Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiently Using Polar Codes in 5G Base Stations to Enhance Rural Connectivity

5G connectivity has become essential to integrate rural communities into the broader digital economy and support critical applications like remote education and remote surgery. A major hindrance to expanding rural broadband coverage, especially in developing countries, is the high cost of installing 5G base stations. Hence, there is a need to reduce the cost of a 5G base station without degrading its performance. Our work proposes a novel approach to efficiently utilize the polar code encoders in a 5G base station. The idea is to use the idle time of the polar encoders during downlink transmission for error correction in the 5G data plane. Polar codes have conventionally been used in the 5G control plane, while LDPC codes are used in the data plane. We perform detailed characterization experiments to show the advantages of using polar codes in the data plane as well. Further, to intelligently distribute the user data packets among the available compute nodes, we propose a set of novel resource allocation algorithms and compare their performance with other algorithms in the literature. Using our proposed optimization techniques, we achieve a 17% reduction in the cost of a 5G base station. Simultaneously, we are able to improve the performance by 24% compared to a conventional base station.
Network-adjusted covariates for community detection

Community detection is a crucial task in network analysis that can be significantly improved by incorporating subject-level information, i.e., covariates. Existing methods have shown the effectiveness of using covariates on the low-degree nodes, but rarely discuss the case where communities have significantly different density levels, i.e. multiscale networks. In this paper, we introduce a novel method that addresses this challenge by constructing network-adjusted covariates, which leverage the network connections and covariates with a node-specific weight for each node. This weight can be calculated without tuning parameters. We present novel theoretical results on the strong consistency of our method under degree-corrected stochastic blockmodels with covariates, even in the presence of misspecification and multiple sparse communities. Additionally, we establish a general lower bound for the community detection problem when both network and covariates are present, and it shows our method is optimal for connection intensity up to a constant factor. Our method outperforms existing approaches in simulations and a LastFM app user network. We then compare our method with others on a statistics publication citation network where 30% of nodes are isolated, and our method produces reasonable and balanced results.
Unsupervised Episode Generation for Graph Meta-learning

We investigate Unsupervised Episode Generation methods to solve Few-Shot Node-Classification (FSNC) task via Meta-learning without labels. Dominant meta-learning methodologies for FSNC were developed under the existence of abundant labeled nodes from diverse base classes for training, which however may not be possible to obtain in the real-world. Although a few studies tried to tackle the label-scarcity problem in graph meta-learning, they still rely on a few labeled nodes, which hinders the full utilization of the information of all nodes in a graph. Despite the effectiveness of graph contrastive learning (GCL) methods in the FSNC task without using the label information, they mainly learn generic node embeddings without consideration of the downstream task to be solved, which may limit its performance in the FSNC task. To this end, we propose a simple yet effective unsupervised episode generation method to benefit from the generalization ability of meta-learning for the FSNC task, while resolving the label-scarcity problem. Our proposed method, called Neighbors as Queries (NaQ), generates training episodes based on pre-calculated node-node similarity. Moreover, NaQ is model-agnostic; hence, it can be used to train any existing supervised graph meta-learning methods in an unsupervised manner, while not sacrificing much of their performance or sometimes even improving them. Extensive experimental results demonstrate the potential of our unsupervised episode generation methods for graph meta-learning towards the FSNC task. Our code is available at: https://github.com/JhngJng/NaQ-PyTorch
Early Planet Formation in Embedded Disks (eDisk). III. A First High-resolution View of Submillimeter Continuum and Molecular Line Emission toward the Class 0 Protostar L1527 IRS

