Miracle-dz/UniFAR-FTUs huggingface dataset:
Uniform convergence to equilibrium for coupled parabolic PDEs and linear evolution equations

We consider systems of heat equations, subject to Neumann boundary conditions or on the whole space $\mathbb{R}^d$, that are coupled by a matrix-valued potential $V$. Our main question is under which conditions each solution to such a system converges to an equilibirum as $t \to \infty$. While this is clearly a fundamental question about systems of parabolic equations, it has been studied, up to now, only under certain positivity assumptions on the potential $V$. Without positivity, Perron-Frobenius theory cannot be applied and the problem seems to be completely open. In this article we close this gap for a large class of potentials. If $V$ is $L^2$-dissipative, the problem turns out to be easily solvable by classical Hilbert space methods; for more general potentials though, the matter becomes much more delicate, and we need to employ recent spectral theoretic results that rely on the geometric structure of $L^p$-spaces. Since we wish to cover also the case $p = \infty$ - and since we further want to study a class of systems with unbounded coefficients - the solutions to our differential equations cannot, in general, be described by means of $C_0$-semigroups. To overcome this difficulty, we prove - in the theoretical part of the paper - a convergence theorem for a very general class of operator semigroups without any time regularity assumptions.
Ordered Reliability Bits Guessing Random Additive Noise Decoding

Modern applications are driving demand for ultra-reliable low-latency communications, rekindling interest in the performance of short, high-rate error correcting codes. To that end, here we introduce a soft-detection variant of Guessing Random Additive Noise Decoding (GRAND) called Ordered Reliability Bits GRAND that can decode any moderate redundancy block-code. For a code of n bits, it avails of no more than ⎾log2(n)⏋ bits of code-book-independent quantized soft detection information per received bit to determine an accurate decoding while retaining the original algorithm’s suitability for a highly parallelized implementation in hardware. ORBGRAND is shown to provide similar block error performance for codes of distinct classes (BCH, CA-Polar and RLC) with low complexity, while providing better block error rate performance than CA-SCL, a state of the art soft detection CA-Polar decoder.
A toolkit for the identification and assessment of new and emerging health technologies 2014

The first toolkit for the identification and assessment of new and emerging health technologies was developed by members of the EuroScan International Network and published in 2009. Five years later this revised toolkit continues to focus on methods that are integral to an early awareness and alert (EAA) system. It is once again a collaborative piece of work to which all members have contributed with new information based on their experiences being added. The main stages involved in EAA systems continue to be: identification of information on new and emerging technologies (horizon scanning); filtration and prioritisation of the identified information; and assessment of the technology or group of technologies. The toolkit provides guidance on these stages and highlights the various approaches that can be taken at each of these stages depending on the context of the EAA system and resources available.
Entropic Decision Making

Using results from neurobiology on perceptual decision making and value-based decision making, the problem of decision making between lotteries is reformulated in an abstract space where uncertain prospects are mapped to corresponding active neuronal representations. This mapping allows us to maximize non-extensive entropy in the new space with some constraints instead of a utility function. To achieve good agreements with behavioral data, the constraints must include at least constraints on the weighted average of the stimulus and on its variance. Both constraints are supported by the adaptability of neuronal responses to an external stimulus. By analogy with thermodynamic and information engines, we discuss the dynamics of choice between two lotteries as they are being processed simultaneously in the brain by rate equations that describe the transfer of attention between lotteries and within the various prospects of each lottery. This model is able to give new insights on risk aversion and on behavioral anomalies not accounted for by Prospect Theory.
First image then video: A two-stage network for spatiotemporal video denoising

Video denoising is to remove noise from noise-corrupted data, thus recovering true signals via spatiotemporal processing. Existing approaches for spatiotemporal video denoising tend to suffer from motion blur artifacts, that is, the boundary of a moving object tends to appear blurry especially when the object undergoes a fast motion, causing optical flow calculation to break down. In this paper, we address this challenge by designing a first-image-then-video two-stage denoising neural network, consisting of an image denoising module for spatially reducing intra-frame noise followed by a regular spatiotemporal video denoising module. The intuition is simple yet powerful and effective: the first stage of image denoising effectively reduces the noise level and, therefore, allows the second stage of spatiotemporal denoising for better modeling and learning everywhere, including along the moving object boundaries. This two-stage network, when trained in an end-to-end fashion, yields the state-of-the-art performances on the video denoising benchmark Vimeo90K dataset in terms of both denoising quality and computation. It also enables an unsupervised approach that achieves comparable performance to existing supervised approaches.
Protecting GANs against privacy attacks by preventing overfitting

