bluuebunny/crossref_metadata_2025 huggingface dataset:
Prevalence and haemodynamic profiles of pulmonary hypertension in cardiac amyloidosis
Authors: Slivnick Jeremy , Zareba Karolina M , Varghese Juliet , Truong Vien , Wallner Alexander L , Tong Matthew S , Hummel Christopher , Mazur Wojciech , Rajpal Saurabh
Published: March 2022

Objectives

While cardiac amyloidosis (CA) classically involves the left ventricle (LV), less is known about its impact on the right ventricle (RV) and pulmonary vasculature. We performed a retrospective analysis to identify the prevalence and types of pulmonary hypertension (PH) profiles in CA and to determine haemodynamic and cardiovascular magnetic resonance (CMR) predictors of major adverse cardiovascular events (MACE).

Methods

Patients with CA who underwent CMR and right heart catheterisation (RHC) within 1 year between 2010 and 2019 were included. Patients were assigned the following haemodynamic profiles based on RHC: no PH, precapillary PH, isolated postcapillary PH (IPCPH), or combined precapillary and postcapillary PH (CPCPH). The relationship between PH profile and MACE (death, heart failure hospitalisation) was assessed using survival analysis. CMR and RV parameters were correlated with MACE using Cox-regression analysis.

Results

A total of 52 patients were included (age 69±9 years, 85% men). RHC was performed during biopsy in 44 (85%) and for clinical indications in 8 (15%) patients. Rates of no PH, precapillary PH, IPCPH and CPCPH were 5 (10%), 3 (6%), 29 (55%) and 15 (29%), respectively. Haemodynamic PH profile did not correlate with risk of death (p=0.98) or MACE (p=0.67). Transpulmonary gradient (TPG) (HR 0.88, CI 0.80 to 0.97), RV, (HR 0.95, CI 0.92 to 0.98) and LV ejection fraction (HR 0.95, CI 0.92 to 0.98) were significantly associated with MACE.

Conclusions

PH is highly prevalent in CA, even at the time of diagnosis. While IPCPH was most common, CPCPH is not infrequent. TPG and RV ejection fraction (RVEF) are prognostic markers in this population.
Aprocitentan and the endothelin system in resistant hypertension
Authors: Clozel Martine
Published: March 2022

Endothelin has emerged as a target for therapeutic intervention in systemic hypertension. As a vasoconstrictor, comitogenic agent, linking pulse pressure and vascular remodeling, and mediator of aldosterone and catecholamine release, endothelin is a key player in hypertension and end-organ damage. In 10%–20% of the hypertensive population, the high blood pressure is resistant to administration of antihypertensive drugs of different classes in combination. Because endothelin is not targeted by the current antihypertensive drugs, this may suggest that this resistance is due, in part at least, to a dependence on endothelin. This hypothesis is supported by the observation that this form of hypertension is often salt-sensitive, and that the endothelin system is stimulated by salt. In addition, the endothelin system is activated in subjects at risk of developing resistant hypertension, such as African Americans or patients with obesity or obstructive sleep apnea. Aprocitentan is an investigational, novel, potent, dual endothelin receptor antagonist (ERA) currently in phase 3 development for the treatment of difficult-to-treat hypertension. This article discusses the research that underpinned the discovery of this ERA and the choice of its first clinical indication for patients with forms of hypertension that cannot be well controlled with classical antihypertensive drugs.
Sulfolane in contaminated sites: environmental toxicity and bioremediation technologies
Authors: Khan Muhammad Faizan , Yu Linlong , Achari Gopal
Published: March 2022

Sulfolane is widely used around the world as an industrial solvent for purifying sour natural gas. However, because of accidental spillage and improper on-site storage/disposal procedures, reports of groundwater, aquifer, and soil contaminations have raised concerns about its potential impacts on humans and the ecosystem. As a contaminant of emerging concern, there is a lack of information on the human toxicity of sulfolane. Several bioremediation technologies have been conducted to assess the biodegradation potential of sulfolane in contaminated groundwater and soils. This review presents and discusses the available literature on the toxicity of sulfolane, which could be useful for developing proper sulfolane guidelines in different media. The oral LD50 of sulfolane varies from 0.6 to 3.5 g·(kg body mass)–1 for different mammalian species, including guinea pig, mouse, rabbit, and rat. In addition, we also review the various sulfolane bioremediation studies to date, highlighting the efficacy of aerobic versus anaerobic bioremediation of sulfolane at contaminated sites. The zero-order biodegradation rate of sulfolane varies from 0.033 to 190 mg·L–1·day–1 depending on the initial concentration of sulfolane, nutrients, oxygen levels, temperature, and other parameters. Effective aerobic treatment technologies can lead to the complete mineralization of sulfolane with sulfuric acid as its major end by-product. Furthermore, the application of aerobic granulation as a promising biotechnology for sulfolane biodegradation is also discussed. This review further discusses the significance of utilizing sulfolane-degrading bacteria to reduce treatment times and presents information for future researchers and scientists on the specific isolates recorded.
Beam-based aperture measurements with movable collimator jaws as performance booster of the CERN Large Hadron Collider
Authors: Fuster-Martínez N. , Aßmann R. W. , Bruce R. , Giovannozzi M. , Hermes P. , Mereghetti A. , Mirarchi D. , Redaelli S. , Wenninger J.
Published: March 2022

