Miracle-dz/UniFAR-FTUs huggingface dataset:
A Data Quarantine Model to Secure Data in Edge Computing

Edge computing provides an agile data processing platform for latency-sensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders. The efficient security solutions in cloud, ad-hoc networks, and computer systems using quarantine have motivated adopting it in edge computing. The data acquisition edge nodes identify the intruders and quarantine all the suspected devices through dimensionality reduction. During quarantine, the proposed concept builds the reputation scores to determine the falsely identified legitimate devices and sanitize their affected data to regain data integrity. As a preliminary investigation, this work identifies an appropriate machine learning method, linear discriminant analysis (LDA), for dimensionality reduction. The LDA results in 72.83% quarantine accuracy and 0.9 seconds training time, which is efficient than other state-of-the-art methods. In future, this would be implemented and validated with ground truth data.
Future Ground-Based Gravitational-Wave Observatories: Synergies with Other Scientific Communities

Planning for the development of a 3rd generation global gravitational-wave detector array is a multifaceted and complex effort that will necessarily need a high level of community input. Interfacing to extant and new stakeholders in the broader scientific constituencies is necessary to keep them aware of the activities taking place in the ground-based gravitational-wave community and receive input to inform and evolve the planning. In this report, we present the approaches GWIC and gravitational-wave collaborations and projects should consider taking to engage with broader community. This report is the fifth in a six part series of reports by the GWIC 3G Subcommittee: i) Expanding the Reach of Gravitational Wave Observatories to the Edge of the Universe, ii) The Next Generation Global Gravitational Wave Observatory: The Science Book, iii) 3G R&D: R&D for the Next Generation of Ground-based Gravitational Wave Detectors, iv) Gravitational Wave Data Analysis: Computing Challenges in the 3G Era, v) Future Ground-based Gravitational-wave Observatories: Synergies with Other Scientific Communities (this report), and vi) An Exploration of Possible Governance Models for the Future Global Gravitational-Wave Observatory Network.
An extended rational dichotomy for certain open books

We study the rational homotopy groups of open books in terms of those of their pages and bindings. Under homotopy conditions on the monodromy we prove a rational dichotomy theorem for open books, as an extension of the classical dichotomy in rational homotopy theory. As a direct application, we show that for Milnor’s open book decomposition of an odd sphere with monodromy of ﬁnite order the induced action of the monodromy on the homology groups of its page cannot be nilpotent. We also calculate the integral homotopy groups of open books in terms of those of their pages and bindings when their monodromies are homotopic to the identity map.
Damage detection of base‐isolated buildings using multi‐input multi‐output subspace identification

A damage detection algorithm of structural health monitoring systems for base‐isolated buildings is proposed. The algorithm consists of the multiple‐input multiple‐output subspace identification method and the complex modal analysis. The algorithm is applicable to linear and non‐linear systems. The story stiffness and damping as damage indices of a shear structure are identified by the algorithm. The algorithm is further tuned for base‐isolated buildings considering their unique dynamic characteristics by simplifying the systems to single‐degree‐of‐freedom systems. The isolation layer and the superstructure of a base‐isolated building are treated as separate substructures as they are distinctly different in their dynamic properties. The effectiveness of the algorithm is evaluated through the numerical analysis and experiment. Finally, the algorithm is applied to the existing 7‐story base‐isolated building that is equipped with an Internet‐based monitoring system. Copyright © 2004 John Wiley & Sons, Ltd.
Stochastic and Worst-Case Generalized Sorting Revisited

The generalized sorting problem is a restricted version of standard comparison sorting where we wish to sort <tex>$n$</tex> elements but only a subset of pairs are allowed to be compared. Formally, there is some known graph <tex>$G=(V, E)$</tex> on the <tex>$n$</tex> elements <tex>$v_{1, \ldots, v_{n}}$</tex>, and the goal is to determine the true order of the elements using as few comparisons as possible, where all comparisons (<tex>$v_{i, v_{j}}$</tex>) must be edges in <tex>$E$</tex>. We are promised that if the true ordering is <tex>$x_{1 < x_{2} < \cdots < x_{n}}$</tex> for <tex>$\{x_{i\}}$</tex> an unknown permutation of the vertices <tex>$\{v_{i\}}$</tex>, then <tex>$(x_{i, x_{i+1})\in E}$</tex> for all <tex>$i$</tex>: this Hamiltonian path ensures that sorting is actually possible. In this work, we improve the bounds for generalized sorting on both random graphs and worst-case graphs. For Erdős-Renyi random graphs <tex>$G(n, p)$</tex> (with the promised Hamiltonian path added to ensure sorting is possible), we provide an algorithm for generalized sorting with an expected <tex>$O(n\ \text{lg}(np))$</tex> comparisons, which we prove to be optimal for query complexity. This strongly improves over the best known algorithm of Huang, Kannan, and Khanna (FOCS 2011), which uses <tex>$\tilde{O(\min(n\sqrt{np},\ n/p^{2}))}$</tex> comparisons. For arbitrary graphs <tex>$G$</tex> with <tex>$n$</tex> vertices and <tex>$m$</tex> edges (again with the promised Hamiltonian path), we provide an algorithm for generalized sorting with <tex>$\tilde{O(\sqrt{mn})}$</tex> comparisons. This improves over the best known algorithm of Huang et al., which uses <tex>$\min(m,\tilde{O}(n^{3/2}))$</tex> comparisons.
THE EVALUATION OF THERMAL PROPERTIES AND IN VITRO TEST OF CARBODIIMIDE OR GLUTARALDEHYDE CROSS-LINKED GELATIN FOR PC 12 CELLS CULTURE

