Miracle-dz/UniFAR-FTUs huggingface dataset:
SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging

Sleep staging is fundamental for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performance, several challenges remain open: 1) How to effectively extract salient waves in multimodal sleep data; 2) How to capture the multi-scale transition rules among sleep stages; 3) How to adaptively seize the key role of specific modality for sleep staging. To address these challenges, we propose SalientSleepNet, a multimodal salient wave detection network for sleep staging. Specifically, SalientSleepNet is a temporal fully convolutional network based on the $U^2$-Net architecture that is originally proposed for salient object detection in computer vision. It is mainly composed of two independent $U^2$-like streams to extract the salient features from multimodal data, respectively. Meanwhile, the multi-scale extraction module is designed to capture multi-scale transition rules among sleep stages. Besides, the multimodal attention module is proposed to adaptively capture valuable information from multimodal data for the specific sleep stage. Experiments on the two datasets demonstrate that SalientSleepNet outperforms the state-of-the-art baselines. It is worth noting that this model has the least amount of parameters compared with the existing deep neural network models.
Mrk 1239: a Type-2 Counterpart of Narrow-line Seyfert-1?

We present new spectrophotometric and spectropolarimetric observations of Mrk 1239, one of the 8 prototypes that defines type-1 narrow-line Seyfert galaxies (NLS1s). Unlike the other typical NLS1s though, a high degree of polarization (P ∼ 5.6%) and red optical–IR (g–W 4 = 12.35) colors suggest that Mrk 1239 is more similar to type-2 active galactic nuclei like NGC 1068. Detailed analysis of spectral energy distribution in the UV–optical–IR yields two components from the nucleus: a direct and transmitted component that is heavily obscured (E B–V ≈ 1.6), and another indirect and scattered one with mild extinction (E B–V ∼ 0.5). Such a two-light-paths scenario is also found in previous reports based on the X-ray data. Comparison of emission lines and the detection of He i*λ10830 BAL at [−3000, −1000] km s−1 indicates that the obscuring clouds are at physical scale between the sublimation radius and that of the narrow emission line regions. The potential existence of powerful outflows is found as both the obscurer and scatterer are outflowing. Similar to many other type-2s, jet-like structure in the radio band is found in Mrk 1239, perpendicular to the polarization angle, suggesting polar scattering. We argue that Mrk 1239 is very probably a type-2 counterpart of NLS1s. The identification of 1 out of 8 prototype NLS1s as a type-2 counterpart implies that there can be a substantial amount of analogs of Mrk 1239 misidentified as type-1s in the optical band. Properties of these misidentified objects are going to be explored in our future works.
Engineering fast bias-preserving gates on stabilized cat qubits

Stabilized cat codes can provide a biased noise channel with a set of bias-preserving (BP) gates, which can significantly reduce the resource overhead for fault-tolerant quantum computing. All existing schemes of BP gates, however, require adiabatic quantum evolution, with performance limited by excitation loss and non-adiabatic errors during the adiabatic gates. In this work, we apply a derivative-based leakage suppression technique to overcome non-adiabatic errors, so that we can implement fast BP gates on Kerr-cat qubits with improved gate fidelity while maintaining high noise bias. When applied to concatenated quantum error correction, the fast BP gates can not only improve the logical error rate but also reduce resource overhead, which enables more efficient implementation of fault-tolerant quantum computing.
ResT: An Efficient Transformer for Visual Recognition

