Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing Storage in Large-Scale Photo Sharing Services using Recompression

The popularity of photo sharing services has increased dramatically in recent years. Increases in users, quantity of photos, and quality/resolution of photos combined with the user expectation that photos are reliably stored indefinitely creates a growing burden on the storage backend of these services. We identify a new opportunity for storage savings with application-specific compression for photo sharing services: photo recompression. We explore new photo storage management techniques that are fast so they do not adversely affect photo download latency, are complementary to existing distributed erasure coding techniques, can efficiently be converted to the standard JPEG user devices expect, and significantly increase compression. We implement our photo recompression techniques in two novel codecs, ROMP and L-ROMP. ROMP is a lossless JPEG recompression codec that compresses typical photos 15% over standard JPEG. L-ROMP is a lossy JPEG recompression codec that distorts photos in a perceptually un-noticeable way and typically achieves 28% compression over standard JPEG. We estimate the benefits of our approach on Facebook's photo stack and find that our approaches can reduce the photo storage by 0.3-0.9x the logical size of the stored photos, and offer additional, collateral benefits to the photo caching stack, including 5-11% fewer requests to the backend storage, 15-31% reduction in wide-area bandwidth, and 16% reduction in external bandwidth.
Attack-Resistant Federated Learning with Residual-based Reweighting

Federated learning has a variety of applications in multiple domains by utilizing private training data stored on different devices. However, the aggregation process in federated learning is highly vulnerable to adversarial attacks so that the global model may behave abnormally under attacks. To tackle this challenge, we present a novel aggregation algorithm with residual-based reweighting to defend federated learning. Our aggregation algorithm combines repeated median regression with the reweighting scheme in iteratively reweighted least squares. Our experiments show that our aggregation algorithm outperforms other alternative algorithms in the presence of label-flipping, backdoor, and Gaussian noise attacks. We also provide theoretical guarantees for our aggregation algorithm.
Infinite time horizon spatially distributed optimal control problems with pde2path algorithms and tutorial examples

We use the continuation and bifurcation package pde2path to numerically analyze infinite time horizon optimal control problems for parabolic systems of PDEs. The basic idea is a two step approach to the canonical systems, derived from Pontryagin's maximum principle. First we find branches of steady or time-periodic states of the canonical systems, i.e., canonical steady states (CSS) respectively canonical periodic states (CPS), and then use these results to compute time-dependent canonical paths connecting to a CSS or a CPS with the so called saddle point property. This is a (high dimensional) boundary value problem in time, which we solve by a continuation algorithm in the initial states. We first explain the algorithms and then the implementation via some example problems and associated pde2path demo directories. The first two examples deal with the optimal management of a distributed shallow lake, and of a vegetation system, both with (spatially, and temporally) distributed controls. These examples show interesting bifurcations of so called patterned CSS, including patterned optimal steady states. As a third example we discuss optimal boundary control of a fishing problem with boundary catch. For the case of CPS-targets we first focus on an ODE toy model to explain and validate the method, and then discuss an optimal pollution mitigation PDE model.
Unsupervised Scene Adaptation with Memory Regularization in vivo

This work focuses on the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data. Existing approaches focus on minoring the inter-domain gap between the source and target domains. However, the intra-domain knowledge and inherent uncertainty learned by the network are under-explored. In this paper, we propose an orthogonal method, called memory regularization in vivo, to exploit the intra-domain knowledge and regularize the model training. Specifically, we refer to the segmentation model itself as the memory module, and minor the discrepancy of the two classifiers, i.e., the primary classifier and the auxiliary classifier, to reduce the prediction inconsistency. Without extra parameters, the proposed method is complementary to most existing domain adaptation methods and could generally improve the performance of existing methods. Albeit simple, we verify the effectiveness of memory regularization on two synthetic-to-real benchmarks: GTA5 → Cityscapes and SYNTHIA → Cityscapes, yielding +11.1% and +11.3% mIoU improvement over the baseline model, respectively. Besides, a similar +12.0% mIoU improvement is observed on the cross-city benchmark: Cityscapes → Oxford RobotCar.
About 200-Year Cycle of Solar Activity in the Mediaeval Korean Records and Reconstructions from Cosmogenic Radionuclides