Generative Adversarial Networks (GANs) have made releasing of synthetic images a viable approach to share data without releasing the original dataset. It has been shown that such synthetic data can be used for a variety of downstream tasks such as training classifiers that would otherwise require the original dataset to be shared. However, recent work has shown that the GAN models and their synthetically generated data can be used to infer the training set membership by an adversary who has access to the entire dataset and some auxiliary information. Current approaches to mitigate this problem (such as DPGAN) lead to dramatically poorer generated sample quality than the original non–private GANs. Here we develop a new GAN architecture (privGAN), where the generator is trained not only to cheat the discriminator but also to defend membership inference attacks. The new mechanism provides protection against this mode of attack while leading to negligible loss in downstream performances. In addition, our algorithm has been shown to explicitly prevent overfitting to the training set, which explains why our protection is so effective. The main contributions of this paper are: i) we propose a novel GAN architecture that can generate synthetic data in a privacy preserving manner without additional hyperparameter tuning and architecture selection, ii) we provide a theoretical understanding of the optimal solution of the privGAN loss function, iii) we demonstrate the effectiveness of our model against several white and black–box attacks on several benchmark datasets, iv) we demonstrate on three common benchmark datasets that synthetic images generated by privGAN lead to negligible loss in downstream performance when compared against non–private GANs.
Band nesting and exciton spectrum in monolayer 
MoS2

We discuss here the effect of band nesting and topology on the spectrum of excitons in a single layer of MoS$_2$, a prototype transition metal dichalcogenide material. We solve for the single particle states using the ab initio based tight-binding model containing metal $d$ and sulfur $p$ orbitals. The metal orbitals contribution evolving from $K$ to $\Gamma$ points results in conduction-valence band nesting and a set of second minima at $Q$ points in the conduction band. There are three $Q$ minima for each $K$ valley. We accurately solve the Bethe-Salpeter equation including both $K$ and $Q$ points and obtain ground and excited exciton states. We determine the effects of the electron-hole single particle energies including band nesting, direct and exchange screened Coulomb electron-hole interactions and resulting topological magnetic moments on the exciton spectrum. The ability to control different contributions combined with accurate calculations of the ground and excited exciton states allows for the determination of the importance of different contributions and a comparison with effective mass and $k\cdot p$ massive Dirac fermion models.
ZeroQ: A Novel Zero Shot Quantization Framework

Quantization is a promising approach for reducing the inference time and memory footprint of neural networks. However, most existing quantization methods require access to the original training dataset for retraining during quantization. This is often not possible for applications with sensitive or proprietary data, e.g., due to privacy and security concerns. Existing zero-shot quantization methods use different heuristics to address this, but they result in poor performance, especially when quantizing to ultra-low precision. Here, we propose \OURS, a novel zero-shot quantization framework to address this. \OURS enables mixed-precision quantization without any access to the training or validation data. This is achieved by optimizing for a Distilled Dataset, which is engineered to match the statistics of batch normalization across different layers of the network. \OURS supports both uniform and mixed-precision quantization. For the latter, we introduce a novel Pareto frontier based method to automatically determine the mixed-precision bit setting for all layers, with no manual search involved. We extensively test our proposed method on a diverse set of models, including ResNet18/50/152, MobileNetV2, ShuffleNet, SqueezeNext, and InceptionV3 on ImageNet, as well as RetinaNet-ResNet50 on the Microsoft COCO dataset. In particular, we show that \OURS can achieve 1.71\% higher accuracy on MobileNetV2, as compared to the recently proposed DFQ~\cite{nagel2019data} method. Importantly, \OURS has a very low computational overhead, and it can finish the entire quantization process in less than 30s (0.5\% of one epoch training time of ResNet50 on ImageNet). We have open-sourced the \OURS framework\footnote{https://github.com/amirgholami/ZeroQ}.
Deflating Super-puffs: Impact of Photochemical Hazes on the Observed Mass–Radius Relationship of Low-mass Planets