The beam aperture of a particle accelerator defines the clearance available for the circulating beams and is a parameter of paramount importance for the accelerator performance. At the CERN Large Hadron Collider (LHC), the knowledge and control of the available aperture is crucial because the nominal proton beams carry an energy of 362 MJ stored in a superconducting environment. Even a tiny fraction of beam losses could quench the superconducting magnets or cause severe material damage. Furthermore, in a circular collider, the performance in terms of peak luminosity depends to a large extent on the aperture of the inner triplet quadrupoles, which are used to focus the beams at the interaction points. In the LHC, this aperture represents the smallest aperture at top-energy with squeezed beams and determines the maximum potential reach of the peak luminosity. Beam-based aperture measurements in these conditions are difficult and challenging. In this paper, we present different methods that have been developed over the years for precise beam-based aperture measurements in the LHC, highlighting applications and results that contributed to boost the operational LHC performance in Run 1 (2010–2013) and Run 2 (2015–2018)
Study of the polishing slurry dispersant for chemical mechanical polishing of 304 stainless steel
Authors: Jianxiu Su , Xu Zhang , Wangting Qi , Xiaojun Cao , Zhankui Wang
Published: March 2022

To further clarify the effect of the polishing slurry dispersant on the chemical mechanical polishing (CMP) performance of 304 stainless steel, a series of tests were carried out. The correlation between the material removal rate (MRR), surface roughness of 304 stainless steel, dispersant composition, and their content was investigated under two kinds of polishing slurry (hydrogen peroxide oxidant and ferric chloride oxidant) conditions. The experimental results indicated that the MRR and surface roughness of 304 stainless steel arrived at the maximum when the content of sodium hexametaphosphate dispersant was 1.2% (wt) under the hydrogen peroxide–oxalic acid polishing slurry condition. The values of MRR and surface roughness were 146 nm/min and 10 nm, respectively. The MRR and surface roughness of 304 stainless also reached the maximum value as the content of the propanetriol dispersant was 1.2% (wt) under the ferric chloride–oxalic acid polishing slurry condition. However, the values of MRR and surface roughness were 457 nm/min and 22 nm, respectively. Therefore, sodium hexametaphosphate was recommended as the dispersant of hydrogen peroxide–oxalic acid polishing, and propanetriol was recommended as the dispersant of ferric chloride–oxalic acid polishing slurry condition, according to the above analysis. This study lays a theoretical foundation for the improvement of 304 stainless steel CMP performance.
NEEDLE–TISSUE INTERACTION MODEL AND EXPERIMENTAL STUDY OF SUBCLAVIAN VEIN PUNCTURE
Authors: WU KAIYU , SUN YIMIN , ZHANG GUANYI , ZHANG YONGDE , ZHANG SHU
Published: March 2022

The subclavian venous puncture catheter has been widely used in treatment of critically ill patients. However, the lack of a mechanical model for subclavian vein puncture and selection of puncture parameters lead to procedure failures. This paper develops a puncture force model, and explores the factors affecting puncture force through a series of experiments. The puncture process is divided into four stages, and a mechanical model is established for each stage. The parameters of the puncture model, including the needle insertion angle and diameter, and venous pressure, are analyzed experimentally using biological tissues. The Box–Behnken experiment design is chosen to account for the cross-interactions of the various factors considered.
Entanglement and nonlocality of four-qubit connected hypergraph states
Authors: Amouzou Grâce , Boffelli Jeoffrey , Jaffali Hamza , Atchonouglo Kossi , Holweck Frédéric
Published: March 2022

We study entanglement and nonlocality of connected four-qubit hypergraph states. One obtains the Stochastic Local Operations and Classical Communication (SLOCC) classification from the known local unitary (LU)-orbits. We then consider Mermin’s polynomials and show that the nonlocal behavior of hypergraph states can be detected by a suitable choice of Mermin’s inequality. Finally, we implement some of the corresponding inequalities on the IBM Quantum Experience and report the detection of the nonlocality of a three-qubit hypergraph states.
Monitoring microenvironment of Hep G2 cell apoptosis using two-photon fluorescence lifetime imaging microscopy
Authors: Wang Kexin , Tang Shiyao , Wang Shiqi , Lin Fangrui , Zou Gengjin , Qu Junle , Liu Liwei
Published: March 2022

