Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative identification of technological discontinuities using simulation modeling

The aim of this paper is to develop and test metrics to quantitatively identify technological discontinuities in a knowledge network. We developed five metrics based on innovation theories and tested the metrics by a simulation model-based knowledge network and hypothetically designed discontinuity. The designed discontinuity is modeled as a node which combines two different knowledge streams and whose knowledge is dominantly persistent in the knowledge network. The performances of the proposed metrics were evaluated by how well the metrics can distinguish the designed discontinuity from other nodes on the knowledge network. The simulation results show that the persistence times # of converging main paths provides the best performance in identifying the designed discontinuity: the designed discontinuity was identified as one of the top 3 patents with 96~99% probability by Metric 5 and it is, according to the size of a domain, 12~34% better than the performance of the second best metric. Beyond the simulation analysis, we tested the metrics using a patent set representative of the Magnetic information storage domain. The three representative patents associated with a well-known breakthrough technology in the domain, the giant magneto-resistance (GMR) spin valve sensor, were selected based on the qualitative studies, and the metrics were tested by how well the metrics identify the selected patents as top-ranked patents. The empirical results fully support the simulation results and therefore the persistence times # of converging main paths is recommended for identifying technological discontinuities for any technology.
Evaluating the impact of automated intrusion response mechanisms

Intrusion detection systems (IDSs) have reached a high level of sophistication and are able to detect intrusions with a variety of methods. Unfortunately, system administrators neither can keep up with the pace that an IDS is delivering alerts, nor can they react upon these within adequate time limits. Automatic response systems have to take over that task. In case of an identified intrusion, these components have to initiate appropriate actions to counter emerging threats. Most current intrusion response systems (IRSs) utilize static mappings to determine adequate response actions in reaction to detected intrusions. The problem with this approach is its inherent inflexibility. Countermeasures (such as changes of firewall rules) often do not only defend against the detected attack but may also have negative effects on legitimate users of the network and its services. To prevent a situation where a response action causes more damage that the actual attack, a mechanism is needed that compares the severity of an attack to the effects of a possible response mechanism. In this paper, we present a network model and an algorithm to evaluate the impact of response actions on the entities of a network. This allows the IRS to select the response among several alternatives which fulfills the security requirements and has a minimal negative effect on legitimate users.
A Simple HPLC-fluorescence Method for Quantitation of Curcuminoids and Its Application to Turmeric Products

An HPLC method using fluorescence detection for the quantitation of curcuminoids, such as curcumin (C), demethoxycurcumin (DMC), and bisdemethoxycurcumin (BDMC) in turmeric products is described. This method involves a simple ultrasonic extraction with methanol as a pretreatment of turmeric products. The separation of curcuminoids and 2,5-xylenol (internal standard) was achieved within 30 min on a Cadenza CD-C_18 column (250 × 4.6 mm; i.d., 3 µm) with a mixture of acetate buffer and CH_3CN. The calibration curves of standard curcuminoids showed good linearities of more than 0.993 of the correlation coefficient. The instrumental detection limits for C, DMC and BDMC (signal-to-noise ratio = 3) were 1.5, 0.9 and 0.09 ng mL^–1, respectively. The relative standard deviations of intra-and inter-day assays by curcuminoids spiked to turmeric powder were less than 6.1%. The proposed method was successfully applied to determine curcuminoids in commercial turmeric products, such as turmeric powders, a tablet, a dressing, a beverage, tea, and crude drugs.
Estimating Primary Demand for Substitutable Products from Sales Transaction Data

We propose a method for estimating substitute and lost demand when only sales and product availability data are observable, not all products are displayed in all periods (e.g., due to stockouts or availability controls), and the seller knows its aggregate market share. The model combines a multinomial logit (MNL) choice model with a nonhomogeneous Poisson model of arrivals over multiple periods. Our key idea is to view the problem in terms of primary (or first-choice) demand; that is, the demand that would have been observed if all products had been available in all periods. We then apply the expectation-maximization (EM) method to this model, and we treat the observed demand as an incomplete observation of primary demand. This leads to an efficient, iterative procedure for estimating the parameters of the model. All limit points of the procedure are provably stationary points of the incomplete data log-likelihood function. Every iteration of the algorithm consists of simple, closed-form calculations. We illustrate the effectiveness of the procedure on simulated data and two industry data sets.
Social brains on drugs: tools for neuromodulation in social neuroscience

