Miracle-dz/UniFAR-FTUs huggingface dataset:
NAF: Nest Axial Attention Fusion Network for Infrared and Visible Images

In recent years, deep learning has been widely used in the field of infrared and visible image fusion. However, the existing methods based on deep learning have the problems of losing details and less consideration of long-range dependence. To address that, we propose a novel encoder-decoder fusion model based on nest connections and Axial-attention, named NAF. The network can extract more multi-scale information as possible and retain more long-range dependencies due to the Axial-attention in each convolution block. The method includes three parts: an encoder consists of convolutional blocks, a fusion strategy based on spatial attention and channel attention, and a decoder to process the fused features. Specifically, the source images are firstly fed into an encoder to extract multi-scale depth features. Then, a fusion strategy is employed to merge the depth features of each scale generated by the encoder. Finally, a decoder based on nested convolutional block is exploited to reconstruct the fused image. The experimental results on public data sets demonstrate that the proposed method has better fusion performance than other state-of-the-art methods in both subjective and objective evaluation.
Analisa Multiwavelet untuk Kompresi Suara

Penghematan pita frekuensi dalam dunia komunikasi dijital merupakan suatu tantangan yang besar. Salah satu cara untuk melakukan penghematan pita frekuensi adalah dengan melakukan kompresi data. Selain menghemat penggunaan pita frekuensi, keuntungan lain dari kompresi data yaitu perpindahan data menjadi cepat serta media penyimpanannya menjadi lebih kecil. Dalam tulisan ini akan diuraikan teknik kompresi suara dengan menggunakan gabungan beberapa induk wavelet (multi wavelet), yaitu induk wavelet Haar, Daubachies, dan Coiflet. Tujuan dari penggunaan teknik ini adalah untuk mendapatkan kualitas suara terkompresi yang lebih baik daripada hanya menggunakan satu induk wavelet saja. Dari hasil simulasi diperoleh bahwa nilai Signal-to-Noise Ratio (SNR) dan compression score untuk yang menggunakan gabungan beberapa induk wavelet lebih besar dibandingkan dengan hanya menggunakan satu induk wavelet saja. Hal ini menunjukkan bahwa sinyal suara hasil dekompresi yang menggunakan gabungan beberapa induk wavelet lebih mirip dengan sinyal asli daripada hanya menggunakan satu induk wavelet saja meskipun terdapat penurunan kualitas. Kata kunci: kompresi data, multiwavelet, SNR, compression score
Traffic Flow Forecasting Research Based on Delay Reconstruction and GRU-SVR

To improve the prediction accuracy of traffic flow, short-term traffic flow prediction based on delayed reconstruction and integrated GRU-SVR model using Stacking strategy is proposed to address the problems of nonlinearity, complexity and time dependence of traffic flow. By solving the phase space reconstruction parameters through the chaotic nature of source traffic to map the sequences into high-dimensional vector matrices, the integrated GRU-SVR model is optimized using iGridSearch CV for prediction. GRU alleviates the long dependence problem among data, can make full use of the before-and-after correlation information in the time dimension to sense the data causality, and the SVR introduction parameters are searched for optimality through improved grid search, and the global optimal solution is obtained in high time efficiency. The global optimal solution can ensure the generalizability of the integrated model. The results show that the RMSE, MAPE and R2 score of the integrated algorithm are better than the other three models. The experiments prove that the method can effectively improve the prediction accuracy and has better generalization ability.
Obfuscating the Dataset: Impacts and Applications

Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties when dataset sharing is essential. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights —in terms of the model accuracy, ℓ2-distance-based model distance, and level of data privacy—and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a tradeoff between the model accuracy and privacy level and a tradeoff between the model difference and privacy level. The results indicate broad application prospects for training outsourcing and guarding against attacks in federated learning both of which have been increasingly attractive in many areas, particularly learning in edge computing.
Task and Memory Mapping of Large Size Embedded Applications over NUMA architecture✱

