Miracle-dz/UniFAR-FTUs huggingface dataset:
CloudShield: Real-time Anomaly Detection in the Cloud

In cloud computing, it is desirable if suspicious activities can be detected by automatic anomaly detection systems. Although anomaly detection has been investigated in the past, it remains unsolved in cloud computing. Challenges are: characterizing the normal behavior of a cloud server, distinguishing between benign and malicious anomalies (attacks), and preventing alert fatigue due to false alarms. We propose CloudShield, a practical and generalizable real-time anomaly and attack detection system for cloud computing. Cloudshield uses a general, pretrained deep learning model with different cloud workloads, to predict the normal behavior and provide real-time and continuous detection by examining the model reconstruction error distributions. Once an anomaly is detected, to reduce alert fatigue, CloudShield automatically distinguishes between benign programs, known attacks, and zero-day attacks, by examining the reconstruction error distributions. We evaluate the proposed CloudShield on representative cloud benchmarks. Our evaluation shows that CloudShield, using model pretraining, can apply to a wide scope of cloud workloads. Especially, we observe that CloudShield can detect the recently proposed speculative execution attacks, e.g., Spectre and Meltdown attacks, in milliseconds. Furthermore, we show that CloudShield accurately differentiates and prioritizes known attacks, and potential zero-day attacks, from benign programs. Thus, it significantly reduces false alarms by up to 99.0%.
Locating earthquake epicenter without a seismic velocity model

We present a method for locating the seismic event epicenters without assuming an Earth model of the seismic velocity structure, based on the linear relationship between logR and log t (where R is the radius of spherical P wave propagated outwards from the hypocenter, t is the travle-time of the P wave). This relationship is derived from the dimensional analysis and a lot of theoretical or real seismic data, in which the earthquake can be considered to be a point source. Application to 1209 events occurred from 2014 to 2017 in the IASPEI Ground Truth (GT) reference events list shows that our method can locate the correct seismic event epicenters in a simple way. ∼ 97.2% of seismic epicenters are located with both longitude and latitude errors ∈ [−0.1◦,+0.1◦]. This ratio can increase if with a finer search grid. As a direct and global-search location, this method may be useful in obtaining the earthquake epicenters occurred in the areas where the seismic velocity structure is poorly known, the starting points or the constraints for other location methods.
Unsupervised Domain-adaptive Hash for Networks

Abundant real-world data can be naturally represented by large-scale networks, which demands eﬃcient and eﬀective learning algorithms. At the same time, labels may only be available for some networks, which demands these algorithms to be able to adapt to unlabeled networks. Domain-adaptive hash learning has enjoyed considerable success in the computer vision com-munity in many practical tasks due to its lower cost in both retrieval time and storage footprint. However, it has not been applied to multiple-domain networks. In this work, we bridge this gap by developing an unsupervised domain-adaptive hash learning method for networks, dubbed UDAH. Specif-ically, we develop four task-speciﬁc yet correlated components: (1) network structure preservation via a hard groupwise contrastive loss, (2) relaxation-free supervised hashing, (3) cross-domain intersected discriminators, and (4) semantic center alignment. We conduct a wide range of experiments to eval-Email uate the eﬀectiveness and eﬃciency of our method on a range of tasks including link prediction, node classiﬁcation, and neighbor recommendation. Our evaluation results demonstrate that our model achieves better performance than the state-of-the-art conventional discrete embedding methods over all the tasks.
K2, Spitzer, and TESS Transits of Four Sub-Neptune Exoplanets