Apoptosis is very important for the maintenance of cellular homeostasis and is closely related to the occurrence and treatment of many diseases. Mitochondria in cells play a crucial role in programmed cell death and redox processes. Nicotinamide adenine dinucleotide (NAD(P)H) is the primary producer of energy in mitochondria, changing NAD(P)H can directly reflect the physiological state of mitochondria. Therefore, NAD(P)H can be used to evaluate metabolic response. In this paper, we propose a noninvasive detection method that uses two-photon fluorescence lifetime imaging microscopy (TP-FLIM) to characterize apoptosis by observing the binding kinetics of cellular endogenous NAD(P)H. The result shows that the average fluorescence lifetime of NAD(P)H and the fluorescence lifetime of protein-bound NAD(P)H will be affected by the changing pH, serum content, and oxygen concentration in the cell culture environment, and by the treatment with reagents such as H2O2 and paclitaxel. Taxol (PTX). This noninvasive detection method realized the dynamic detection of cellular endogenous substances and the assessment of apoptosis.
Tell, Imagine, and Search: End-to-end Learning for Composing Text and Image to Image Retrieval
Authors: Zhang Feifei , Xu Mingliang , Xu Changsheng
Published: March 2022

Composing Text and Image to Image Retrieval (
CTI-IR
) is an emerging task in computer vision, which allows retrieving images relevant to a query image with text describing desired modifications to the query image. Most conventional cross-modal retrieval approaches usually take one modality data as the query to retrieve relevant data of another modality. Different from the existing methods, in this article, we propose an end-to-end trainable network for simultaneous image generation and
CTI-IR
. The proposed model is based on Generative Adversarial Network (GAN) and enjoys several merits. First, it can learn a generative and discriminative feature for the query (a query image with text description) by jointly training a generative model and a retrieval model. Second, our model can automatically manipulate the visual features of the reference image in terms of the text description by the adversarial learning between the synthesized image and target image. Third, global-local collaborative discriminators and attention-based generators are exploited, allowing our approach to focus on both the global and local differences between the query image and the target image. As a result, the semantic consistency and fine-grained details of the generated images can be better enhanced in our model. The generated image can also be used to interpret and empower our retrieval model. Quantitative and qualitative evaluations on three benchmark datasets demonstrate that the proposed algorithm performs favorably against state-of-the-art methods.
Generating Fast Specialized Simulators for Stochastic Reaction Networks via Partial Evaluation
Authors: Köster Till , Warnke Tom , Uhrmacher Adelinde M.
Published: March 2022

Domain-specific modeling languages allow a clear separation between simulation model and simulator and, thus, facilitate the development of simulation models and add to the credibility of simulation results. Partial evaluation provides an effective means for efficiently executing models defined in such languages. However, it also implies some challenges of its own. We illustrate this and solutions based on a simple domain-specific language for biochemical reaction networks as well as on the network representation of the established BioNetGen language. We implement different approaches adopting the same simulation algorithms: one generic simulator that parses models at runtime and one generator that produces a simulator specialized to a given model based on partial evaluation and code generation. For the purpose of better understanding, we additionally generate intermediate variants, where only some parts are partially evaluated. Akin to profile-guided optimization, we use dynamic execution of the model to further optimize the simulators. The performance of the approaches is carefully benchmarked using representative models of small to large biochemical reaction networks. The generic simulator achieves a performance similar to state-of-the-art simulators in the domain, whereas the specialized simulator outperforms established simulation tools with a speedup of more than an order of magnitude. Technical limitations in regard to the size of the generated code are discussed and overcome using a combination of link-time optimization and code separation. A detailed performance study is undertaken, investigating how and where partial evaluation has the largest effect.
Shuffle-invariant Network for Action Recognition in Videos
Authors: Shi Qinghongya , Zhang Hong-Bo , Li Zhe , Du Ji-Xiang , Lei Qing , Liu Jing-Hua
Published: March 2022

The local key features in video are important for improving the accuracy of human action recognition. However, most end-to-end methods focus on global feature learning from videos, while few works consider the enhancement of the local information in a feature. In this article, we discuss how to automatically enhance the ability to discriminate the local information in an action feature and improve the accuracy of action recognition. To address these problems, we assume that the critical level of each region for the action recognition task is different and will not change with the region location shuffle. We therefore propose a novel action recognition method called the shuffle-invariant network. In the proposed method, the shuffled video is generated by regular region cutting and random confusion to enhance the input data. The proposed network adopts the multitask framework, which includes one feature backbone network and three task branches: local critical feature shuffle-invariant learning, adversarial learning, and an action classification network. To enhance the local features, the feature response of each region is predicted by a local critical feature learning network. To train this network, an
L
1-based critical feature shuffle-invariant loss is defined to ensure that the ordered feature response list of these regions remains unchanged after region location shuffle. Then, the adversarial learning is applied to eliminate the noise caused by the region shuffle. Finally, the action classification network combines these two tasks to jointly guide the training of the feature backbone network and obtain more effective action features. In the testing phase, only the action classification network is applied to identify the action category of the input video. We verify the proposed method on the HMDB51 and UCF101 action datasets. Several ablation experiments are constructed to verify the effectiveness of each module. The experimental results show that our approach achieves the state-of-the-art performance.
Efficient Protocol Testing Under Temporal Uncertain Event Using Discrete-event Network Simulations
Authors: Vu Minh , Xu Lisong , Elbaum Sebastian , Sun Wei , Qiao Kevin
Published: March 2022

