Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automatic Error Tagger for German

Automatically classifying errors by language learners facilitates corpus analysis and tool development. We present a tag set and a rule-based classiﬁer for automatically assigning error tags to edits in learner texts. In our manual evaluation, the tags assigned by the classiﬁer are considered to be the best or close to best ﬁtting tag by both raters in 91% of the cases.
Dosimetric comparison of intensity-modulated radiation therapy and volumetric-modulated arc therapy plans for the treatment of glioma using flattening filter-free and flattening filter modes

In the present study, the dose verification between 6X and 6X flattening filter-free (FFF) in intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) was compared, and the advantages and disadvantages of different radiotherapy plans were evaluated. All four plans achieved comparable heterogeneity and conformity indices. For frontal tumor, VMAT demonstrated more improved sparing of the brainstem compared with the IMRT (P=0.045); while in the model of FFF, the Dmax of eye lens was significantly reduced by 16–21% (P<0.001). The organs at risk (OARs) in the temporal lobe tumor were spared well in the IMRT plan. With the removal of FF, the low-dose volume for both tumor locations was significantly reduced (P<0.05). By contrast, there was no significant difference in monitor units (MUs) with FFF, but the MUs were significantly reduced in the VMAT plan (P<0.001). Regarding the protection of OARs, FFF appeared to be superior compared with FF. For the frontal glioma, the VMAT plan had more advantages, and for temporal lobe tumor, dynamic IMRT was more appropriate. The VMAT plan reduces the low-dose volume of normal brain tissues and the MUs. While the removal of FF may increase the dose rate, the shortened treatment delivery time may improve the accuracy of treatment due to intra-fractional patient motion.
The Bank, the Bank-Run, and the Central Bank: The Impact of Early Deposit Withdrawals in a New Keynesian Framework

Currently, private trust in commercial banks declines as a consequence of today´s financial crisis. As past crises, e.g. the Asian crisis, show, the loss of confidence in the financial sector typically causes private agents to withdraw their capital from financial institutions. Thus, the purpose of this paper is to implement the feature of early deposit withdrawal in a New Keynesian framework with commercial banks in order to analyze the implications of a loss of confidence. In addition, we present the optimal monetary policy to ensure a stabilized system.
Time geography rediscovered: a common language for location-oriented services

We propose that the concepts of Time Geography be evaluated as a framework for use within location-oriented services. Originally conceived as a system to describe patterns in human migration, Time Geography is ideally suited for providing the common language and concepts necessary for dialogue within this evolving area. Location-oriented services have been the focus of a great deal of attention, but with research occurring in many disparate disciplines, the lack of a common model that can conceptualize these ideas has not received appropriate attention. To demonstrate its applicability within location-oriented services, we present a research activity which makes explicit use of concepts from Time Geography, with the hope that it can be seen as a tractable and practical solution for several difficulties facing this fast growing area of interest.
UvA-DARE ( Digital Academic Repository ) Learning to Recognize Human Activities from Soft Labeled Data

An activity recognition system is a very important component for assistant robots, but training such a system usually requires a large and correctly labeled dataset. Most of the previous works only allow training data to have a single activity label per segment, which is overly restrictive because the labels are not always certain. It is, therefore, desirable to allow multiple labels for ambiguous segments. In this paper, we introduce the method of soft labeling, which allows annotators to assign multiple, weighted, labels to data segments. This is useful in many situations, e.g. when the labels are uncertain, when part of the labels are missing, or when multiple annotators assign inconsistent labels. We treat the activity recognition task as a sequential labeling problem. Latent variables are embedded to exploit sub-level semantics for better estimation. We propose a novel method for learning model parameters from soft-labeled data in a max-margin framework. The model is evaluated on a challenging dataset (CAD-120), which is captured by a RGBD sensor mounted on the robot. To simulate the uncertainty in data annotation, we randomly change the labels for transition segments. The results show significant improvement over the state-of-the-art approach.
ADMM for consensus on colored networks

We propose a novel distributed algorithm for one of the most fundamental problems in networks: the average consensus. We view the average consensus as an optimization problem, which allows us to use recent techniques and results from the optimization area. Based on the assumption that a coloring scheme of the network is available, we derive a decentralized, asynchronous, and communication-efficient algorithm that is based on the Alternating Direction Method of Multipliers (ADMM). Our simulations with other state-of-the-art consensus algorithms show that the proposed algorithm is the one exhibiting the most stable performance across several network models.
Epoxy-Based Composites Embedded with High Performance BZT-0.5BCT Piezoelectric Nanoparticles Powders for Damping Vibration Absorber Application