We present new Spitzer transit observations of four K2 transiting sub-Neptunes: K2-36 c, K2-79b, K2-167b, and K2-212b. We derive updated orbital ephemerides and radii for these planets based on a joint analysis of the Spitzer, TESS, and K2 photometry. We use the EVEREST pipeline to provide improved K2 photometry, by detrending instrumental noise and K2's pointing jitter. We used a pixel-level decorrelation method on the Spitzer observations to reduce instrumental systematic effects. We modeled the effect of possible blended eclipsing binaries, seeking to validate these planets via the achromaticity of the transits (K2 versus Spitzer). However, we find that Spitzer’s signal-to-noise ratio for these small planets is insufficient to validate them via achromaticity. Nevertheless, by jointly fitting radii between K2 and Spitzer observations, we were able to independently confirm the K2 radius measurements. Due to the long time baseline between the K2 and Spitzer observations, we were also able to increase the precision of the orbital periods compared to K2 observations alone. The improvement is a factor of 3 for K2-36 c, and more than an order of magnitude for the remaining planets. Considering possible JWST observations in 1/2023, previous 1σ uncertainties in transit times for these planets range from 74–434 minutes, but we have reduced them to the range of 8–23 minutes.
Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images

We present a method for creating 3D indoor scenes with a generative model learned from a collection of semantic-segmented depth images captured from different unknown scenes. Given a room with a specified size, our method automatically generates 3D objects in a room from a randomly sampled latent code. Different from existing methods that represent an indoor scene with the type, location, and other properties of objects in the room and learn the scene layout from a collection of complete 3D indoor scenes, our method models each indoor scene as a 3D semantic scene volume and learns a volumetric generative adversarial network (GAN) from a collection of 2.5D partial observations of 3D scenes. To this end, we apply a differentiable projection layer to project the generated 3D semantic scene volumes into semantic-segmented depth images and design a new multiple-view discriminator for learning the complete 3D scene volume from 2.5D semantic-segmented depth images. Compared to existing methods, our method not only efficiently reduces the workload of modeling and acquiring 3D scenes for training, but also produces better object shapes and their detailed layouts in the scene. We evaluate our method with different indoor scene datasets and demonstrate the advantages of our method. We also extend our method for generating 3D indoor scenes from semantic-segmented depth images inferred from RGB images of real scenes.1
Suspicious ARP Activity Detection and Clustering Based on Autoencoder Neural Networks

The rapidly increasing number of smart devices on the Internet necessitates an efficient inspection system for safeguarding our networks from suspicious activities such as Address Resolution Protocol (ARP) probes. In this research, we analyze sequence data of ARP traffic on LAN based on the numerical count and degree of its packets. A dynamic threshold is employed to detect underlying suspicious activities, which are further converted into feature vectors to train an unsupervised autoencoder neural network. Then, we leverage K-means clustering to separate the extracted latent features of suspicious activities from the autoencoder into various patterns.
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction

Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors. By leveraging state-of-the-art deep learning technologies on such data, urban traffic prediction has drawn a lot of attention in AI and Intelligent Transportation System community. The problem can be uniformly modeled with a 3D tensor (T, N, C), where T denotes the total time steps, N denotes the size of the spatial domain (i.e., mesh-grids or graph-nodes), and C denotes the channels of information. According to the specific modeling strategy, the state-of-the-art deep learning models can be divided into three categories: grid-based, graph-based, and multivariate time-series models. In this study, we first synthetically review the deep traffic models as well as the widely used datasets, then build a standard benchmark to comprehensively evaluate their performances with the same settings and metrics. Our study named DL-Traff is implemented with two most popular deep learning frameworks, i.e., TensorFlow and PyTorch, which is already publicly available as two GitHub repositories https://github.com/deepkashiwa20/DL-Traff-Grid and https://github.com/deepkashiwa20/DL-Traff-Graph. With DL-Traff, we hope to deliver a useful resource to researchers who are interested in spatiotemporal data analysis.
Search for heavy particles in the b -tagged dijet mass distribution with additional b -tagged jets in proton-proton collisions at

