of the underlying velocity field. By adding this neural network component, we also correct drifter trajectories near sub-mesoscale features missed by deterministic models using only satellite and wind reanalysis data. The effect of varying similarity between training and testing trajectory datasets for the blended model was evaluated, as was the effect of seasonality in the Gulf of Mexico.
Resolution Limits of Noisy 20 Questions Estimation

We establish fundamental limits on estimation accuracy for the noisy 20 questions problem with measurement dependent noise and introduce optimal non-adaptive procedures that achieve this limits. The minimal achievable resolution is defined as the absolute difference between the estimated and the true values of the target random variable, given a finite number of queries constrained by the excess-resolution probability. Inspired by the relationship between the 20 questions problem and the channel coding problem, we derive non-asymptotic bounds on the minimal achievable resolution. Furthermore, applying the Berry-Esseen theorem to our non-asymptotic bounds, we obtain a second-order asymptotic approximation to finite blocklength performance, specifically the achievable resolution of optimal non-adaptive query procedures with a finite number of queries subject to the excess-resolution probability constraint. We specialize our second-order results to measurement dependent versions of several channel models including the binary symmetric, the binary erasure and the binary Z- channels. Our results are then extended to adaptive query procedures, establishing a lower bound on the resolution gain associated with adaptive querying.
Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference

Model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. The uncertainty does not represent the model error well. In this paper, temperature scaling is extended to dropout variational inference to calibrate model uncertainty. Expected uncertainty calibration error (UCE) is presented as a metric to measure miscalibration of uncertainty. The effectiveness of this approach is evaluated on CIFAR-10/100 for recent CNN architectures. Experimental results show, that temperature scaling considerably reduces miscalibration by means of UCE and enables robust rejection of uncertain predictions. The proposed approach can easily be derived from frequentist temperature scaling and yields well-calibrated model uncertainty. It is simple to implement and does not affect the model accuracy.
Network Differential Connectivity Analysis

Identifying differences in networks has become a canonical problem in many biological applications. Here, we focus on testing whether two Gaussian graphical models are the same. Existing methods try to accomplish this goal by either directly comparing their estimated structures, or testing the null hypothesis that the partial correlation matrices are equal. However, estimation approaches do not provide measures of uncertainty, e.g., $p$-values, which are crucial in drawing scientific conclusions. On the other hand, existing testing approaches could lead to misleading results in some cases. To address these shortcomings, we propose a qualitative hypothesis testing framework, which tests whether the connectivity patterns in the two networks are the same. Our framework is especially appropriate if the goal is to identify nodes or edges that are differentially connected. No existing approach could test such hypotheses and provide corresponding measures of uncertainty, e.g., $p$-values. We investigate theoretical and numerical properties of our proposal and illustrate its utility in biological applications. Theoretically, we show that under appropriate conditions, our proposal correctly controls the type-I error rate in testing the qualitative hypothesis. Empirically, we demonstrate the performance of our proposal using simulation datasets and applications in cancer genetics and brain imaging studies.
Self-gravitating planetary envelopes and the core-nucleated instability

Planet formation scenarios can be constrained by the ratio of the gaseous envelope mass relative to the solid core mass in the observed exoplanet populations. One-dimensional calculations find a critical (maximal) core mass for quasi-static envelopes to exist, suggesting that envelopes around more massive cores should collapse due to a ‘core-nucleated’ instability. We study self-gravitating planetary envelopes via hydrodynamic simulations, progressively increasing the dimensionality of the problem. We characterize the core-nucleated instability and its non-linear evolution into runaway gas accretion in one-dimensional spherical envelopes. We show that rotationally supported envelopes can enter a runaway accretion regime via polar shocks in a two-dimensional axisymmetric model. This picture remains valid for high-mass cores in three dimensions, where the gas gravity mainly adds up to the core gravity and enhances the mass accretion rate of the planet in time. We relate the core-nucleated instability to the absence of equilibrium connecting the planet to its parent disc and discuss its relevance for massive planet formation.
Shredder: Learning Noise Distributions to Protect Inference Privacy

