Miracle-dz/UniFAR-FTUs huggingface dataset:
Min-max Entropy for Weakly Supervised Pointwise Localization

Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain. In this work, we focus on weakly supervised localization (WSL) where a model is trained to classify an image and localize regions of interest at pixel-level using only global image annotation. Typical convolutional attentions maps are prune to high false positive regions. To alleviate this issue, we propose a new deep learning method for WSL, composed of a localizer and a classifier, where the localizer is constrained to determine relevant and irrelevant regions using conditional entropy (CE) with the aim to reduce false positive regions. Experimental results on a public medical dataset and two natural datasets, using Dice index, show that, compared to state of the art WSL methods, our proposal can provide significant improvements in terms of image-level classification and pixel-level localization (low false positive) with robustness to overfitting. A public reproducible PyTorch implementation is provided in: this https URL .
Query Optimization Properties of Modified VBS

Valuation-Based~System can represent knowledge in different domains including probability theory, Dempster-Shafer theory and possibility theory. More recent studies show that the framework of VBS is also appropriate for representing and solving Bayesian decision problems and optimization problems. In this paper after introducing the valuation based system (VBS) framework, we present Markov-like properties of VBS and a method for resolving queries to VBS.
Chemodynamics of outer halo dwarf stars, including potential members of $\textit{Gaia}$-Enceladus and $\textit{Gaia}$-Sequoia

High resolution Keck HIRES and Gemini GRACES spectra are combined with Gaia DR2 photometry and astrometry to re-examine the low-metallicity, kinematically interesting dwarf stars studied by Stephens & Boesgaard (2002). These stars, spanning a metallicity range of $-1.5>$ [Fe/H] $>-3$, were originally selected as outer halo dwarf stars because of their large apocentric radii, large distances from the Milky Way plane and/or highly elliptical or retrograde orbits. Using additional chemical and kinematic criteria, we select a subsample of six stars (test cases) to redetermine stellar parameters and abundances using new model atmospheres and updated atomic data. We confirm two of the six stars in our sample to be very metal-poor, with [Fe/H]$\sim -2.3$ (G158-100, G037-037) and find two other stars to be Mg-poor at intermediate metallicities [Fe/H]$\sim-1.5$ (G184-007 and G189-050). The latter signature is suggestive of origins in an accreted dwarf galaxy. Gaia DR2 data is used to redetermine the orbital parameters for the entire SB02 sample. We find nine stars to be dynamically coincident with the \textit{Gaia}-Enceledas satellite merger, including one very low metallicity star (G238-030) ([Fe/H]$\sim-3.6$). Another 17 stars are found to be dynamically coincident with the \textit{Gaia}-Sequoia accretion event, including one very metal-poor star near [Fe/H] $=-3.5$. Both metal-poor stars have low masses and isochrone ages older than 10 Gyr. A knee in [$\alpha$/Fe] is found in the \textit{Gaia}-Enceladus associated stars, near [Fe/H]$\sim-1.6$, while the location of an [$\alpha$/Fe] is less clear in the \textit{Gaia}-Sequoia sample. If the metal-poor stars in these samples are true members of the \textit{Gaia}-Enceledas and \textit{Gaia}-Sequoia remnants, they present unique opportunities to probe the low metallicity tail and early star formation history of these systems.
Keyphrase Generation for Scientific Articles using GANs

In this paper, we present a keyphrase generation approach using conditional Generative Adversarial Networks (GAN). In our GAN model, the generator outputs a sequence of keyphrases based on the title and abstract of a scientific article. The discriminator learns to distinguish between machine-generated and human-curated keyphrases. We evaluate this approach on standard benchmark datasets. Our model achieves state-of-the-art performance in generation of abstractive keyphrases and is also comparable to the best performing extractive techniques. We also demonstrate that our method generates more diverse keyphrases and make our implementation publicly available1.
Correctional Nurses' Knowledge and Perceptions of Methicillin-Resistant Staphylococcus aureus

Correctional Nurses’ Knowledge and Perceptions of Methicillin-Resistant Staphylococcus aureus by Deborah Hall Winbush MSN, University of North Carolina at Charlotte, 2005 BSN, University of North Carolina at Charlotte, 2000 Project Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Nursing Practice
Direct measurement of the Kepler Space Telescope CCD’s intrapixel response function

