Miracle-dz/UniFAR-FTUs huggingface dataset:
Fitting an Analytic Magnetic Field to a Prestellar Core

We deploy and demonstrate the capabilities of the magnetic field model developed by Ewertowski & Basu by fitting observed polarimetry data of the prestellar core FeSt 1–457. The analytic hourglass magnetic field function derived directly from Maxwell’s equations yields a central-to-surface magnetic field strength ratio in the equatorial plane, as well as magnetic field directions with relative magnitudes throughout the core. This fit emerges from a comparison of a single plane of the model with the polarization map that results from the integrated properties of the magnetic field and dust throughout the core. Importantly, our fit is independent of any assumed density profile of the core. We check the robustness of the fit by using the POLARIS code to create synthetic polarization maps that result from the integrated scattering and emission properties of the dust grains and their radiative transfer, employing an observationally motivated density profile. We find that the synthetic polarization maps obtained from the model also provide a good fit to the observed polarimetry. Our model fits the striking feature of significant curvature of magnetic field lines in the outer part of FeSt 1–457. Combined with independent column density estimates, we infer that the core of size R gas has a mildly supercritical mass-to-flux ratio and may have formed through dynamical motions starting from a significantly larger radius R. A breakdown of flux freezing through neutral-ion slip (ambipolar diffusion) could be responsible for effecting such a transition from a large-scale magnetic field structure to a more compact gas structure.
NemaNet: A convolutional neural network model for identification of nematodes soybean crop in brazil

Phytoparasitic nematodes (or phytonematodes) are causing severe damage to crops and generating large-scale economic losses worldwide. In soybean crops, annual losses are estimated at 10.6% of world production. Besides, identifying these species through microscopic analysis by an expert with taxonomy knowledge is often laborious, time-consuming, and susceptible to failure. In this perspective, robust and automatic approaches are necessary for identifying phytonematodes capable of providing correct diagnoses for the classification of species and subsidizing the taking of all control and prevention measures. This work presents a new public data set called NemaDataset containing 3,063 microscopic images from five nematode species with the most significant damage relevance for the soybean crop. Additionally, we propose a new Convolutional Neural Network (CNN) model defined as NemaNet and a comparative assessment with thirteen popular models of CNNs, all of them representing the state of the art classification and recognition. The general average calculated for each model, on a from-scratch training, the NemaNet model reached 96.99% accuracy, while the best evaluation fold reached 98.03%. In training with transfer learning, the average accuracy reached 98.88\%. The best evaluation fold reached 99.34% and achieve an overall accuracy improvement over 6.83% and 4.1%, for from-scratch and transfer learning training, respectively, when compared to other popular models.
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples

Designing deep networks robust to adversarial examples remains an open problem. Likewise, recent zeroth order hard-label attacks on image classification models have shown comparable performance to their first-order, gradient-level alternatives. It was recently shown in the gradient-level setting that regular adversarial examples leave the data manifold, while their on-manifold counterparts are in fact generalization errors. In this paper, we argue that query efficiency in the zeroth-order setting is connected to an adversary's traversal through the data manifold. To explain this behavior, we propose an information-theoretic argument based on a noisy manifold distance oracle, which leaks manifold information through the adversary's gradient estimate. Through numerical experiments of manifold-gradient mutual information, we show this behavior acts as a function of the effective problem dimensionality and number of training points. On real-world datasets and multiple zeroth-order attacks using dimension-reduction, we observe the same universal behavior to produce samples closer to the data manifold. This results in up to two-fold decrease in the manifold distance measure, regardless of the model robustness. Our results suggest that taking the manifold-gradient mutual information into account can thus inform better robust model design in the future, and avoid leakage of the sensitive data manifold.
Tail forecasts of inflation using time-varying parameter quantile regressions