A search optimized for new heavy particles decaying to two b -quarks and produced in association with additional b -quarks is reported. The sensitivity is improved by b -tagging at least one lower- p T jet in addition to the two highest- p T jets. The data used in this search correspond to an integrated luminosity of 103 fb − 1 collected with a dedicated trijet trigger during the 2017 and 2018 ﬃﬃﬃ s p ¼ 13 TeV proton-proton collision runs with the ATLAS detector at the LHC. The search looks for resonant peaks in the b -tagged dijet invariant mass spectrum over a smoothly falling background. The background is estimated with an innovative data-driven method based on orthonormal functions. The observed b -tagged dijet invariant mass spectrum is compatible with the background-only hypothesis. Upper limits at 95% confidence level on a heavy vector-boson production cross section times branching ratio to a pair of b -quarks are derived.
Path Planning With Naive-Valley-Path Obstacle Avoidance and Global Map-Free

—In this paper, we present a complete Path Planning approach divided into two main categories: Global Path Planning (GPP) and Local Path Planning (LPP). Unlike most other works, the GPP layer, instead of complex and heavy maps, uses road and intersections graphs obtained directly from internet applications like OpenStreetMaps (OSM). This map-free GPP frees us from the common area-size restrictions. In the LPP layer, we use a novel Naive-Valley-Path method (NVP) to generate a local path avoiding obstacles in the road in an extremely-low execution time period. This approach exploits the concept of ”valley” areas around local minima, i.e., the ones always away from obstacles. We demonstrate the robustness of the system in our research platform BLUE, driving autonomously across the University of Alicante Scientiﬁc Park for more than 20 km in a 12.33 ha area. Our vehicle avoids different static persistent and non-persistent obstacles in the road and even dynamic ones, such as vehicles and pedestrians. Code is available at https://github.com/AUROVA-LAB/lib_planning .
Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer

With the strength of deep generative models, 3D pose transfer regains intensive research interests in recent years. Existing methods mainly rely on a variety of constraints to achieve the pose transfer over 3D meshes, e.g., the need for manually encoding for shape and pose disentanglement. In this paper, we present an unsupervised approach to conduct the pose transfer between any arbitrate given 3D meshes. Specifically, a novel Intrinsic-Extrinsic Preserved Generative Adversarial Network (IEP-GAN) is presented for both intrinsic (i.e., shape) and extrinsic (i.e., pose) information preservation. Extrinsically, we propose a co-occurrence discriminator to capture the structural/pose invariance from distinct Laplacians of the mesh. Meanwhile, intrinsically, a local intrinsic-preserved loss is introduced to preserve the geodesic priors while avoiding heavy computations. At last, we show the possibility of using IEP-GAN to manipulate 3D human meshes in various ways, including pose transfer, identity swapping and pose interpolation with latent code vector arithmetic. The extensive experiments on various 3D datasets of humans, animals and hands qualitatively and quantitatively demonstrate the generality of our approach. Our proposed model produces better results and is substantially more efficient compared to recent state-of-the-art methods. Code is available: https://github.com/mikecheninoulu/Unsupervised_IEPGAN
Associations of Biomarkers of Inflammation and Breast Cancer in the Breast Adipose Tissue of Women with Combined Measures of Adiposity

Background Mechanisms underlying the obesity-breast cancer link involve inflammation but need to be elucidated. Determining obesity by combining body mass index (BMI) with the waist circumference (WC) may clarify the role of inflammatory and hormonally related markers in breast cancer. We examined the effect of combining adiposity indices (BMI/WC) with the gene expression of several biomarkers involved in breast cancer. Methods Expression of cytochrome P450 family 19 subfamily A member 1 (CYP19A1), estrogen receptor-alpha (ER-α), allograft inflammatory factor 1 (AIF1), cyclooxygenase-2 (COX2), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and leptin (LEP) in 141 adipose breast tissues was quantified using qPCR method. BMI and WC were measured by a trained nurse and categorized using the median split, BMILOWCLO, BMILOWCHI, BMIHIWCLO, and BMIHIWCHI. Results Gene expression of IL-6 (3-fold), TNF-α (2-fold), and LEP (2-fold) was higher in the breast adipose tissue of women with high WC regardless of BMI, that is, BMILOWCHI and BMIHIWCHI women (all P < 0.01). Compared to BMILOWCLO women, gene expression of CYP19A1, COX2, and AIF1 was increased by two-fold in breast adipose tissue of BMIHIWCHI women (P < 0.10). ER-α was not different across adiposity categories. Conclusions The expression of some biomarkers, particularly those related to inflammation, is elevated in breast adipose tissue of women with a high WC independent of BMI. Obesity monitoring should also include women with normal or low BMI, but with central adiposity.
Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis

The emergence of machine learning (ML) and blockchain (BC) technology has greatly enriched the functions and services of healthcare, giving birth to the new field of “smart healthcare. ” This study aims to review the application of ML and BC technology in the smart medical industry by Web of Science (WOS) using bibliometric visualization. Through our research, we identify the countries with the greatest output, the major research subjects, funding funds, and the research hotspots in this field. We also find out the key themes and future research areas in application of ML and BC technology in healthcare area. We reveal the different aspects of research under the two technologies and how they relate to each other around five themes.
Acute kidney injury identification for pharmacoepidemiologic studies: Use of laboratory electronic acute kidney injury alerts versus electronic health records in Hospital Episode Statistics

A laboratory‐based acute kidney injury (AKI) electronic‐alert (e‐alert) system, with e‐alerts sent to the UK Renal Registry (UKRR) and collated in a master patient index (MPI), has recently been implemented in England. The aim of this study was to determine the degree of correspondence between the UKRR‐MPI and AKI International Classification Disease‐10 (ICD‐10) N17 coding in Hospital Episode Statistics (HES) and whether hospital N17 coding correlated with 30‐day mortality and emergency re‐admission after AKI.
Medicaid Expansion and Enhanced Coverage of Direct Oral Antivirals: Improving Care for Patients With Liver Disease

Effective health policies are crucial for mitigating barriers to adequate access to treatment for our most vulnerable patients, including those with liver disease. Patients with liver disease are especially reliant on sound healthcare policies as their mortality is strongly tied to expensive medications (e.g., direct acting antivirals [DAAs]) and surgical procedures such as transplantation.
Technetium‐99m‐MAG3 and technetium‐99m‐DTPA: Renal clearance measured by the constant infusion technique – Old news?

Accurate, precise and straightforward methods for measuring glomerular filtration rate (GFR) and/or renal plasma flow (RPF) are still in demand today. The time‐consuming constant infusion technique (CIT) is the gold standard and preferred for research, whereas the simple, but less precise, single injection technique (SIT) is used in clinical settings.
Bilingualism alters infants' cortical organization for attentional orienting mechanisms.

A bilingual environment is associated with changes in the brain's structure and function. Some suggest that bilingualism also improves higher-cognitive functions in infants as young as 6-months, yet whether this effect is associated with changes in the infant brain remains unknown. In the present study, we measured brain activity using functional near-infrared spectroscopy in monolingual- and bilingual-raised 6- and 10-month-old infants. Infants completed an orienting attention task, in which a cue was presented prior to an object appearing on the same (Valid) or opposite (Invalid) side of a display. Task performance did not differ between the groups but neural activity did. At 6-months, both groups showed greater activity for Valid (>Invalid) trials in frontal regions (left hemisphere for bilinguals, right hemisphere for monolinguals). At 10-months, bilinguals showed greater activity for Invalid (>Valid) trials in bilateral frontal regions, while monolinguals showed greater brain activity for Valid (>Invalid) trials in left frontal regions. Bilinguals' brain activity trended with their parents' reporting of dual-language mixing when speaking to their child. These findings are the first to indicate how early (dual) language experience can alter the cortical organization underlying broader, non-linguistic cognitive functions during the first year of life. This article is protected by copyright. All rights reserved.
Reversible Curvature Reversal of Monolithic Liquid Crystal Elastomer Film and its Smart Valve Application.