We investigated the Korean records of naked-eye sunspot observations and found an implication of periodicity of about 200-year. Adding the Chinese records we showed that the historical naked-eye sunspot observations have the similar periodicity. Recent some works showed that there would be no intrinsic periodicities except 11-year cycle. We adopt the new approach called samplogram to test sampling stability of cycles in terms of power spectra and difference series and show that the Suess/de Vries cycle of about 207-year is a deterministic cycle of the stochastic solar activity. Also we show that occurrences of grand minimum are not necessarily expected with the Suess/de Vries cycle and it is possible to appear double or multiple grand maxima without a grand minimum within them.
Computation Reallocation for Object Detection

The allocation of computation resources in the backbone is a crucial issue in object detection. However, classification allocation pattern is usually adopted directly to object detector, which is proved to be sub-optimal. In order to reallocate the engaged computation resources in a more efficient way, we present CR-NAS (Computation Reallocation Neural Architecture Search) that can learn computation reallocation strategies across different feature resolution and spatial position diectly on the target detection dataset. A two-level reallocation space is proposed for both stage and spatial reallocation. A novel hierarchical search procedure is adopted to cope with the complex search space. We apply CR-NAS to multiple backbones and achieve consistent improvements. Our CR-ResNet50 and CR-MobileNetV2 outperforms the baseline by 1.9% and 1.7% COCO AP respectively without any additional computation budget. The models discovered by CR-NAS can be equiped to other powerful detection neck/head and be easily transferred to other dataset, e.g. PASCAL VOC, and other vision tasks, e.g. instance segmentation. Our CR-NAS can be used as a plugin to improve the performance of various networks, which is demanding.
Containing rumors spreading on correlated multiplex networks

Rumors flooding on rapidly-growing online social networks have geared much attention from many fronts. Individuals can transmit rumors via numerous channels since they can be active on multiple platforms. However, no systematic theoretical research of rumors containing dynamics on multiplex networks has been conducted yet. In this study, we propose a family of containing strategies based on the degree product of each user on the multiplex networks. Then, we develop a heterogeneous edge-based compartmental theory to comprehend the containing dynamics. The simulation results demonstrate that strategies with preference to block users with large can significantly reduce the rumor outbreak size and enlarge the threshold. Besides, better performance can be expected on heterogeneous multiplex networks with the increasing of preference intensity and degree heterogeneity. Moreover, take the inter-layer degree correlations rs into consideration, the strategy performs best on multiplex networks with rs = −1, rs = 1 the second, and rs = 0 the last. On the contrary, if we prefer to block users with small rather than large , the containing performance will be worse than that of blocking users randomly on most multiplex networks except for uncorrelated multiplex networks with uniform degree distribution. We found that the blocking preferences have no influence on the containing results on uncorrelated multiplex networks with uniform degree distribution. Our theoretical analysis can well predict the rumors containing results and performance differences in all the cases studied. The systematic theoretical research of rumors containing dynamics on multiplex networks in this study will offer inspirations for further investigations on this issue.
The complete mitochondrial genome of stag beetle Lucanus cervus (Coleoptera: Lucanidae) and phylogenetic analysis

