Miracle-dz/UniFAR-FTUs huggingface dataset:
The mortality problem of learning and mimetic practice in emerging industries: Dying to be legitimate

This study seeks to disentangle claims of institutional and organizational learning theories and to shed light on the impact of Knightian (environmental) uncertainty in discovery opportunities. This article suggests, and finds empirical support for, the concept that emerging professional service industries retain high levels of causal ambiguity. High uncertainty interferes with institutional theory's claim of mortality reduction through isomorphism, but leads to superstitious learning, increasing organizational mortality hazard. Education and experience of entrepreneurs help them identify discovery (exogenous) opportunities for entrepreneurial rents, while high (but untheorized) levels of uncertainty interfere in their ability to successfully exploit these same opportunities. Copyright © 2012 Strategic Management Society.
Ensemble molecular dynamics yields submillisecond kinetics and intermediates of membrane fusion

Lipid membrane fusion is critical to cellular transport and signaling processes such as constitutive secretion, neurotransmitter release, and infection by enveloped viruses. Here, we introduce a powerful computational methodology for simulating membrane fusion from a starting configuration designed to approximate activated prefusion assemblies from neuronal and viral fusion, producing results on a time scale and degree of mechanistic detail not previously possible to our knowledge. We use an approach to the long time scale simulation of fusion by constructing a Markovian state model with large-scale distributed computing, yielding an understanding of fusion mechanisms on time scales previously impossible to simulate to our knowledge. Our simulation data suggest a branched pathway for fusion, in which a common stalk-like intermediate can either rapidly form a fusion pore or remain in a metastable hemifused state that slowly forms fully fused vesicles. This branched reaction pathway provides a mechanistic explanation both for the biphasic fusion kinetics and the stable hemifused intermediates previously observed experimentally. Our distributed computing and Markovian state model approaches provide sufficient sampling to detect rare transitions, a systematic process for analyzing reaction pathways, and the ability to develop quantitative approximations of reaction kinetics for fusion.
A Rendering-Based Method for Selecting the Main Data Region in Web Pages

Extracting data from web pages is an important task for several applications, such as comparison shopping and data mining. Much of that data is provided by search result pages, in which each result, called search result record, represents a record from a database. One of the most important steps for extracting such records is identifying, among different data regions from a page, one that contains the records to be extracted. An incorrect identification of this region may lead to an incorrect extraction of the search result records. In this paper, we propose a simple but efficient method that generates path expression to select the main data region from a given page, based on the rendering area information of its elements. The generated path expression may be used by wrappers for extracting the search result records and its data units, reducing its complexity and increasing its accuracy. Experimental results using web pages from several domains show that the method is highly effective.
A Fluorescent Probe for Detecting Mycobacterium tuberculosis and Identifying Genes Critical for Cell Entry

The conventional method for quantitating Mycobacterium tuberculosis (Mtb) in vitro and in vivo relies on bacterial colony forming unit (CFU) enumeration on agar plates. Due to the slow growth rate of Mtb, it takes 3–6 weeks to observe visible colonies on agar plates. Imaging technologies that are capable of quickly quantitating both active and dormant tubercle bacilli in vitro and in vivo would accelerate research toward the development of anti-TB chemotherapies and vaccines. We have developed a fluorescent probe that can directly label the Mtb cell wall components. The fluorescent probe, designated as DLF-1, has a strong affinity to the D-Ala-D-Ala unit of the late peptidoglycan intermediates in the bacterial cell wall. We demonstrate that DLF-1 is capable of detecting Mtb in both the actively replicating and dormant states in vitro at 100 nM without inhibiting bacterial growth. The DLF-1 fluorescence signal correlated well with CFU of the labeled bacteria (R2 = 1 and 0.99 for actively replicating and dormant Mtb, respectively). DLF-1 can also quantitate labeled Mtb inside of cells. The utility of DLF-1 probe to quantitate Mtb was successfully applied to identify genes critical for cell invasion. In conclusion, this novel near infrared imaging probe provides a powerful new tool for enumerating Mtb with potential future use in bacterial virulence study.
Pose Estimation with Unknown Focal Length Using Points, Directions and Lines