Abstract: Lead-free, high piezoelectric performance, Ba(Zr0.2Ti0.8)O3-0.5(Ba0.7Ca0.3)TiO3 (BZT-0.5BCT) sub-micron powders with perovskite structure were fabricated using the sol-gel process. A 0-3 type composite was obtained by choosing epoxy resin as matrix and BZT-0.5BCT, acetylene black as functional phases. Particular attention was paid to the damping behavior of composite with different content of BZT-0.5BCT powders, the influence of frequency and loading force on the damping properties were also analyzed. A mathematical model was developed to characterize the damping properties of the composites. It found that the piezoelectric effects and interfacial friction play a key role in damping behavior of composites, and a large dissipated loss factor of tanδ was found at the BZT-0.5BCT content of 20 vol %.
Decision-Making in Complex Dynamic and Evolutive Systems: The Need for a New Paradigm

For contemporary psychology, decision-making represents behaviours, which are very different from automatic responses. They are developed by implementing integrative cognitive functions adapted to the finalities sought and the situation to treat. Through the diversity of epistemological choices for instance, research in previous decades focused on the individual choices expressed by situations or contexts with a stable structure. The new problems of life in today’s society lead to making decisions on societal problems (climate, energy, etc.), which bring into play systems and no longer variables. This chapter has four aspects. After having characterised the decision-making process as a cognitive behaviour (1), having recalled the best known traditional models (those of Economics and Psychology) (2), this chapter deals with the properties of complex systems (globality, interactivity, dynamism, and scalability), which render decision-making difficult (3), and concludes with the necessity of a change of paradigm by pointing to paths to follow (4).
Temporal monitoring of plans in Command and Control (C2): Application to Combat Search and Rescue

Planning missions is one of the most important tasks in Command and Control (C2) domain. Many efforts have been devoted to improve the planning process in order to deal with new constraints such as planning in a multi-user, multi-perspective, distributed and network-enabled environment. Unfortunately, less effort has been devoted to monitor the execution of plans in-progress. In this work, we propose a new technique to monitor temporally constrained tasks part of a plan that must be executed by different partners in a network-enabled environment.
Explaining a Productive Decade

This paper analyzes the sources of U.S. productivity growth in recent years using both aggregate and industry-level data. We confirm the central role for information technology (IT) in the productivity revival during 1995-2000 and show that IT played a significant, though smaller, role after 2000. Productivity growth after 2000 appears to have been boosted by industry restructuring and cost cutting in response to profit pressures, an unlikely source of future strength. In addition, the incorporation of intangible capital into the growth accounting framework takes some of the luster off the performance of labor productivity since 2000 and makes the gain during 1995-2000 look larger than in the official data. Finally, we examine the outlook for trend growth in labor productivity; our estimate, though subject to much uncertainty, is centered at a year, faster than the lackluster pace that prevailed before 1995 but somewhat slower than the 1995-2006 average.
IGFBP-3 Inhibits Cytokine-Induced Insulin Resistance and Early Manifestations of Atherosclerosis

Metabolic syndrome is associated with visceral obesity, insulin resistance and an increased risk of cardiovascular diseases. Visceral fat tissue primarily consists of adipocytes that secrete cytokines leading to a state of systemic inflammation in obese conditions. One of the IGF-independent functions of IGFBP-3 is its role as an anti-inflammatory molecule. Our study in obese adolescents show a decrease in total IGFBP-3 levels and increase in proteolyzed IGFBP-3 in circulation when compared to their normal counterparts and establishes a positive correlation between IGFBP-3 proteolysis and adiposity parameters as well as insulin resistance. In human adipocytes, we show that IGFBP-3 inhibits TNF-α-induced NF-κB activity in an IGF-independent manner, thereby restoring the deregulated insulin signaling and negating TNF-α-induced inhibition of glucose uptake. IGFBP-3 further inhibits TNF-α, CRP and high glucose-induced NF-κB activity in human aortic endothelial cells (HAECs) and subsequently suppresses monocyte adhesion to HAEC through the IGFBP-3 receptor. In conclusion, these findings suggest that reduced levels of IGFBP-3 in circulation and reduced expression of IGFBP-3 in macrophages in obesity may result in suppression of its anti-inflammatory functions and therefore IGFBP-3 may present itself as a therapeutic for obesity-induced insulin resistance and for events occurring in the early stages of atherosclerosis.
Fiber orientation mapping using generalized diffusion tensor imaging