Space missions designed for high precision photometric monitoring of stars often under-sample the point-spread function, with much of the light landing within a single pixel. Missions like MOST, Kepler, BRITE, and TESS, do this to avoid uncertainties due to pixel-to-pixel response nonuniformity. This approach has worked remarkably well. However, individual pixels also exhibit response nonuniformity. Typically, pixels are most sensitive near their centers and less sensitive near the edges, with a difference in response of as much as 50%. The exact shape of this fall-off, and its dependence on the wavelength of light, is the intra-pixel response function (IPRF). A direct measurement of the IPRF can be used to improve the photometric uncertainties, leading to improved photometry and astrometry of under-sampled systems. Using the spot-scan technique, we measured the IPRF of a flight spare e2v CCD90 imaging sensor, which is used in the Kepler focal plane. Our spot scanner generates spots with a full-width at half-maximum of $\lesssim$3 microns across the range of 400 nm - 850 nm. We find that Kepler's CCD shows similar IPRF behavior to other back-illuminated devices, with a decrease in responsivity near the edges of a pixel by $\sim$50%. The IPRF also depends on wavelength, exhibiting a large amount of diffusion at shorter wavelengths and becoming much more defined by the gate structure in the near-IR. This method can also be used to measure the IPRF of the CCDs used for TESS, which borrows much from the Kepler mission.
Segmentation of lesioned brain anatomy with deep volumetric neural networks and multiple spatial priors achieves human-level performance

Conventional automated segmentation of MRI of the brain and head distinguishes different tissues based on image intensities and prior tissue probability maps (TPM). This works well for normal head anatomies, but fails in the presence of unexpected lesions. Deep convolutional neural networks leverage instead spatial patterns and can learn to segment lesions, but have thus far not leveraged prior probabilities. Here we add to a three-dimensional convolutional network spatial priors with a TPM, morphological priors with conditional random fields, and context with a wider field-of-view at lower resolution. We train and test these networks on images of 43 stroke patients and 4 healthy individuals which have been manually segmented. The analysis demonstrates the benefits of leveraging the three sources of prior information. We also provide an out-of-sample validation and clinical application of the approach on an additional 47 patients with disorders of consciousness. Importantly, we demonstrate that the new architecture, which we call MultiPrior network, surpaces the performance of expert human segmenters. We make the code and trained networks freely available.
Reduced Side Lobe Elliptical-cylindrical Antenna Array Design Using Sade Optimized Hyper Beam

Now a time with the advancement of Radar technology, detecting objects, determining the structure of the target, estimating the direction and the speed is prominently increasing. There is no doubt that small cross section targets are hardly identified and determined. An antenna array with economical number of elements having increased range, reduced multipath is the heart-desire of communication engineers. Besides, reduced side lobe with a sharp main beam can determine the wanted signals precisely while rejecting the non-interested signals. An optimization technique that can be able to learn and behave intelligently is integrated to design an elliptical-cylindrical antenna array. The evolutionary algorithm in general and Self-adaptive Differential Evolution (SaDE) is implemented to design the proposed antenna design. A reduced number of active elements with non-uniform excitation currents of ECAA synthesis is performed to obtain reduced SLL, low cost and good radiation pattern. With increased antenna gain, small cross section objects can be accurately determined at an unforgiving environment where there are elevated interferences. In general, the results of this study clearly revealed that the SLL is highly reduced. The proposed antenna array offers high system availability over wide range of applications. Reduction of the SLL, the FNBW and the required power consumption are achieved.
Unusual valence state in the antiperovskites 
Sr3SnO
 and 
Sr3PbO
 revealed by x-ray photoelectron spectroscopy

The class of antiperovskite compounds $A_3B$O ($A$ = Ca, Sr, Ba; $B$ = Sn, Pb) has attracted interest as a candidate 3D Dirac system with topological surface states protected by crystal symmetry. A key factor underlying the rich electronic structure of $A_3B$O is the unusual valence state of $B$, i.e., a formal oxidation state of $-4$. Practically, it is not obvious whether anionic $B$ can be stabilized in thin films, due to its unusual chemistry, as well as the polar surface of $A_3B$O, which may render the growth-front surface unstable. We report X-ray photoelectron spectroscopy (XPS) measurements of single-crystalline films of Sr$_3$SnO and Sr$_3$PbO grown by molecular beam epitaxy (MBE). We observe shifts in the core-level binding energies that originate from anionic Sn and Pb, consistent with density functional theory (DFT) calculations. Near the surface, we observe additional signatures of neutral or cationic Sn and Pb, which may point to an electronic or atomic reconstruction with possible impact on putative topological surface states.
A bivariate logistic regression model based on latent variables

Bivariate observations of binary and ordinal data arise frequently and require a bivariate modeling approach in cases where one is interested in aspects of the marginal distributions as separate outcomes along with the association between the two. We consider methods for constructing such bivariate models based on latent variables with logistic marginals and propose a model based on the Ali‐Mikhail‐Haq bivariate logistic distribution. We motivate the model as an extension of that based on the Gumbel type 2 distribution as considered by other authors and as a bivariate extension of the logistic distribution, which preserves certain natural characteristics. Basic properties of the obtained model are studied and the proposed methods are illustrated through analysis of two data sets: a basic science cognitive experiment of visual recognition and awareness and a clinical data set describing assessments of walking disability among multiple sclerosis patients.
Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures.

Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal methods for design and analysis of nanophotonic systems.
The $f$-Divergence Expectation Iteration Scheme.

This paper introduces the $f$-EI$(\phi)$ algorithm, a novel iterative algorithm which operates on measures and performs $f$-divergence minimisation in a Bayesian framework. We prove that for a rich family of values of $(f,\phi)$ this algorithm leads at each step to a systematic decrease in the $f$-divergence and show that we achieve an optimum. In the particular case where we consider a weighted sum of Dirac measures and the $\alpha$-divergence, we obtain that the calculations involved in the $f$-EI$(\phi)$ algorithm simplify to gradient-based computations. Empirical results support the claim that the $f$-EI$(\phi)$ algorithm serves as a powerful tool to assist Variational methods.
Microreversibility, nonequilibrium response, and Euler’s polynomials

Microreversibility constrains the fluctuations of the nonequilibrium currents that cross an open system. This can be seen from the so-called fluctuation relations, which are a direct consequence of microreversibility. Indeed, the latter are known to impose time-reversal symmetry relations on the statistical cumulants of the currents and their responses at arbitrary orders in the deviations from equilibrium. Remarkably, such relations can be analyzed by means of Euler’s polynomials. Here we show that fluctuation relations can actually be explicitly written in terms of the constant terms of these particular polynomials. We hence demonstrate that Euler’s polynomials are indeed fundamentally rooted in fluctuation relations, both in the absence and the presence of an external magnetic field.
"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks via Reward Shaping

In order to learn effectively, robots must be able to extract the intangible context by which task progress and mistakes are defined. In the domain of reinforcement learning, much of this information is provided by the reward function. Hence, reward shaping is a necessary part of how we can achieve state-of-the-art results on complex, multi-step tasks. However, comparatively little work has examined how reward shaping should be done so that it captures task context, particularly in scenarios where the task is long-horizon and failure is highly consequential. Our Schedule for Positive Task (SPOT) reward trains our Efficient Visual Task (EVT) model to solve problems that require an understanding of both task context and workspace constraints of multi-step block arrangement tasks. In simulation EVT can completely clear adversarial arrangements of objects by pushing and grasping in 99% of cases vs an 82% baseline in prior work. For random arrangements EVT clears 100% of test cases at 86% action efficiency vs 61% efficiency in prior work. EVT + SPOT is also able to demonstrate context understanding and complete stacks in 74% of trials compared to a baseline of 5% with EVT alone. To our knowledge, this is the first instance of a Reinforcement Learning based algorithm successfully completing such a challenge. Code is available at https://github.com/jhu-lcsr/good_robot .
First Time of nanoscopic electrostatic drives pushing for ultrasonic transmission for gesture recognition

The design, fabrication, and acoustic characterization of an electrostatic ultrasound transducer based on lateral actuation is documented in this letter. Unlike cMUTs or pMUTs, this device-NED-MUT -does not use a membrane to generate pressure waves but instead displaces air using the volume of the chip. A single transmitter unit, composed of 258 actuators in an area of 3×3 mm2 operating at 40 kHz, was capable of generating a pressure level of 87 dB (relative to 20 μPa rms) at a distance of 8.9 cm with an input voltage of 24V DC plus 24V AC. The measured performance makes the NED-MUT suitable for gesture recognition using ultrasound ranging.
How Does Learning Rate Decay Help Modern Neural Networks

Learning rate decay (lrDecay) is a \emph{de facto} technique for training modern neural networks. It starts with a large learning rate and then decays it multiple times. It is empirically observed to help both optimization and generalization. Common beliefs in how lrDecay works come from the optimization analysis of (Stochastic) Gradient Descent: 1) an initially large learning rate accelerates training or helps the network escape spurious local minima; 2) decaying the learning rate helps the network converge to a local minimum and avoid oscillation. Despite the popularity of these common beliefs, experiments suggest that they are insufficient in explaining the general effectiveness of lrDecay in training modern neural networks that are deep, wide, and nonconvex. We provide another novel explanation: an initially large learning rate suppresses the network from memorizing noisy data while decaying the learning rate improves the learning of complex patterns. The proposed explanation is validated on a carefully-constructed dataset with tractable pattern complexity. And its implication, that additional patterns learned in later stages of lrDecay are more complex and thus less transferable, is justified in real-world datasets. We believe that this alternative explanation will shed light into the design of better training strategies for modern neural networks.
Extending the Message Passing Interface (MPI) with User-Level Schedules