In this paper, we study the geometry problems of estimating camera pose with unknown focal length using combination of geometric primitives. We consider points, lines and also rich features such as quivers, i.e.\ points with one or more directions. We formulate the problems as polynomial systems where the constraints for different primitives are handled in a unified way. We develop efficient polynomial solvers for each of the derived cases with different combinations of primitives. The availability of these solvers enables robust pose estimation with unknown focal length for wider classes of features. Such rich features allow for fewer feature correspondences and generate larger inlier sets with higher probability. We demonstrate in synthetic experiments that our solvers are fast and numerically stable. For real images, we show that our solvers can be used in RANSAC loops to provide good initial solutions.
miR-130a acts as a potential diagnostic biomarker and promotes gastric cancer migration, invasion and proliferation by targeting RUNX3

MicroRNAs (miRNAs) are abnormally expressed in various types of cancer. miR-130a expression and function in gastric cancer has yet to be elucidated. The aim of the present study was to identify the miR-130a expression and function in gastric cancer. miR-130a expression was examined in gastric cancer cell lines and tissues by RT-qPCR. The diagnostic and prognostic significance of miR-130a in gastric cancer was analyzed by receiver-operating characteristic (ROC) curve and Kaplan-Meier analysis. miR130a expression was identified and the diagnostic significance in the serum of gastric cancer patients and healthy controls was analyzed using RT-qPCR and ROC curves, respectively. A target gene for miR-130a was identified using luciferase reporter assays, and gastric cancer tumorigenesis ability was examined by 3-(4,5-dimethylthazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and Transwell assays. The results showed that miR-130a was upregulated in gastric cancer. The low-miR-130a group had significantly improved overall survival compared to the high-miR-130a group. Furthermore, the expression of miR-130a in plasma in gastric cancer patients was upregulated and diagnostic value for gastric cancer of miR-130a is more effective than the tumor markers carcinoembryonic antigen (CEA) and CA-199. miR-130a directly targeted runt-related transcription factor 3 (RUNX3) and promoted gastric cancer tumorigenesis by targeting RUNX3. miR-130a may therefore be a useful marker for the diagnosis and prognosis of gastric cancer. Additionally, miR-130a was identified as an oncogene that promotes gastric cancer tumorigenesis by targeting RUNX3.
Integrated compilation and scalability analysis for parallel systems

Despite the performance potential of parallel systems, several factors have hindered their widespread adoption. Of these, performance variability is among the most significant. Data parallel languages, which facilitate the programming of those systems, increase the semantic distance between the program's source code and its observable performance, thus aggravating the optimization problem. In this paper, we present a new methodology to automatically predict the performance scalability of data parallel applications on multicomputers. Our technique represents the execution time of a program as a symbolic expression that includes the number of processors (P), problem size (N), and other system-dependent parameters. This methodology is strongly based on information collected at compile time. By extending an existing data parallel compiler (Fortran D95), we derive during compilation, a symbolic cost model that represents the expected cost of each high-level code section and, inductively, of the complete program.
Guidelines for Exposure Assessment in Health Risk Studies Following a Nuclear Reactor Accident

Background: Worldwide concerns regarding health effects after the Chernobyl and Fukushima nuclear power plant accidents indicate a clear need to identify short- and long-term health impacts that might result from accidents in the future. Fundamental to addressing this problem are reliable and accurate radiation dose estimates for the affected populations. The available guidance for activities following nuclear accidents is limited with regard to strategies for dose assessment in health risk studies. Objectives: Here we propose a comprehensive systematic approach to estimating radiation doses for the evaluation of health risks resulting from a nuclear power plant accident, reflected in a set of seven guidelines. Discussion: Four major nuclear reactor accidents have occurred during the history of nuclear power production. The circumstances leading to these accidents were varied, as were the magnitude of the releases of radioactive materials, the pathways by which persons were exposed, the data collected afterward, and the lifestyle factors and dietary consumption that played an important role in the associated radiation exposure of the affected populations. Accidents involving nuclear reactors may occur in the future under a variety of conditions. The guidelines we recommend here are intended to facilitate obtaining reliable dose estimations for a range of different exposure conditions. We recognize that full implementation of the proposed approach may not always be feasible because of other priorities during the nuclear accident emergency and because of limited resources in manpower and equipment. Conclusions: The proposed approach can serve as a basis to optimize the value of radiation dose reconstruction following a nuclear reactor accident. Citation: Bouville A, Linet MS, Hatch M, Mabuchi K, Simon SL. 2014. Guidelines for exposure assessment in health risk studies following a nuclear reactor accident. Environ Health Perspect 122:1–5; http://dx.doi. org/10.1289/ehp.1307120
Certificate Translation