This paper proposes methods for Bayesian inference in time-varying parameter (TVP) quantile regression (QR) models. We use data augmentation schemes to facilitate the conditional likelihood, and render the model conditionally Gaussian to develop an efficient Gibbs sampling algorithm. Regularization of the highdimensional parameter space is achieved via flexible dynamic shrinkage priors. A simple version of the TVP-QR based on an unobserved component (UC) model is applied to dynamically trace the quantiles of the distribution of inflation in the United States (US), the United Kingdom (UK) and the euro area (EA). We conduct an out-of-sample inflation forecasting exercise to assess predictive accuracy of the proposed framework versus several benchmarks using metrics to capture performance in different parts of the distribution. The proposed model is competitive and performs particularly well for higher-order and tail forecasts. We analyze the resulting predictive distributions and find that they are often skewed and feature heavier than normal tails.
Mountain pass connections for elliptic Allen-Cahn systems

Given a nonnegative, smooth potential V : Rk → R (k ≥ 2) with multiple zeros, we say that a curve q : R→ Rk is a connection if it solves the system of ordinary differential equations q′′ = ∇uV (q), in R, and tends to a zero of V at ±∞. While (to the best of our knowledge) the research conducted so far has exclusively focused in connections which minimize the associated energy functional in a suitable function space, in this paper we provide some existence results regarding non-minimizing connections. Our approach is based on several refined versions of the classical Mountain Pass Lemma.
Multilingual Byte2Speech Text-To-Speech Models Are Few-shot Spoken Language Learners

We present a multilingual end-to-end Text-To-Speech framework that maps byte inputs to spec-trograms, thus allowing arbitrary input scripts. Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to various new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording without the need of lexicon, extra corpus, auxiliary models, or particular linguistic expertise, while retains satisfactory intelligibility and naturalness matching rich-resource models. Exhaustive comparative studies are performed to reveal the potential of the framework for low-resource application and the impact of various factors contributory to adaptation. Furthermore, we propose a novel method to extract language-speciﬁc sub-networks for a better understanding of the mechanism of multilingual models.
R andomized eigen - spectrograms extraction for an effective fault diagnosis of bearings

The Intelligent Fault Diagnosis of rotating machinery proposes some captivating challenges in light of the imminent big data era. Large amounts of data are expected to populate the Internet of Things (IoT) diagnostic services. Consequently, today’s deep learning strategies are evolving towards e ﬀ ective approaches such as transfer learning to uncover hidden paths in extensive vibration data. However, this ﬁeld is characterized by several open issues. Models’ interpretation is still buried under the foundations of data driven science, thus requiring attention to the development of new opportunities also for machine learning theories. This study proposes a diagnosis model, based on intelligent spectrogram recognition, via image processing. The novel approach is embodied by the introduction of the eigen-spectrograms and randomized linear algebra in fault diagnosis. The eigen-spectrograms hierarchically display inherent structures underlying spectrogram images. Also, di ﬀ erent combinations of eigen-spectrograms are expected to describe multiple machine health states. Randomized algebra and eigen-spectrograms enable the construction of a signiﬁcant feature space, which nonetheless emerges as a viable device to explore models’ interpretations. The computational e ﬃ ciency of randomized approaches further collocates this methodology in the big data perspective and provides new reading keys of well-established statistical learning theories, such as the Support Vector Machine (SVM). The conjunction of randomized algebra and Support Vector Machine for spectrogram recognition shows to be extremely accurate and e ﬃ cient as compared to state of the art results and transfer learning strategies.
Contrastive Disentanglement in Generative Adversarial Networks