Neuromodulators such as serotonin, oxytocin and testosterone play an important role in social behavior. Studies examining the effects of these neuromodulators and others on social cognition and behavior (and their neural underpinnings) are becoming increasingly common. Here, we provide an overview of methodological considerations for those wishing to evaluate or conduct empirical studies of neuromodulation in social neuroscience.
Semi-Analytical Modeling of Distributed Phase-Shifts Applied on Chirped Fiber Bragg Gratings

Tunable notch filters with multiple transmission lines have been proposed and demonstrated by temporarily inducing phase-shifts in chirped fiber Bragg gratings through the application of stress or thermal perturbations. These filters find application in many fields including microwave photonics or optical communications. So far, study of this type of filter has been mostly experimental with occasional numerical modeling based on coupled-mode theory. This paper presents a theoretical analysis using the WKB approximation that highlightens the presence of resonators and quantitatively describes both the phase-shift accumulated in the propagation region and the reflection strength of the surrounding grating regions. The model provides an approximated function of the transmission peak amplitude that gives good insight into its dependence on the grating parameters and perturbation profile. This analysis predicts the filter bandwidth, tuning capability and minimum spectral separation between two transmission lines leading to straightforward design and optimization of the filter parameters.
Channeling of ammonia in glucosamine-6-phosphate synthase.

Glucosamine-6-phosphate synthase catalyses the first and rate-limiting step in hexosamine metabolism, converting fructose 6-phosphate into glucosamine 6-phosphate in the presence of glutamine. The crystal structure of the Escherichia coli enzyme reveals the domain organisation of the homodimeric molecule. The 18 A hydrophobic channel sequestered from the solvent connects the glutaminase and isomerase active sites, and provides a means of ammonia transfer from glutamine to sugar phosphate. The C-terminal decapeptide sandwiched between the two domains plays a central role in the transfer. Based on the structure, a mechanism of enzyme action and self-regulation is proposed. It involves large domain movements triggered by substrate binding that lead to the formation of the channel.
Use of a Real-Time Remote Monitoring Network (RTRM) to Characterize the Guadalquivir Estuary (Spain)

The temporal variability of hydrological variables in the Guadalquivir estuary was examined during three years through a real-time remote monitoring network (RTRM). The network was developed with the aim of studying the influence of hydrodynamical and hydrological features within the estuary on the functioning of the pelagic ecosystem. Completing this data-gathering network, monthly cruises were performed in order to measure biogeochemical variables that are indicative of the trophic status of the aquatic environment. The results showed that several sources of physical forcing, such as wind, tide-associated currents and river discharge were responsible for the spatio-temporal patterns of dissolved oxygen, salinity and turbidity in the estuary. The analysis was conducted under tidal and flood regime, which allowed us to identify river discharge as the main forcing agent of the hydrology inside the estuary. In particular, episodes of elevated turbidity detected by the network, together with episodes of low salinity and dissolved oxygen were closely related to the increase in water supply from a dam located upstream. The network installed provided accurate data that can be rapidly used for research or educational applications and by policy-makers or agencies in charge of the management of the coastal area.
Real Time Dynamic Trajectory Smoothing for Uninhabited Aerial Vehicles

E. P. Anderson is a graduate research assistant in the Department of Electrical Engineering at Stanford University, Stanford, CA 94305. R. W. Beard is an associate professor in the Electrical and Computer Engineering Department at Brigham Young University, Provo, Utah 84602. T. W. McLain is an associate professor in the Mechanical Engineering Department at Brigham Young University, Provo, Utah 84602. Email: eandersn@stanford.edu, beard@ee.byu. edu, tmclain@et.byu. edu. R.W. Beard is the corresponding author.
Obesity Is Associated with Inflammation and Elevated Aromatase Expression in the Mouse Mammary Gland