Multicore architectures provide the increased performance required by modern embedded real-time systems. Most platforms exhibit a non-uniform memory access (NUMA). In NUMA, memory banks with different access time can be explicitly addressed. Such an architecture, however, is challenging predictability given the significant impact of the allocation of variables on the execution times. At software level, real-world embedded applications (e.g. automotive) are composed by thousands of functions often communicating through shared variables stored in memory, with a variable access time because of NUMA. This paper addresses the mapping of complex embedded applications onto NUMA multicore architectures. The developed problem formulation offers a solution to the following problems: (i) allocating variables (called labels in the automotive context) over memories of different characteristics, (ii) mapping functionalities (called runnables) onto CPUs, (iii) creating OS tasks from runnables, and (iv) assigning priorities to tasks. Our developed implementation is capable to handle an application composed by 1K+ runnables, all sharing 10K+ labels and finds a solution in at most 3 minutes on a standard laptop, enabling interactive design space exploration.
Instance-level Weighted Graph Learning for Incomplete Multi-view Clustering

Incomplete multi-view clustering has attracted board attention due to the frequent absent of some views of real-world objects. Existing incomplete multi-view clustering methods usually assign different weights to different views to learn the consensus graph of multi-views, which however cannot preserve properly the non-noise information in the views of lower weight. In this paper, unlike existing view-level weighted graph learning, we propose a simple yet effective instance-level weighted graph learning for incomplete multi-view clustering. Specifically, we first use the similarity information of the available views to estimate and recover the missing views, such that the harmful impact of the missing views can be reduced. Then, we adaptively assign the weights to the similarities between different perspectives such that negative effects of noises are reduced. Finally, by combining graph fusion and rank constraints, we can learn a new consensus representation of multi-view data for incomplete multi-view analysis. Experimental results on five widely used incomplete multi-view datasets clearly demonstrate the effectiveness of our proposed method.
Rice Disease Recognition and Feature Visualization Using a Convolutional Neural Network

To achieve fast and accurate identification of rice diseases in the field, we propose an automatic rice disease classifier, in which the process of characterizing rice diseases is visualized and analyzed by a deconvolutional neural network. An AlexNet model, pretrained by ImageNet, is constructed and trained on rice disease images to classify them. After the training is completed, the signal is repositioned to the corresponding position of the input image by a deconvolutional neural network corresponding to the AlexNet structure. The set of pixels that contribute most to the prediction of the convolutional neural network is identified from the deconvolution visualization map. The experimental results demonstrated the effectiveness of the proposed method. The classifier achieved an accuracy of 90.03% for the rice disease dataset, which was 8.39% and 16.78% higher than the accuracies achieved by the LeNet and BP neural networks, respectively. The features of the middle layer of the convolutional neural network perform a hierarchical transformation from low-level information, such as color, to high-level information, such as contours and edges of disease spots. This transformation process matches the criteria for the actual identification of rice diseases. The proposed method lays the foundation for the accurate identification of crop diseases and the design and adjustment of deep convolutional neural network structures.
Increasing the Robustness of TERO-TRNGs Against Process Variation

The transition effect ring oscillator is a popular design for building entropy sources because it is compact, built from digital elements only, and is very well suited for FPGAs. However, it is known to be quite sensitive to process variation. Although the latter is useful for building physical unclonable functions, it is interfering with the application as an entropy source. In this article, we investigate an approach to increase reliability. We show that adding a third stage eliminates much of the susceptibility to process variation and how a resulting gigahertz oscillation can be evaluated on an FPGA. The design is supported by physical and stochastic modeling. The physical model is validated using an experiment with dynamically reconfigurable look-up tables.
Analysis of Shared Cache Interference in Multi-Core Systems using Event-Arrival Curves