Program verification techniques based on programming logics and verification condition generators provide a powerful means to reason about programs. Whereas these techniques have very often been employed in the context of high-level languages in order to benefit from their structural nature, it is often required, especially in the context of mobile code, to prove the correctness of compiled programs. Thus it is highly desirable to have a means of bringing the benefits of source code verification to code consumers. Certificate translation is a general method to transfer to code consumers evidence gained through verification of source code; it relies on the notion of certificate, used in Proof-Carrying Code to convey to the code consumer independently verifiable evidence that programs respect policies. The talk provides sufficient conditions of existence for algorithms that transform certificates of source programs into certificates of compiled programs, and show that many common transformations comply with these conditions. Joint work with Benjamin Grégoire, César Kunz, and Tamara Rezk
Description of a Database Containing Wrist PPG Signals Recorded during Physical Exercise with Both Accelerometer and Gyroscope Measures of Motion

Wearable heart rate sensors such as those found in smartwatches are commonly based upon Photoplethysmography (PPG) which shines a light into the wrist and measures the amount of light reflected back. This method works well for stationary subjects, but in exercise situations, PPG signals are heavily corrupted by motion artifacts. The presence of these artifacts necessitates the creation of signal processing algorithms for removing the motion interference and allowing the true heart related information to be extracted from the PPG trace during exercise. Here, we describe a new publicly available database of PPG signals collected during exercise for the creation and validation of signal processing algorithms extracting heart rate and heart rate variability from PPG signals. PPG signals from the wrist are recorded together with chest electrocardiography (ECG) to allow a reference/comparison heart rate to be found, and the temporal alignment between the two signal sets is estimated from the signal timestamps. The new database differs from previously available public databases because it includes wrist PPG recorded during walking, running, easy bike riding and hard bike riding. It also provides estimates of the wrist movement recorded using a 3-axis low-noise accelerometer, a 3-axis wide-range accelerometer, and a 3-axis gyroscope. The inclusion of gyroscopic information allows, for the first time, separation of acceleration due to gravity and acceleration due to true motion of the sensor. The hypothesis is that the improved motion information provided could assist in the development of algorithms with better PPG motion artifact removal performance.
The Efficacy of the Growing Pro-social Program in Reducing Anger, Shame, and Paranoia over Time in Male Prison Inmates

Objectives: This randomized controlled trial aimed to assess the efficacy of a cognitive-behavioral group program, Growing Pro-Social (GPS), in reducing anger, shame, and paranoia over time in Portuguese male prison inmates. Methods: Participants were randomized to the GPS treatment (n = 121) or control group (n = 133). The State-Trait Anger Expression Inventory, the Other as Shamer Scale, and the Paranoia Scale were completed at baseline, at the middle of treatment, at posttreatment, and at 12 months’ follow-up. Intervention effects were tested with latent growth curve models (LGCM). Results: At baseline, no significant differences between groups were found. Results from LGCM showed that condition was a significant predictor of change observed in all outcome measures over time. While treatment participants showed a significant increase in anger-control over time, controls presented a significant decrease over time in this same variable. For the remaining dimensions of anger, as well as for external shame and paranoia, while the treatment group showed a significant decrease over time, the control group showed a significant increase or no change. Conclusions: These results pointed out the GPS’s ability to promote significant change in cognitive and emotional relevant variables associated with antisocial behavior.
Observers’ performance at evaluating truthfulness when provided with comparable truth or small talk baselines

Research has shown that the comparable truth baseline technique outperforms the small talk with respect to the elicitation of cues to deception. However, their impact on observers’ accuracy has not been evaluated yet. In this experiment, participants (N = 74) watched ten interviews where senders either lied or told the truth about a set of tasks. Half of the interviews were conducted with a comparable truth baseline, the other half with a small talk baseline. As predicted, results showed that observers in the comparable truth baseline condition outperformed participants in the small talk baseline condition in terms of total accuracy rates. The article sheds light on the impact of the two baseline techniques in distinguishing truth-tellers from liars and discourages the use of a small talk baseline. It also provides insights for future studies.
Multistage SVM as a Clinical Decision Making Tool for Predicting Post Operative Patient Status

Because applying machine learning techniques in support of clinical decision would improve decision makers in healthcare, we present in this paper a comparative framework of Support Vector Machine (SVM) classifiers based on post operative patient (POP) data. We compare the performance of a single multiclass SVM and a multistage SVM (MSVM) to those obtained by a number of other classifiers presented in the literature and show that both SVM approaches significantly outperform the other methods resulting in 84.4% and 94.3% overall accuracy respectively. Results for the non-SVM classifiers ranged from 48% 77.7% accuracy.
Pengaruh Gaya Kepemimpinan terhadap Kinerja Karyawan melalui Variabel Intervening Motivasi Kerja di Head Office PT Marifood

