Miracle-dz/UniFAR-FTUs huggingface dataset:
Supermassive Black Holes as Possible Sources of Ultrahigh-energy Cosmic Rays

The production and acceleration mechanisms of ultrahigh-energy cosmic rays (UHECRs) of energy >1020 eV, clearly beyond the GZK cutoff limit, remain unclear, which points to the exotic nature of the phenomena. Recent observations of extragalactic neutrinos may indicate that the source of UHECRs is an extragalactic supermassive black hole (SMBH). We demonstrate that ultraefficient energy extraction from a rotating SMBH driven by the magnetic Penrose process (MPP) could indeed fit the bill. We envision ionization of neutral particles, such as neutron beta decay, skirting close to the black hole horizon that energizes protons to over 1020 eV for an SMBH of mass 109M⊙ and magnetic field 104 G. Applied to the Galactic center SMBH, we have a proton energy of order ≈1015.6 eV that coincides with the knee of the cosmic-ray spectra. We show that large γz factors of high-energy particles along the escaping directions occur only in the presence of an induced charge of the black hole, which is known as the Wald charge in the case of a uniform magnetic field. It is remarkable that the process requires neither an extended acceleration zone nor fine-tuning of accreting-matter parameters. Further, this leads to certain verifiable constraints on the SMBH’s mass and magnetic field strength as the source of UHECRs. This clearly makes the ultraefficient regime of the MPP one of the most promising mechanisms for fueling the UHECR powerhouse.
Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability

With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER.
Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images

Deep neural networks (DNN) have been widely used to carry out segmentation tasks in both electron microscopy (EM) and light/fluorescence microscopy (LM/FM). Most DNNs developed for this purpose are based on some variation of the encoder-decoder U-Net architecture. Here we show how Res-CR-Net, a new type of fully convolutional neural network that does not adopt a U-Net architecture, excels at segmentation tasks traditionally considered very hard, like recognizing the contours of nuclei, cytoplasm and mitochondria in densely packed cells in either EM or LM/FM images.
Localizing Interpretable Multi-scale informative Patches Derived from Media Classification Task

Deep convolutional neural networks (CNN) always depend on wider receptive field (RF) and more complex non-linearity to achieve state-of-the-art performance, while suffering the increased difficult to interpret how relevant patches contribute the final prediction. In this paper, we construct an interpretable AnchorNet equipped with our carefully designed RFs and linearly spatial aggregation to provide patch-wise interpretability of the input media meanwhile localizing multi-scale informative patches only supervised on media-level labels without any extra bounding box annotations. Visualization of localized informative image and text patches show the superior multi-scale localization capability of AnchorNet. We further use localized patches for downstream classification tasks across widely applied networks. Experimental results demonstrate that replacing the original inputs with their patches for classification can get a clear inference acceleration with only tiny performance degradation, which proves that localized patches can indeed retain the most semantics and evidences of the original inputs.
Low intrinsic emittance in modern photoinjector brightness

Reducing the intrinsic emittance of photocathodes is one of the most promising routes to improving the brightness of electron sources. However, when emittance growth occurs during beam transport (for example, due to space-charge), it is possible that this emittance growth overwhelms the contribution of the photocathode and, thus, in this case source emittance improvements are not beneficial. Using multi-objective genetic optimization, we investigate the role intrinsic emittance plays in determining the final emittance of several space-charge dominated photoinjectors, including those for high repetition rate free electron lasers and ultrafast electron diffraction. We introduce a new metric to predict the scale of photocathode emittance improvements that remain beneficial and explain how additional tuning is required to take full advantage of new photocathode technologies. Additionally, we determine the scale of emittance growth due to point-to-point Coulomb interactions with a fast tree-based space-charge solver. Our results show that in the realistic high brightness photoinjector applications under study, the reduction of thermal emittance to values as low as 50 pm/um (1 meV MTE) remains a viable option for the improvement of beam brightness.
Extended Distribution of Hyptis Brevipes Poit. in India