Caches are used to bridge the gap between main memory and the significantly faster processor cores. In multi-core architectures, the last-level cache is often shared between cores. However, sharing a cache causes inter-core interference to emerge. Concurrently running tasks will experience additional cache misses as the competing tasks issue interfering accesses and trigger the eviction of data contained in the shared cache. Thus, to compute a task’s worst-case execution time (WCET), a safe bound on the effects of inter-core cache interference has to be determined. In this paper, we propose a novel analysis approach for shared caches using the least recently used (LRU) replacement policy. The presented analysis leverages timing information to produce tight bounds on the worst-case interference. We describe how inter-core cache interference may be expressed as a function of time using event-arrival curves. Thus, by determining the maximal duration between subsequent accesses to a cache block, it is possible to bound the inter-core interference. This enables us to classify accesses as cache hits or potential misses. We implemented the analysis in a WCET analyzer and evaluated its performance for multi-core systems containing 2, 4, and 8 cores using shared caches from 4 KB to 32 KB. The analysis achieves significant improvements compared to a standard interference analysis with WCET reductions of up to 60%. The average WCET reduction is 9% for dual-core, 15% for quad-core, and 11% for octa-core systems. The analysis runtime overhead ranges from a factor of 4 × to 7 × compared to the baseline analysis.
Diagnosis of primary amoebic meningoencephalitis by metagenomic next-generation sequencing: A case report

Abstract Primary amoebic meningoencephalitis (PAM) caused by Naegleria fowleri is a fatal infection with a mortality rate of more than 95%, despite advances in antimicrobial chemotherapy and supportive care. Initial manifestations of PAM are indistinguishable from bacterial meningitis. Prompt diagnosis and antifungal treatment may help decline the overall mortality. Here we present a case of a 38-year-old man transferred to our hospital due to mild headache, which deteriorated quickly. Severe increased intracranial pressure was found. The cerebrospinal fluid (CSF) was yellowish with significantly increased leukocyte and protein. Smear and culture were negative. The patient was first diagnosed with pyogenic meningoencephalitis. However, the symptoms deteriorated. Metagenomic next-generation sequencing (mNGS) of CSF was applied and finally confirmed N. fowleri as the protist pathogen within 24 h. However, due to the time cost of sampling and transportation (2 days), the diagnosis came too late, and the patient passed away 1 day before. In summary, mNGS is a rapid and accurate diagnostic method for clinical practices, especially for rare central nervous system infections. It should be used as quickly as possible for acute infections, such as PAM. All aspects of patient interrogation and prompt identification should be paramount to ensure appropriate treatment and decline the overall mortality.
Interactive Depixelization of Pixel Art through Spring Simulation

We introduce an approach for converting pixel art into high‐quality vector images. While much progress has been made on automatic conversion, there is an inherent ambiguity in pixel art, which can lead to a mismatch with the artist's original intent. Further, there is room for incorporating aesthetic preferences during the conversion. In consequence, this work introduces an interactive framework to enable users to guide the conversion process towards high‐quality vector illustrations. A key idea of the method is to cast the conversion process into a spring‐system optimization that can be influenced by the user. Hereby, it is possible to resolve various ambiguities that cannot be handled by an automatic algorithm.
Robust Pointset Denoising of Piecewise‐Smooth Surfaces through Line Processes

Denoising is a common, yet critical operation in geometry processing aiming at recovering high‐fidelity models of piecewise‐smooth objects from noise‐corrupted pointsets. Despite a sizable literature on the topic, there is a dearth of approaches capable of processing very noisy and outlier‐ridden input pointsets for which no normal estimates and no assumptions on the underlying geometric features or noise type are provided. In this paper, we propose a new robust‐statistics approach to denoising pointsets based on line processes to offer robustness to noise and outliers while preserving sharp features possibly present in the data. While the use of robust statistics in denoising is hardly new, most approaches rely on prescribed filtering using data‐independent blending expressions based on the spatial and normal closeness of samples. Instead, our approach deduces a geometric denoising strategy through robust and regularized tangent plane fitting of the initial pointset, obtained numerically via alternating minimizations for efficiency and reliability. Key to our variational approach is the use of line processes to identify inliers vs. outliers, as well as the presence of sharp features. We demonstrate that our method can denoise sampled piecewise‐smooth surfaces for levels of noise and outliers at which previous works fall short.
SPHINGOLIPIDS AND Δ8-SPHINGOLIPID DESATURASE FROM THE PICOALGA OSTREOCOCCUS TAURI AND INVOLVEMENT IN TEMPERATURE ACCLIMATION