Elevated circulating estrogen levels are associated with increased risk of breast cancer in obese postmenopausal women. Following menopause, the biosynthesis of estrogens through CYP19 (aromatase)-mediated metabolism of androgen precursors occurs primarily in adipose tissue, and the resulting estrogens are then secreted into the systemic circulation. The potential links between obesity, inflammation, and aromatase expression are unknown. In both dietary and genetic models of obesity, we observed necrotic adipocytes surrounded by macrophages forming crown-like structures (CLS) in the mammary glands and visceral fat. The presence of CLS was associated with activation of NF-κB and increased levels of proinflammatory mediators (TNF-α, IL-1β, Cox-2), which were paralleled by elevated levels of aromatase expression and activity in the mammary gland and visceral fat of obese mice. Analyses of the stromal-vascular and adipocyte fractions of the mammary gland suggested that macrophage-derived proinflammatory mediators induced aromatase and estrogen-dependent gene expression (PR, pS2) in adipocytes. Saturated fatty acids, which have been linked to obesity-related inflammation, stimulated NF-κB activity in macrophages leading to increased levels of TNF-α, IL-1β, and Cox-2, each of which contributed to the induction of aromatase in preadipocytes. The discovery of the obesity → inflammation → aromatase axis in the mammary gland and visceral fat and its association with CLS may provide insight into mechanisms underlying the increased risk of hormone receptor-positive breast cancer in obese postmenopausal women, the reduced efficacy of aromatase inhibitors in the treatment of breast cancer in these women, and their generally worse outcomes. The presence of CLS may be a biomarker of increased breast cancer risk or poor prognosis. Cancer Prev Res; 4(3); 329–46. ©2011 AACR.
Power-efficient spectrum sharing for noncooperative underwater acoustic communication systems

This paper aims at studying underwater acoustic OFDM communication systems interfering with each others in the same channel. We propose a decentralized spectrum sharing method that minimizes the total power consumed while satisfying a constraint related to their information rate. The considered systems are supposed noncooperative, i.e. unable to communicate with each others so that they cannot agree on a fair resource sharing scheme. The problem is formulated within the framework of game theory and solved according to the Nash Equilibrium concept. Several results are presented and show that interfering UA systems can share the spectrum in a more efficient way, both in terms of energy consumption and information rate.
BDNF pro-peptide actions facilitate hippocampal LTD and are altered by the common BDNF polymorphism Val66Met

Significance Brain-derived neurotrophic factor (BDNF) is a neurotrophin that elicits biological effects on synaptic plasticity. BDNF is initially synthesized as precursor proBDNF, and then the BDNF pro-peptide is simultaneously produced from the precursor protein. However, the physiological functions of the pro-peptide are largely unknown. Here, we demonstrate that the BDNF pro-peptide is a facilitator of hippocampal long-term depression (LTD), requiring the activation of GluN2B-containing NMDA-type receptors and the pan-neurotrophin receptor p75NTR. Second, a common BDNF polymorphism substitutes valine for methionine at amino acid position 66 (Val66Met) in the pro-peptide of BDNF and impairs memory function. Unexpectedly, the pro-peptide with Met mutation completely inhibits hippocampal LTD. These findings provide insights into the physiological role of the BDNF pro-peptide in the brain. Most growth factors are initially synthesized as precursor proteins and subsequently processed into their mature form by proteolytic cleavage, resulting in simultaneous removal of a pro-peptide. However, compared with that of mature form, the biological role of the pro-peptide is poorly understood. Here, we investigated the biological role of the pro-peptide of brain-derived neurotrophic factor (BDNF) and first showed that the pro-peptide is expressed and secreted in hippocampal tissues and cultures, respectively. Interestingly, we found that the BDNF pro-peptide directly facilitates hippocampal long-term depression (LTD), requiring the activation of GluN2B-containing NMDA receptors and the pan-neurotrophin receptor p75NTR. The BDNF pro-peptide also enhances NMDA-induced α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor endocytosis, a mechanism crucial for LTD expression. Thus, the BDNF pro-peptide is involved in synaptic plasticity that regulates a mechanism responsible for promoting LTD. The well-known BDNF polymorphism valine for methionine at amino acid position 66 (Val66Met) affects human memory function. Here, the BDNF pro-peptide with Met mutation completely inhibits hippocampal LTD. These findings demonstrate functional roles for the BDNF pro-peptide and a naturally occurring human BDNF polymorphism in hippocampal synaptic depression.
Bootstrapping simultaneous confidence bands

