Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Flexible Architecture for Industrial Networking

The digitalization of manufacturing processes is expected to lead to a growing interconnection of production sites, as well as machines, tools and work pieces. In the course of this development, new use-cases arise which have challenging requirements from a communication technology point of view. In this paper we propose a communication network architecture for Industry 4.0 applications, which combines new 5G and non-cellular wireless network technologies with existing (wired) fieldbus technologies on the shop floor. This architecture includes the possibility to use private and public mobile networks together with local networking technologies to achieve a flexible setup that addresses many different industrial use cases. It is embedded into the Industrial Internet Reference Architecture and the RAMI4.0 reference architecture. The paper shows how the advancements introduced around the new 5G mobile technology can fulfill a wide range of industry requirements and thus enable new Industry 4.0 applications. Since 5G standardization is still ongoing, the proposed architecture is in a first step mainly focusing on new advanced features in the core network, but will be developed further later.
Substrate rigidity regulates fibronectin matrix assembly in fibroblasts

Cells sense and respond to the mechanical properties of their microenvironment. We investigated whether these properties affect the ability of cells to assemble a fibrillar fibronectin (FN) matrix. Analysis of matrix assembled by cells grown on FN-coated polyacrylamide gels of varying stiffnesses showed that rigid substrates stimulate FN matrix assembly, whereas assembly is inhibited on softer substrates. Stimulating integrins with Mn2+ treatment increased FN assembly on softer gels, suggesting that integrin binding is deficient on soft substrates. Guanidine hydrochloride-induced extension of the substrate-bound FN rescued assembly on soft substrates to a similar degree as Mn2+ treatment and also increased the initiation of assembly at FN matrix assembly sites. In contrast, increasing actin-mediated cell contractility did not rescue FN matrix assembly on soft substrates. Thus, rigidity-dependent FN matrix assembly is determined by extracellular events, namely, the engagement of FN by cells and the induction of FN conformational changes. Extensibility of FN in response to substrate stiffness may serve as a mechanosensing mechanism whereby cells use pericellular FN to probe the stiffness of their environment.
Forest Service-- National AgroforestryCenter 1-1-2009 Determinants of Trust for Public Lands : Fire and Fuels Management on the Bitterroot National Forest

Management of public lands occurs today with high levels of scrutiny and controversy. To succeed, managers seek the support, involvement, and endorsement of the public. This study examines trust as an indicator of managerial success and attempts to identify and measure the components that most influence it. A review of trust literature yielded 14 attributes that were hypothesized to contribute to trust, grouped into the three dimensions of Shared Norms and Values, Willingness to Endorse, and Perceived Efficacy. Operationalizing these attributes and dimensions, a telephone survey was administered to a sample of Montana, USA, residents living adjacent to the Bitterroot National Forest (n = 1,152). Each of the attributes was measured in the context of federal lands fire and fuel management. Structural equation modeling showed that all 14 attributes were found to be influential contributors to levels of trust. Results suggest that if managers are to maintain or increase levels of public trust, they need to consider each of trust’s attributes as they make social, ecological, and economic resource decisions.
Technical Report: Adding Missing Words to Regular Expressions

Regular expressions (regexes) are patterns that are used in many applications to extract words or tokens from text. However, even hand-crafted regexes may fail to match all the intended words. In this paper, we propose a novel way to generalize a given regex so that it matches also a set of missing (previously non-matched) words. Our method finds an approximate match between the missing words and the regex, and adds disjunctions for the unmatched parts appropriately. We show that this method can not just improve the precision and recall of the regex, but also that it generates much shorter regexes than baselines and competitors on various datasets. This report complements our paper at the PAKDD 2018 conference. [18] Rapport technique: Ajout de mots manquants aux expressions regulieres Resume Les expressions regulieres (regex) sont des modeles utilises dans de nombreuses applications pour extraire des mots ou des parties du texte. Cependant, meme les regex faites a la main ne correspondent pas toujours a l'ensemble des mots prevus. Dans cet article, nous proposons une nouvelle facon de generaliser une expression reguliere donnee afin qu'elle corresponde egalement a un ensemble de mots manquants (precedemment non reconnus). Notre methode trouve une correspondance approximative entre les mots manquants et l'expression reguliere, et ajoute des disjonctions pour les parties non recon-nues de facon appropriee. Nous montrons que cette methode ameliore la precision et le rappel de la regex, et aussi qu'elle genere des expressions re-gulieres beaucoup plus courtes que l'approche naive et que les algorithmes concurrents sur differents jeux de donnees. Ce rapport complete notre article soumis a la conference PAKDD 2018. [18]
The Impact of Overweight/Obesity Duration and Physical Activity on Medical Multimorbidity: Examining the WATCH Paradigm