Studying the physical and chemical conditions of young embedded disks is crucial to constrain the initial conditions for planet formation. Here we present Atacama Large Millimeter/submillimeter Array observations of dust continuum at ∼0. ″06 (8 au) resolution and molecular line emission at ∼0. ″17 (24 au) resolution toward the Class 0 protostar L1527 IRS from the Large Program eDisk (Early Planet Formation in Embedded Disks). The continuum emission is smooth without substructures but asymmetric along both the major and minor axes of the disk as previously observed. The detected lines of 12CO, 13CO, C18O, H2CO, c-C3H2, SO, SiO, and DCN trace different components of the protostellar system, with a disk wind potentially visible in 12CO. The 13CO brightness temperature and the H2CO line ratio confirm that the disk is too warm for CO freezeout, with the snowline located at ∼350 au in the envelope. Both molecules show potential evidence of a temperature increase around the disk–envelope interface. SO seems to originate predominantly in UV-irradiated regions such as the disk surface and the outflow cavity walls rather than at the disk–envelope interface as previously suggested. Finally, the continuum asymmetry along the minor axis is consistent with the inclination derived from the large-scale (100″ or 14,000 au) outflow, but opposite to that based on the molecular jet and envelope emission, suggesting a misalignment in the system. Overall, these results highlight the importance of observing multiple molecular species in multiple transitions to characterize the physical and chemical environment of young disks.
Prediction of non-SUSY AdS conjecture on the lightest neutrino mass revisited

We study the constraint of the non-SUSY AdS conjecture on the three-dimensional vacua obtained from the compactification of the Standard Model coupled to Einstein gravity on a circle where the three-dimensional components of the four-dimensional metric are general functions of both non-compact and compact coordinates. We find from studying the wavefunction profile of the three-dimensional metric in the compactified dimension that the radius of the compactified dimension must be quantized. Consequently, the three-dimensional vacua are constrained by not only the non-SUSY AdS conjecture but also the quantization rule of the circle radius, leading to both upper and lower bounds for the mass of the lightest neutrino as $\sqrt{2}\leq m_\nu/\sqrt{\Lambda_4}<\sqrt{3}$ where $\Lambda_4\simeq5.06\times10^{-84}$ GeV$^2$ is the observed cosmological constant. This means that the lightest neutrino should have a mass around $10^{-32}$ eV or it would be well-approximately massless. With this prediction, we reconstruct the light neutrino mass matrix that is fixed by the neutrino oscillation data and in terms of three new mixing angles and six new phases for both the normal ordering and inverted ordering. In the situation that the light neutrino mass matrix is Hermitian, we calculate its numerical value in the $3\sigma$ range.
Multilayer random dot product graphs: Estimation and online change point detection

In this paper, we first introduce the multilayer random dot product graph (MRDPG) model, which can be seen as an extension of the random dot product graph model to multilayer networks. The MRDPG model is convenient for incorporating nodes' latent positions when understanding connectivity. By modelling a multilayer network as an MRDPG, we further deploy a tensor-based method and demonstrate its superiority over the state-of-the-art methods. We then move from a static to a dynamic MRDPG and are concerned with online change point detection problems. At every time point, we observe a realisation from an $L$-layered MRDPG. Across layers, we assume shared common node sets and latent positions, but allow for different connectivity matrices. In this paper we unfold a comprehensive picture concerning a range of problems. For both fixed and random latent position cases, we propose efficient online change point detection algorithms, minimising the delay in detection while controlling the false alarms. Notably, in the random latent position case, we devise a novel nonparametric change point detection algorithm with a kernel estimator in its core, allowing for the case when the density does not exist, accommodating stochastic block models as special cases. Our theoretical findings are supported by extensive numerical experiments, with the code available online https://github.com/MountLee/MRDPG.
Early Planet Formation in Embedded Disks (eDisk). I. Overview of the Program and First Results