Testing network protocol implementations is difficult mainly because of the temporal uncertain nature of network events. To evaluate the worst-case performance or detect the bugs of a network protocol implementation using network simulators, we need to systematically simulate the behavior of the network protocol under all possible cases of the temporal uncertain events, which is time consuming. The recently proposed Symbolic Execution based Interval Branching (SEIB) simulates a group of uncertain cases together in a single simulation branch and thus is more efficient than brute force testing. In this article, we argue that the efficiency of SEIB could be further significantly improved by eliminating unnecessary comparisons of the event timestamps. Specifically, we summarize and present three general types of unnecessary comparisons when SEIB is applied to a general network simulator, and then correspondingly propose three novel techniques to eliminate them. Our extensive simulations show that our techniques can improve the efficiency of SEIB by several orders of magnitude, such as from days to minutes.
A Multimodal Framework for Large-Scale Emotion Recognition by Fusing Music and Electrodermal Activity Signals
Authors: Yin Guanghao , Sun Shouqian , Yu Dian , Li Dejian , Zhang Kejun
Published: March 2022

Considerable attention has been paid to physiological signal-based emotion recognition in the field of affective computing. For reliability and user-friendly acquisition, electrodermal activity (EDA) has a great advantage in practical applications. However, EDA-based emotion recognition with large-scale subjects is still a tough problem. The traditional well-designed classifiers with hand-crafted features produce poorer results because of their limited representation abilities. And the deep learning models with auto feature extraction suffer the overfitting drop-off because of large-scale individual differences. Since music has a strong correlation with human emotion, static music can be involved as the external benchmark to constrain various dynamic EDA signals. In this article, we make an attempt by fusing the subject’s individual EDA features and the external evoked music features. And we propose an end-to-end multimodal framework, the one-dimensional residual temporal and channel attention network (RTCAN-1D). For EDA features, the channel-temporal attention mechanism for EDA-based emotion recognition is first involved in mine the temporal and channel-wise dynamic and steady features. The comparisons with single EDA-based SOTA models on DEAP and AMIGOS datasets prove the effectiveness of RTCAN-1D to mine EDA features. For music features, we simply process the music signal with the open-source toolkit openSMILE to obtain external feature vectors. We conducted systematic and extensive evaluations. The experiments on the current largest music emotion dataset PMEmo validate that the fusion of EDA and music is a reliable and efficient solution for large-scale emotion recognition.
A Cluster-then-label Approach for Few-shot Learning with Application to Automatic Image Data Labeling
Authors: Wu Renzhi , Das Nilaksh , Chaba Sanya , Gandhi Sakshi , Chau Duen Horng , Chu Xu
Published: March 2022

Few-shot learning (FSL) aims at learning to generalize from only a small number of labeled examples for a given target task. Most current state-of-the-art FSL methods typically have two limitations. First, they usually require access to a source dataset (in a similar domain) with abundant labeled examples, which may not always be possible due to privacy concerns and copyright issues. Second, they typically do not offer any estimation of the generalization error on the target FSL task, because the handful of labeled examples must be used for training and cannot spare a validation subset. In this article, we propose a cluster-then-label approach to perform few-shot learning. Our approach does not require access to the labeled source dataset and provides an estimation of generalization error. We show empirically, on four benchmark datasets, that our approach provides competitive predictive performance to state-of-the-art FSL approaches and our generalization error estimation is accurate. Finally, we explore the application of our proposed method to automatic image data labeling. We compare our method with existing automatic data labeling systems. The end-to-end performance of our method outperforms the state-of-the-art automatic data labeling system Snuba by 26% and is only 7% away from the fully supervised upper bound.
Improving Feature Discrimination for Object Tracking by Structural-similarity-based Metric Learning
Authors: Wu Jingjing , Jiang Jianguo , Qi Meibin , Chen Cuiqun , Liu Yimin
Published: March 2022

Existing approaches usually form the tracking task as an appearance matching procedure. However, the discrimination ability of appearance features is insufficient in these trackers, which is caused by their weak feature supervision constraints and inadequate exploitation of spatial contexts. To tackle this issue, this article proposes a novel appearance matching tracking (AMT) method to strengthen the feature restraints and capture discriminative spatial representations. Specifically, we first utilize a triplet structural loss function, which improves the learning capability of features by applying a structural similarity constraint with a triplet metric format on the features. It leverages feature statistics to capture the complex interactions of visual parts. Second, we put forward an adaptive matching module that exploits the dual spatial enhancement module to reinforce target feature discrimination. This not only boosts the representation ability of spatial context but also realizes spatially dynamic feature selection by attending to target deformation information. Moreover, this model introduces a simple but effective matching unit to intuitively evaluate the relative appearance differences between the target and the proposals. In addition, with the obtained discriminative features, AMT is capable of providing precise localization for the target. Therefore, the impact of spatial suppression imposed by window functions can be alleviated, allowing for effective tracking of high-speed moving objects. Extensive experiments prove that AMT outperforms state-of-the-art methods on six public datasets and demonstrate the effectiveness of each component in AMT.
Scribble-Supervised Meibomian Glands Segmentation in Infrared Images
Authors: Liu Xiaoming , Wang Shuo , Zhang Ying , Yuan Quan
Published: March 2022

