Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning

We present Deep-n-Cheap -- an open-source AutoML framework to search for deep learning models. This search includes both architecture and training hyperparameters, and supports convolutional neural networks and multi-layer perceptrons. Our framework is targeted for deployment on both benchmark and custom datasets, and as a result, offers a greater degree of search space customizability as compared to a more limited search over only pre-existing models from literature. We also introduce the technique of 'search transfer', which demonstrates the generalization capabilities of the models found by our framework to multiple datasets. Deep-n-Cheap includes a user-customizable complexity penalty which trades off performance with training time or number of parameters. Specifically, our framework results in models offering performance comparable to state-of-the-art while taking 1-2 orders of magnitude less time to train than models from other AutoML and model search frameworks. Additionally, this work investigates and develops various insights regarding the search process. In particular, we show the superiority of a greedy strategy and justify our choice of Bayesian optimization as the primary search methodology over random / grid search.
Understanding the Lightning Process Approach to CFS/ME; a Review of the Disease Process and the Approach

Introduction: The Lightning Process (LP) is a neuro-physiological training programme based on self-coaching, concepts from Positive Psychology, Osteopathy and Neuro Linguistic Programming (NLP). It has a developing evidence base for its efficacy with a range of issues, including Chronic Fatigue Syndrome/ Myalgic Encephalomyelitis (CFS/ME), but little has been published about its hypothesis on the disease processes, and its approach to this disabling disease. Objectives: This paper aims to resolve these gaps in the research and contextualise the approach within current theories and research into the disease. Methods: A literature review was undertaken of the published evidence supporting the main current models of aetiology and disease process for CFS/ME. An evaluation of the LP’s conceptualisation of the aetiology of the disease and the hypothesis behind its approach was undertaken, through a review of the literature and semi-structured interviews with the programme’s original researcher (this paper’s lead author). These models were then compared to identify similarities and differences. Results: The review identified that the LP adopts a multifactorial, multisystem disease process for the disease, which is well aligned with current research and established conceptual frameworks for CFS/ME pathology. It identified that the LP shared the established perspective that the illness is a physiological, and not a psychological, one. It found the LP applies a self-regulation approach to neuro-physiology processes to influence the physical disease process. Conclusions: This paper resolves the identified gaps in the research and clarifies the hypotheses behind this approach, which has been identified by the evidence base as providing successful outcomes for some. It is hoped this clearer understanding of the approach will assist researchers, clinicians and those with this disabling disease to identify some additional options for potential recovery.
Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them

Different ways of calculating mortality during epidemics have yielded very different results, particularly during the current COVID-19 pandemic. For example, the ‘CFR’ has been interchangeably called the case fatality ratio, case fatality rate, and case fatality risk, often without standard mathematical definitions. The most commonly used CFR is the case fatality ratio, typically constructed using the estimated number of deaths to date divided by the estimated total number of confirmed infected cases to date. How does this CFR relate to an infected individual’s probability of death? To explore such issues, we formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality measures to show that neither of these are directly represented by the case fatality ratio. The key parameters that affect the dynamics of different mortality estimates are the incubation period and the time individuals were infected before confirmation of infection. Using data on the recent SARS-CoV-2 outbreaks, we estimate and compare the different dynamic mortality estimates and highlight their differences. Informed by our modeling, we propose more systematic methods to determine mortality during epidemic outbreaks and discuss sensitivity to confounding effects and uncertainties in the data arising from, e.g., undertesting and heterogeneous populations.
Effects of Dietary Fiber Level in Weaning Pig Diets on Growth Performance, Nutrient Digestibility and Intestinal Morphology