A wide variety of deep neural applications increasingly rely on the cloud to perform their compute-heavy inference. This common practice requires sending private and privileged data over the network to remote servers, exposing it to the service provider and potentially compromising its privacy. Even if the provider is trusted, the data can still be vulnerable over communication channels or via side-channel attacks in the cloud. To that end, this paper aims to reduce the information content of the communicated data with as little as possible compromise on the inference accuracy by making the sent data noisy. An undisciplined addition of noise can significantly reduce the accuracy of inference, rendering the service unusable. To address this challenge, this paper devises Shredder, an end-to-end framework, that, without altering the topology or the weights of a pre-trained network, learns additive noise distributions that significantly reduce the information content of communicated data while maintaining the inference accuracy. The key idea is finding the additive noise distributions by casting it as a disjoint offline learning process with a loss function that strikes a balance between accuracy and information degradation. The loss function also exposes a knob for a disciplined and controlled asymmetric trade-off between privacy and accuracy. While keeping the DNN intact, Shredder divides inference between the cloud and the edge device, striking a balance between computation and communication. In the separate phase of inference, the edge device takes samples from the Laplace distributions that were collected during the proposed offline learning phase and populates a noise tensor with these sampled elements. Then, the edge device merely adds this populated noise tensor to the intermediate results to be sent to the cloud. As such, Shredder enables accurate inference on noisy intermediate data without the need to update the model or the cloud, or any training process during inference. We also formally show that Shredder maximizes privacy with minimal impact on DNN accuracy while the tradeoff between privacy and accuracy is controlled through a mathematical knob. Experimentation with six real-world DNNs from text processing and image classification shows that Shredder reduces the mutual information between the input and the communicated data to the cloud by 74.70% compared to the original execution while only sacrificing 1.58% loss in accuracy. On average, Shredder also offers a speedup of 1.79x over Wi-Fi and 2.17x over LTE compared to cloud-only execution when using an off-the-shelf mobile GPU (Tegra X2) on the edge.
Neutrino Oscillations in the Vacuum

The initial configuration of a neutrino experiment consists of wave packets, created by the experimenter. Unmeasured final states are essentially delocalized, but it is conventional to nonetheless treat them as localized wave packets as they are in a sense measured by the environment. In this note we consider an experiment in the vacuum, and so there is no environment and the unmeasured final states are not localized. We introduce a simplified but consistent model of neutrino interactions and numerically evolve the system over a finite time using the Hamiltonian to obtain the oscillation probability. All particles involved in neutrino production or detection, localized or not, are treated and evolved consistently in quantum field theory, keeping all entanglements.
Feature Level Fusion from Facial Attributes for Face Recognition

We introduce a deep convolutional neural networks (CNN) architecture to classify facial attributes and recognize face images simultaneously via a shared learning paradigm to improve the accuracy for facial attribute prediction and face recognition performance. In this method, we use facial attributes as an auxiliary source of information to assist CNN features extracted from the face images to improve the face recognition performance. Specifically, we use a shared CNN architecture that jointly predicts facial attributes and recognize face images simultaneously via a shared learning parameters, and then we use facial attribute features an an auxiliary source of information concatenated by face features to increase the discrimination of the CNN for face recognition. This process assists the CNN classifier to better recognize face images. The experimental results show that our model increases both the face recognition and facial attribute prediction performance, especially for the identity attributes such as gender and race. We evaluated our method on several standard datasets labeled by identities and face attributes and the results show that the proposed method outperforms state-of-the-art face recognition models.
Pentagon OPE resummation in 
N=4
 SYM: Hexagons with one effective particle contribution

We present the technique for resummation of flux tube excitations series arising in pentagon operator expansion program for polygonal Wilson loops in N=4 SYM. Here we restrict ourselves with contributions of one-particle effective states and consider as a particular example NMHV 6 particle amplitude at one-loop. The presented technique is also applicable at higher loops for one effective particle contributions and has the potential for generalization for contributions with more effective particles.
Manifold Fitting in Ambient Space