Sphingolipids are crucial components of cell membranes. Sphingolipid Δ8-unsaturation is more specific to plants and is involved in the regulation of stress responses. The structure and functions of sphingolipids in microalgae are still poorly understood. Ostreococus tauri is a minimal microalga at the base of the green lineage, and is therefore a key organism for understanding lipid evolution. The present work reports the characterisation as well as the temperature regulation of sphingolipids and Δ8-sphingolipid desaturase from O. tauri. Complex sphingolipids are glycosylceramides with unique glycosyl moieties encompassing hexuronic acid residues, reminiscent of bacterial glucuronosylceramides, with up to three additional hexose residues. In contrast, the ceramide backbones show limited variety, with dihydroxylated C18/C18:1EΔ8 sphingoid bases and C16:0 fatty-acyl chain being the main compounds. The sphingolipid Δ8-desaturase from O. tauri, although phylogenetically related to plant homologues has a substrate preference similar to the diatom homologue. Both sphingolipid Δ8-desaturase transcripts and sphingolipid Δ8-unsaturation are regulated in a temperature- dependent manner being higher at 14°C than 24°C. Overexpressing the sphingolipid Δ8- desaturase in O. tauri at 24°C results in higher sphingolipid unsaturation and impairs the increase in cell size, structure and chlorophyll. In particular, the cell-size defect is not detected in cells acclimated to 14°C and is furthermore suppressed upon transfer from 24°C to 14°C. Our work provides the first functional evidence for the involvement of sphingolipid Δ8-unsaturation for temperature acclimation in microalgae, suggesting that this function is an ancestral feature in the green lineage.
Subtype-specific circadian clock dysregulation modulates breast cancer biology, invasiveness, and prognosis

Studies in shift workers and model organisms link circadian disruption to breast cancer. However, molecular rhythms in non-cancerous and cancerous human breast tissues are largely unknown. We reconstructed rhythms informatically, integrating locally collected, time-stamped biopsies with public datasets. For non-cancerous tissue, the inferred order of core-circadian genes matches established physiology. Inflammatory, epithelial-mesenchymal transition (EMT), and estrogen responsiveness pathways show circadian modulation. Among tumors, clock correlation analysis demonstrates subtype-specific changes in circadian organization. Luminal A organoids and informatic ordering of Luminal A samples exhibit continued, albeit disrupted rhythms. However, CYCLOPS magnitude, a measure of global rhythm strength, varied widely among Luminal A samples. Cycling of EMT pathway genes was markedly increased in high-magnitude Luminal A tumors. Patients with high-magnitude tumors had reduced 5-year survival. Correspondingly, 3D Luminal A cultures show reduced invasion following molecular clock disruption. This study links subtype-specific circadian disruption in breast cancer to EMT, metastatic potential, and prognosis.
Uracil-DNA Glycosylase of Murine Gammaherpesvirus 68 Binds Cognate Viral Replication Factors Independently of its Catalytic Residues