Once a regression has been fitted to data, it is usually necessary to add confidence intervals to indicate the accuracy of the fitted regression line. This can easily be done for individual explanatory variable values. However sometimes confidence limits are needed simultaneously for the whole range of explanatory variable values of interest. In other words the problem is to construct a confidence band within which the entire unknown true regression line lies with given confidence. This article discusses computer intensive methods for doing this. The advantage of such methods compared with classical asymptotic methods is that accurate coverages can be obtained quite easily using bootstrap resampling.
Cognitive modeling and verbal semantics: a representational framework based on UML

This book presents a unique approach towards the study of verbal meaning. It develops a frame-work for capturing the semantics of verbs that is based on the Unified Modeling Language (UML), the lingua franca for the design and modeling of object-oriented systems in computer science. The new graphical framework combines formal precision with conceptual flexibility and allows the representation of very complicated details of verbal meaning, thereby offering a solution for different semantic problems such as polysemy and context-dependency. Besides the framework's development, the book contains a cognitive interpretation of important modeling elements, a discussion of general issues in connection with and well-elaborated applications of the framework. Since the framework is graphical in nature, the book contains many annotated figures, and the framework's modeling elements are illustrated by example diagrams. Not only scholars working in the field of linguistics, in particular insemantics, will find this book illuminating because of its new graphical approach, but also researchers of cognitive science, computational linguistics and computer science in general will surely appreciate it.
Extra-Specific Multiword Expressions for Language-Endowed Intelligent Agents

Language-endowed intelligent agents benefit from leveraging lexical knowledge falling at different points along a spectrum of compositionality. This means that robust computational lexicons should include not only the compositional expectations of argument-taking words, but also non-compositional collocations (idioms), semi-compositional collocations that might be difficult for an agent to interpret (e.g., standard metaphors), and even collocations that could be compositionally analyzed but are so frequently encountered that recording their meaning increases the efficiency of interpretation. In this paper we argue that yet another type of string-to-meaning mapping can also be useful to intelligent agents: remembered semantic analyses of actual text inputs. These can be viewed as super-specific multi-word expressions whose recorded interpretations mimic a person’s memories of knowledge previously learned from language input. These differ from typical annotated corpora in two ways. First, they provide a full, context-sensitive semantic interpretation rather than select features. Second, they are are formulated in the ontologically-grounded metalanguage used in a particular agent environment, meaning that the interpretations contribute to the dynamically evolving cognitive capabilites of agents configured in that environment.
Transition To Adulthood And Family Relations: An Intergenerational Perspective

Introduction. Part I: The Theoretical and Methodological Background. The Identity of the Family. "Family Research" vs "Family Related Research": a Crucial Difference. Part II: An Analysis of Family Relationships by Gender and Generations. Family Relations, Psychosocial Risk, Pro-social Behaviour, Planning for the Future: Parents and Children, Face to Face. Concluding Remarks and New Research Questions. Appendix. References.
Estimated effects of solvents and mineral oils on cancer incidence and mortality in a cohort of aerospace workers.

