Miracle-dz/UniFAR-FTUs huggingface dataset:
SmartTags: bridging applications and network for proactive performance management

Sudden changes in the applications and events in the network are often related. Many of the datacenter applications go through sudden state changes (such as a query-response in MemCached application) that may result in an event in the network (such as utilization, packet drops, etc.). Existing works do not fully leverage the relationship between application state changes and network events and as a result provide limited performance improvements. An ideal application and network management system should be able to automatically identify the sources of sudden changes in the application, host or network and relate these changes to network events to enable proactive network management. In this work, we propose SmartTags, a system that automatically learns which of the application state changes (Tags) are related to the network events, and uses this information for proactive network management. At a high level, smartTags is orthogonal to current NAI approaches. It provides a systemic way for application developers and network designers to automatically learn the relationship between application behavior and network events. Through very simple small scale real testbed based experiments, we demonstrate that smartTags can improve the training time of a distributed machine learning application by 27% while minimizing loss to zero. Similarly, it can improve the query completion time of MemCached by 32% while achieving near zero loss. We envision much more gains in large scale distributed systems.
Mask Wearing Recognition Based on Fusion Algorithm

In view of the low accuracy of mask wearing recognition caused by factors such as multiple and small target recognition, extreme lighting conditions and low definition, this paper proposes a fusion algorithm that integrates the attention mechanism FcaNet and dense convolution blocks into YOLOv5s. The fusion algorithm is used to address the issue including small target identification and low-resolution facial image recognition. This paper obtains data from open-source datasets RMFD (Real-World Masked Face Dataset) and celebA, uses fusion algorithm for feature extraction, and finally uses FaceNet and support vector machine (SVM) for face mask wearing recognition. After experimental comparison, the fusion algorithm can achieve 97.55% accuracy of face mask wearing recognition under different complex environments, and the average frame rate is 30.3FPS. The algorithm model improves the recognition rate of mask wearing under occlusion conditions and with different clarity, has higher recognition performance, and can be used for mask wearing recognition when entering public places.
Enzyme Biosensors for Point-of-Care Testing Enzyme Biosensors for Point-of-Care Testing

Biosensors are devices that integrate a variety of technologies, containing biology, elec- tronics, chemistry, physics, medicine, informatics, and correlated technology. Biosensors act as transducer with a biorecognition element and transform a biochemical reaction on the transducer surface directly into a measurable signal. The biosensors have the advantages of rapid analysis, low cost, and high precision, which are widely used in many fields, such as medical care, disease diagnosis, food detection, environmental monitoring, and fermentation industry. The enzyme biosensors show excellent application value owing to the development of fixed technology and the characteristics of specific identification, which can be combined with point-of-care testing (POCT) technology. POCT technology is attracting more and more attention as a very effective method of clinic detection. We outline the recent advances of biosensors in this chapter, focusing on the principle and classification of enzyme biosensor, immobilization method of biorecognition layers, and fabrication of amperometric biosensors, as well as the applications of POCT. A summary of glucose biosensor development and integrated setups is included. The latest applications of enzyme biosensors in diagnostic applications focusing on POCT of bio- markers in real samples were described.
Boosting cross‐task adversarial attack with random blur

Deep neural networks are highly vulnerable to adversarial examples, and these adversarial examples stay malicious when transferred to other neural networks. Many works exploit this transferability of adversarial examples to execute black‐box attacks. However, most existing adversarial attack methods rarely consider cross‐task black‐box attacks that are more similar to real‐world scenarios. In this paper, we propose a class of random blur‐based iterative methods (RBMs) to enhance the success rates of cross‐task black‐box attacks. By integrating the random erasing and Gaussian blur into the iterative gradient‐based attacks, the proposed RBM augments the diversity of adversarial perturbation and alleviates the marginal effect caused by iterative gradient‐based methods, generating the adversarial examples of stronger transferability. Experimental results on ImageNet and PASCAL VOC data sets show that the proposed RBM generates more transferable adversarial examples on image classification models, thereby successfully attacking cross‐task black‐box object detection models.
Exploiting cross‐session information for knowledge‐aware session‐based recommendation via graph attention networks