Generalized diffusion tensor imaging uses tensors of arbitrary ranks to model the angular variations in the diffusivities measured by magnetic resonance imaging (MRI) methods. However, a diffusivity profile alone is not readily capable of producing distinct fiber orientations. In this work, we show how it is possible to get the displacement probability profile for water molecules from the higher rank diffusion tensors and validate the technique via simulations of one, two and three fiber systems. Finally, we present fiber orientation results for an image from an excised rat brain.
Enhancement of HCCA utilizing capture effect to support high QoS and DCF friendliness

With increase of mobile wireless LAN systems, frequency contamination by the overlapping basic service set (OBSS) becomes a critical issue. In this paper, we focus on HCF controlled channel access (HCCA) to alleviate the OBSS problem. HCCA considers single BSS (SBSS) environment only and suppresses traffic of co-existing DCF (distributed coordination function) based WiFi access points (APs). We propose two methods that utilize capture effects to improve the coexistence capability with DCF networks, that we call “DCF friendliness”. The first method adjusts transmission timing in HCCA WLAN by applying inter-AP coordination. The second method enables simultaneous communication with surrounding DCF WLANs by changing the frame interval of HCCA to “DIFS+1 SlotTime. ” Simulations show that both the proposed method can achieve higher throughput and better DCF friendliness.
Competency Models at Work: The Value of Perceived Relevance and Fair Rewards for Employee Outcomes

The purpose of this study was to generate empirical knowledge on how employees perceive and respond to competency models at work. A cross-sectional survey of 278 employees in four Norwegian organizations showed that competency models perceived as strategically and personally relevant and fairly rewarded were positively related to supervisor-rated work effort and organizational citizenship behaviors (OCB) and self-rated ‘employability’ outcomes. Exchange relationships mediated the link between competency model perceptions and work effort and OCB. Social exchange relationships marginally mediated the link between competency model perceptions and employees’ openness to job-related changes and engagement in activities aimed at improving one’s employability.
Comparing Various Classification Algorithms by WEKA

In knowledge discovery process classification is an important technique of data mining and widely used in various fields. The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. In this paper we present the comparison of different classification techniques using Waikato Environment for Knowledge Analysis or in short, WEKA. WEKA is open source software which consists of a collection of machine learning algorithms for data mining tasks. The aim of this paper is to investigate the performance of different classification methods for a set of large data. The algorithm or methods tested are K-NN, C4.5, SVM and Bayes Network algorithm. Experimental results demonstrate that in the datasets with few numbers of records, comparing classifiers with regard the AUC may be in correct while the number of the records and the number of the attributes in each record are increased, the results become more stable. Resultants also show that the most accurate algorithm based on the generated data sets is Bayes network classifier with an accuracy of 89.71%.
Location of drug binding sites on human serum albumin.

The location of Site I on the human serum albumin (HSA) molecule was investigated.Naphthol yellow- S (NY-S) was used as a representative ligand for Site I, since the bilirubin-displacing effect of NY-S was similar to that of Site I drugs such as warfarin and phenylbutazone.NY-S was bound to HSA with at least three classes of binding sites. The binding parameters of the primary class were one binding site (n1=1) and a binding constant (K1) of >107M-1, and the complex at equimolar ratio of dye to albumin exhibited metachromasy. The intrinsic fluorescence intensity, attributed to the single tryptophan residue (position 214), decreased significantly in this complex. Fragments A and C (residues 299-585 and 124-298, respectively, in the amino acid sequence of HSA) obtained by cyanogen bromide cleavage of HSA showed NY-S-binding ability.The binding of NY-S to fragment A or C aused the same metachromasy as seen with NY-S-HSA complex. Fragment C is markedly hydrophobic and contains the single tryptophan residue. The binding of NY-S to HSA was diminished by the chemical modification of lysine residues with pyridoxal 5'-phosphate (PLP). Further, lysine residues modified by PLP existed in fragment C.These results suggest that the primary binding site of NY-S, which corresponds to Site I, is located in the fragment C region of the HSA molecule.
A computational auditory scene analysis system for robust speech recognition

We present a computational auditory scene analysis system for separating and recognizing target speech in the presence of competing speech or noise. We estimate, in two stages, the ideal binary time-frequency (T-F) mask which retains the mixture in a local TF unit if and only if the target is stronger than the interference within the unit. In the first stage, we use harmonicity to segregate the voiced portions of individual sources in each time frame based on multipitch tracking. Additionally, unvoiced portions are segmented based on an onset/offset analysis. In the second stage, speaker characteristics are used to group the T-F units across time frames. The resulting T-F masks are used in conjunction with missing-data methods for recognition. Systematic evaluations on a speech separation challenge task show significant improvement over the baseline performance.
Safe Haven Assets and Investor Behaviour Under Uncertainty