Beyond a traditional stimuli-responsive soft actuator that shows a single motion by a stimulus, multi-directional actuation reversal with a single stimulus is highly required in applications such as shape morphing sensors and soft robotics. Liquid crystal elastomers (LCEs) are one of the most attractive candidates for the soft-actuator due to their capability of stimuli-responsive shape changing in 3D, that is programmable with local orientation of LC mesogens. Here, we report a simple but effective method to fabricate monolithic LCE actuators that are capable of reversible curvature reversal in bending and twisting deformation by a single stimulus, heat. The curvature reversal of the LCE film can be programmed by means of asymmetric crosslinking density along the thickness and the orientation of the LC mesogens. The curvature reversal of the monolithic LCE film exhibits highly reversible (more than 100 times) and fast actuation(∼3-5s) by heating and cooling, allowing new concept of a practical application using LCE material: a self-regulated smart valve that is capable of qualitatively sorting liquids by temperature. We believe that our system is potentially applied to a self-regulated sorting platform for various endothermic and exothermic chemical or biological reactions This article is protected by copyright. All rights reserved.
PqsR‐independent quorum‐sensing response of Pseudomonas aeruginosa ATCC 9027 outlier‐strain reveals new insights on the PqsE effect on RhlR activity

Pseudomonas aeruginosa is a ubiquitous environmental bacterium and an opportunistic pathogen that represents an important health hazard. The quorum‐sensing response regulates the expression of several virulence factors and involves three regulons: Las, Rhl, and Pqs. The P. aeruginosa ATCC 9027 strain, which belongs to the genetically diverse PA7 clade, contains a frame‐shift mutation in the pqsR gene that encodes a transcriptional activator necessary for pyocyanin (PYO) synthesis in type strains PAO1 and PA14. Here we characterize the PqsE‐dependent production of PYO in strain ATCC 9027. We show that this strain expresses pqsE independently of PqsR and in the absence of quinolone production, and that PqsE promotes the RhlR‐dependent production of PYO, yet this production is not strictly dependent on PqsE. In addition, we show that in both strains ATCC 9027 and PAO1, PqsE overexpression causes an increased concentration of RhlR and enhances PYO production but does not affect rhamnolipids (RL) production in the same way. These results suggest that PqsE interaction with RhlR preferentially modifies its ability to activate transcription of genes involved in PYO production and provide new evidence about PqsE‐dependent RhlR activation, highlighting the variability of the QS response among different P. aeruginosa clades and strains.
Classification algorithm using halftone features of counterfeit bills and CNN

With recent advancements in image processing and printing technology, home printers have improved in performance and grown more widespread. As such, they have been increasingly used in counterfeiting and forgery. Most counterfeit bills in Korea have been created using home scanners and printers. The identification of printer model is thus necessary to rapidly track down criminals and solve crimes. Household printers can be largely divided into inkjet and laser printers. These two types of printers print halftone textures instead of continuous images. This study proposed a technique of printer classification based on halftone textures that can be observed in printed documents. Since halftone textures are expressed as periodic lattices, the images were transformed via FFT, which is highly effective at expressing periodicity. ResNet, known for its superior gradient flow, was used for training. The experiment was conducted on 12 color laser jets and 2 inkjets. Scans of bills printed by each printer were used, and halftone texture analysis was performed on these images for printer model classification. Each image was cropped into several parts; one of the cropped parts was analyzed. The analysis showed that laser printers could be 100% distinguished from inkjet printers. An accuracy of 98.44% was achieved in make classification. When 50 cropped images were used instead of a single image, the technique achieved 100% accuracy in model classification. The proposed technique is non‐destructive; it offers high accessibility and efficiency as it can be performed using a scanner alone, without requiring additional optical equipment.
Solutions of Perturbed Fractional Schr¨odinger–Poisson System with Critical Nonlinearity in R 3

In this paper, we study the existence of semiclassical solutions of the fractional Schr¨odinger–Poisson system with critical exponent in R 3 . Under suitable assumptions, we show that the problem has at least one nontrivial solution provided that (cid:15) ≤ ε δ where ε δ is suﬃciently small positive number.