Hyptis brevipes Poit. (Lamiaceae), an exotic species had been reported earlier from Andaman Islands but so far not yet been reported from mainland of India. Now this species has been collected from Mahananda Wildlife Sanctuary, Darjeeling District, West Bengal and reported here as new distributional record from mainland of India.
Spontaneous Surface Collapse and Reconstruction in Antiferromagnetic Topological Insulator MnBi$_2$Te$_4$

MnBi$_2$Te$_4$ is an antiferromagnetic topological insulator which stimulates intense interests due to the exotic quantum phenomena and promising device applications. Surface structure is a determinant factor to understand the novel magnetic and topological behavior of MnBi2Te4, yet its precise atomic structure remains elusive. Here, we discovered a spontaneous surface collapse and reconstruction in few-layer MnBi2Te4 exfoliated under delicate protection. Instead of the ideal septuple-layer structure in the bulk, the collapsed surface is shown to reconstruct as Mn-doped Bi$_2$Te$_3$ quintuple-layer and Mn$_x$Bi$_y$Te double-layer with a clear van der Waals gap in between. Combining with first-principles calculations, such spontaneous surface collapse is attributed to the abundant intrinsic Mn-Bi antisite defects and tellurium vacancy in the exfoliated surface, which is further supported by in-situ annealing and electron irradiation experiments. Our results shed light on the understanding of the intricate surface-bulk correspondence of MnBi$_2$Te$_4$, and provide insightful perspective of the surface-related quantum measurements in MnBi$_2$Te$_4$ few-layer devices.
Enhancing Routing Security in IoT: Performance Evaluation of RPL’s Secure Mode Under Attacks

As the routing protocol for low power and lossy networks (RPL)s became the standard for routing in the Internet-of-Things (IoT) networks, many researchers had investigated the security aspects of this protocol. However, no work (to the best of our knowledge) has investigated the use of the security mechanisms included in RPL’s standard, mainly because there was no implementation for these features in any Internet of Things (IoT) operating systems yet. A partial implementation of RPL’s security mechanisms was presented recently for the Contiki operating system (by Perazzo et al.), which provided us with an opportunity to examine RPL’s security mechanisms. In this article, we investigate the effects and challenges of using RPL’s security mechanisms under common routing attacks. First, a comparison of RPL’s performance, with and without its security mechanisms, under four routing attacks [Blackhole, Selective-Forward (SF), Neighbor, and Wormhole (WH) attacks] is conducted using several metrics (e.g., average data packet delivery rate, average data packet latency, average power consumption, etc.). This comparison is performed using two commonly used radio duty-cycle protocols. Second, and based on the observations from this comparison, we propose two techniques that could reduce the effects of such attacks, without having added security mechanisms for RPL. An evaluation of these techniques shows improved performance of RPL under the investigated attacks, except for the WH.
Quality Prediction on Deep Generative Images

In recent years, deep neural networks have been utilized in a wide variety of applications including image generation. In particular, generative adversarial networks (GANs) are able to produce highly realistic pictures as part of tasks such as image compression. As with standard compression, it is desirable to be able to automatically assess the perceptual quality of generative images to monitor and control the encode process. However, existing image quality algorithms are ineffective on GAN generated content, especially on textured regions and at high compressions. Here we propose a new “naturalness”-based image quality predictor for generative images. Our new GAN picture quality predictor is built using a multi-stage parallel boosting system based on structural similarity features and measurements of statistical similarity. To enable model development and testing, we also constructed a subjective GAN image quality database containing (distorted) GAN images and collected human opinions of them. Our experimental results indicate that our proposed GAN IQA model delivers superior quality predictions on the generative image datasets, as well as on traditional image quality datasets.
Landau-like theory for buckling phenomena and its application to the elastica hypoarealis