Insoluble dietary fiber is benefit on gut volume and morphology of the pigs. Rice hull is the by-product from rice processing that rich of dietary fiber content. The objectives of our study were to evaluate the effects of dietary fiber level from grounded rice hull on growth performance, nutrient digestibility, intestinal morphology and bacterial cell count in hindgut of weaning pigs. Thirty weaning pigs (21±3 days of age) were fed randomly with three experimental diets consisting of three levels of total Dietary Fiber (DF); 130, 140 and 150 g/kg with equal ratios of insoluble per soluble dietary fiber. On the 29th day, four pigs per treatment were euthanized for the collection of small intestinal tissue samples for morphological determination and digesta samples were collected for microbial counts. For digestibility determination, 12 weaned pigs were used to determine nutrient digestibility. The Feed Conversion Ratio (FCR) pigs at Week 1 was improved, however, there was overall no significant difference for growth performance and diarrhea incidence among the groups. The nutrient digestibility of energy and protein did not differ among treatments, whereas the digestibility of crude fat and fiber tended to increase (P = 0.082 and 0.074). Increasing of dietary fiber level tended to increase villus height and enhance villus height per crypt depth ratio (VH:CD) of ileum (VH:CD ratio were 1.94, 2.10 and 2.35 in pigs fed 130, 140 and 150 g/kg dietary fiber diets respectively; P = 0.062). The bacterial count in the gut content was not affected by dietary fiber level. In conclusion, the dietary fiber level between 130 to150 g/kg was not altering overall growth performance nutrient digestibility, intestinal morphology intestinal bacteria population. However, the dietary fiber 140-150 g/ kg in diet could improve FCR in the first week after weaning and tended to improve the ileal morphology.
Mergers Do Trigger AGNs out to z $\sim$ 0.6

Aims. The fueling and feedback of Active Galactic Nuclei (AGNs) are important in understanding the co-evolution between black holes and host galaxies. Mergers are thought to have the capability to bring gas inwards and ignite nuclear activity, especially for more powerful AGNs. However, there is still significant ongoing debate on whether mergers can trigger AGNs and, if they do, whether mergers are a significant triggering mechanism. Methods. We select a low-redshift ($0.005<z<0.1$) sample from the Sloan Digital Sky Survey (SDSS) and a high-redshift ($0 < z < 0.6$) sample from the Galaxy And Mass Assembly (GAMA) survey. We take advantage of the convolutional neural network technique to identify mergers. We use mid-infrared (MIR) color cut and optical emission line diagnostics to classify AGNs. We also include Low Excitation Radio Galaxies (LERGs) to investigate the connection between mergers and low accretion rate AGNs. Results. We find that AGNs are more likely to be found in mergers than non-mergers, with an AGN excess up to $1.81\pm{0.16}$, suggesting that mergers can trigger AGNs. We also find the fraction of mergers in AGNs is higher than that in non-AGN controls, for both MIR and optically selected AGNs, as well as LERGs, with values between $16.40\pm{0.5}\%$ and $39.23\pm{2.10}\%$, implying a non-negligible to potentially significant role of mergers in triggering AGNs. This merger fraction in AGNs increases as stellar mass increases which supports the idea that mergers are more important for triggering AGNs in more massive galaxies. In terms of merger fraction as a function of AGN power we find a positive trend for MIR selected AGNs and a complex trend for optically selected AGNs, which we interpret under an evolutionary scenario proposed by previous studies. In addition, obscured MIR selected AGNs are more likely to be hosted in mergers than unobscured MIR selected AGNs.
DOPS: Learning to Detect 3D Objects and Predict Their 3D Shapes

We propose DOPS, a fast single-stage 3D object detection method for LIDAR data. Previous methods often make domain-specific design decisions, for example projecting points into a bird-eye view image in autonomous driving scenarios. In contrast, we propose a general-purpose method that works on both indoor and outdoor scenes. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. 3D bounding box parameters are estimated in one pass for every point, aggregated through graph convolutions, and fed into a branch of the network that predicts latent codes representing the shape of each detected object. The latent shape space and shape decoder are learned on a synthetic dataset and then used as supervision for the end-to-end training of the 3D object detection pipeline. Thus our model is able to extract shapes without access to ground-truth shape information in the target dataset. During experiments, we find that our proposed method achieves state-of-the-art results by ~5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Waymo Open Dataset, while reproducing the shapes of detected cars.
$H$-space structures on spaces of metrics of positive scalar curvature