Modern data sets in many applications no longer comprise samples of real vectors in a real vector space but samples of much more complex structures which may be represented as points in a space with certain underlying geometric structure, namely a manifold. Manifold learning is an emerging field for learning the underlying structure. The study of manifold learning can be split into two main branches, namely dimension reduction and manifold fitting. With the aim of interacting statistics and geometry, we tackle the problem of manifold fitting in the ambient space. Inspired by the relation between the eigenvalues of the Laplace-Beltrami operator and the geometry of a manifold, we aim to find a small set of points that preserve the geometry of the underlying manifold. Based on this relationship, we extend the idea of subsampling to noisy datasets in high dimensional space and utilize the Moving Least Squares (MLS) approach to approximate the underlying manifold. We analyze the two core steps in our proposed method theoretically and also provide the bounds for the MLS approach. Our simulation results and real data analysis demonstrate the superiority of our method in estimating the underlying manifold from noisy data.
Automatic Fact-guided Sentence Modification

Online encyclopediae like Wikipedia contain large amounts of text that need frequent corrections and updates. The new information may contradict existing content in encyclopediae. In this paper, we focus on rewriting such dynamically changing articles. This is a challenging constrained generation task, as the output must be consistent with the new information and fit into the rest of the existing document. To this end, we propose a two-step solution: (1) We identify and remove the contradicting components in a target text for a given claim, using a neutralizing stance model; (2) We expand the remaining text to be consistent with the given claim, using a novel two-encoder sequence-to-sequence model with copy attention. Applied to a Wikipedia fact update dataset, our method successfully generates updated sentences for new claims, achieving the highest SARI score. Furthermore, we demonstrate that generating synthetic data through such rewritten sentences can successfully augment the FEVER fact-checking training dataset, leading to a relative error reduction of 13%. 1
Cool-core cycles and Phoenix

Recent observations show that the star formation rate (SFR) in the Phoenix cluster’s central galaxy is ∼500 M⊙ yr−1. Even though Phoenix is a massive cluster (M200 ≈ 2.0 × 1015 M⊙; z ≈ 0.6) such a high central SFR is not expected in a scenario in which feedback from an active galactic nucleus (AGN) maintains the intracluster medium in a state of rough thermal balance. It has been argued that either AGN feedback saturates in very massive clusters or the central supermassive black hole is too small to produce enough kinetic feedback and hence is unable to quench the catastrophic cooling. In this work, we present an alternate scenario wherein intense short-lived cooling and star formation phases followed by strong AGN outbursts are part of the AGN feedback loop. Using results from a 3D hydrodynamic simulation of a standard cool-core cluster (M200 ∼ 7 × 1014 M⊙; z = 0), scaled to account for differences in mass and redshift, we argue that Phoenix is at the end of a cooling phase in which an AGN outburst has begun but has not yet arrested core cooling. This state of high cooling rate and star formation is expected to last for ≲100 Myr in Phoenix.
The transitional gap transient AT 2018hso: new insights into the luminous red nova phenomenon

Context. The absolute magnitudes of luminous red novae (LRNe) are intermediate between those of novae and supernovae (SNe), and show a relatively homogeneous spectro-photometric evolution. Although they were thought to derive from core instabilities in single stars, there is growing support for the idea that they are triggered by binary interaction that possibly ends with the merging of the two stars. Aims. AT 2018hso is a new transient showing transitional properties between those of LRNe and the class of intermediate-luminosity red transients (ILRTs) similar to SN 2008S. Through the detailed analysis of the observed parameters, our study supports that it actually belongs to the LRN class and was likely produced by the coalescence of two massive stars. Methods. We obtained ten months of optical and near-infrared photometric monitoring, and 11 epochs of low-resolution optical spectroscopy of AT 2018hso. We compared its observed properties with those of other ILRTs and LRNe. We also inspected the archival Hubble Space Telescope (HST) images obtained about 15 years ago to constrain the progenitor properties. Results. The light curves of AT 2018hso show a first sharp peak (reddening-corrected Mr = −13.93 mag), followed by a broader and shallower second peak that resembles a plateau in the optical bands. The spectra dramatically change with time. Early-time spectra show prominent Balmer emission lines and a weak [Ca II] doublet, which is usually observed in ILRTs. However, the strong decrease in the continuum temperature, the appearance of narrow metal absorption lines, the great change in the Hα strength and profile, and the emergence of molecular bands support an LRN classification. The possible detection of a MI ∼ −8 mag source at the position of AT 2018hso in HST archive images is consistent with expectations for a pre-merger massive binary, similar to the precursor of the 2015 LRN in M101. Conclusions. We provide reasonable arguments to support an LRN classification for AT 2018hso. This study reveals growing heterogeneity in the observables of LRNe than has been thought previously, which is a challenge for distinguishing between LRNe and ILRTs. This suggests that the entire evolution of gap transients needs to be monitored to avoid misclassifications.
Additive Powers-of-Two Quantization: A Non-uniform Discretization for Neural Networks