Disentanglement is defined as the problem of learninga representation that can separate the distinct, informativefactors of variations of data. Learning such a representa-tion may be critical for developing explainable and human-controllable Deep Generative Models (DGMs) in artificialintelligence. However, disentanglement in GANs is not a triv-ial task, as the absence of sample likelihood and posteriorinference for latent variables seems to prohibit the forwardstep. Inspired by contrastive learning (CL), this paper, froma new perspective, proposes contrastive disentanglement ingenerative adversarial networks (CD-GAN). It aims at dis-entangling the factors of inter-class variation of visual datathrough contrasting image features, since the same factorvalues produce images in the same class. More importantly,we probe a novel way to make use of limited amount ofsupervision to the largest extent, to promote inter-class dis-entanglement performance. Extensive experimental resultson many well-known datasets demonstrate the efficacy ofCD-GAN for disentangling inter-class variation.
Variational Structured Attention Networks for Deep Visual Representation Learning

Convolutional neural networks have enabled major progresses in addressing pixel-level prediction tasks such as semantic segmentation, depth estimation, surface normal prediction and so on, benefiting from their powerful capabilities in visual representation learning. Typically, state of the art models integrate attention mechanisms for improved deep feature representations. Recently, some works have demonstrated the significance of learning and combining both spatial- and channel-wise attentions for deep feature refinement. In this paper, we aim at effectively boosting previous approaches and propose a unified deep framework to jointly learn both spatial attention maps and channel attention vectors in a principled manner so as to structure the resulting attention tensors and model interactions between these two types of attentions. Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework, leading to VarIational STructured Attention networks (VISTA-Net). We implement the inference rules within the neural network, thus allowing for end-to-end learning of the probabilistic and the CNN front-end parameters. As demonstrated by our extensive empirical evaluation on six large-scale datasets for dense visual prediction, VISTA-Net outperforms the state-of-the-art in multiple continuous and discrete prediction tasks, thus confirming the benefit of the proposed approach in joint structured spatial-channel attention estimation for deep representation learning. The code is available at https://github.com/ygjwd12345/VISTA-Net.
Environmental Sound Classification on the Edge: Deep Acoustic Networks for Extremely Resource-Constrained Devices

Significant efforts are being invested to bring the classification and recognition powers of desktop and cloud systemsdirectly to edge devices. The main challenge for deep learning on the edge is to handle extreme resource constraints(memory, CPU speed and lack of GPU support). We present an edge solution for audio classification that achieves close to state-of-the-art performance on ESC-50, the same benchmark used to assess large, non resource-constrained networks. Importantly, we do not specifically engineer thenetwork for edge devices. Rather, we present a universalpipeline that converts a large deep convolutional neuralnetwork (CNN) automatically via compression and quantization into a network suitable for resource-impoverishededge devices. We first introduce a new sound classification architecture, ACDNet, that produces above state-of-the-art accuracy on both ESC-10 and ESC-50 which are 96.75% and 87.05% respectively. We then compress ACDNet using a novel network-independent approach to obtain an extremely small model. Despite 97.22% size reduction and 97.28% reduction in FLOPs, the compressed network still achieves 82.90% accuracy on ESC-50, staying close to the state-of-the-art. Using 8-bit quantization, we deploy ACD-Net on standard microcontroller units (MCUs). To the best of our knowledge, this is the first time that a deep network for sound classification of 50 classes has successfully been deployed on an edge device. While this should be of interestin its own right, we believe it to be of particular impor-tance that this has been achieved with a universal conver-sion pipeline rather than hand-crafting a network for mini-mal size.
EXCITATION ENERGY TRANSFER IN THE CHROMATICALLY ADAPTED PHYCOBILIN SYSTEMS OF BLUE‐GREEN ALGAE: DIFFERENCE IN THE ENERGY TRANSFER KINETICS AT PHYCOCYANIN LEVEL