Infrared imaging is currently the most effective clinical method to evaluate the morphology of the meibomian glands (MGs) in patients. As an important indicator for monitoring the development of MG dysfunction, it is necessary to accurately measure gland-drop and gland morphology. Although there are existing methods for automatic segmentation of MGs using deep learning frameworks, they require fully annotated ground-truth labels for training, which is time-consuming and laborious. In this article, we proposed a new scribble-supervised deep learning framework for segmenting the MGs, which only requires easily attainable scribble annotations for training. To cope with the shortage of supervision and regularize the network, a transformation consistent strategy is incorporated, which requires the prediction to follow the same transformation if a transform is performed on an input image of the network. The proposed segmentation method consists of two stages. In the first stage, a U-Net network is used to obtain the meibomian region segmentation map. In the second stage, we concentrate on segmenting glands in the meibomian region. We utilize the gradient prior information of the original image at the decoder part of the segmentation network, which can coarsely locate the target contour. We automatically generate reliable labels using the exponential moving average of the predictions during training and filter out the unreliable pseudo-label by uncertainty threshold. Experimental results on a local MG dataset and two other public medical image datasets demonstrate the effectiveness of the proposed segmentation framework.
Quantum Linear System Solver Based on Time-optimal Adiabatic Quantum Computing and Quantum Approximate Optimization Algorithm
Authors: An Dong , Lin Lin
Published: March 2022

We demonstrate that with an optimally tuned scheduling function, adiabatic quantum computing (AQC) can readily solve a quantum linear system problem (QLSP) with
O
(κ poly(log (κ ε))) runtime, where κ is the condition number, and ε is the target accuracy. This is near optimal with respect to both κ and ε, and is achieved without relying on complicated amplitude amplification procedures that are difficult to implement. Our method is applicable to general non-Hermitian matrices, and the cost as well as the number of qubits can be reduced when restricted to Hermitian matrices, and further to Hermitian positive definite matrices. The success of the time-optimal AQC implies that the quantum approximate optimization algorithm (QAOA) with an optimal control protocol can also achieve the same complexity in terms of the runtime. Numerical results indicate that QAOA can yield the lowest runtime compared to the time-optimal AQC, vanilla AQC, and the recently proposed randomization method.
Harmonious Multi-branch Network for Person Re-identification with Harder Triplet Loss
Authors: Tang Zengming , Huang Jun
Published: March 2022

Recently, advances in person re-identification (Re-ID) has benefitted from use of the popular multi-branch network. However, performing feature learning in a single branch with uniform partitioning is likely to separate meaningful local regions, and correlation among different branches is not well established. In this article, we propose a novel harmonious multi-branch network (HMBN) to relieve these intra-branch and inter-branch problems harmoniously. HMBN is a multi-branch network with various stripes on different branches to learn coarse-to-fine pedestrian information. We first replace the uniform partition with a horizontal overlapped partition to cover meaningful local regions between adjacent stripes in a single branch. We then incorporate a novel attention module to make all branches interact by modeling spatial contextual dependencies across branches. Finally, in order to train the HMBN more effectively, a harder triplet loss is introduced to optimize triplets in a harder manner. Extensive experiments are conducted on three benchmark datasets — DukeMTMC-reID, CUHK03, and Market-1501 — demonstrating the superiority of our proposed HMBN over state-of-the-art methods.
Towards Corruption-Agnostic Robust Domain Adaptation
Authors: Xu Yifan , Sheng Kekai , Dong Weiming , Wu Baoyuan , Xu Changsheng , Hu Bao-Gang
Published: March 2022

Great progress has been achieved in domain adaptation in decades. Existing works are always based on an ideal assumption that testing target domains are independent and identically distributed with training target domains. However, due to unpredictable corruptions (e.g., noise and blur) in real data, such as web images and real-world object detection, domain adaptation methods are increasingly required to be corruption robust on target domains. We investigate a new task, corruption-agnostic robust domain adaptation (CRDA), to be accurate on original data and robust against unavailable-for-training corruptions on target domains. This task is non-trivial due to the large domain discrepancy and unsupervised target domains. We observe that simple combinations of popular methods of domain adaptation and corruption robustness have suboptimal CRDA results. We propose a new approach based on two technical insights into CRDA, as follows: (1) an easy-to-plug module called domain discrepancy generator (DDG) that generates samples that enlarge domain discrepancy to mimic unpredictable corruptions; (2) a simple but effective teacher-student scheme with contrastive loss to enhance the constraints on target domains. Experiments verify that DDG maintains or even improves its performance on original data and achieves better corruption robustness than baselines. Our code is available at:
https://github.com/YifanXu74/CRDA
.
NR-CNN: Nested-Residual Guided CNN In-loop Filtering for Video Coding
Authors: Lin Kai , Jia Chuanmin , Zhang Xinfeng , Wang Shanshe , Ma Siwei , Gao Wen
Published: March 2022