We proposed Additive Powers-of-Two~(APoT) quantization, an efficient non-uniform quantization scheme that attends to the bell-shaped and long-tailed distribution of weights in neural networks. By constraining all quantization levels as a sum of several Powers-of-Two terms, APoT quantization enjoys overwhelming efficiency of computation and a good match with weights' distribution. A simple reparameterization on clipping function is applied to generate better-defined gradient for updating of optimal clipping threshold. Moreover, weight normalization is presented to refine the input distribution of weights to be more stable and consistent. Experimental results show that our proposed method outperforms state-of-the-art methods, and is even competitive with the full-precision models demonstrating the effectiveness of our proposed APoT quantization. For example, our 3-bit quantized ResNet-34 on ImageNet only drops 0.3% Top-1 and 0.2% Top-5 accuracy without bells and whistles, while the computation of our model is approximately 2x less than uniformly quantized neural networks.
Understanding and Stabilizing GANs' Training Dynamics Using Control Theory

Generative adversarial networks (GANs) are effective in generating realistic images but the training is often unstable. There are existing efforts that model the training dynamics of GANs in the parameter space but the analysis cannot directly motivate practically effective stabilizing methods. To this end, we present a conceptually novel perspective from control theory to directly model the dynamics of GANs in the function space and provide simple yet effective methods to stabilize GANs’ training. We ﬁrst analyze the training dynamic of a prototypical Dirac GAN and adopt the widely-used closed-loop control (CLC) to improve its stability. We then extend CLC to stabilize the training dynamic of normal GANs, where CLC is implemented as a squared L 2 regularizer on the output of the discriminator. Empirical re-sults show that our method can effectively stabilize the training and obtain state-of-the-art performance on data generation tasks.
Resolving the puzzle of type IIP SN 2016X

The enigmatic type IIP SN 2016X demonstrates the unprecedented asphericity in the nebular Hα line profile, the absence of nebular [O i] emission, and the unusual occultation effect due to the internal dust. The hydrodynamic modelling of the bolometric light curve and expansion velocities suggests that the event is an outcome of the massive star explosion that ejected 28 M$\odot$ with the kinetic energy of 1.7 × 1051 erg and 0.03 M$\odot$ of radioactive 56Ni. We recover the bipolar distribution of 56Ni from the Hα profile via the simulation of the emissivity produced by non-spherical 56Ni ejecta. The conspicuous effect of the dust absorption in the Hα profile rules out the occultation by the dusty sphere or dusty thick disc, but turns out consistent with the thin dusty disc-like structure in the plane perpendicular to the bipolar axis. We speculate that the absence of the nebular [O i] emission might originate from the significant cooling of the oxygen-rich matter mediated by CO and SiO molecules.
Locally Constant Networks

We show how neural models can be used to realize piece-wise constant functions such as decision trees. Our approach builds on ReLU networks that are piece-wise linear and hence their associated gradients with respect to the inputs are locally constant. We formally establish the equivalence between the classes of locally constant networks and decision trees. Moreover, we highlight several advantageous properties of locally constant networks, including how they realize decision trees with parameter sharing across branching / leaves. Indeed, only $M$ neurons suffice to implicitly model an oblique decision tree with $2^M$ leaf nodes. The neural representation also enables us to adopt many tools developed for deep networks (e.g., DropConnect (Wan et al. 2013)) while implicitly training decision trees. We demonstrate that our method outperforms alternative techniques for training oblique decision trees in the context of molecular property classification and regression tasks.