Purpose: The “fat-but-fit” paradigm has been evaluated. However, the duration of overweight/obesity within the “fat-but-fit” paradigm (ie, assessing body mass at more than 1 time point) has not been extensively evaluated, which was this study’s purpose. Design: Cross-sectional. Setting: National Health and Nutrition Examination Survey 2003 to 2006. Participants: Ages 36 to 85; N = 3621. Measures: Physical activity assessed via accelerometry. Medical multimorbidity was assessed via physician diagnosis of 13 chronic diseases. Height and body mass were directly measured for current body mass index (BMI), and 10-year prior BMI was calculated using current height and self-reported weight 10 years prior. Six mutually exclusive groups were created: (1) active, normal weight now and 10 years ago; (2) inactive, normal weight now and 10 years ago; (3) active, overweight/obese now but not 10 years ago; (4) active, overweight/obese now and 10 years ago; (5) inactive, overweight/obese now but not 10 years ago; and (6) inactive, overweight/obese now and 10 years ago. Analysis: Logistic regression. Results: Compared to group 1, adjusted odds ratios (ORs) were as follows: group 2: OR = 2.0 (P = .004), group 3: OR = 1.7 (P = .004), group 4: OR = 2.5 (P < .001), group 5: OR = 2.9 (P < .001), and group 6: OR = 4.8 (P < .001). Conclusions: All patterns of weight change/duration and activity level altered the odds of medical multimorbidity, suggesting that the duration of overweight/obesity should also be taken into consideration when assessing the “fat-but-fit” paradigm.
Orientation of native versus translocated juvenile lesser spotted eagles (Clanga pomarina) on the first autumn migration

ABSTRACT The ontogeny of migration routines used by wild birds remains unresolved. Here we investigated the migratory orientation of juvenile lesser spotted eagles (LSE; Clanga pomarina) based on translocation and satellite tracking. Between 2004 and 2016, 85 second-hatched juveniles (Abels) were reared in captivity for release into the declining German population, including 50 birds that were translocated 940 km from Latvia. In 2009, we tracked 12 translocated juveniles, as well as eight native juveniles and nine native adults, to determine how inexperienced birds come to use strategic migration routes. Native juveniles departed around the same time as the adults and six of eight used the eastern flyway around the Mediterranean, which was used by all adults. In contrast, translocated juveniles departed on average 6 days before native LSEs, and five travelled southward and died in the central Mediterranean region. Consequently, fewer translocated juveniles (4/12) than native juveniles (7/8) reached Africa. We conclude that juvenile LSEs have a much better chance of learning the strategic southeastern flyway if they leave at an appropriate time to connect with experienced elders upon departure. It is not clear why translocated juveniles departed so early. Regardless, by the end of the year, most juveniles had perished, whether they were translocated (10/12) or not (6/8). The small number of surviving translocated juveniles thus still represents a significant increase in the annual productivity of the German LSE population in 2009. Highlighted Article: In 2009, most juvenile eagles translocated from Latvia to Germany departed earlier than native juveniles and adults, and consequently failed to learn the species' traditional eastern detour around the Mediterranean.
General properties of nonsignaling theories

This article identifies a series of properties common to all theories that do not allow for superluminal signaling and predict the violation of Bell inequalities. Intrinsic randomness, uncertainty due to the incompatibility of two observables, monogamy of correlations, impossibility of perfect cloning, privacy of correlations, bounds in the shareability of some states; all these phenomena are solely a consequence of the no-signaling principle and nonlocality. In particular, it is shown that for any distribution, the properties of i nonlocal, ii no arbitrarily shareable, and iii positive secrecy content are equivalent.
Strategic foresight, leadership, and the future of rural healthcare staffing in the United States