Recently, deep learning for video coding, such as deep predictive coding, deep transform coding, and deep in-loop filtering, has been an emerging research area. The coding gain of hybrid coding framework could be extensively promoted by the data-driven models. However, previous deep coding tools especially deep in-loop filtering mainly consider the performance improvement while pay less attention to the reliability, usability, and adaptivity of the networks. In this article, a nested-residual guided convolutional neural network (NR-CNN) structure with cascaded global shortcut and configurable residual blocks is proposed for in-loop filtering. By taking advantage of the correlation between different color components, we further extend the NR-CNN by utilizing luminance as textural and structural guidance for chrominance filtering, which significantly improves the filtering performance. To fully exploit the proposed network into codec integration, we subsequently introduce an efficient and adaptive framework consisting of an adaptive granularity optimization and a parallel inference pipeline for deep learning based filtering. The former contributes to the coding performance improvement through an adaptive decision-making based on rate-distortion analysis at various granularities. The latter reduces the running time of network inference. The extensive experimental results show the superiority of the proposed method, achieving 8.2%, 14.9%, and 13.2% BD-rate savings on average under random access (RA) configuration. Meanwhile, the proposed method also obtains better subjective quality.
Dynamic Evaluation of Microservice Granularity Adaptation
Authors: Hassan Sara , Bahsoon Rami , Minku Leandro , Ali Nour
Published: March 2022

Microservices have gained acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. Among the critical microservice architecture design decisions is when to adapt the granularity of a microservice architecture by merging/decomposing microservices. No existing work investigates the following question: How can we reason about the trade-off between predicted benefits and cost of pursuing microservice granularity adaptation under uncertainty? To address this question, we provide a novel formulation of the decision problem to pursue granularity adaptation as a real options problem. We propose a novel evaluation process for dynamically evaluating granularity adaptation design decisions under uncertainty. Our process is based on a novel combination of real options and the concept of Bayesian surprises. We show the benefits of our evaluation process by comparing it to four representative industrial microservice runtime monitoring tools, which can be used for retrospective evaluation for granularity adaptation decisions. Our comparison shows that our process can supersede and/or complement these tools. We implement a microservice application—Filmflix—using Amazon Web Service Lambda and use this implementation as a case study to show the unique benefit of our process compared to traditional application of real options analysis.
Scenario-Aware Recurrent Transformer for Goal-Directed Video Captioning
Authors: Man Xin , Ouyang Deqiang , Li Xiangpeng , Song Jingkuan , Shao Jie
Published: March 2022

Fully mining visual cues to aid in content understanding is crucial for video captioning. However, most state-of-the-art video captioning methods are limited to generating captions purely based on straightforward information while ignoring the scenario and context information. To fill the gap, we propose a novel, simple but effective scenario-aware recurrent transformer (SART) model to execute video captioning. Our model contains a “scenario understanding” module to obtain a global perspective across multiple frames, providing a specific scenario to guarantee a goal-directed description. Moreover, for the sake of achieving narrative continuity in the generated paragraph, a unified recurrent transformer is adopted. To demonstrate the effectiveness of our proposed SART, we have conducted comprehensive experiments on various large-scale video description datasets, including ActivityNet, YouCookII, and VideoStory. Additionally, we extend a story-oriented evaluation framework for assessing the quality of the generated caption more precisely. The superior performance has shown that SART has a strong ability to generate correct, deliberative, and narrative coherent video descriptions.
Online Correction of Camera Poses for the Surround-view System: A Sparse Direct Approach
Authors: Zhang Tianjun , Deng Hao , Zhang Lin , Zhao Shengjie , Liu Xiao , Zhou Yicong
Published: March 2022