We construct and study an $H$-space multiplication on $\mathcal R^+(M)$ for manifolds $M$ which are nullcobordant in their own tangential $2$-type. This is applied to give a rigidity criterion for the action of the diffeomorphism group on $\mathcal R^+(M)$ via pullback. We also compare this to other known multiplicative structures on $\mathcal R^+(M)$.
Field stability of Majorana spin liquids in antiferromagnetic Kitaev models

Magnetic fields can give rise to a plethora of phenomena in Kitaev spin systems, such as the formation of nontrivial spin liquids in two and three spatial dimensions. For the original honeycomb Kitaev model, it has recently been observed that the sign of the bond-directional exchange is of crucial relevance for the field-induced physics, with antiferromagnetic couplings giving rise to an intermediate spin liquid regime between the low-field gapped Kitaev spin liquid and the high-field polarized state, which is not present in the ferromagnetically coupled model. Here, by employing a Majorana mean-field approach for a magnetic field pointing along the [001] direction, we present a systematic study of field-induced spin liquid phases for a variety of two and three-dimensional lattice geometries. We find that antiferromagnetic couplings generically lead to (i) spin liquid phases that are considerably more stable in field than those for ferromagnetic couplings, and (ii) an intermediate spin liquid phase which arises from a change in the topology of the Majorana band structure. Close inspection of the mean-field parameters reveal that the intermediate phase occurs due to a field-driven sign change in an `effective' $z$-bond energy parameter. Our results clearly demonstrate the richness of the Majorana physics of the antiferromagnetic Kitaev models, in comparison to their ferromagnetic counterparts.
Cauchy theory and mean-field limit for general Vicsek models in collective dynamics

In this paper we provide a local Cauchy theory both on the torus and in the whole space for general Vicsek dynamics at the particle and at the kinetic level. We consider rather general interaction kernels, nonlinear viscosity and nonlinear friction. We also establish the mean-field limit in the large particle limit. Particularly, we include normalised kernels which display a singularity when the flux of particles vanishes. Thus, in terms of the Cauchy theory for the kinetic equation, we extend to more general interactions and complete the program initiated in Gamba et. al. (2016) (where the authors assume that the singularity does not take place) and in Figalli et. al. (2017) (where the authors prove that the singularity does not happen in the space homogeneous case). Moreover, we derive an explicit lower time of existence as well as a global existence criterion that is applicable, among other cases, to obtain a long time theory for non-renormalised kernels and for the original Vicsek problem without any a priori assumptions.
Stability in turbulence: The interplay between shocks and vorticity in a superfluid with higher-order dispersion

Higher-order dispersion can lead to intriguing dynamics that are becoming a focus of modern hydrodynamics research. Such systems occur naturally, for example in shallow water waves and nonlinear optics, for which several types of novel dispersive shocks structures have been identified. Here we introduce ultracold atoms as a tunable quantum simulations platform for higher-order systems. Degenerate quantum gases are well controlled model systems for the experimental study of dispersive hydrodynamics in superfluids and have been used to investigate phenomena such as vortices, solitons, dispersive shock waves and quantum turbulence. With the advent of Raman-induced spin-orbit coupling, the dispersion of a dilute gas Bose-Einstein condensate can be modified in a flexible way, allowing for detailed investigations of higher-order dispersion dynamics. Here we present a combined experimental and theoretical study of shock structures generated in such a system. The breaking of Galilean invariance by the spin-orbit coupling allows two different types of shock structures to emerge simultaneously in a single system. Numerical simulations suggest that the behavior of these shock structures is affected by interactions with vortices in a manner reminiscent of emerging viscous hydrodynamics due to an underlying quantum turbulence in the system. This result suggests that spin-orbit coupling can be used as a powerful means to tun the effective viscosity in cold-atom experiments serving as quantum simulators of turbulent hydrodynamics, with applications from condensed matter and optics to quantum simulations of neutron stars.
Swift Observations of Mrk 421 in Selected Epochs. III. Extreme X-Ray Timing/Spectral Properties and Multiwavelength Lognormality during 2015 December–2018 April