The thermal and degradable properties of carbodiimide (EDC) or glutaraldehyde (GTA) cross-linked gelatin membranes have been investigated in order to evaluate the effects of different concentrations of two kinds of cross-linking reagent on the stability of membranes. In the thermogram recorded from a gelatin membrane cross-linked with EDC solution, the endothermic peak of 0.8% EDC cross-linking gelatin was centered at about 61°C that was higher than other samples treated with EDC solutions. Denaturation temperature (Td) of gelatin samples increased on increasing EDC concentration (0.2% to 0.8%), in agreement with the simultaneous increased of the extent of cross-linking. But increasing GTA concentration from 0.05% to 0.6%, the Td values of gelatin samples were decreased from 66.2°C to 56.3°C . In addition, two endothermic peaks were observed in 0.4% and 0.6% GTA cross-linking groups because of the GTA concentration was too high to complete cross-linking reaction. Therefore, partial of gelatin membrane was cross-linked completely but others were not. In the thermogravimetric analysis, the proportion of cracking endothermic peak of 0.6% GTA cross-linking gelatin (g15G0.6) was higher than the peak of 0.6% EDC cross-linking gelatin (g15C0.6). Therefore, g15G0.6 cracked to smaller molecules has to absorb more calorific capacity than g15C0.6. The increase in the strength of covalent binding on increasing the proportion of endothermic peak was evident. The results of degradable rate were in agreement with the lower concentration of cross-linked reagent the faster degraded rate of gelatin membrane. The MTT assay showed that 15% gelatin cross-linked by 0.8% EDC has the least cytotoxicity, and cell activity of this group was similar to control group (blank dish). As the concentration of GTA in gelatin membranes was down to 0.05% or 0.1% the cell viability was returned to approach the value of control group.
Double-sided cooling for high power IGBT modules using flip chip technology

A new technique for the packaging of IGBT modules has been developed. The components are sandwiched between two DBC (direct bond copper) substrates with aluminum nitride. Wire bonds are replaced with flip chip solder bumps, which allows to cool components on both sides. Microchannel heat sinks are directly integrated in the package to decrease the thermal resistance of the module. Thus, a very compact module with high thermal performance is obtained. A prototype with two IGBTs (insulated gate bipolar transistor) and four diodes associated in parallel was realised and tested. In this paper, the innovative packaging technique is described, and results of thermal tests are presented.
The Worker-Establishment Characteristics Database

A data set combining information on the characteristics of both workers and their employers has long been a grail for labor economists. The reason for this interest is that while a number of theoretical models in labor economics stress the importance of employer-employee matching in determining labor market outcomes, almost all empirical work relies on either worker surveys with little information about employers or establishment surveys with little information about workers. The Worker-Establishment Characteristic Database (WECD) represents just such an employer-employee-matched database. Containing 199,557 manufacturing workers matched to 16,144 manufacturing establishments, the WECD is the largest worker-firm matched data set available for the U.S. This paper describes how this data set was constructed and assesses the usefulness of these data for economic research. In addition, I discuss some of the issues that can be addressed using employer-employee-matched data and plans for creating future versions of the WECD.
Identifying urban air pollution hot-spots by dispersion modeling when data are scarce: application to diesel generators in Beirut, Lebanon