We present an overview of the Large Program, “Early Planet Formation in Embedded Disks (eDisk),” conducted with the Atacama Large Millimeter/submillimeter Array (ALMA). The ubiquitous detections of substructures, particularly rings and gaps, in protoplanetary disks around T Tauri stars raise the possibility that at least some planet formation may have already started during the embedded stages of star formation. In order to address exactly how and when planet formation is initiated, the program focuses on searching for substructures in disks around 12 Class 0 and 7 Class I protostars in nearby (<200 pc) star-forming regions through 1.3 mm continuum observations at a resolution of ∼7 au (0.″04). The initial results show that the continuum emission, mostly arising from dust disks around the sample protostars, has relatively few distinctive substructures, such as rings and spirals, in marked contrast to Class II disks. The dramatic difference may suggest that substructures quickly develop in disks when the systems evolve from protostars to Class II sources, or alternatively that high optical depth of the continuum emission could obscure internal structures. Kinematic information obtained through CO isotopologue lines and other lines reveals the presence of Keplerian disks around protostars, providing us with crucial physical parameters, in particular, the dynamical mass of the central protostars. We describe the background of the eDisk program, the sample selection and their ALMA observations, and the data reduction, and we also highlight representative first-look results.
Debiasing Piecewise Deterministic Markov Process samplers using couplings

Monte Carlo methods - such as Markov chain Monte Carlo (MCMC) and piecewise deterministic Markov process (PDMP) samplers - provide asymptotically exact estimators of expectations under a target distribution. There is growing interest in alternatives to this asymptotic regime, in particular in constructing estimators that are exact in the limit of an infinite amount of computing processors, rather than in the limit of an infinite number of Markov iterations. In particular, Jacob et al. (2020) introduced coupled MCMC estimators to remove the non-asymptotic bias, resulting in MCMC estimators that can be embarrassingly parallelised. In this work, we extend the estimators of Jacob et al. (2020) to the continuous-time context and derive couplings for the bouncy, the boomerang and the coordinate samplers. Some preliminary empirical results are included that demonstrate the reasonable scaling of our method with the dimension of the target.
Bose-Einstein Condensation of Light in a Semiconductor Quantum Well Microcavity

When particles with integer spin accumulate at low temperature and high density they undergo Bose-Einstein condensation (BEC). Atoms, solid-state excitons and excitons coupled to light all exhibit BEC, which results in high coherence due to massive occupation of the respective system's ground state. Surprisingly, photons were shown to exhibit BEC much more recently in organic dye-filled optical microcavities, which, owing to the photon's low mass, occurs at room temperature. Here we demonstrate that photons within an inorganic semiconductor microcavity also thermalise and undergo BEC. Although semiconductor lasers are understood to operate out of thermal equilibrium, we identify a region of good thermalisation in our system where we can clearly distinguish laser action from BEC. Based on well-developed technology, semiconductor microcavities are a robust system for exploring the physics and applications of quantum statistical photon condensates. Notably, photon BEC is an alternative to exciton-based BECs, which dissociate under high excitation and often require cryogenic operating conditions. In practical terms, photon BECs offer their critical behaviour at lower thresholds than lasers. Our study shows two further advantages of photon BEC in semiconductor materials: the lack of dark electronic states allows these BECs to be sustained continuously; and semiconductor quantum wells offer strong photon-photon scattering. We measure an unoptimised interaction parameter, $\tilde{g}=0.0023\pm0.0003$, which is large enough to access the rich physics of interactions within BECs, such as superfluid light or vortex formation.
Representations of the Fermion-Virasoro algebras

Let $\delta=0$ or $\frac{1}{2}$. In this paper, we introduce the Fermion algebra $F(\delta)$ and the Fermion-Virasoro algebra $\mathcal S(\delta)$. They are infinite-dimensional Lie superalgebras. All simple smooth $F(\delta)$-modules, all simple weight $F(\delta)$-modules, all simple smooth $\mathcal S(\delta)$-modules of nonzero level, and all simple Harish-Chandra $\mathcal S(\delta)$-modules are determined. Surprisingly, we have found four different $\mathcal S(\delta)$-module structures on free $\mathbb C[L_0]$-modules of rank $2$.
A non-backtracking method for long matrix and tensor completion