The observed mass–radius relationship of low-mass planets informs our understanding of their composition and evolution. Recent discoveries of low-mass, large-radius objects (“super-puffs”) have challenged theories of planet formation and atmospheric loss, as their high inferred gas masses make them vulnerable to runaway accretion and hydrodynamic escape. Here we propose that high-altitude photochemical hazes could enhance the observed radii of low-mass planets and explain the nature of super-puffs. We construct model atmospheres in radiative-convective equilibrium and compute rates of atmospheric escape and haze distributions, taking into account haze coagulation, sedimentation, diffusion, and advection by an outflow wind. We develop mass–radius diagrams that include atmospheric lifetimes and haze opacity, which is enhanced by the outflow, such that young (∼0.1–1 Gyr), warm (Teq ≥ 500 K), low-mass objects (Mc < 4 M⊕) should experience the most apparent radius enhancement due to hazes, reaching factors of three. This reconciles the densities and ages of the most extreme super-puffs. For Kepler-51b, the inclusion of hazes reduces its inferred gas mass fraction to <10%, similar to that of planets on the large-radius side of the sub-Neptune radius gap. This suggests that Kepler-51b may be evolving toward that population and that some warm sub-Neptunes may have evolved from super-puffs. Hazes also render transmission spectra of super-puffs and sub-Neptunes featureless, consistent with recent measurements. Our hypothesis can be tested by future observations of super-puffs’ transmission spectra at mid-infrared wavelengths, where we predict that the planet radius will be half of that observed in the near-infrared.
The Carnegie-Irvine Galaxy Survey. IX. Classification of Bulge Types and Statistical Properties of Pseudo Bulges

We study the statistical properties of 320 bulges of disk galaxies in the Carnegie-Irvine Galaxy Survey, using robust structural parameters of galaxies derived from image fitting. We apply the Kormendy relation to classify classical and pseudo bulges and characterize the bulge dichotomy with respect to the structural properties of the bulges and the physical properties of the host galaxies. We confirm previous findings that pseudo bulges on average have smaller Sérsic indices, lower bulge-to-total ratios, and fainter surface brightnesses than classical bulges. Our sizable sample statistically shows that pseudo bulges are more intrinsically flattened than classical bulges. Pseudo bulges are most frequent (incidence ≳80%) in late-type spirals (later than Sc). Our measurements support the picture in which pseudo bulges arose from star formation induced by inflowing gas, while classical bulges were born out of violent processes such as mergers and coalescence of clumps. We reveal differences with the literature that warrant attention: (1) the bimodal distribution of Sérsic indices presented by previous studies is not reproduced in our study; (2) classical and pseudo bulges have similar relative bulge sizes; and (3) the pseudo bulge fraction is considerably smaller in early-type disks than reported in previous studies based on one-dimensional surface brightness profile fitting. We attribute these differences to our improved image quality, more robust bulge-to-disk decomposition technique, and to the different classification criteria that we applied. Moreover, we find that barred galaxies do not host more pseudo bulges or more prominent pseudo bulges than unbarred galaxies. Various implications of these findings are discussed.
An ALMA survey of the SCUBA-2 cosmology legacy survey UKIDSS/UDS field: Dust attenuation in high-redshift Lyman-break galaxies

We analyse 870 $\mu$m Atacama Large Millimetre Array (ALMA) dust continuum detections of 41 canonically selected $z$ ≃ 3 Lyman-break galaxies (LBGs), as well as 209 ALMA-undetected LBGs, in follow-up of SCUBA-2 mapping of the UKIDSS Ultra Deep Survey (UDS) field. We find that our ALMA-bright LBGs lie significantly off the local IRX-beta relation and have relatively bluer rest-frame UV slopes (as parametrized by β), given their high values of the ‘infrared excess’ (IRX ≡ LIR/LUV), relative to the average ‘local’ IRX-β relation. We attribute this finding in part to the young ages of the underlying stellar populations but we find that the main reason behind the unusually blue UV slopes are the relatively shallow slopes of the corresponding dust attenuation curves. We show that, when stellar masses, M*, are being established via SED fitting, it is absolutely crucial to allow the attenuation curves to vary (rather than fixing it on Calzetti-like law), where we find that the inappropriate curves may underestimate the resulting stellar masses by a factor of ≃2–3× on average. In addition, we find these LBGs to have relatively high specific star-formation rates (sSFRs), dominated by the dust component, as quantified via the fraction of obscured star formation $(f_{\rm obs}\equiv {\rm SFR_{\rm IR}/{\rm SFR}_{\rm UV+IR}})$. We conclude that the ALMA-bright LBGs are, by selection, massive galaxies undergoing a burst of a star formation (large sSFRs, driven, for example, by secular or merger processes), with a likely geometrical disconnection of the dust and stars, responsible for producing shallow dust attenuation curves.
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

Many real-world control problems involve both discrete decision variables - such as the choice of control modes, gear switching or digital outputs - as well as continuous decision variables - such as velocity setpoints, control gains or analogue outputs. However, when defining the corresponding optimal control or reinforcement learning problem, it is commonly approximated with fully continuous or fully discrete action spaces. These simplifications aim at tailoring the problem to a particular algorithm or solver which may only support one type of action space. Alternatively, expert heuristics are used to remove discrete actions from an otherwise continuous space. In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously. In our experiments, we first demonstrate that the proposed approach efficiently solves such natively hybrid reinforcement learning problems. We then show, both in simulation and on robotic hardware, the benefits of removing possibly imperfect expert-designed heuristics. Lastly, hybrid reinforcement learning encourages us to rethink problem definitions. We propose reformulating control problems, e.g. by adding meta actions, to improve exploration or reduce mechanical wear and tear.
3D UAV Trajectory and Communication Design for Simultaneous Uplink and Downlink Transmission