Abstract— Excitation energy transfer in chromatically adapted phycobilin system was investigated with the blue‐green algae Tolypothrix tenuis and, supplementary, Fremyella diplosiphon with use of time‐resolved fluorescence spectrum (Yamazaki et al, 1984). Special attention was paid to the energy migration at the phycocyanin (PC) level in the phycoerythrin (PE)‐rich and PE excited system and in the PE‐less and PC excited system. The energy transfer from PC to allophycocyanin was far faster in the former than in the latter in both organisms. Such feature was the same as our previous observation for PE‐rich system of Porphyridium cruentum and PE‐less system of Anacystis nidulans (Yamazaki et al, 1984). Thus, the difference in phycobilisome structure is not a cause for such difference. Based on simulation analysis, we interpreted our observation as that (1) all PC chtomophores do not equally participate to the energy migration within PC compartment but (2) a short transfer path through PC compartment is formed probably by f‐type chromophores and (3) the difference in the “length” of this path is a main determinant for kinetic difference between PE‐rich and PE‐less systems.
The Design of Dual Band Stacked Metasurfaces Using Integral Equations

An integral equation-based approach for the design of dual-band stacked metasurfaces that are invariant in 1-D is presented. The stacked metasurface will generate collimated beams at the desired angles in each band upon reflection. The conductor-backed stacked metasurface consists of two metasurfaces (a patterned metallic cladding supported by a dielectric spacer) stacked one upon the other. The stacked metasurface is designed in three phases. First, the patterned metallic cladding of each metasurface is homogenized and modeled as an inhomogeneous impedance sheet. An electric field integral equation (EFIE) is written to model the mutual coupling between the homogenized elements within each metasurface and from metasurface to metasurface. The EFIE is transformed into matrix equations by the method of moments. The nonlinear matrix equations are solved at both bands iteratively resulting in dual-band complex-valued impedance sheets. In the second phase, optimization is applied to transform these complex-valued impedance sheets into purely reactive sheets suitable for printed circuit board fabrication by introducing surface waves. In the third phase, the metallic claddings of each metasurface are patterned for full-wave simulation of the dual-band stacked metasurface. Using this approach, two dual-band stacked metasurfaces are designed.
Analytic genericity of diffusing orbits in a priori unstable Hamiltonian systems

The genericity of Arnold diffusion in the analytic category is an open problem. In this paper, we study this problem in the following a priori unstable Hamiltonian system with a time-periodic perturbation Hε(p,q,I,φ,t)=h(I)+∑i=1n±12pi2+Vi(qi)+εH1(p,q,I,φ,t), where (p,q)∈Rn×Tn , (I,φ)∈Rd×Td with n, d ⩾ 1, V i are Morse potentials, and ɛ is a small non-zero parameter. The unperturbed Hamiltonian is not necessarily convex, and the induced inner dynamics does not need to satisfy a twist condition. Using geometric methods we prove that Arnold diffusion occurs for generic analytic perturbations H 1. Indeed, the set of admissible H 1 is C ω dense and C 3 open (a fortiori, C ω open). Our perturbative technique for the genericity is valid in the C k topology for all k ∈ [3, ∞) ∪ {∞, ω}.
Point Cloud based Hierarchical Deep Odometry Estimation

Processing point clouds using deep neural networks is still a challenging task. Most existing models focus on object detection and registration with deep neural networks using point clouds. In this paper, we propose a deep model that learns to estimate odometry in driving scenarios using point cloud data. The proposed model consumes raw point clouds in order to extract frame-to-frame odometry estimation through a hierarchical model architecture. Also, a local bundle adjustment variation of this model using LSTM layers is implemented. These two approaches are comprehensively evaluated and are compared against the state-of-the-art.
Fast Outage Analysis of Large-Scale Production Clouds with Service Correlation Mining