Background The stag beetle Lucanus cervus (Coleoptera: Lucanidae) is widely distributed in Europe. Habitat loss and fragmentation has led to significant reductions in numbers of this species. In this study, we sequenced the complete mitochondrial genome of L. cervus and reconstructed phylogenetic relationships among Lucanidae using complete mitochondrial genome sequences. Methods Raw data sequences were generated by the next generation sequencing using Illumina platform from genomic DNA of L. cervus. The mitochondrial genome was assembled by IDBA and annotated by MITOS. The aligned sequences of mitochondrial genes were partitioned using PartitionFinder 2. Phylogenetic relationships among 19 stag beetle species were constructed using Maximum Likelihood (ML) method implemented in IQ-TREE web server and Bayesian method implemented in PhyloBayes MPI 1.5a. Three scarab beetles were used as outgroups. Results The complete mitochondrial genome of L. cervus is 20,109 bp in length, comprising 13 protein-coding genes, 22 transfer RNA genes, two ribosomal RNAs and a control region. The A + T content is 69.93% for the majority strand. All protein-coding genes start with the typical ATN initiation codons except for cox1, which uses AAT. Phylogenetic analyses based on ML and Bayesian methods shown consistent topologies among Lucanidae.
Reduced GRAMD1C expression correlates to poor prognosis and immune infiltrates in kidney renal clear cell carcinoma

There has been an increase in the mortality rate and morbidity of kidney cancer (KC) with kidney renal clear cell carcinoma (KIRC) being the most common subtype of KC. GRAMD1C (GRAM Domain Containing 1C) has not been reported to relate to prognosis and immunotherapy in any cancers. Using bioinformatics methods, we judged the prognostic value of GRAMD1C expression in KIRC and investigated the underlying mechanisms of GRAMD1C affecting the overall survival of KIRC based on data downloaded from The Cancer Genome Atlas (TCGA). The outcome revealed that reduced GRAMD1C expression could be a promising predicting factor of poor prognosis in kidney renal clear cell carcinoma. Meanwhile, GRAMDIC expression was significantly correlated to several tumor-infiltrating immune cells (TIICs), particularly the regulatory T cells (Tregs). Furthermore, GRAMD1C was most significantly associated with the mTOR signaling pathway, RNA degradation, WNT signaling pathway, toll pathway and AKT pathway in KIRC. Thus, GRAMD1C has the potential to become a novel predictor to evaluate prognosis and immune infiltration for KIRC patients.
Simulating more realistic predation threat using attack playbacks

Use of virtual proxies of live animals are rapidly gaining ground in studies of animal behaviour. Such proxies help to reduce the number of live experimental animals needed to stimulate the behaviour of experimental individuals and to increase standardisation. However, using too simplistic proxies may fail to induce a desired effect and/or lead to quick habituation. For instance, in a predation context, prey often employ multimodal cues to detect predators or use specific aspects of predator behaviour to assess threat. In a live interaction, predator and prey often show behaviours directed towards each other, which are absent in virtual proxies. Here we compared the effectiveness of chemical and visual predator cues in the cooperatively breeding cichlid Neolamprologus pulcher, a species in which predation pressure has been the evolutionary driver of its sociality. We created playbacks of predators simulating an attack and tested their effectiveness in comparison to a playback showing regular activity and to a live predator. We further compared the effectiveness of predator odour and conspecific skin extracts on behaviours directed towards a predator playback. Regular playbacks of calmly swimming predators were less effective than live predators in stimulating a focal individual’s aggression and attention. However, playbacks mimicking an attacking predator induced responses much like a live predator. Chemical cues did not affect predator directed behaviour.
LOW-QUALITY JPG IMAGES

Super Resolution (SR) is a fundamental and important low-level computer vision (CV) task. Different from traditional SR models, this study concentrates on a specific but realistic SR issue: How can we obtain satisfied SR results from compressed JPG (C-JPG) image, which widely exists on the Internet. In general, C-JPG can release storage space while keeping considerable quality in visual. However, further image processing operations, e.g., SR, will suffer from enlarging inner artificial details and result in unacceptable outputs. To address this problem, we propose a novel SR structure with two specifically designed components, as well as a cycle loss. In short, there are mainly three contributions to this paper. First, our research can generate high-qualified SR images for prevalent C-JPG images. Second, we propose a functional sub-model to recover information for C-JPG images, instead of the perspective of noise elimination in traditional SR approaches. Third, we further integrate cycle loss into SR solver to build a hybrid loss function for better SR generation. Experiments show that our approach achieves outstanding performance among state-of-the-art methods.
Signal / Collect Processing Large Graphs in Seconds