Session‐based recommendation (SBR) aims to predict the next item based on anonymous behavior session, which has become increasingly essential in various online services. Prior efforts mainly focus on modeling user preference based on the current session. Although some of them have been proven effective, they fail to address two main challenges in SBR. First, SBR suffers more from the problem of data sparsity due to the very limited user–item interactions, and hence it cannot sufficiently capture complicated item dependency relationships. Second, most of the user‐item interaction sequences may be with noisy preference signals due to the uncertainty of user's behaviors, and it is difficult to distill high‐quality item for recommendation. In this study, we propose a novel SBR model that exploits Cross‐session information for Knowledge‐aware Session‐based Recommendation (CKSR) to address these two issues. Specifically, cross‐session graph and knowledge graph are combined to model a cross‐session knowledge graph, based on which a knowledge‐aware attention mechanism is performed to capture the complicated transition pattern among interacted items. Each session is then represented as the composition of the global preference and the current interest of that session. Moreover, we leverage the similar sessions for the target session to establish a similar session referral circle and apply an influence coupler to judge the significance of different session referrals. An attentive network is designed to distill session preferences from its unique session referral circle. It dynamically extracts high‐quality item from noisy session. Experiments on two benchmark data sets demonstrate that CKSR outperforms the state‐of‐the‐art methods consistently.
The Wide Field Imager at the 2.2-m MPG/ESO telescope: first views with a 67-million-facette eye.

The basic capabilities of the Wide Field Imager (WFI) were described in a previous Messenger article (No. 93, p. 13; see also Table 1) which also includes a brief account of the contributions by the three participating institutes, namely ESO (Garching and La Silla), the Max-PlanckInstitut für Astronomie in Heidelberg, and the Osservatorio Astronomico di Capodimonte in Napoli. Table 1 summarises the most important characteristics. A first fairly complete version of the user manual is now offered via the WFI home page on the Web (URL: http://www.ls.eso.org/ lasilla/Telescopes/2p2T/E2p2M/WFI/WFI. html). The same page also provides access to some examples of pictures obtained with the instrument. An exposure time calculator is available under the URL: http://www.eso.org/observing/etc/ Following the completion of the 2.2-m Telescope Upgrade Plan (cf. The Messenger, No. 93, p. 19 and No. 94, p. 12), the WFI was installed on La Silla as the only instrument offered at the 2.2-m MPG/ESO Telescope. Since January 18, it is used by ESO Visiting Astronomers and MPI-A observers. Every night, the WFI’s 67 million pixels produce an amount of data comparable to that generated by all other ESO telescopes on La Silla plus the VLT UT1!
Multiplex Real-Time PCR Melting Curve Assay To Detect Drug-Resistant Mutations of Mycobacterium tuberculosis (cid:1)

Early diagnosis of drug-resistant Mycobacterium tuberculosis is urgently needed to optimize treatment regi-mens and to prevent the transmission of resistant strains. Real-time PCR assays have been developed to detect drug resistance rapidly, but none of them have been widely applied due to their complexity, high cost, or requirement for advanced instruments. In this study, we developed a real-time PCR method based on melting curve analysis of dually labeled probes. Six probes targeting the rpoB 81-bp core region, katG315 , the inhA promoter, the ahpC promoter, and embB306 were designed and validated with clinical isolates. First, 10 multidrug-resistant (MDR) strains with a wide mutation spectrum were used to analyze the melting temper- ature ( T m ) deviations of different mutations by single real-time PCR. All mutations can be detected by signiﬁcant T m reductions compared to the wild type. Then, three duplex real-time PCRs, with two probes in each, were developed to detect mutations in 158 MDR isolates. Comparison of the results with the sequencing data showed that all mutations covered by the six probes were detected with 100% sensitivity and 100% speciﬁcity. Our method
Evaluating and Optimizing Real-Time Software Transactional Memory