Cloud-based services are surging into popularity in recent years. However, outages, i.e., severe incidents that always impact multiple services, can dramatically affect user experience and incur severe economic losses. Locating the root-cause service, i.e., the service that contains the root cause of the outage, is a crucial step to mitigate the impact of the outage. In current industrial practice, this is generally performed in a bootstrap manner and largely depends on human efforts: the service that directly causes the outage is identified first, and the suspected root cause is traced back manually from service to service during diagnosis until the actual root cause is found. Unfortunately, production cloud systems typically contain a large number of interdependent services. Such a manual root cause analysis is often time-consuming and labor-intensive. In this work, we propose COT, the first outage triage approach that considers the global view of service correlations. COT mines the correlations among services from outage diagnosis data. After learning from historical outages, COT can infer the root cause of emerging ones accurately. We implement COT and evaluate it on a real-world dataset containing one year of data collected from Microsoft Azure, one of the representative cloud computing platforms in the world. Our experimental results show that COT can reach a triage accuracy of 82.1%-83.5%, which outperforms the state-of-the-art triage approach by 28.0%-29.7%.
Optical Constants of a Solar System Organic Analog and the Allende Meteorite in the Near- and Mid-infrared (1.5–13 μm)

Measurements of visible and near-infrared reflection (0.38–5 μm) and mid-to-far-infrared emission (5–200 μm) from telescope and satellite remote-sensing instruments make it possible to investigate the composition of planetary surfaces via electronic transitions and vibrational modes of chemical bonds. Red spectral slopes at visible and near-infrared wavelengths and absorption features at 3.3 and 3.4 μm observed in circumstellar disks, in the interstellar medium (ISM), and on the surfaces of solar system bodies are interpreted to be due to the presence of organic material and other carbon compounds. Identifying the origin of these features requires measurements of the optical properties of a variety of relevant analog and planetary materials. Spectroscopic models of dust within circumstellar disks and the ISM, as well as planetary regoliths, often incorporate just one such laboratory measurement despite the wide variation in absorption and extinction properties of organic and other carbon-bearing materials. Here we present laboratory measurements of transmission spectra in the 1.5–13 μm region and use these to derive real and imaginary indices of refraction for two samples: (1) an analog to meteoritic insoluble organic matter and (2) a powdered Allende meteorite sample. We also test our refractive index retrieval method on a previously published transmission spectrum of an Mg-rich olivine. We compare optical measurements of the insoluble organic matter analog to those of other solar system and extrasolar organic analogs, such as amorphous carbon and tholins, and find that the indices of refraction of the newly characterized material differ significantly from other carbonaceous samples.
Interplay between Zhang-Rice singlets and high-spin states in a model for doped 
NiO2
 planes

Superconductivity found in doped $\mathrm{Nd}\mathrm{Ni}{\mathrm{O}}_{2}$ is puzzling as two local symmetries of doped $\mathrm{Ni}{\mathrm{O}}_{2}$ layers compete, with presumably far-reaching implications for the involved mechanism: A cupratelike regime with Zhang-Rice singlets is replaced by local triplet states at realistic values of charge-transfer energy, which would suggest a rather different superconductivity scenario from high-${T}_{c}$ cuprates. We address this competition by investigating ${\mathrm{Ni}}_{4}{\mathrm{O}}_{8}$ clusters with periodic boundary conditions in the parameter range relevant for the superconducting nickelates. With increasing value of charge-transfer energy we observe upon hole doping the expected crossover from the cuprate regime dominated by Zhang-Rice singlets to the local triplet states. We find that smaller charge-transfer energy $\mathrm{\ensuremath{\Delta}}$ is able to drive this change in the ground-state character when realistic values for nickel-oxygen repulsion ${U}_{dp}$ are taken into account. For large values of the charge-transfer energy, oxygen orbitals are less important than in superconducting cuprates as their spectral weight is found only at rather high excitation energies. However, a second $\mathrm{Ni}(3d$) orbital can easily become relevant, with either the $xy$ or the $3{z}^{2}\ensuremath{-}{r}^{2}$ orbitals contributing in addition to the ${x}^{2}\ensuremath{-}{y}^{2}$ orbital to the formation of triplet states. In addition, our result that ${U}_{dp}$ (acting between Ni and O) favors on-site triplets implies that correlation effects beyond purely on-site interactions should be taken into account when obtaining effective two-band models.