We consider the problem of low-rank rectangular matrix completion in the regime where the matrix $M$ of size $n\times m$ is"long", i.e., the aspect ratio $m/n$ diverges to infinity. Such matrices are of particular interest in the study of tensor completion, where they arise from the unfolding of a low-rank tensor. In the case where the sampling probability is $\frac{d}{\sqrt{mn}}$, we propose a new spectral algorithm for recovering the singular values and left singular vectors of the original matrix $M$ based on a variant of the standard non-backtracking operator of a suitably defined bipartite weighted random graph, which we call a non-backtracking wedge operator. When $d$ is above a Kesten-Stigum-type sampling threshold, our algorithm recovers a correlated version of the singular value decomposition of $M$ with quantifiable error bounds. This is the first result in the regime of bounded $d$ for weak recovery and the first for weak consistency when $d\to\infty$ arbitrarily slowly without any polylog factors. As an application, for low-rank orthogonal $k$-tensor completion, we efficiently achieve weak recovery with sample size $O(n^{k/2})$, and weak consistency with sample size $\omega(n^{k/2})$.
A novel method for efficient closure of large mucosal defects using nylon loops combined with titanium clips after endoscopic submucosal dissection

Endoscopic submucosal dissection (ESD) is an effective treatment for early-stage gastrointestinal cancer [1, 2]. However, difficult site lesions (e. g., colon flexion, right-side colon) and large mucosal defects (greater than 5 cm) result in postESD defect closures as an ongoing challenge in clinical practice [3]. Therefore, we report a novel method for the efficient closure of large mucosal defects after ESD using nylon loops combined with titanium clips. We demonstrate the application of this method in a real case of post-ESD large defect closure (▶Video 1). An 80-yearoldman was found to have a circumferential 1/3 perineal lateral developmental tumor in the left posterior wall of the rectum 4–7 cm from the anal orifice with a pathology of high-grade intraepithelial neoplasia. The operation procedure was as follows. After complete debridement of the lesion by ESD (▶Fig. 1), one titanium clip with a nylon loop was fixed at the midpoint of one side of the incision margin (▶Fig. 2). Another titanium clip was fixed at the midpoint of the opposite side of the incision margin (▶Fig. 3), and then the nylon loop was gathered to transform the oval-shaped defect into a “figure-eight-shaped” defect (▶Fig. 4). The remaining defect was closed with an appropriate amount of additional titanium clips. This method was applied to 50 patients with lesions ranging from 1/3–1/2 of the circumference of the bowel, and the mean closure time was 12.6 minutes. The mean number of titanium clips was Video 1 The application of nylon loop combined with titanium clips in a real case of large mucosal defect closure after endoscopic submucosal dissection. ▶ Fig. 1 Complete dissection of the lesion, leaving a large mucosal defect.
Multiscale semantic alignment graph convolution network for single-shot learning based hyperspectral image classification

Graph convolutional networks (GCN) can extract features from non-Euclidean space very effectively, and it has been successfully applied in various fields of hyperspectral images (HSIs). However, due to the limited labeled HSI data, GCN often performs not well and encounters over-smoothing problems as the number of network layers increases. Furthermore, building a GCN adjacency matrix for HSI classification directly is computationally complex. This paper proposes a multiscale semantic alignment graph convolutional network (MSAGCN) for HSI classification to solve the problems mentioned above. The proposed method mainly consists of three parts, superpixel segmentation, semantic alignment and multiscale graphs. Firstly, superpixel segmentation is performed on the original HSI, and each superpixel region contains similar spatial and spectral information. Secondly, semantic features of labeled nodes are extracted using identity aggregation with fixed receptive fields. The class-center similarity is adopted using these semantic features to align nodes semantically. This semantic alignment technique alleviates over-smoothing in GCN. Finally, the multiscale technique enables MSAGCN to obtain different scale spectral-spatial features. Experimental results show that our proposed model exhibits competitive results on open source hyperspectral datasets with only one labeled sample per class.
DRA-Net: densely residual attention based low-light image enhancement