Software transactional memory (STM) is a proposed solution to the challenge of developing correct concurrent code. STM allows programmers to annotate sections of their code that need to be synchronized, and the STM implementation resolves synchronization issues behind the scenes. One application domain where STM can be particularly useful is real-time systems, where schedulability is a crucial metric is system certiﬁcation. However, STM usually relies on retries to resolve contention, which makes theoretical worst-case behavior, and thus schedulability, highly pessimistic and therefore impractical in the real-time application domain. Previous work on real-time STM has failed to give both theoretical and practical solutions to this problem. Work in this thesis is part of a large eﬀort to present real-time STM that is both schedulable and high-performing, achieved using a retry-free, and thus entirely lock-based, STM implementation. This work details multiple metrics for evaluating retry-free STM compared to a known retry-based lock-based solution, as well as presents optimizations to a locking protocol for increased performance in this context. Evaluations show a retry-free STM implementation with optimized locking protocols is signiﬁcantly more schedulable and higher performing on single-socket machines than other lock-based STM.
HArtMuT—modeling eye and muscle contributors in neuroelectric imaging

Objective. Magneto- and electroencephalography (M/EEG) measurements record a mix of signals from the brain, eyes, and muscles. These signals can be disentangled for artifact cleaning e.g. using spatial filtering techniques. However, correctly localizing and identifying these components relies on head models that so far only take brain sources into account. Approach. We thus developed the Head Artifact Model using Tripoles (HArtMuT). This volume conduction head model extends to the neck and includes brain sources as well as sources representing eyes and muscles that can be modeled as single dipoles, symmetrical dipoles, and tripoles. We compared a HArtMuT four-layer boundary element model (BEM) with the EEGLAB standard head model on their localization accuracy and residual variance (RV) using a HArtMuT finite element model (FEM) as ground truth. We also evaluated the RV on real-world data of mobile participants, comparing different HArtMuT BEM types with the EEGLAB standard head model. Main results. We found that HArtMuT improves localization for all sources, especially non-brain, and localization error and RV of non-brain sources were in the same range as those of brain sources. The best results were achieved by using cortical dipoles, muscular tripoles, and ocular symmetric dipoles, but dipolar sources alone can already lead to convincing results. Significance. We conclude that HArtMuT is well suited for modeling eye and muscle contributions to the M/EEG signal. It can be used to localize sources and to identify brain, eye, and muscle components. HArtMuT is freely available and can be integrated into standard software.
The Ecosystem Integrity Index: a novel measure of terrestrial ecosystem integrity with global coverage

We present a novel index that represents, in one combined metric, the integrity of terrestrial ecosystems globally at 1km 2 resolution: the Ecosystem Integrity Index (EII). The index provides a simple, yet scientifically robust, way of measuring, monitoring and reporting on ecosystem integrity at any geographical scale. It is formed of three components, structure, composition, and function, and measured against a natural (current potential) baseline on a scale of 0 to 1. The index has been developed to help national governments measure and report on various of the goals and targets being developed within the draft post-2020 global biodiversity framework being negotiated under the Convention on Biological Diversity, and for non-state actor contributions to also be recognized. In doing so, it will enable these actors to make informed decisions on the conservation, restoration and sustainable use of ecosystems for which they are wholly or partly responsible. We find that ecosystem integrity is rapidly declining in terrestrial areas globally, with countries, on average, retaining less than half of their natural ecosystem integrity with a mean EII across all countries of 0.45 (SD: 0.20). This poses a challenge for nations and businesses seeking to halt and reverse decades of loss of nature around the world, but one that is not insurmountable. With sufficient effort, values of EII can increase at national and other scales, representing progress towards the vision of living in harmony with nature.
Growth hormone remodels the 3D-structure of the mitochondria of inflammatory macrophages and promotes metabolic reprogramming