We present the results from the timing and spectral study of Mrk 421 based mainly on the Swift data in the X-ray energy range obtained during the time interval 2015 December–2018 April. The most extreme X-ray flaring activity on long-term, daily, and intraday timescales was observed during the 2 month period that started in 2017 December, when the 0.3–10 keV flux exceeded a level of 5 × 10−9 erg cm−2 s−1, recorded only twice previously. While the TeV-band and X-ray variabilities were mostly correlated, the source often varied in a complex manner in the MeV–GeV and radio–UV energy ranges, indicating that the multifrequency emission of Mrk 421 could not always be generated in a single zone. The longer-term flares at X-rays and γ-rays showed a lognormal character, possibly indicating a variability imprint of the accretion disk onto the jet. A vast majority of the 0.3–10 keV spectra were consistent with the log-parabolic model, showing relatively low spectral curvature and correlations between the different spectral parameters, predicted in the case of the first- and second-order Fermi processes. The position of the synchrotron spectral energy distribution peak showed an extreme variability on diverse timescales between the energies Ep < 0.1 and >15 keV, with 15% of the spectra peaking at the hard X-ray, and was related to the peak height as with α ∼ 0.6, which is expected for the transition from Kraichnan-type turbulence into the “hard sphere” one. The 0.3–300 GeV spectra showed features of the hadronic contribution, jet–star interaction, and upscatter in the Klein–Nishina regime in different time intervals.
Improving Auditory Attention Decoding Performance of Linear and Non-Linear Methods using State-Space Model

Identifying the target speaker in hearing aid applications is crucial to improve speech understanding. Recent advances in electroencephalography (EEG) have shown that it is possible to identify the target speaker from single-trial EEG recordings using auditory attention decoding (AAD) methods. AAD methods reconstruct the attended speech envelope from EEG recordings, based on a linear least-squares cost function or non-linear neural networks, and then directly compare the reconstructed envelope with the speech envelopes of speakers to identify the attended speaker using Pearson correlation coefficients. Since these correlation coefficients are highly fluctuating, for a reliable decoding a large correlation window is used, which causes a large processing delay. In this paper, we investigate a state-space model using correlation coefficients obtained with a small correlation window to improve the decoding performance of the linear and the non-linear AAD methods. The experimental results show that the state-space model significantly improves the decoding performance.
A 1201 s Orbital Period Detached Binary: The First Double Helium Core White Dwarf LISA Verification Binary

We report the discovery of a 1201 s orbital period binary, the third shortest-period detached binary known. Sloan Digital Sky Survey J232230.20 + 050942.06 contains two He-core white dwarfs orbiting with a 27° inclination. Located 0.76 kpc from the Sun, the binary has an estimated Laser Interferometer Space Antenna (LISA) 4 yr signal-to-noise ratio of 40. J2322 + 0509 is the first He + He white dwarf LISA verification binary, a source class that is predicted to account for one-third of resolved LISA ultra-compact binary detections.
Episode-Based Prototype Generating Network for Zero-Shot Learning