This paper presents an efficient multi-scale vision Transformer, called ResT, that capably served as a general-purpose backbone for image recognition. Unlike existing Transformer methods, which employ standard Transformer blocks to tackle raw images with a fixed resolution, our ResT have several advantages: (1) A memory-efficient multi-head self-attention is built, which compresses the memory by a simple depth-wise convolution, and projects the interaction across the attention-heads dimension while keeping the diversity ability of multi-heads; (2) Position encoding is constructed as spatial attention, which is more flexible and can tackle with input images of arbitrary size without interpolation or fine-tune; (3) Instead of the straightforward tokenization at the beginning of each stage, we design the patch embedding as a stack of overlapping convolution operation with stride on the 2D-reshaped token map. We comprehensively validate ResT on image classification and downstream tasks. Experimental results show that the proposed ResT can outperform the recently state-of-the-art backbones by a large margin, demonstrating the potential of ResT as strong backbones. The code and models will be made publicly available at https://github.com/wofmanaf/ResT.
Gas/Surface Interaction Study of Low-Density Ablators in Sub- and Supersonic Plasmas

Ablation experiments have been carried out with the carbon-phenolic material AQ61 and a non-pyrolyzing carbon fiber preform in suband supersonic air and nitrogen plasmas in the VKI Plasmatron facility. We performed an in-situ recession analysis, including volumetric ablation, and observed the temperature and radiance of the surface for emissivity estimations, as well as spatial molecular radiation profiles in the boundary layer, which were compared to a numerical approach. Surface temperatures as low as 1600 K allowed observation of the temperature distribution over the surface using an infrared camera. The carbon preform test samples were varied in shape as well as regarding their fiber direction. Sublimation of the surface was reached in supersonic plasma flow at a cold wall heat flux of 9.5 MW/m. Carbon preform emissivities were found to be in the order of 0.86 0.97. A stagnation line description with an ablation boundary condition helped to reproduce experimentally measured boundary layer emission profiles of the CN violet molecule. In spite of the very simplified model, emission intensities were in the same order of magnitude compared to the experimental data. The model was able to reconstruct the location of the maximum emission for two cases. The char layer was examined by Scanning Electron Microscopy, illustrating degradation after nitrogen ablation.
Nano-sized Ag inserted into ITO films prepared by continuous roll-to-roll sputtering for high-performance, flexible, transparent film heaters

We demonstrate high-performance, flexible, transparent film heaters fabricated on a conductive Ag layer inserted into ITO films prepared by pilot-scale roll-to-roll (RTR) sputtering. The RTR sputtered ITO/Ag/ITO (IAI) multilayer on a 700 mm wide PET substrate at room temperature exhibits a high optical transmittance of 88.2% and a very low sheet resistance of 3.0 ohm per square, which are ideal for a low-voltage transparent film heater. The time–temperature profiles and heat distribution analysis demonstrate that the performance of the transparent film heater with the IAI multilayer is superior to that of a film heater with graphene, carbon nanotubes, amorphous ITO, Ag nanowires, or Ag networks, due to its very low sheet resistance (3–17 ohm per square). In particular, the thickness of the Ag interlayer in the IAI multilayer critically impedes the voltage for attaining 100 °C in the transparent film heater because the Ag interlayer provides a main conduction path in the IAI multilayer. In addition, the bending and cycling fatigue tests demonstrate comparable flexibility of the IAI multilayer to other transparent electrodes, indicating the potential of the IAI multilayer as an electrode for a highly flexible film heater. Therefore, the RTR sputtered IAI multilayer is the best substitute for conventional metallic Ag nanowire or ITO film heaters in order to realize a large-area, transparent, flexible film heater that is applicable to flexible and transparent defogging/deicing and heating systems.
On uniqueness and nonuniqueness of ancient ovals

In this paper, we prove that any nontrivial SO(k)×SO(n+ 1−k)-symmetric ancient compact noncollapsed solution of the mean curvature flow agrees up to scaling and rigid motion with the O(k)×O(n+ 1 − k)-symmetric ancient ovals constructed in [HH16]. This confirms a conjecture by Angenent-Daskalopoulos-Sesum. On the other hand, for every k ≥ 2 we also construct a (k − 1)-parameter family of uniformly (k + 1)-convex ancient ovals that are only Z2 ×O(n+ 1− k)-symmetric. This gives counterexamples to a conjecture of Daskalopoulos.
Stabilization of the extended horizons