Both researchers and industry are confronted with the need to process increasingly large amounts of data, much of which has a natural graph representation. Some use MapReduce for scalable processing, but this abstraction is not designed for graphs and has shortcomings when it comes to both iterative and asynchronous processing, which are particularly important for graph algorithms. This paper presents the Signal/Collect programming model for scalable synchronous and asynchronous graph processing. We show that this abstraction can capture the essence of many algorithms on graphs in a concise and elegant way by giving Signal/Collect adaptations of algorithms that solve tasks as varied as clustering, inferencing, ranking, classification, constraint optimisation, and even query processing. Furthermore, we built and evaluated a parallel and distributed framework that executes algorithms in our programming model. We empirically show that our framework efficiently and scalably parallelises and distributes algorithms that are expressed in the programming model. We also show that asynchronicity can speed up execution times. Our framework can compute a PageRank on a large (>1.4 billion vertices, >6.6 billion edges) real-world graph in 112 seconds on eight machines, which is competitive with other graph processing approaches.
Tumor Cell–Intrinsic USP22 Suppresses Antitumor Immunity in Pancreatic Cancer

USP22, a deubiquitinating enzyme, is a tumor-intrinsic factor that can modulate the immune tumor microenvironment in pancreatic cancer. Loss of USP22 sensitizes therapy-resistant tumors, thus, representing a potential target to boost responses of pancreatic tumors to immunotherapy. Although immune checkpoint blockade (ICB) improves clinical outcome in several types of malignancies, pancreatic ductal adenocarcinoma (PDA) remains refractory to this therapy. Preclinical studies have demonstrated that the relative abundance of suppressive myeloid cells versus cytotoxic T cells determines the efficacy of combination immunotherapies, which include ICB. Here, we evaluated the role of the ubiquitin-specific protease 22 (USP22) as a regulator of the immune tumor microenvironment (TME) in PDA. We report that deletion of USP22 in pancreatic tumor cells reduced the infiltration of myeloid cells and promoted the infiltration of T cells and natural killer (NK) cells, leading to an improved response to combination immunotherapy. We also showed that ablation of tumor cell–intrinsic USP22 suppressed metastasis of pancreatic tumor cells in a T-cell–dependent manner. Finally, we provide evidence that USP22 exerted its effects on the immune TME by reshaping the cancer cell transcriptome through its association with the deubiquitylase module of the SAGA/STAGA transcriptional coactivator complex. These results indicated that USP22 regulates immune infiltration and immunotherapy sensitivity in preclinical models of pancreatic cancer.
The optimal range of gestational weight gain for singleton term nulliparous pregnancies in north region of China

Objective:To approach the optimal range of gestational weight gain for singloten term nulliparous pregnancies in north region of china.Methods:The information of 5 072 singleton nulliparous pregnancies delivered in Shenyang region were collected.The information contained pre-pregnancy,pregnancy and postpartum conditions of each woman.The 5 072 women were divided into three categories according to Chinese classification of body mass index(BMI):pre-pregnancy underweight(18.5 kg/m2),normal weight(18.5～24 kg/m2),overweight and obesity(≥24 kg/m2). The relationship between gestational weight gain and adverse pregnancy outcome and recommending optimal range for gestational weight gain were analyzed.Results:The rate of pre-pregnancy underweight,normal weight,overweight and obesity was 16.8 %,66.8% and 16.4%,respectively.The ranges of gestational weight gain which caused the lowest mobility of adverse pregnancy outcome were 12～18 kg,10.0～16.0 kg and 8.0～14.0 kg(P0.05),respectively.The recommending optimal ranges of gestational weight gain were 12～18 kg,10.0～16.0 kg and 8.0～14.0 kg respectively.Conclusion:The average gestational weight gain is higher in north region of china.The recommending optimal ranges should be 12～18 kg,10.0～16.0 kg and 8.0～14.0 kg for the three categories respectively.
Effects of the replacement of fishmeal by soy protein concentrate on growth performance, apparent digestibility, and retention of protein and amino acid in juvenile pearl gentian grouper