Macrophages are a heterogeneous population of innate immune cells that support tissue homeostasis through their involvement in tissue development and repair, and pathogen defense. Emerging data reveal that metabolism may control macrophage polarization and function and, conversely, phenotypic polarization may drive metabolic reprogramming. Here, using biochemical analysis, correlative cryogenic fluorescence microscopy and cryo-focused ion-beam scanning electron microscopy, we demonstrate that growth hormone (GH) functions as a metabolic modulator to reprogram inflammatory GM-CSF-primed monocyte-derived macrophages (GM-MØ). We found that exogenous treatment of GM-MØ with recombinant human GH suppressed glycolysis, lactate production and non-mitochondrial respiration, and enhanced mitochondrial oxidative phosphorylation. Likewise, GH treatment augmented mitochondrial volume and altered mitochondrial dynamics, including the remodeling of the inner membrane to increase the density of cristae. Our data demonstrate that GH likely serves a modulatory role in the metabolism of inflammatory macrophages and suggest that metabolic reprogramming of macrophages should be considered a new target to intervene in multiple inflammatory diseases.
Detection of Frog virus 3 via the system integrating RPA-CRISPR/Cas12a-SPM with deep learning

Frog virus 3 (FV3, genera Ranavirus, family Iridoviridae), a double-stranded DNA virus, results in irreparable damage to biodiversity and significant economic losses to aquaculture. Although the existing FV3 detection methods are of high sensitivity and specificity, the complex procedure and requirement of expensive instruments limit their practical implantation. Herein, we develop a fast, easy-to-implement, highly sensitive, and point-of-care (POC) detection system for FV3. Combining recombinase polymerase amplification (RPA) and CRISPR/Cas12a, we achieve a limit of detection (LoD) of 100 aM (60.2 copies/μL) by optimizing RPA primers and CRISPR RNAs (crRNAs). For POC detection, we build a smartphone microscopy (SPM) and achieve an LoD of 10 aM within 40 minutes. Four positive animal-derived samples with a quantitation cycle (Cq) value of quantitative PCR (qPCR) in the range of 13 to 32 are detectable by the proposed system. In addition, we deploy deep learning models for binary classification (positive or negative samples) and multiclass classification (different concentrations of FV3 and negative samples), achieving 100% and 98.75% accuracy, respectively. Without temperature regulation and expensive equipment, RPA-CRISPR/Cas12a combined with a smartphone readout and artificial intelligence (AI) assisted classification shows great potential for FV3 detection. This integrated system holds great promise for POC detection of aquatic DNA pathogens. Highlights An integrated DNA detection system is developed by combining RPA, CRISPR/Cas12a, smartphone microscopy, and deep learning. The LoD of frog virus 3 is 10 aM within 40 min. The detection system shows good performance on animal-derived samples.
Comparison of de novo and reference genome-based transcriptome assembly pipelines for differential expression analysis of RNA sequencing data

Objective As sequencing technologies become more accessible and bioinformatic tools improve, genomic resources are increasingly available for non-model species. Using a draft genome to guide transcriptome assembly from RNA sequencing data, rather than performing assembly de novo, affects downstream analyses. Yet, direct comparisons of these approaches are rare. Here, we compare the results of the standard de novo assembly pipeline (‘Trinity’) and two reference genome-based pipelines (‘Tuxedo’ and the ‘new Tuxedo’) for differential expression and gene ontology enrichment analysis of a companion study on Atlantic cod (Gadus morhua). Results The new Tuxedo pipeline produced a higher quality assembly than the Tuxedo suite. However, greater enrichment of Trinity-identified differentially expressed genes suggests that a higher proportion of them represent biologically meaningful differences in transcription, as opposed to transcriptional noise or false positives. Coupled with the ability to annotate novel loci, the increased sensitivity of the Trinity pipeline might make it preferable over the reference genome-based approaches for studies aimed at broadly characterizing variation in the magnitude of expression differences and biological processes. However, the ‘new Tuxedo’ pipeline might be appropriate when a more conservative approach is warranted, such as for the identification of candidate genes.
Intestinal permeability and peripheral immune cell composition are altered by pregnancy and adiposity at mid- and late-gestation in the mouse