Herpesviruses are large double-stranded DNA viruses that encode core replication proteins and accessory factors involved in nucleotide metabolism and DNA repair. Mammalian Uracil-DNA glycosylases (UNG) excise deleterious uracil residues from their genomic DNA. Each herpesvirus UNG studied to date has demonstrated conservation of the enzymatic function to excise uracil residues from DNA. We previously reported that a murine gammaherpesvirus (MHV68) with a stop codon in ORF46 (ORF46.stop) that encodes for vUNG was defective in lytic replication and latency in vivo. However, a mutant virus that expressed a catalytically inactive vUNG (ORF46.CM) had no replication defect, unless coupled with additional mutations in the catalytic motif of the viral dUTPase (ORF54.CM). The disparate phenotypes observed in the vUNG mutants led us to explore the non-enzymatic properties of vUNG. Immunoprecipitation of vUNG followed by mass spectrometry in MHV68-infected fibroblasts identified a complex comprised of the cognate viral DNA polymerase, vPOL encoded by ORF9, and the viral DNA polymerase processivity factor, vPPF encoded by ORF59. MHV68 vUNG colocalized with vPOL and vPPF in subnuclear structures consistent with viral replication compartments. In reciprocal co-immunoprecipitations, the vUNG formed a complex with the vPOL and vPPF upon transfection with either factor alone, or in combination. Last, we determined that key catalytic residues of vUNG are not required for interactions with vPOL and vPPF upon transfection or in the context of infection. We conclude that the vUNG of MHV68 associates with vPOL and vPPF independently of its catalytic activity. IMPORTANCE Gammaherpesviruses encode a uracil-DNA glycosylase (vUNG) that is presumed to excise uracil residues from viral genomes. We previously identified the vUNG enzymatic activity, but not the protein itself, as dispensable for gammaherpesvirus replication in vivo. In this study, we report a non-enzymatic role for the viral UNG of a murine gammaherpesvirus to form a complex with two key components of the viral DNA replication machinery. Understanding the role of the vUNG in this viral DNA replication complex may inform the development of antiviral drugs that combat gammaherpesvirus associated cancers.
Energy Landscapes and Heat Capacity Signatures for Monomers and Dimers of Amyloid-Forming Hexapeptides

Amyloid formation is a hallmark of various neurodegenerative disorders. In this contribution, energy landscapes are explored for various hexapeptides that are known to form amyloids. Heat capacity (CV) analysis at low temperature for these hexapeptides reveals that the low energy structures contributing to the first heat capacity feature above a threshold temperature exhibit a variety of backbone conformations for amyloid forming monomers. The corresponding control sequences do not exhibit such structural polymorphism, as diagnosed via end-to-end distance and a dihedral angle defined for the monomer. A similar heat capacity analysis for dimer conformations obtained using basin-hopping global optimisation, shows clear features in end-to-end distance versus dihedral correlation plots, where amyloid-forming sequences exhibit a preference for larger end-to-end distances and larger positive dihedrals. These results hold for sequences taken from tau, amylin, insulin A chain, a de-novo designed peptide, and various control sequences. While there is a little overall correlation between the aggregation propensity and the temperature at which the low-temperature CV feature occurs, further analysis suggests that the amyloid forming sequences exhibit the key CV feature at a lower temperature compared to control sequences derived from the same protein.
Identification of plants functional counterparts of the metazoan Mediator of DNA Damage Checkpoint 1

Induction of DNA damage triggers rapid phosphorylation of the histone H2A.X (γH2A.X). In animals, mediator of DNA damage checkpoint 1 (MDC1) binds γH2A.X through a tandem BRCA1 carboxyl-terminal (tBRCT) domain and mediates recruitment of downstream effectors of DNA damage response (DDR). However, readers of this modification in plants have remained elusive. We report that from Arabidopsis BRCT domain proteome, BCP1-4 proteins with tBRCT domains are involved in DDR. Through its tBRCT domain BCP4 binds γH2A.X in vitro and localizes to DNA damage-induced foci in an H2A.X dependent manner. BCP4 also contains a domain that interacts directly with NBS1 and thus acts as a functional counterpart of MDC1. We also show that BCP1, that contains two tBRCT domains, co-localizes with γH2A.X but it does not bind γH2A.X suggesting functional similarity with human PAXIP1. A phylogenetic analysis supports that PAXIP1 and MDC1 in metazoa and their plant counterparts evolved independently from common ancestors with tBRCT domains. Collectively, our study reveals missing components and provides mechanistic and evolutionary insights into plant DDR.