ABSTRACT This article uses a strategic foresight tool, megatrends, to examine forces influencing long-term healthcare staffing in the rural United States. Two megatrends—exponential advances in science and technology and the continued evolution of the decentralized global marketplace—will influence and ultimately help shape the future of rural healthcare. Successful health ecosystems of the future will need to be customer-driven, more affordable, and tech-savvy. Successful evolution in an era of continuous change will require a blend of intentional engagement with stakeholders, strategic foresight, and future-focused leadership. More research is needed to fully understand not only the challenges of rural healthcare but also the emerging opportunities.
Microstructural Investigation of Routes for Gamma Titanium Aluminides Production by Powder Metallurgy

The alloy design and efficient routes of TiAl processing are important technological challenges for the development of new aerospace systems. Gamma-TiAl alloys are potential replacements for nickel and conventional titanium alloys in hot sections of turbine engines, as well as in sub-structures of orbital platform vehicles. Powder metallurgy (P/M) of Ti-based alloys may lead to the obtainment of components having weak-to-absent textures, uniform grain structure and higher homogeneity compared with conventional wrought products. This paper aims to investigate the microstructural evolution and densification aspects involved in the obtainment of Ti-48Al-2Cr-2Nb (at.%) alloy by three P/M-processing routes. Samples were prepared from elemental and pre-alloyed powders mixed for 2 h, followed by cold uniaxial and isostatic pressing followed by sintering and hot pressing stages between 1100°C up to 1400°C, for 1 h. After metallographic preparation, sintered samples were characterized by means of scanning electron microscopy (SEM) in the backscattered mode (BSE), X-ray diffraction (XRD), and density measurements. The results showed the potential of TiAl pre-alloyed powders to prevent Kirkendall porosity. A full lamellar microstructure was obtained by the pressureless route while a duplex microstructure was observed in samples produced by the hot pressing route.
Skill Assessment using Computer Vision based Analysis

In this paper, we propose a computer vision based method to assess the technical skill level of surgeons by analyzing the movement of robotic instruments in robotic surgical videos. First, our method leverages the power of crowd workers on the internet to obtain high quality data in a scalable and cost-efficient way. Second, we utilize the high quality data to train an accurate and efficient robotic instrument tracker based on the state-of-theart Hourglass Networks. Third, we assess the movement of the robotic instruments and automatically classify the technical level of a surgeon with a linear classifier, using peer evaluations of skill as the reference standard. Since the proposed method relies only on video data, this method has the potential to be transferred to other minimally invasive surgical procedures.
Hybrid-IoT: Hybrid Blockchain Architecture for Internet of Things - PoW Sub-Blockchains

From its early days the Internet of Things (IoT) has evolved into a decentralized system of cooperating smart objects with the requirement, among others, of achieving distributed consensus. Yet, current IoT platform solutions are centralized cloud based computing infrastructures, manifesting a number of significant disadvantages, such as, among others, high cloud server maintenance costs, weakness for supporting time-critical IoT applications, security and trust issues. Enabling blockchain technology into IoT can help to achieve a proper distributed consensus based IoT system that overcomes those disadvantages. While this is an ideal match, it is still a challenging endeavor. In this paper we take a first step towards that goal by designing Hybrid-IoT, a hybrid blockchain architecture for IoT. In Hybrid-IoT, subgroups of IoT devices form PoW blockchains, referred to as PoW sub-blockchains. Then, the connection among the PoW sub-blockchains employs a BFT inter-connector framework, such as Polkadot or Cosmos. In this paper, we focus on the PoW sub-blockchains formation, guided by a set of guidelines based on a set of dimensions, metrics and bounds. In order to prove the validity of the approach we carry on a performance and security evaluation.
SEARCHING FOR PULSARS USING IMAGE PATTERN RECOGNITION

In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ∼9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The performance of this system can be improved over time as more training data are accumulated. This AI system has been integrated into the PALFA survey pipeline and has discovered six new pulsars to date.
The Effect of Carbon Black Surface Properties and Structure on Rheometer Cure Behavior

Abstract The cure behavior of SBR and NR compounds was examined using the Monsanto Rheometer. The three cure parameters—induction period (td), rate of crosslinking reaction (k2) and maximum increase in torque (ΔL)—were derived from the Rheometer torque curves. The data so obtained were used to calculate Westlinning's αF-values. It was shown that (1) k2 decreases with increasing total acid concentration at the carbon black surface and (2) the value of αF is related to the filler structure and the crosslink density of the rubber matrix. It was also demonstrated that the crosslink density, as measured by ΔL, is influenced by the surface chemistry of carbon black. In SBR stocks the crosslink density increases with increasing total acid concentration, while in NR compounds the reverse is true. These findings were confirmed by examining the tensile properties of regular and oxidized HAF-LS black in SBR and NR compositions. Thus the effect of carbon black surface chemistry on crosslink density can be estimated, .
DCN: Detector-Corrector Network Against Evasion Attacks on Deep Neural Networks