Composability is one of seven reasons for the long-standing and continuing success of MPI. Extending MPI by composing its operations with user-level operations provides useful integration with the progress engine and completion notification methods of MPI. However, the existing extensibility mechanism in MPI (generalized requests) is not widely utilized and has significant drawbacks. MPI can be generalized via scheduled communication primitives, for example, by utilizing implementation techniques from existing MPI-3 nonblocking collectives and from forthcoming MPI-4 persistent and partitioned APIs. Non-trivial schedules are used internally in some MPI libraries; but, they are not accessible to end-users. Message-based communication patterns can be built as libraries on top of MPI. Such libraries can have comparable implementation maturity and potentially higher performance than MPI library code, but do not require intimate knowledge of the MPI implementation. Libraries can provide performance-portable interfaces that cross MPI implementation boundaries. The ability to compose additional user-defined operations using the same progress engine benefits all kinds of general purpose HPC libraries. We propose a definition for MPI schedules: a user-level programming model suitable for creating persistent collective communication composed with new application-specific sequences of user-defined operations managed by MPI and fully integrated with MPI progress and completion notification. The API proposed offers a path to standardization for extensible communication schedules involving user-defined operations. Our approach has the potential to introduce event-driven programming into MPI (beyond the tools interface), although connecting schedules with events comprises future work. Early performance results described here are promising and indicate strong overlap potential.
Optimizing Polar Codes Compatible with Off-the-Shelf LDPC Decoders

Previous work showed that polar codes can be decoded using off-the-shelf LDPC decoders by imposing special constraints on the LDPC code structure, which, however, resulted in some performance degradation. In this paper we show that this loss can be mitigated; in particular, we demonstrate how the gap between LDPC-style decoding and Arıkan’s Belief Propagation (BP) decoding of polar codes can be closed by taking into account the underlying graph structure of the LDPC decoder while jointly designing the polar code and the parity-check matrix of the corresponding LDPC-like code. The resulting polar codes under conventional LDPC-style decoding are shown to have similar error-rate performance when compared to some well-known and standardized LDPC codes. Moreover, we obtain performance gains in the high SNR region.
Stochastic thermodynamics for active matter.

We propose a generalization of stochastic thermodynamics to systems of active particles, which move under the combined influence of stochastic internal self-propulsions (activity) and a heat bath. The main idea is to consider joint trajectories of particles' positions and self-propulsions. It is then possible to exploit formal similarity of an active system and a system consisting of two subsystems interacting with different heat reservoirs and coupled by a non-symmetric interaction. The resulting thermodynamic description closely follows the standard stochastic thermodynamics. In particular, total entropy production, $\Delta s_\text{tot}$, can be decomposed into housekeeping, $\Delta s_\text{hk}$, and excess, $\Delta s_\text{ex}$, parts. Both $\Delta s_\text{tot}$ and $\Delta s_\text{hk}$ satisfy fluctuation theorems. The average rate of the steady-state housekeeping entropy production can be related to the violation of the fluctuation-dissipation theorem via a Harada-Sasa relation. The excess entropy production enters into a Hatano-Sasa-like relation, which leads to a generalized Clausius inequality involving the change of the system's entropy and the excess entropy production. Interestingly, although the evolution of particles' self-propulsions is free and uncoupled from that of their positions, non-trivial steady-state correlations between these variables lead to the non-zero excess dissipation in the reservoir coupled to the self-propulsions.
Adversarial Deep Embedded Clustering: On a Better Trade-off Between Feature Randomness and Feature Drift

To overcome the absence of concrete supervisory signals, deep clustering models construct their own labels based on self-supervision and pseudo-supervision. However, applying these techniques can cause Feature Randomness and Feature Drift. In this paper, we formally characterize these two new concepts. On one hand, Feature Randomness takes place when a considerable portion of the pseudo-labels is deemed to be random. In this regard, the trained model can learn non-representative features. On the other hand, Feature Drift takes place when the pseudo-supervised and the reconstruction losses are jointly minimized. While penalizing the reconstruction loss aims to preserve all the inherent data information, optimizing the embedded-clustering objective drops the latent between-cluster variances. Due to this compromise, the clustering-friendly representations can be easily drifted. In this context, we propose ADEC (Adversarial Deep Embedded Clustering) a novel autoencoder-based clustering model, which relies on a discriminator network to reduce random features while avoiding the drifting effect. Our new metrics <inline-formula><tex-math notation="LaTeX">$\Delta _{FR}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>Δ</mml:mi><mml:mrow><mml:mi>F</mml:mi><mml:mi>R</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="bouguessa-ieq1-2997772.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\Delta _{FD}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>Δ</mml:mi><mml:mrow><mml:mi>F</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="bouguessa-ieq2-2997772.gif"/></alternatives></inline-formula> allows to, respectively, assess the level of Feature Randomness and Feature Drift. We empirically demonstrate the suitability of our model on handling these problems using benchmark real datasets. Experimental results validate that our model outperforms state-of-the-art autoencoder-based clustering methods.