We study two different safe haven assets, US government bonds and gold, and examine how the price changes of these assets can be used to infer investor behaviour under uncertainty. We find that investors are ambiguity-averse, that is they buy gold when faced with extreme uncertainty about the state of the economy or thefinancial system and when they receive ambiguous signals. In contrast, investors buyUS government bonds when faced with extreme but unambiguous signals. We also show that there is overreaction to ambiguous signals.
Testing Social Cognitive Career Theory in Colombian adolescent secondary students: a study in the field of mathematics and science

This study aims to validate the model for vocational interests and choices defined by Social Cognitive Career Theory (SCCT) in the context of Colombian secondary education, with a view to extending previous research in various ways. Firstly, in terms of cross-cultural validation, it examines the whole core of the model of vocational interests and choices in the science/mathematics area for the first time with Latin-American secondary school students. Secondly, it tests the SCCT model for the first time in an ethnically diverse population outside the United States, including minority groups. Finally, it examines the role in this new context of the perceived social support in career development processes. 2,787 Colombian secondary school students took part in the study. The subjects were evaluated for self-efficacy, outcome expectations, interests, occupational aspirations and perceived social support for entry to careers in the science/mathematics area. Structural equation modeling (including multigroup analysis for gender and ethnic group) was used to test the fit of the hypothesized model to the data. In the full sample, results indicated global support for SCCT as a way to predict Colombian students’ interests and occupational aspirations in the field of mathematics and science (CFI=.96 and RMSEA=.046). All the hypotheses formulated were verified with the exception of those related to the direct effects of self-efficacy and outcome expectations in connection with occupational aspirations. Collectively, the predictors accounted for 52% of the variance in interests and for 40% of the variance in occupational aspirations. In addition, results also suggest that the model is invariant across gender and ethnic groups (White Colombian, Mestizo Colombian and Afro-Colombian). The implications for both future research on SCCT and intervention are discussed.
Enabling IoT automation using local clouds

Various forms of cloud computing principles and technologies are becoming important recently. This paper addresses cloud computing for automation and control applications. It's argued that the open Internet cloud idea has such limitations that its not appropriate for automation. Since automation is physically and geographically local, it is inevitable to introduce the concept of local automation clouds. It's here proposed that local automation clouds should be self contained an be able to execute the intended automation functionalities without any external resources. Thus providing a fence at the rim of the local cloud preventing any inbound or outbound communication. Such a local cloud provides possibilities to address key requirements of both todays and future automation solutions. Adding mechanisms for secure inter-cloud administration and data tranfere enables local automation cloud to meet IoT automation system requirements as: 1) Interoperability of a wide range of IoT and legacy devices 2) Automation requirement on latency guarantee/prediction for communication and control computations. 3) Scalability of automation systems enabling very large integrated automation systems 4) Security and related safety of automation systems 5) Ease of application engineering 6) Multi stakeholder integration and operations agility. How these requirements can be met in such a local automation cloud is discussed with references to proposed solutions. The local automation cloud concept is further verified for a compartment climate control application. The control application included an IoT controller, four IoT sensors and actuators, and a physical layer communication gateway. The gateway acted as host for local cloud core functionalities. The climate control application has successfully been implemented using the open source Arrowhead Framework and its supports for design and implementation of self contained local automation clouds.
A Comparison of Structural and Dynamic Properties of Different Simulation Methods Applied to SH 3

The dynamic and static properties of molecular dynamics simulations using various methods for treating solvent were compared. The SH3 protein domain was chosen as a test case because of its small size and high surface-to-volume ratio. The simulations were analyzed in structural terms by examining crystal packing, distribution of polar residues, and conservation of secondary structure. In addition, the “essential dynamics" method was applied to compare each of the molecular dynamics trajectories with a full solvent simulation. This method proved to be a powerful tool for the comparison of large concerted atomic motions in SH3. It identified methods of simulation that yielded significantly different dynamic properties compared to the full solvent simulation. Simulating SH3 using the stochastic dynamics algorithm with a vacuum (reduced charge) force field produced properties close to those of the full solvent simulation. The application of a recently described solvation term did not improve the dynamic properties. The large concerted atomic motions in the full solvent simulation as revealed by the essential dynamics method were analyzed for possible biological implications. Two loops, which have been shown to be involved in ligand binding, were seen to move in concert to open and close the ligand-binding site.