Soy protein concentrate (SPC), as a protein source, is widely used to replace partial fishmeal (FM) in aquafeeds, especially for carnivorous fish. This study investigated the effects of partial FM replacement by SPC for juvenile pearl gentian grouper. The fish were fed with diets containing six levels of SPC (SPC 0, 15, 30, 45, 60, and 75) for 6 weeks. At the end of the feeding trial, average body weight gain (BWG), specific growth ratio (SGR), and weight gain ratio (WGR) had the highest values in fish fed with diet SPC 15, followed by that of fish fed with SPC 0 while fish fed with SPC 75 had the lowest values (P < 0.05). Fish fed with diet SPC 15 and SPC 30 had the highest protein efficiency ratio (PER) while fish fed with diet SPC 15, SPC 30, and SPC 45 had the highest feed conversion ratio (FCR) (P < 0.05). Daily feed intake (DFI) was significantly decreased in fish fed with diets containing any level of SPC (P < 0.05). Survival rate was significantly higher in fish fed with diets SPC 15, SPC 30, and SPC 45 as compared to other treatments. Fish fed the diet including less than 30% FM replacement showed a higher protein content in the muscle. The ADC of dietary protein and some amino acids were significantly higher in diets SPC 0, followed by SPC 15; while SPC 75 had the lowest content (P < 0.05). Similarly, fish fed with SPC 30 and SPC 15 showed significantly higher protein and amino acid (AA) retention than other dietary treatments. The optimal FM replacement with SPC for specific growth ratio (SGR) was estimated to be 14.41% using a non-linear higher order regression model. These results indicated that pearl gentian grouper has a limited ability to utilize SPC as a protein source, and the FM replacement with SPC should be less than 30% (FM45.5 g 100g -1 and SPC18g 100g -1).
Self-Assembled Gemcitabine Prodrug Nanoparticles Show Enhanced Efficacy against Patient-Derived Pancreatic Ductal Adenocarcinoma.

Effective new therapies for pancreatic ductal adenocarcinoma (PDAC) are desperately needed, as the prognosis of PDAC patients is dismal and treatment remains a major challenge. Gemcitabine (GEM) is commonly used to treat PDAC; however, the clinical use of GEM has been greatly compromised by its low delivery efficacy and drug resistance. Here we describe a very simple yet cost-effective approach that synergistically combines drug reconstitution, supramolecular nanoassembly and tumor-specific targeting to address the multiple challenges posed by the delivery of the chemotherapeutic drug GEM. Using our developed PUFAylation technology, the GEM prodrug was able to spontaneously self-assemble into colloidal-stable nanoparticles with sub-100 nm size on covalent attachment of hydrophobic linoleic acid via amide linkage. The prodrug nanoassemblies could be further refined by PEGylation and PDAC-specific peptide ligand for preclinical studies. In vitro cell-based assays showed that not only were GEM nanoparticles superior to free GEM, but also the decoration with PDAC-homing peptide facilitated the intracellular uptake of nanoparticles and thereby augmented the cytotoxic activity. In two separate xenograft models of human PDAC, one of which was a patient-derived xenograft model, the administration of targeted nanoparticles resulted in marked inhibition of tumor progression as well as alleviated systemic toxicity. Together, these data unequivocally confirm that the hydrophilic and rapidly metabolized drug GEM can be feasibly transformed into a pharmacologically efficient nanomedicine through exploiting the PUFAylation technology. This strategy could also potentially be applied to rescue many other therapeutics that show unfavorable outcomes in the preclinical studies due to pharmacologic obstacles.