BACKGROUND A retrospective cohort study of workers employed at a California aerospace company between 1950 and 1993 was conducted; it examined cancer mortality from exposures to the rocket fuel hydrazine. METHODS In this study, we employed a job exposure matrix (JEM) to assess exposures to other known or suspected carcinogens-including trichloroethylene (TCE), polycyclic aromatic hydrocarbons (PAHs), mineral oils, and benzene-on cancer mortality (1960-2001) and incidence (1988-2000) in 6,107 male workers. We derived rate- (hazard-) ratios estimates from Cox proportional hazard models with time-dependent exposures. RESULTS High levels of TCE exposure were positively associated with cancer incidence of the bladder (rate ratio (RR): 1.98, 95% confidence interval (CI) 0.93-4.22) and kidney (4.90; 1.23-19.6). High levels of exposure to mineral oils increased mortality and incidence of lung cancer (1.56; 1.02-2.39 and 1.99; 1.03-3.85), and incidence of melanoma (3.32; 1.20-9.24). Mineral oil exposures also contributed to incidence and mortality of esophageal and stomach cancers and of non-Hodgkin's lymphoma and leukemia when adjusting for other chemical exposures. Lagging exposure measures by 20 years changed effect estimates only minimally. No associations were observed for benzene or PAH exposures in this cohort. CONCLUSIONS Our findings suggest that these aerospace workers who were highly exposed to mineral oils experienced an increased risk of developing and/or dying from cancers of the lung, melanoma, and possibly from cancers of the esophagus and stomach and non-Hodgkin's lymphoma and leukemia. These results and the increases we observed for TCE and kidney cancers are consistent with findings of previous studies.
An Interactive Physical Synthesis Methodology for High-Frequency FPGA Designs

State-of-the-art FPGA design has become a very complex process primarily due to the aggressive timing requirements of the designs. Designers spend significant amount of time and effort trying to close the timing on their latest designs. In that timing closure methodology, Physical Synthesis plays a key role to boost the design performance. In traditional approaches, user performs placement followed by physical synthesis. As the design complexity increases, physical synthesis cannot perform all the optimization steps due to the physical constraints imposed by the placement operation. In this work, we propose an interactive methodology to perform physical synthesis in the pre-placement stage of the FPGA timing closure flow. The approach will work in two iterations of the design flow. In the first iteration, the designer will perform the regular post-placement physical synthesis operation on the design. That phase will automatically write a replayable-file which will contain information about all the optimization actions. That file also contains all the attempted optimization moves what physical synthesis deemed beneficial from QoR perspective, but was not able to accept due to the physical constraint. In the second iteration of the design flow, the designer will perform all those physical synthesis optimizations by importing the replayable file in the pre-placement stage. In addition to performing the physical synthesis flow's changes, it also performs the optimizations that were not possible in the traditional physical synthesis flow. After these changes are made in the logical stage of the design flow, the crucial placement step can adapt to the optimized/better netlist structure. As a result, this approach will greatly help the users reach their challenging timing closure goal. We have evaluated the effectiveness and performance of our proposed approach on a large set of industrial designs. All these designs were targeted towards the latest Xilinx Ultrascale™ devices. Our experimental data indicates that the proposed approach improves the design performance by 4% to 5%, on an average.
Studying the Lexicon of Dialogue Acts

Dialogue Acts have been well studied in linguistics and attracted computational linguistics research for a long time: they constitute the basis of everyday conversations and can be identified with the communicative goal of a given utterance (e.g. asking for information, stating facts, expressing opinions, agreeing or disagreeing). Even if not constituting any deep understanding of the dialogue, automatic dialogue act labeling is a task that can be relevant for a wide range of applications in both human-computer and human-human interaction. We present a qualitative analysis of the lexicon of Dialogue Acts: we explore the relationship between the communicative goal of an utterance and its affective content as well as the salience of specific word classes for each speech act. The experiments described in this paper fit in the scope of a research study whose long-term goal is to build an unsupervised classifier that simply exploits the lexical semantics of utterances for automatically annotate dialogues with the proper speech acts.