We extend Kodama-Ishibashi and Jansen-Rostworowski-Rutkowski master field framework to study quasinormal modes of black branes and black holes of Einstein gravity in D = 5 space-time dimensions with multiple scalars and an arbitrary bulk potential. As an application, we consider a thermodynamically unstable state of mass deformed N = 4 supersymmetric Yang-Mills theory at strong coupling. The corresponding gravitational dual is N = 2 black brane with a dynamical instability of its translationary invariant horizon. We study the dependence of the quasinormal mode spectra as the boundary gauge theory is compactified on S — the black brane is turned into the black hole.
Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures

Sound event detection is an important facet of audio tagging that aims to identify sounds of interest and define both the sound category and time boundaries for each sound event in a continuous recording. With advances in deep neural networks, there has been tremendous improvement in the performance of sound event detection systems, although at the expense of costly data collection and labeling efforts. In fact, current state-of-the-art methods employ supervised training methods that leverage large amounts of data samples and corresponding labels in order to facilitate identification of sound category and time stamps of events. As an alternative, the current study proposes a semi-supervised method for generating pseudo-labels from unsupervised data using a student-teacher scheme that balances self-training and cross-training. Additionally, this paper explores post-processing which extracts sound intervals from network prediction, for further improvement in sound event detection performance. The proposed approach is evaluated on sound event detection task for the DCASE2020 challenge. The results of these methods on both “validation” and “public evaluation” sets of DESED database show significant improvement compared to the state-of-the art systems in semi-supervised learning.
Dynamics of freely rising spheres: the effect of moment of inertia

Abstract The goal of this study is to elucidate the effect the particle moment of inertia (MOI) has on the dynamics of spherical particles rising in a quiescent and turbulent fluid. To this end, we performed experiments with varying density ratios $\varGamma$, the ratio of the particle density and fluid density, ranging from $0.37$ up to $0.97$. At each $\varGamma$ the MOI was varied by shifting mass between the shell and the centre of the particle to vary $I^*$ (the particle MOI normalised by the MOI of a particle with the same weight and a uniform mass distribution). Helical paths are observed for low, and ‘three-dimensional (3-D) chaotic’ trajectories at higher values of $\varGamma$. The present data suggest no influence of $I^*$ on the critical value for this transition $0.42<\varGamma _{{crit}}<0.52$. For the ‘3-D chaotic’ rise mode, we identify trends of decreasing particle drag coefficient ($C_d$) and amplitude of oscillation with increasing $I^*$. Due to limited data it remains unclear if a similar dependence exists in the helical regime as well. Path oscillations remain finite for all cases studied and no ‘rectilinear’ mode is encountered, which may be the consequence of allowing for a longer transient distance in the present compared with earlier work. Rotational dynamics did not vary significantly between quiescent and turbulent surroundings, indicating that for the present configuration these are predominantly wake driven.
Generation of maximal three-state field-free molecular orientation with terahertz pulses

X iv :2 10 5. 13 69 0v 1 [ qu an tph ] 2 8 M ay 2 02 1 Optimal three-state field-free molecular orientation with terahertz pulses Qian-Qian Hong, Li-Bao Fan, Chuan-Cun Shu, ∗ and Niels E. Henriksen Hunan Key Laboratory of Super-Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha 410083, China Department of Chemistry, Technical University of Denmark, Building 207, DK-2800 Kgs. Lyngby, Denmark Abstract We present a combined analytical and numerical investigation to show how an optimal control field can be designed to generate maximum field-free orientation of molecules for three populated rotational states. Based on a model involving pure rotational ladder-climbing excitation between rotational states, a set of optimal amplitude and phase conditions are analytically derived for the applied control fields. The maximum degree of orientation can be achieved when the field satisfies amplitude and phase conditions at the two transition frequencies. Multiple optimal solutions exist and to examine these conditions, we devise a quantum coherent control scheme using two terahertz pulses and successfully apply it to the linear polar molecule HCN at ultracold temperature. The sensitivity of both populations and phases of rotational states to control field parameters, i.e., the detuning, bandwidth, and time delay, is analyzed for understanding the optimal orientation mechanism. This work thus examines the frequency domain landscape belonging to optimal pulses.
Generic monodromy group of Riemann surfaces for inverses to entire functions of finite order