In this paper, we investigate the unmanned aerial vehicle (UAV)-aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem’s hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity.
LXVII Ultrastructure of the Tracheal Ciliated Mucosa in Rat and Man

New techniques in morphology have been developed during the last eight years. Among these techniques, that of electron microscopy has opened up a new world of details inside a cell, the knowledge of which seems to be of great importance for the understanding of the normal and pathologic activity of cells. The application of the electron microscope in studying the structure of the tracheal epitheli­ um 4 did contribute to the understanding of problems like ciliary activity, mucus secretion, and cell regeneration. The data to be dis­ cussed here are taken mainly from this reference. However, some recent experiments have been done with human trachea, generously placed at my disposal by Dr. John F. Daly, Department of Otorhinolaryngology at New York University, Bellevue Medical Center. The micrographs in this communication represent all human trachea, whereas the schematic drawings are the summary of a study of the rat trachea. The tissue is fixed in osmium tetroxide, 2 , 3 , 8 dehydrated in alcohols and embedded in liquid plastic which subsequently is made to harden. The specimen can then be sectioned with a special type of microtome which produces sections with a thickness of about onemillionth of an inch. The sections may immediately be examined in the electron microscope without dissolving the embedding medium or staining the tissue. The magnifications used ordinarily range between 2,000 and 20,000 diameters. By subsequent printing, a magnification of 50,000 to 200,000 diameters may easily be achieved, at which structures of molecular size may be pictured.
Joint 3D Maneuver and Power Adaptation for Secure UAV Communication With CoMP Reception

This paper studies a secrecy unmanned aerial vehicle (UAV) communication system with coordinated multi-point (CoMP) reception, in which one UAV sends confidential messages to a set of cooperative ground receivers (GRs), in the presence of several suspicious eavesdroppers. In particular, we consider two types of eavesdroppers that are non-colluding and colluding, respectively. Under this setup, we exploit the UAV’s maneuver in three dimensional (3D) space together with transmit power adaptation for optimizing the secrecy communication performance. First, we consider the quasi-stationary UAV scenario, in which the UAV is placed at a fixed but optimizable location during the communication period. In this scenario, we jointly optimize the UAV’s 3D placement and transmit power control to maximize the secrecy rate. Under both non-colluding and colluding eavesdroppers, we obtain the optimal solutions to the joint 3D placement and transmit power control problems in well structures. Next, we consider the mobile UAV scenario, in which the UAV has a mission to fly from an initial location to a final location during the communication period. In this scenario, we jointly optimize the UAV’s 3D trajectory and transmit power allocation to maximize the average secrecy rate during the whole communication period. To deal with the difficult joint 3D trajectory and transmit power allocation problems, we present alternating-optimization-based approaches to obtain high-quality solutions. Finally, we provide numerical results to validate the performance of our proposed designs. It is shown that due to the consideration of CoMP reception, our proposed design with 3D maneuver significantly outperforms the conventional design with two dimensional (2D) (horizontal) maneuver only, by exploiting the additional degrees of freedom in altitudes. It is also shown that the non-colluding and colluding eavesdroppers lead to distinct 3D UAV maneuver behaviors, e.g., under colluding eavesdroppers, the UAV should fly farther apart from them (than that under the non-colluding ones) for avoiding their collaborative interception.
Origin of Strong Two-Magnon Scattering in Heavy-Metal/Ferromagnet/Oxide Heterostructures

We experimentally investigate the origin of the two-magnon scattering (TMS) in heavy-metal (HM)/ferromagnet (FM)/oxide heterostructures (FM = Co, Ni81Fe19, or Fe60Co20B20) by varying the materials located above and below the FM layers. We show that strong TMS in HM/FM/oxide systems arises primarily at the HM/FM interface and increases with the strength of interfacial spin-orbit coupling and magnetic roughness at this interface. TMS at the FM/oxide interface is relatively weak, even in systems where spin-orbit coupling at this interface generates strong interfacial magnetic anisotropy. We also suggest that the spin-current-induced excitation of non-uniform short-wavelength magnon at the HM/FM interface may function as a mechanism of spin memory loss for the spin-orbit torque exerted on the uniform mode.