Deep neural networks are extensively used in image recognition. However, its integrity is compromised by evasion attacks. Attackers can easily craft adversarial examples that make DNNs unknowingly output the labels they want rather than the right labels. In a recent study, it was shown that existing detection methods are not effective in identifying these adversarial examples, i.e., it is a realistic threat to existing systems. Unlike the previous detection methods, we observe that the classification probability distributions of adversarial examples and those of untampered examples exhibit a big difference, which can be easily identified based on the output of a DNN without getting into the complicated DNN internal structure. Based on this new insight, we propose a new light-weight detection method by transforming the detection of adversarial examples into a binary classification problem. The detector we train achieves almost 100% accuracy on adversarial examples. Moreover, we propose a detector-corrector network that effectively reduces successful rate of existing state-of-the-art evasion attacks under three commonly used distance metrics. In particular, for the common L2 attack, DCN mitigates 99% adversarial examples on MNIST and 95% on CIFAR-10. Our evaluation demonstrates that DCN is significantly more effective and efficient against various evasion attacks than existing methods.
Language-integrated provenance in Haskell

Scientific progress increasingly depends on data management, particularly to clean and curate data so that it can be systematically analyzed and reused. A wealth of techniques for managing and curating data (and its provenance) have been proposed, largely in the database community. In particular, a number of influential papers have proposed collecting provenance information explaining where a piece of data was copied from, or what other records were used to derive it. Most of these techniques, however, exist only as research prototypes and are not available in mainstream database systems. This means scientists must either implement such techniques themselves or (all too often) go without. This is essentially a code reuse problem: provenance techniques currently cannot be implemented reusably, only as ad hoc, usually unmaintained extensions to standard databases. An alternative, relatively unexplored approach is to support such techniques at a higher abstraction level, using metaprogramming or reflection techniques. Can advanced programming techniques make it easier to transfer provenance research results into practice? We build on a recent approach called language-integrated provenance, which extends language-integrated query techniques with source-to-source query translations that record provenance. In previous work, a proof of concept was developed in a research programming language called Links, which supports sophisticated Web and database programming. In this paper, we show how to adapt this approach to work in Haskell building on top of the Database-Supported Haskell (DSH) library. Even though it seemed clear in principle that Haskell's rich programming features ought to be sufficient, implementing language-integrated provenance in Haskell required overcoming a number of technical challenges due to interactions between these capabilities. Our implementation serves as a proof of concept showing how this combination of metaprogramming features can, for the first time, make data provenance facilities available to programmers as a library in a widely-used, general-purpose language. In our work we were successful in implementing forms of provenance known as where-provenance and lineage. We have tested our implementation using a simple database and query set and established that the resulting queries are executed correctly on the database. Our implementation is publicly available on GitHub. Our work makes provenance tracking available to users of DSH at little cost. Although Haskell is not widely used for scientific database development, our work suggests which languages features are necessary to support provenance as library. We also highlight how combining Haskell's advanced type programming features can lead to unexpected complications, which may motivate further research into type system expressiveness.
Study of deformability behaviour and failure mechanism by simulating rock joints sample under different loading conditions

Through the RMT-150C testing system,artificial concrete joint samples are employed to study the shearing stiffness and failure mode for rock joint under different shearing deformation velocities,undulation angles and normal stresses. It is suggested that the failure modes of joint samples are dominated by the undulation angles. Under small undulation angle,the joint samples are in abrasion failure mode,while under high undulation angles,the joint samples are in snip failure mode. The failure modes of joint sample are also influenced by the normal stress and shearing deformation velocity. It is also reported that the shearing stiffnesses of joint samples increase with the increasing shearing deformation velocities and undulation angles; and the rising rates tend to decrease with the increment of shearing deformation velocities. As well,the shearing stiffnesses of joint samples linearly increase with the increasing normal stresses. The shear stiffness formula is further presented to consider the effect of shearing deformation velocity and undulation angle. Based on the experimental results,the regression results for the formula are also conducted.