Bifurcation phenomena are ubiquitous in elasticity, but their study is often limited to linear perturbation or numerical analysis since second or higher variations are often beyond an analytic treatment. Here, we review two key mathematical ideas, namely, the splitting lemma and the determinacy of a function, and show how they can be fruitfully used to derive a reduced function, named Landau expansion in the paper, that allows us to give a simple but rigorous description of the bifurcation scenario, including the stability of the equilibrium solutions. We apply these ideas to a paradigmatic example with potential applications to various softly constrained physical systems and biological tissues: a stretchable elastic ring under pressure. We prove the existence of a tricritical point and find bistability effects and hysteresis when the stretching modulus is sufficiently small. These results seem to be in qualitative agreement with some recent experiments on heart cells.
STT-CBS: A Conflict-Based Search Algorithm for Multi-Agent Path Finding with Stochastic Travel Times

We address the Multi-Agent Path Finding problem on a graph for agents assigned to goals in a known environment and under uncertainty. Our algorithm, called STT-CBS, uses Conflict-Based Search (CBS) with a stochastic travel time (STT) model for the agents. We model robot travel time along each edge of the graph by independent gamma-distributed random variables and propose probabilistic conflict identification and constraint creation methods to robustly handle travel time uncertainty. We show that under reasonable assumptions our algorithm is complete and optimal in terms of expected sum of travel times, while ensuring an upper bound on each pairwise conflict probability. Simulations and hardware experiments show that STT-CBS is able to significantly decrease conflict probability over CBS, while remaining within the same complexity class.
On the equivariant K– and KO–homology of
some special linear groups

We compute the equivariant $KO$-homology of the classifying space for proper actions of $\textrm{SL}_3(\mathbb{Z})$ and $\textrm{GL}_3(\mathbb{Z})$. We also compute the Bredon homology and equivariant $K$-homology of the classifying spaces for proper actions of $\textrm{PSL}_2(\mathbb{Z}[\frac{1}{p}])$ and $\textrm{SL}_2(\mathbb{Z}[\frac{1}{p}])$ for each prime $p$. Finally, we prove the unstable Gromov-Lawson-Rosenberg conjecture for $\textrm{PSL}_2(\mathbb{Z}[\frac{1}{p}])$ when $p\equiv11\pmod{12}$.
A 7-lncRNA signature associated with the prognosis of colon adenocarcinoma

Background Colon adenocarcinoma (COAD) is the most common colon cancer exhibiting high mortality. Due to their association with cancer progression, long noncoding RNAs (lncRNAs) are now being used as prognostic biomarkers. In the present study, we used relevant clinical information and expression profiles of lncRNAs originating from The Cancer Genome Atlas database, aiming to construct a prognostic lncRNA signature to estimate the prognosis of patients. Methods The samples were randomly spilt into training and validation cohorts. In the training cohort, prognosis-related lncRNAs were selected from differentially expressed lncRNAs using the univariate Cox analysis. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were employed for identifying prognostic lncRNAs. The prognostic signature was constructed by these lncRNAs. Results The prognostic model was able to calculate each COAD patient’s risk score and split the patients into groups of low and high risks. Compared to the low-risk group, the high-risk group had significant poor prognosis. Next, the prognostic signature was validated in the validation, as well as all cohorts. The receiver operating characteristic (ROC) curve and c-index were determined in all cohorts. Moreover, these prognostic lncRNA signatures were combined with clinicopathological risk factors to construct a nomogram for predicting the prognosis of COAD in the clinic. Finally, seven lncRNAs (CTC-273B12.10, AC009404.2, AC073283.7, RP11-167H9.4, AC007879.7, RP4-816N1.7, and RP11-400N13.2) were identified and validated by different cohorts. The Kyoto Encyclopedia of Genes and Genomes analysis of the mRNAs co-expressed with the seven prognostic lncRNAs suggested four significantly upregulated pathways, which were AGE-RAGE, focal adhesion, ECM-receptor interaction, and PI3K/Akt signaling pathways. Conclusion Thus, our study verified that the seven lncRNAs mentioned can be used as biomarkers to predict the prognosis of COAD patients and design personalized treatments.
Functional characterization of maize heat shock transcription factor gene ZmHsf01 in thermotolerance