It is clear that the gastrointestinal tract influences metabolism and immune function. Most studies to date have used male test subjects, with a focus on effects of obesity and dietary challenges. Despite significant physiological adaptations that occur across gestation, relatively few studies have examined pregnancy-related gut function. In this study, we investigated the impacts of pregnancy and adiposity on maternal intestinal epithelium morphology, in vivo intestinal permeability, and peripheral blood immunophenotype, using control (CTL) and high-fat (HF) fed non-pregnant female mice and pregnant mice at mid-(embryonic day (E)14.5) and late (E18.5) gestation. We found that small intestine length increased between non-pregnant mice and dams at late-gestation, but ileum villus length, and ileum and colon crypt depths and goblet cell numbers remained similar. Compared to CTL-fed mice, HF-fed mice had reduced small intestine length, ileum crypt depth and villus length. Goblet cell numbers were only consistently reduced in HF-fed non-pregnant mice. Pregnancy increased in vivo gut permeability, with a greater effect at mid-versus late-gestation. Non-pregnant HF-fed mice had greater gut permeability, and permeability was also increased in HF-fed pregnant dams at mid but not late-gestation. The impaired maternal gut barrier in HF-fed dams at mid-gestation coincided with changes in maternal blood and bone marrow immune cell composition, including an expansion of circulating inflammatory Ly6Chigh monocytes. In summary, pregnancy has temporal effects on maternal intestinal structure and barrier function, and on peripheral immunophenotype, which are further modified by HF diet-induced maternal adiposity.
Model based control can give rise to devaluation insensitive choice

Influential recent work aims to ground psychiatric dysfunction in the brain’s basic computational mechanisms. For instance, compulsive symptoms as in drug abuse have been argued to arise from imbalance between multiple systems for instrumental learning. Computational models suggest that such multiplicity arises because the brain adaptively simplifies laborious “model-based” deliberation by sometimes relying on a cheaper, more habitual “model-free” shortcut. Support for this account comes in part from failures to appropriately change behavior in light of new events. Notably, instrumental responding can, in some circumstances, persist despite reinforcer devaluation, perhaps reflecting control by model-free mechanisms that are driven by past reinforcement rather than knowledge of the (now devalued) outcome. However, another important line of theory – heretofore mostly studied in Pavlovian conditioning – posits a different mechanism that can also modulate behavioral change. It concerns how animals identify different rules or contingencies that may apply in different circumstances, by covertly clustering experiences into distinct groups identified with different “latent causes” or contexts. Such clustering has been used to explain the return of Pavlovian responding following extinction. Here we combine both lines of theory to investigate the consequences of latent cause inference on instrumental sensitivity to reinforcer devaluation. We show that because segregating events into different latent clusters prevents generalization between them, instrumental insensitivity to reinforcer devaluation can arise in this theory even using only model-based planning, and does not require or imply any habitual, model-free component. In simulations, these ersatz habits (like laboratory ones) emerge after overtraining, interact with contextual cues, and show preserved sensitivity to reinforcer devaluation on a separate consumption test, a standard control. While these results do not rule out a contribution of model-free learning per se, they point to a subtle and important role of state inference in instrumental learning and highlight the need for caution in using reinforcer devaluation procedures to rule in (or out) the contribution of different learning mechanisms. They also offer a new perspective on the neurocomputational substrates of drug abuse and the relevance of laboratory reinforcer devaluation procedures to this phenomenon.