The surround-view module is an indispensable component of a modern advanced driving assistance system. By calibrating the intrinsics and extrinsics of the surround-view cameras accurately, a top-down surround-view can be generated from raw fisheye images. However, poses of these cameras sometimes may change. At present, how to correct poses of cameras in a surround-view system online without re-calibration is still an open issue. To settle this problem, we introduce the sparse direct framework and propose a novel optimization scheme of a cascade structure. This scheme is actually composed of two levels of optimization and two corresponding photometric error based models are proposed. The model for the first-level optimization is called the ground model, as its photometric errors are measured on the ground plane. For the second level of the optimization, it’s based on the so-called ground-camera model, in which photometric errors are computed on the imaging planes. With these models, the pose correction task is formulated as a nonlinear least-squares problem to minimize photometric errors in overlapping regions of adjacent bird’s-eye-view images. With a cascade structure of these two levels of optimization, an appropriate balance between the speed and the accuracy can be achieved. Experiments show that our method can effectively eliminate the misalignment caused by cameras’ moderate pose changes in the surround-view system. Source code and test cases are available online at
https://cslinzhang.github.io/CamPoseCorrection/
.
Career Interview Readiness in Virtual Reality (CIRVR): A Platform for Simulated Interview Training for Autistic Individuals and Their Employers
Authors: Adiani Deeksha , Itzkovitz Aaron , Bian Dayi , Katz Harrison , Breen Michael , Hunt Spencer , Swanson Amy , Vogus Timothy J. , Wade Joshua , Sarkar Nilanjan
Published: March 2022

Employment outcomes for autistic

1

individuals are often poorer relative to their neurotypical (NT) peers, resulting in a greater need for other forms of financial and social support. While a great deal of work has focused on developing interventions for autistic children, relatively less attention has been paid to directly addressing the employment challenges faced by autistic adults. One key impediment to autistic individuals securing employment is the job interview. Autistic individuals often experience anxiety in interview situations, particularly with open-ended questions and unexpected interruptions. They also exhibit atypical gaze patterns that may be perceived as, but not necessarily indicative of, disinterest or inattention. In response, we developed a closed-loop adaptive virtual reality (VR)–based job interview training platform, which we have named Career Interview Readiness in VR (CIRVR). CIRVR is designed to provide an engaging, adaptive, and individualized experience to practice and refine interviewing skills in a less anxiety-inducing virtual context. CIRVR contains a real-time physiology-based stress detection module, as well as a real-time gaze detection module, to permit individualized adaptation. We also present the first prototype of the CIRVR Dashboard, which provides visualizations of data to help autistic individuals as well as potential employers and job coaches make sense of the data gathered from interview sessions. We conducted a feasibility study with 9 autistic and 8 NT individuals to assess the preliminary usability and feasibility of CIRVR. Results showed differences in perceived usability of the system between autistic and NT participants, and higher levels of stress in autistic individuals during interviews. Participants across both groups reported satisfaction with CIRVR and the structure of the interview. These findings and feedback will support future work in improving CIRVR’s features in hopes for it to be a valuable tool to support autistic job candidates as well as their potential employers.
Word Level Script Identification Using Convolutional Neural Network Enhancement for Scenic Images
Authors: Mahajan Shilpa , Rani Rajneesh
Published: March 2022

Script identification from complex and colorful images is an integral part of the text recognition and classification system. Such images may contain twofold challenges: (1) Challenges related to the camera like blurring effect, non-uniform illumination and noisy background, and so on, and (2) Challenges related to the text shape, orientation, and text size. The present work in this area is much focused on non-Indian scripts. In contrast, Gurumukhi, Hindi, and English scripts play a vital role in communication among Indians and foreigners. In this article, we focus on the above said challenges in the field of identifying the script. Additionally, we have introduced a new dataset that contains Hindi, Gurumukhi, and English scripts from scenic images collected from different sources. We also proposed a CNN-based model, which is capable of distinguishing between the scripts with good accuracy. Performance of the method has been evaluated for own dataset, i.e., NITJDATASET and other benchmarked datasets available for Indian scripts, i.e., CVSI-2015 (Task-1 and Task 4) and ILST. This work is an extension to find the script from strict text background.
Machine Learning and Data Cleaning: Which Serves the Other?
Authors: Ilyas Ihab F. , Rekatsinas Theodoros
Published: March 2022

The last few years witnessed significant advances in building automated or semi-automated data quality, data cleaning and data integration systems powered by machine learning (ML). In parallel, large deployment of ML systems in business, science, environment and various other areas started to realize the strong dependency on the quality of the input data to these ML models to get reliable predictions or insights. That dual relationship between ML and data cleaning has been addressed by many recent research works under terms such as “Data cleaning for ML” and “ML for automating data cleaning and data preparation”. In this article, we highlight this symbiotic relationship between ML and data cleaning and discuss few challenges that require collaborative efforts of multiple research communities.
ESRNet: Efficient Search and Recognition Network for Image Manipulation Detection
Authors: Ren Ruyong , Niu Shaozhang , Ren Hua , Zhang Shubin , Han Tengyue , Tong Xiaohai
Published: March 2022