We consider the vector space $E_{\rho,p}$ of entire functions of finite order, whose types are not more than $p>0$, endowed with Frechet topology, which is generated by a sequence of weighted norms. We call a function $f\in E_{\rho,p}$ {\it typical} if it is surjective and has an infinite number critical points such that each of them is non-degenerate and all the values of $f$ at these points are pairwise different. We prove that the set of all typical functions contains a set which is $G_\delta$ and dense in $E_{\rho,p}$. Furthermore, we show that inverse to any typical function has Riemann surface whose monodromy group coincides with finitary symmetric group of permutations of naturals, which is unsolvable in the following strong sense: it does not have a normal tower of subgroups, whose factor groups are or abelian or finite. As a consequence from these facts and Topological Galois Theory, we obtain that generically (in the above sense) for $f\in E_{\rho,p}$ the solution of equation $f(w)=z$ cannot be represented via $z$ and complex constants by a finite number of the following actions: algebraic operations (i.e., rational ones and solutions of polynomial equations) and quadratures (in particular, superpositions with elementary functions).
Domain-Adaptive Pretraining Methods for Dialogue Understanding

Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adaptive pretraining objectives on downstream tasks. In particular, three objectives, including a novel objective focusing on modeling predicate-argument relations, are evaluated on two challenging dialogue understanding tasks. Experimental results demonstrate that domain-adaptive pretraining with proper objectives can significantly improve the performance of a strong baseline on these tasks, achieving the new state-of-the-art performances.
Language Models Use Monotonicity to Assess NPI Licensing

We investigate the semantic knowledge of language models (LMs), focusing on (1) whether these LMs create categories of linguistic environments based on their semantic monotonicity properties, and (2) whether these categories play a similar role in LMs as in human language understanding, using negative polarity item licensing as a case study. We introduce a series of experiments consisting of probing with diagnostic classifiers (DCs), linguistic acceptability tasks, as well as a novel DC ranking method that tightly connects the probing results to the inner workings of the LM. By applying our experimental pipeline to LMs trained on various filtered corpora, we are able to gain stronger insights into the semantic generalizations that are acquired by these models.
MODISSA: a multipurpose platform for the prototypical realization of vehicle-related applications using optical sensors

We present the current state of development of the sensor-equipped car MODISSA, with which Fraunhofer IOSB realizes a configurable experimental platform for hardware evaluation and software development in the context of mobile mapping and vehicle-related safety and protection. MODISSA is based on a van that has successively been equipped with a variety of optical sensors over the past few years, and contains hardware for complete raw data acquisition, georeferencing, real-time data analysis, and immediate visualization on in-car displays. We demonstrate the capabilities of MODISSA by giving a deeper insight into experiments with its specific configuration in the scope of three different applications. Other research groups can benefit from these experiences when setting up their own mobile sensor system, especially regarding the selection of hardware and software, the knowledge of possible sources of error, and the handling of the acquired sensor data.
Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data

Application of Machine Learning algorithms to the medical domain is an emerging trend that helps to advance medical knowledge. At the same time, there is a significant a lack of explainable studies that promote informed, transparent, and interpretable use of Machine Learning algorithms. In this paper, we present explainable multi-class classification of the Covid-19 mental health data. In Machine Learning study, we aim to find the potential factors to influence a personal mental health during the Covid-19 pandemic. We found that Random Forest (RF) and Gradient Boosting (GB) have scored the highest accuracy of 68.08% and 68.19% respectively, with LIME prediction accuracy 65.5% for RF and 61.8% for GB. We then compare a Post-hoc system (Local Interpretable Model-Agnostic Explanations, or LIME) and an Ante-hoc system (Gini Importance) in their ability to explain the obtained Machine Learning results. To the best of these authors knowledge, our study is the first explainable Machine Learning study of the mental health data collected during Covid-19 pandemics.
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks

We present a new class of Langevin based algorithms, which overcomes many of the known shortcomings of popular adaptive optimizers that are currently used for the fine tuning of deep learning models. Its underpinning theory relies on recent advances of Euler's polygonal approximations for stochastic differential equations (SDEs) with monotone coefficients. As a result, it inherits the stability properties of tamed algorithms, while it addresses other known issues, e.g. vanishing gradients in neural networks. In particular, we provide a nonasymptotic analysis and full theoretical guarantees for the convergence properties of an algorithm of this novel class, which we named TH$\varepsilon$O POULA (or, simply, TheoPouLa). Finally, several experiments are presented with different types of deep learning models, which show the superior performance of TheoPouLa over many popular adaptive optimization algorithms.
Galactic Extinction: How Many Novae Does It Hide and How Does It Affect the Galactic Nova Rate?

There is a long-standing discrepancy between the observed Galactic classical nova rate of ∼10 yr−1 and the predicted rate from Galactic models of ∼30–50 yr−1. One explanation for this discrepancy is that many novae are hidden by interstellar extinction, but the degree to which dust can obscure novae is poorly constrained. We use newly available all-sky three-dimensional dust maps to compare the brightness and spatial distribution of known novae to that predicted from relatively simple models in which novae trace Galactic stellar mass. We find that only half (53%) of the novae are expected to be easily detectable (g ≲ 15) with current all-sky optical surveys such as the All-Sky Automated Survey for Supernovae (ASAS-SN). This fraction is much lower than previously estimated, showing that dust does substantially affect nova detection in the optical. By comparing complementary survey results from the ASAS-SN, OGLE-IV, and Palomar Gattini IR surveys using our modeling, we find a tentative Galactic nova rate of ∼30 yr−1, though this could be as high as ∼40 yr−1, depending on the assumed distribution of novae within the Galaxy. These preliminary estimates will be improved in future work through more sophisticated modeling of nova detection in ASAS-SN and other surveys.
Deviations from Arrhenius dynamics in high temperature liquids, a possible collapse, and a viscosity bound

Liquids realize a highly complex state of matter in which strong competing kinetic and interaction eﬀects come to life. As such, liquids are, generally, more challenging to understand than either gases or solids. In weakly interacting gases, the kinetic eﬀects dominate. By contrast, low temperature solids typically feature far smaller ﬂuctuations about their ground state. Notwithstanding their complexity, with the exception of quantum ﬂuids (e.g., superﬂuid Helium) and supercooled liquids (including glasses), various aspects of common liquid dynamics such as their dynamic viscosity are often assumed to be given by rather simple, Arrhenius-type, activated forms with nearly constant (i.e., temperature independent) energy barriers. In this work, we analyze experimentally measured viscosities of numerous liquids far above their equilibrium melting temperature to see how well this assumption fares. We ﬁnd, for the investigated liquids, marked deviations from simple activated dynamics. Even far above their equilibrium melting temperatures, as the temperature drops, the viscosity of these liquids increases more strongly than predicted by activated dynamics dominated by a single uniform energy barrier. For metallic ﬂuids, the scale of the prefactors of the best Arrhenius ﬁts for the viscosity is typically consistent with that given by the product ( 𝑛ℎ ) with 𝑛 the number density and ℎ Planck’s constant. More generally, in various ﬂuids (whether metallic or non-metallic) that we examined, ( 𝑛ℎ ) constitutes a lower bound scale on the viscosity. We ﬁnd that a scaling of the temperature axis (complementing that of the viscosity) leads to a partial collapse of the temperature dependent viscosities of diﬀerent ﬂuids; such a scaling allows for a functional dependence of the viscosity on temperature that includes yet is far more general than activated Arrhenius form alone. We speculate on relations between non-Arrhenius dynamics and thermodynamic observables.