We introduce a simple yet effective episode-based training framework for zero-shot learning (ZSL), where the learning system requires to recognize unseen classes given only the corresponding class semantics. During training, the model is trained within a collection of episodes, each of which is designed to simulate a zero-shot classification task. Through training multiple episodes, the model progressively accumulates ensemble experiences on predicting the mimetic unseen classes, which will generalize well on the real unseen classes. Based on this training framework, we propose a novel generative model that synthesizes visual prototypes conditioned on the class semantic prototypes. The proposed model aligns the visual-semantic interactions by formulating both the visual prototype generation and the class semantic inference into an adversarial framework paired with a parameter-economic Multi-modal Cross-Entropy Loss to capture the discriminative information. Extensive experiments on four datasets under both traditional ZSL and generalized ZSL tasks show that our model outperforms the state-of-the-art approaches by large margins.
Projected Neural Network for a Class of Sparse Regression with Cardinality Penalty

In this paper, we consider a class of sparse regression problems, whose objective function is the summation of a convex loss function and a cardinality penalty. By constructing a smoothing function for the cardinality function, we propose a projected neural network and design a correction method for solving this problem. The solution of the proposed neural network is unique, global existent, bounded and globally Lipschitz continuous. Besides, we prove that all accumulation points of the proposed neural network have a common support set and a unified lower bound for the nonzero entries. Combining the proposed neural network with the correction method, any corrected accumulation point is a local minimizer of the considered sparse regression problem. Moreover, we analyze the equivalent relationship on the local minimizers between the considered sparse regression problem and another sparse problem. Finally, some numerical experiments are provided to show the efficiency of the proposed neural networks in solving some sparse regression problems in practice.
Nonlogarithmic divergence of a deflection angle by a marginally unstable photon sphere of the Damour-Solodukhin wormhole in a strong deflection limit

Static, spherically symmetric black holes and compact objects without an event horizon have unstable (stable) circular orbits of a light called photon (antiphoton) sphere. A Damour-Solodukhin wormhole has been suggested as a simple black hole mimicker and the difference of its metric tensors from a black hole is described by a dimensionless parameter λ. The wormhole with two flat regions has two photon spheres and an antiphoton sphere for λ<2/2 and a photon sphere for λ≥2/2. When the parameter λ is 2/2, the photon sphere is marginally unstable because of degeneration of the photon spheres and antiphoton sphere. We investigate gravitational lensing by the wormhole in weak and strong gravitational fields. We find that the deflection angle of a light ray reflected by the marginally unstable photon sphere diverges nonlogarithmically in a strong deflection limit for λ=2/2, while the deflection angle reflected by the photon sphere diverges logarithmically for λ≠2/2. We extend a strong deflection limit analysis for the nonlogarithmic divergence case. We expect that our method can be applied for gravitational lenses by marginally unstable photon spheres of various compact objects.
The Effect of Patellar Denervation by Circumpatellar Electrocautery on Anterior Knee Pain Following Total Knee Replacement – An Experimental Study

ABSTRACT OBJECTIVES Anterior knee pain is a common problem in patients who have undergone TKR which causes dissatisfaction among them. There are Various methods for prevention of anterior knee pain following TKR . The objective of this study is to determine the effect of circumpatellar electrocautery on anterior knee pain following TKR and to compare the results with that of those patients who have undergone TKR without circumpatellar denervation. METHODS This is a cohort study conducted in Dept. of Orthopedics, Govt. Medical College, Kozhikode,kerala, 2014. Total sample size was 90.out of which 2 patients died during the study period. We lost follow up of 7 patients. Among the remaining 81 patients 42 had undergone TKR with circumpatellar denervation using electocautery and 39 without circumpatellar denervation. They were kept under follow up. Patients were followed up postoperatively at 1 month, 3 months, 6 months and at one year. At all postoperative visits, a clinical score was determined using the Knee Society score and the clinical anterior knee pain rating system described by Waters and Bentley RESULTS There is no statistically significant difference in AKP score between both groups.There is a statistically significant difference in the knee society score at 1 st month(p value <.001). But there is no difference on further follow up visits . CONCLUSION There is no statistically significant difference between final outcome of patients who underwent patella denervation using circumpatellar electrocauterisation and those without denervation with respect to anterior knee pain among patients who have undergone TKR.