With the widespread use of smartphones and the rise of intelligent software, we can manipulate captured photos anytime and anywhere, so the fake photos finally obtained look “Real.” If these intelligent operation methods are maliciously applied to our daily life, then fake news, fake photos, rumors, slander, fraud, threats, and other information security issues around us can happen all the time. Today’s intelligent retouching software can make various modifications to photos, some of which do not change the content that the photos themselves want to express, such as retouching, contrast improvement, and so on. In this article, we mainly study the three operation modes of changing the authenticity of photo contents, which are Copy-move, Splicing, and Removal. Few scholars have done relevant research due to the lack of a corresponding dataset. To address this issue, we elaborately collect a novel dataset, called the multi-realistic scene manipulation dataset (
MSM30K
), which consists of 30,000 images, including three types of tampering methods, and covering 32 different tampering scenes in life. In addition, we propose a unified detection network: the efficient search and recognition network (
ESRNet
) for three tampering methods. It mainly includes four main modules: Efficient feature pyramid network (
EFPN
), Residual receptive field block with attention (
RFBA
), Hierarchical decoding identification (
HDI
), and Cascaded group-reversal attention (
GRA
) blocks. On these three datasets, ESRNet can reach 0.81 on the S-measure, 0.72 on the F-measure, and 0.85 on the E-measure. The inference speed is ~53 fps on a single GPU without I/O time. ESRNet outperforms various state-of-the-art manipulation detection baselines on three image manipulation datasets.
Binary Fuse Filters: Fast and Smaller Than Xor Filters
Authors: Graf Thomas Mueller , Lemire Daniel
Published: March 2022

Bloom and cuckoo filters provide fast approximate set membership while using little memory. Engineers use them to avoid expensive disk and network accesses. The recently introduced xor filters can be faster and smaller than Bloom and cuckoo filters. The xor filters are within 23% of the theoretical lower bound in storage as opposed to 44% for Bloom filters. Inspired by Dietzfelbinger and Walzer, we build probabilistic filters—called
binary fuse filters
—that are within 13% of the storage lower bound—without sacrificing query speed. As an additional benefit, the construction of the new binary fuse filters can be more than twice as fast as the construction of xor filters. By slightly sacrificing query speed, we further reduce storage to within 8% of the lower bound. We compare the performance against a wide range of competitive alternatives such as Bloom filters, blocked Bloom filters, vector quotient filters, cuckoo filters, and the recent ribbon filters. Our experiments suggest that binary fuse filters are superior to xor filters.
Localizing RFIDs in Pixel Dimensions
Authors: An Zhenlin , Lin Qiongzheng , Yang Lei , Guo Yi , Li Ping
Published: March 2022

Radio Frequency IDentification (RFID) is emerging as a vital technology of the Internet of Things (IoT). Billions of RFID tags have been deployed to locate daily objects such as equipment, pharmaceuticals, vehicles, and so on. Unlike previous solutions that focus on localizing tagged objects in the world coordinate system in reference to reader antennas, this work exploits a system, called RFCamera, that can identify and locate RFID-tagged objects in images with pixel dimensions. Our core insight is that an image is a visual AoA profile in terms of lights, which is resulted from the pinhole camera model. Similarly, we generate an RF image derived from the AoA profile of a tag using the same pinhole model as the camera. Consequently, the locations of visual entities corresponding to tagged objects are highlighted by comparing two types of images. To this end, we customized a camera system equipped with a pair of rotatable reader antennas. Our experimental evaluation demonstrates that RFCamera enables a mean error of 5.7∘ and 2.9∘ at azimuth and elevation angle estimation, respectively. It can locate a visual entity with a mean error of 51 pixels (i.e., ≈1.3 cm at 96 dpi) in a 640× 480 image.
Evaluation of Systems-of-Systems Software Architectures: State of the Art and Future Perspectives
Authors: Santos Daniel S. , Oliveira Brauner R. N. , Kazman Rick , Nakagawa Elisa Y.
Published: March 2022

The quality of large and complexSystems-of-Systems (SoS)that have emerged in critical application domains depends on the quality of their architectures, which are inherently dynamic in terms of reorganization at runtime to comply with domain needs. However, the way to model and evaluate the quality of these architectures is not clear. This article presents the state-of-the-art regarding how SoS architectures have been evaluated. For this, we systematically examined the literature and, as a result, we discovered and summarized relevant architectural evaluation methods and associated modeling techniques and quality attributes, the maturity of these methods, as well as the benefits and costs of adopting them. We also address open issues and research opportunities and recommend that the mindset for SoS architecture evaluation must be changed to ensure the quality of SoS in critical domains.
Online Learning for Orchestration of Inference in Multi-user End-edge-cloud Networks
Authors: Shahhosseini Sina , Seo Dongjoo , Kanduri Anil , Hu Tianyi , Lim Sung-Soo , Donyanavard Bryan , Rahmani Amir M. , Dutt Nikil
Published: March 2022

Deep-learning-based intelligent services have become prevalent in cyber-physical applications, including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protection, responsiveness, and reliability. Resource-constrained end-devices must be carefully managed to meet the latency and energy requirements of computationally intensive deep learning services. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). However, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy. We demonstrate the effectiveness of our solution for edge devices in an end-edge-cloud system and evaluate with a real-setup implementation using multiple AWS and ARM core configurations. Our solution provides 35% speedup in the average response time compared to the state-of-the-art with less than 0.9% accuracy reduction, demonstrating the promise of our online learning framework for orchestrating DL inference in end-edge-cloud systems.