Background Heat waves can critically influence maize crop yields. Plant heat shock transcription factors (HSFs) play a key regulating role in the heat shock (HS) signal transduction pathway. Method In this study, a homologous cloning method was used to clone HSF gene ZmHsf01 (accession number: MK888854) from young maize leaves. The transcript levels of ZmHsf01 were detected using qRT-PCR in different tissues and treated by HS, abscisic acid (ABA), hydrogen peroxide (H2O2), respectively, and the functions of gene ZmHsf01 were studied in transgenic yeast and Arabidopsis. Result ZmHsf01 had a coding sequence (CDS) of 1176 bp and encoded a protein consisting of 391 amino acids. The homologous analysis results showed that ZmHsf01 and SbHsfA2d had the highest protein sequence identities. Subcellular localization experiments confirmed that ZmHsf01 was localized in the nucleus. ZmHsf01 was expressed in many maize tissues. It was up-regulated by HS, and up-regulated in roots and down-regulated in leaves under ABA and H2O2treatments. ZmHsf01-overexpressing yeast cells showed increased thermotolerance. In Arabidopsis seedlings, ZmHsf01 compensated for the thermotolerance defects of mutant athsfa2, and ZmHsf01-overexpressing lines showed enhanced basal and acquired thermotolerance. When compared to wild type (WT) seedlings, ZmHsf01-overexpressing lines showed higher chlorophyll content and survival rates after HS. Heat shock protein (HSP) gene expression levels were more up-regulated in ZmHsf01-overexpressing Arabidopsis seedlings than WT seedlings. These results suggest that ZmHsf01 plays a vital role in response to HS in plant.
Development of Bullous Pemphigoid after Tenckhoff Catheter Placement in a Peritoneal Dialysis Patient

Bullous pemphigoid (BP), a chronic autoimmune subepidermal blistering skin disease, has been described in end-stage renal disease patients requiring dialysis after the placement of an artero-venous fistula. We report a case of a novel onset of BP following a peritoneal dialysis abdominal Tenckhoff catheter placement. The 3-month treatment with systemic doxycycline and topical clobetasol propionate allowed a rapid disappearing of the blisters and left the patient free of symptoms in the follow-up. To our knowledge, this is the first case describing a new BP onset after a peritoneal dialysis catheter placement.
Stability of Plasma Amyloid-β 1–40, Amyloid-β 1–42, and Total Tau Protein over Repeated Freeze/Thaw Cycles

Introduction: Blood biomarkers of Alzheimer’s disease (AD) have attracted much attention of researchers in recent years. In clinical studies, repeated freeze/thaw cycles often occur and may influence the stability of biomarkers. This study aims to investigate the stability of amyloid-β 1–40 (Aβ1–40), amyloid-β 1–42 (Aβ1–42), and total tau protein (T-tau) in plasma over freeze/thaw cycles. Methods: Plasma samples from healthy controls (n = 2), AD patients (AD, n =3) and Parkinson’s disease patients (PD, n = 3) were collected by standardized procedure and immediately frozen at –80°C. Samples underwent 5 freeze/thaw (–80°C/room temperature) cycles. The concentrations of Aβ1–40, Aβ1–42, and T-tau were monitored during the freeze/thaw tests using an immunomagnetic reduction (IMR) assay. The relative percentage of concentrations after every freeze/thaw cycle was calculated for each biomarker. Results: A tendency of decrease in the averaged relative percentages over samples through the freeze and thaw cycles for Aβ1–40 (100 to 97.11%), Aβ1–42 (100 to 94.99%), and T-tau (100 to 95.65%) was found. However, the decreases were less than 6%. For all three biomarkers, no statistical significance was found between the levels of fresh plasma and those of the plasma experiencing 5 freeze/thaw cycles (p > 0.1). Conclusions: Plasma Aβ1–40, Aβ1–42, and T-tau are stable through 5 freeze/thaw cycles measured with IMR.
A Depth Image Fusion Network for 3D Point Cloud Semantic Segmentation