The visual quality of nighttime photographs diminishes greatly due to low contrast and high noise. We need a robust image enhancement methodology to improve such low-light images close to standard daylight images. Due to deteriorated conditions of uneven light and noise, this image enhancement problem becomes ill-posed. Our paper has proposed a Densely Residual Attention Network (DRANet), an end-to-end attention base densely residual network. The architecture of DRANet consists of sub-modules convolution block (CB) and densely residual feature - convolutional block attention module (DRF-CBAM). DFR-CBAM also has sub-modules, deep residual feature block (DRFB), and convolutional block attention module (CBAM). Using the most recent results from attention and deep residual-based convolution networks in number of computer vision problems, we have used DRFB to enhance the features in-depth by using its dense and residual skip connections. Similarly, features in both spatial and channel axis have been extracted by using a lightweight CBAM attention module. Contrast, luminosity, and noise of the enhanced images have been balanced by additionally implementing a color balancing function at the end of the proposed network. Furthermore, we have used a combination of LLab, LSSIM and LMAE loss functions to make the proposed network stable and recover both contextual and local details while training. MAE, PSNR, SSIM, MS-SSIM, FSIM, Cosine Similarity, and deltaE2000 have been used as referenced while NIQE as nonreferenced based image quality assessment (IQA) metric. Experiment results showed that our proposed methodology is very effective with higher referenced and lower non-referenced image IQA metrics values. Furthermore, the effectiveness of our method has also demonstrated by the visual and perceptual quality of enhanced images.
Semantic segmentation and image quality assessment of anterior segment images for smartphones

Eye diseases have always been a threat to public health worldwide. Many people suffer from various eye diseases, but there are not enough skilled ophthalmologists to meet the demand for medical care. Thus, finding a method to perform ophthalmic examinations automatically and conveniently is necessary. Although many well-designed ophthalmic diagnosis systems have been proposed to diagnose ophthalmic disorders using artificial intelligence algorithms, they tend to depend on high-quality anterior segment images to perform appropriately. In order to capture high-quality anterior segment images simply with a smartphone, we proposed a system including a semantic segmentation model and an image quality assessment method for anterior segment images. Our proposed segmentation model, namely the multi-task anterior segment image semantic segmentation (MT-ASISS) model, has a designed multitask learning network structure and achieves an accuracy of 92.63% in Dice and a processing speed of 138ms per frame on smartphones. Our anterior segment image quality assessment method, namely Mixed-Parameters Quality Assessment (MPQA) method, has an accuracy of 92.6% in mean average precision (mAP). The system can help reduce the demand for professional image collecting equipment, share the burden of choosing satisfactory images manually and improve the efficiency of acquiring anterior segment images.
The Isotopic Pattern Variability in the Breeding and Non-Breeding Black-Vented Shearwater

Abstract. Seabirds utilize different foraging strategies based on their physical needs and environmental conditions during the stages of their reproductive cycle. During their breeding season, seabirds assume a central-place foraging strategy, while after the reproductive season, during their molt, they migrate to distant non-breeding grounds where they remain for several months. Until recently, little was known about their distribution and ecology during migration. This gap in knowledge regarding large-scale movements and ecology could be filled by analyzing isotopic ratios of carbon (δ13C) and nitrogen (δ15N) in the feathers. Analyzing stable isotope ratios provides insights into the species' foraging ecology and variations along the molting period. We first ascertained the molting strategy of the Black-vented Shearwaters (Puffinus opisthomelas). We then inferred their non-breeding period distribution range and variations in the trophic niche by analysing stable isotopes in feathers. The observed δ13C values reveal that these birds all migrate to similar terminal non-breeding areas. Values of δ15N were similar between sexes during winter and up to the reproductive period; however, their levels were distinct during the post-breeding period. Sexes exploited slightly different trophic levels during the post-breeding period, differently using coastal and pelagic waters, likely to reduce competition during their post-breeding molt.