Diesel generators are emerging as community-initiated solutions to compensate for electricity shortage in cities marred by economical crisis and/or conflict. The resulting pollution distribution in dense urban environments is a major source of concern to the population. In the absence of periodic observations from properly distributed sensors, as is the case in Beirut, physically based computational modeling stand out as an effective tool for predicting the pollutant distribution in complex environments, and a cost-effective framework for investigating what-if scenarios and assessing mitigation strategies. Here, we present a Lagrangian transport model-based study of PM2.5 dispersion originating from a large number of diesel generators in Beirut. We explore large and small scale dispersion patterns in selected smalls domains and over the entire city. The scenarios considered investigate the impact of topography, atmospheric stability, presence of buildings, diesel generators distribution, and stacks elevations for representative meteorological conditions. Assessment of these scenarios is carried out in terms of small and large scale dispersion patterns and the mean concentration at street level and population exposure proxy indicators. We also report on the efficacy of elevating the stack height as a mitigation measure at different representative wind and atmospheric stability conditions.
Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system’s composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO’s autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO’s search when used as a prior.
Constraining the cosmic-ray ionization rate and spectrum with NIR spectroscopy of dense clouds. A testbed for JWST

Low-energy cosmic-rays (CRs) control the thermo-chemical state and the coupling between gas and magnetic fields in dense molecular clouds, the sites of star-formation. However, current estimates of the low-energy CR spectrum ($E \lesssim 1$ GeV) and the associated CR ionization rate are highly uncertain. We apply, for the first time, a new method for constraining the CR ionization rate and the CR spectral shape using H$_2$ rovibrational lines from cold molecular clouds. Using the MMIRS instrument on the MMT, we obtained deep near-infrared (NIR) spectra in six positions within four dense cores, G150, G157, G163, G198, with column densities $N_{\rm H_2} \approx 10^{22}$ cm$^{-2}$. We derive 3$\sigma$ upper limits on the H$_2$ $(1-0)$S(0) line (2.22 $\mu$m) brightness in the range $I = 5.9 \times 10^{-8}$ to $1.2 \times 10^{-7}$ erg cm$^{-2}$ s$^{-1}$ sr$^{-1}$ for the different targets. Using both an analytic model and a numerical model of CR propagation, we convert these into upper limits on the CR ionization rate in the clouds' interior, $\zeta = 1.5$ to $3.6 \times 10^{-16}$ s$^{-1}$, and lower limits on the low-energy spectral slope of interstellar CR protons, $\alpha = -0.97$ to $-0.79$. We show that while MMT was unable to detect the H$_2$ lines due to high atmospheric noise, JWST/NIRSpec will be able to efficiently detect the CR-excited H$_2$ lines, making it the ideal method for constraining the otherwise elusive low-energy CRs, shedding light on the sources and propagation modes of CRs.
Measurement of power spread histograms in chains of erbium-doped fiber amplifiers (EDFAs) fed by live local area network traffic

A serious problem facing wavelength division multiplexed (WDM) networks with fiber amplifier cascades is transient cross saturation or gain dynamics of fiber amplifiers. Attention has been focused primarily on circuit-switched scenarios. When the number of WDM channels transmitted through a circuit-switching network varies, channel addition/removal will tend to perturb signals at the surviving channels that share all or part of the route. Even more serious bit error rate deterioration can arise in WDM packet switched burst mode networks. In this contribution we present experimental results demonstrating the effect of fast power transients in erbium-doped fiber amplifiers (EDFAs) on packetized traffic transmitted through a chain of five EDFAs. Traffic of a LAN has been transmitted over one channel, the effect of EDFA cross gain saturation due to the bursteness of the traffic has been observed in a cw channel. The stabilizing effect of gain clamping the first EDFA in the cascade has been investigated.
Beep: Fine-grained Fix Localization by Learning to Predict Buggy Code Elements

Software Fault Localization refers to the activity of finding code elements (e.g., statements) that are related to a software failure. The state-of-the-art fault localization techniques, however, produce coarse-grained results that can deter manual debugging or mislead automated repair tools. In this work, we focus specifically on the fine-grained identification of code elements (i.e., tokens) that must be changed to fix a buggy program: we refer to it as fix localization. This paper introduces a neural network architecture (named Beep) that builds on AST paths to predict the buggy code element as well as the change action that must be applied to repair a program. Leveraging massive data of bugs and patches within the CoCoNut dataset, we trained a model that was (1) effective in localizing the buggy tokens with the Mean First Rank significantly higher than a statistics based baseline and a machine learning-based baseline, and (2) effective in predicting the repair operators (with the associated buggy code elements) with a Recall@1= 30-45% and the Mean First Rank=7-12 (evaluated by CoCoNut, ManySStuBs4J, and Defects4J datasets). To showcase how fine-grained fix localization can help program repair, we employ it in two repair pipelines where we use either a code completion engine to predict the correct token or a set of heuristics to search for the suitable donor code. A key strength of accurate fix localization for program repair is that it reduces the chance of patch overfitting, a challenge in generate-and-validate automated program repair: both two repair pipelines achieve a correctness ratio of 100%, i.e., all generated patches are found to be correct. Moreover, accurate fix localization helps enhance the efficiency of program repair.
Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction

Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Although methods based on edge features or node similarity have been proposed to solve the link prediction problem, many technical challenges still exist due to the unique structural properties of networks, especially when the networks are sparse. From the graph mining perspective, we first give empirical evidence of the inconsistency between heuristic and learned edge features. Then, we propose a novel link prediction framework, AdaSim, by introducing an Adaptive Similarity function using features obtained from network embedding based on random walks. The node feature representations are obtained by optimizing a graph-based objective function. Instead of generating edge features using binary operators, we perform link prediction solely leveraging the node features of the network. We define a flexible similarity function with one tunable parameter, which serves as a penalty of the original similarity measure. The optimal value is learned through supervised learning and thus is adaptive to data distribution. To evaluate the performance of our proposed algorithm, we conduct extensive experiments on eleven disparate networks of the real world. Experimental results show that AdaSim achieves better performance than state-of-the-art algorithms and is robust to different sparsities of the networks.
Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture

Automatically understanding and recognising human affective states using images and computer vision can improve human-computer and human-robot interaction. However, privacy has become an issue of great concern, as the identities of people used to train affective models can be exposed in the process. For instance, malicious individuals could exploit images from users and assume their identities. In addition, affect recognition using images can lead to discriminatory and algorithmic bias, as certain information such as race, gender, and age could be assumed based on facial features. Possible solutions to protect the privacy of users and avoid misuse of their identities are to: (1) extract anonymised facial features, namely action units (AU) from a database of images, discard the images and use AUs for processing and training, and (2) federated learning (FL) i.e. process raw images in users' local machines (local processing) and send the locally trained models to the main processing machine for aggregation (central processing). In this paper, we propose a two-level deep learning architecture for affect recognition that uses AUs in level 1 and FL in level 2 to protect users' identities. The architecture consists of recurrent neural networks to capture the temporal relationships amongst the features and predict valence and arousal affective states. In our experiments, we evaluate the performance of our privacy-preserving architecture using different variations of recurrent neural networks on RECOLA, a comprehensive multimodal affective database. Our results show state-of-the-art performance of $0.426$ for valence and $0.401$ for arousal using the Concordance Correlation Coefficient evaluation metric, demonstrating the feasibility of developing models for affect recognition that are both accurate and ensure privacy.
Folded‐loop antenna with a reflector for mobile handsets at 2.0 GHz

A folded‐loop antenna with a reflector for mobile handsets at the 2.0‐GHz band is analyzed, taking the effect of the human head into account. The reflector is arranged between the human head and the driver. The width and height of the reflector are chosen to be slightly smaller and larger than those of the driver, respectively. Based upon numerical investigations, this antenna is found to enhance radiation efficiency by 20% and reduce peak specific absorption rates (SARs) averaged over 1 g and 10 g of tissues by 70%–85%. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 40: 272–275, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.11350
Self-suppression of the Giant CARS Background for Detection of Buried Interface with Sub-monolayer Sensitivity

The past decades have witnessed marked progresses on the research of interfacial science in complex systems promoted by the advances in novel experimental techniques. Despite its success in many fields, implementation of coherent anti-Stokes Raman spectroscopy (CARS) for tackling the problems at interfaces was hindered by the huge resonant and non-resonant background from the bulk. Here we have developed a novel CARS scheme that is capable of probing a buried interface via suppression of the non-resonant and resonant bulk contribution by at least 105 times. The method utilizes self-destructive interference between the forward and backward CARS generated in the bulk near the Brewster angle. As a result, we are able to resolve the vibrational spectrum of sub-monolayer interfacial species immersed in the surrounding media with huge CARS responses. We expect our approach not only opens up the opportunity for interrogation of the interfaces that involve apolar molecules, but also benefits other nonlinear optical spectroscopic techniques in promoting signal-to-background noise ratio.
A Unified View of Relational Deep Learning for Drug Pair Scoring

In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction, and combination therapy design tasks have been proposed. Here, we present a unified theoretical view of relational machine learning models which can address these tasks. We provide fundamental definitions, compare existing model architectures and discuss performance metrics, datasets, and evaluation protocols. In addition, we emphasize possible high-impact applications and important future research directions in this domain.