It is widely known that the 3D point cloud is the most intuitive way to depict real scenes. Till now, some deep learning networks for point cloud data that have been proposed. In this paper, we propose a simple and intuitive network framework for 3D point cloud semantic segmentation in small-scale scenes. PointSIFT is improved by designing the depth image features extraction module. Through the module structure, constructing a simple mapping relationship between the two feature space. By fusing two features, we achieve state-of-the-art performance, moreover higher training efficiency. We also test our network to real workbench scenarios. And there is no performance degradation due to real scene noise.
Static Transfer Switch Applied to Single-Phase Uninterruptible Power Supply

This paper presents a Static Transfer Switch (STS) applied to Single-Phase Uninterruptible Power Supply. The performance of two STS topologies was evaluated, one traditional composed of thyristors and the other formed by the association of IGBTs and diodes. In addition, two approaches for detection of disturbances in the grid voltage waveform are explored: the first method is based on the dq0 transformation and the second one is performed through a PLL (Phase Locked Loop) algorithm. Simulation and experimental results were performed to evaluate the performance of topologies and methods for disturbances detection.
A Congestion-adaptive Fault-tolerant Routing Algorithm on HNoC

The Heterogeneous Network on Chip (HNoC) integrates CPU cores and GPU cores, as well as other components, such as Last Level Cache and Memory Controller. The various components on the chip work together. The quality of communication between components becomes critical. The GPUs have a large amount of computing resources in parallel, so it has a large number of threads. This feature makes the communication between the GPUs and the last level cache and the memory controller more frequent. The communication of each link in the traditional network on-chip is relatively average. However, the congestion near the GPU node in the heterogeneous on-chip network is serious, which affects the performance of the on-chip network. Therefore, we propose a fault-tolerant routing algorithm based on congestion awareness on HNoC. We also monitor the traffic of the network by implementing the local congestion monitor module. Heuristically determine the node and direction of the on-chip network application turning mode. In order to accurately evaluate the impact of the experimental scheme on system performance, this paper selects the Noxim NoC simulator as the basic experimental platform. Experimental comparison the proposed algorithm with the traditional fault-tolerant algorithm, in the heterogeneous network with failure rates of 5%, 10%, 15% and 20%, the average latency is reduced by 35%, 22%, 25% and 24 respectively. The average throughput saturation decreased by 12.5%, 41.7%, 23.7% and 26.3%, respectively. It has a significant improvement in performance.
Thermal Error Modeling for Machine Tools: Mechanistic Analysis and Solution for the Pseudocorrelation of Temperature-Sensitive Points

The temperature-sensitive point is the input variable of the thermal error compensation model of computer numerical control (CNC) machine tools. At present, the most commonly used selection method is to measure the multipoint temperature and thermal error of the machine tool synchronously and select several temperature measurement points with the highest thermal error correlation as temperature-sensitive points according to the measurement data. This study reveals that this method sometimes has obvious mis-selection and causes the model to fail. The reason is that the weak correlation temperature measurement point away from the machine tool heat source amplifies the volatility of the correlation evaluation result, owing to the small overall change. If the calculated result exceeds the true strong correlation temperature measurement point, it will be incorrectly selected as the temperature-sensitive point. This scenario has been termed herein as the pseudocorrelation problem. With the gradual popularization of thermal error compensation technology for CNC machine tools, pseudocorrelation will seriously affect the mass production pass rate. Therefore, the study analyzes and rigorously proves the mathematical mechanism of this problem, and the temperature measurement point preselection algorithm based on the correlation coefficient volatility determination factor (CCVDF) is proposed to eliminate potential pseudocorrelation temperature points before selecting temperature-sensitive points. After 1000 random simulation experiments, the failure rate of Z-direction thermal error modeling after preselection decreased from 5.1% to 0.5%, and the Y-direction error decreased from 33.7% to 1.5%. This algorithm can greatly improve the qualification rate of large-scale equipment of thermal error compensation technology.