Dalam mencapai target penjualan oleh Perusahaan, diperlukan kinerja karyawan yang baik yang mana dipengaruhi oleh motivasi kerja dan kemampuan setiap karyawan. Disisi lain, motivasi kerja seorang karyawan dapat dipengaruhi oleh gaya kepemimpinan seorang pemimpin. Penelitian ini bertujuan untuk mengetahui pengaruh gaya kepemimpinan terhadap kinerja karyawan melalui motivasi kerja pada suatu Perusahaan.Jenis Penelitian yang digunakan adalah penelitian kuantitatif dengan metode survey. Penelitian ini menggunakan teknik pengambilan sampel simple random sampling. Teknik analisis data yang digunakan adalah Partial Least Square. Hasil penelitian menunjukan bahwa gaya kepemimpinan memiliki pengaruh yang signifikan terhadap kinerja karyawan melalui motivasi kerja yang ada dalam diri karyawan.
Longitudinal changes in neurodevelopmental outcomes between 18 and 36 months in children with prenatal triptan exposure: findings from the Norwegian Mother and Child Cohort Study

Objective This study sought to determine whether changes in neurodevelopmental outcomes between 18 and 36 months of age were associated with prenatal exposure to triptan medications, a class of 5-HT receptor agonists used in the treatment of migraine. Method Using data from the Norwegian Mother and Child Cohort Study, a prospective birth cohort that includes nearly 40% of all pregnancies in Norway from 1999 to 2008, we identified 50 469 mother–child dyads who met inclusion criteria and were present for at least one follow-up assessment at 18 or 36 months postpartum. Neurodevelopment was assessed using the Child Behaviour Checklist, the Emotionality, Activity, and Shyness Questionnaire, and the Ages and Stages Questionnaire. We used generalised estimating equations to evaluate change from 18 to 36 months for children prenatally exposed to triptans, relative to contrast groups, and used marginal structural models with inverse probability of treatment and censoring weights to address time-varying exposure and confounding as well as loss to follow-up. Results Among eligible participants (n=50 469), 1.0% used a triptan during pregnancy, 2.0% used triptans prior to pregnancy only, 8.0% reported migraine without triptan use and 89.0% had no history of migraine. Children with prenatal triptan exposure had greater increases in emotionality (r-RR 2.18, 95% CI 1.03 to 4.53) and activity problems (r-RR 1.70, 95% CI 1.02 to 2.8) compared to children born to mothers who discontinued triptan use prior to pregnancy. Conclusion Prenatal triptan exposure was associated with changes over time in externalising-type behaviours such as emotionality and activity, but not with internalising-type behaviours.
Intelligent Checkers to Improve Attack Detection in Cyber Physical Systems

An approach to improve the security of cyber physical systems (CPS) through Intelligent Checkers (IC) is presented. ICs are designed to be behaviorally and structurally independent of the cyber-portion of a CPS and hence not subject to cyber attacks. An IC monitors the status of a physical process and raises an alarm when process measurements violate predefined constraints. A strict one-way communication from an IC to the CPS command center validates the authenticity of the data received from CPS sensors. An IC is able to raise an alarm despite communication failure or undetected cyber attacks. Strategic placement of one or more ICs throughout a CPS is expected to improve the potential for the early detection of attacks.
Collaborative Personalized Digital Interactive TV Basics

With the introduction of interactive user oriented TV, digitalization and ipTV, a whole broadcast world is facing new challenges as well as new opportunities. The world of personalized digital interactive TV today is a fast developing area with well established and proven research concepts. Nowadays devices should be designed to learn for themselves and according to the accepted knowledge to prioritize the available programs for the final user. The emergence of the Digital TV technologies calls for investigating consumer preferences and behavioural models towards this new promising communication services. In the paper we are presenting the basic principles of a user based recognition and modelling of personalised interactive digital TV technologies which might increase and create new opportunities and home services.
Formalization and Parsing of Typed Unification-Based ID/LP Grammars

This paper defines unification based ID/LP grammars based on typed feature structures as nonterminals and proposes a variant of Earley's algorithm to decide whether a given input sentence is a member of the language generated by a particular typed unification ID/LP grammar. A solution to the problem of the nonlocal flow of information in unification ID/LP grammars as discussed in Seiffert (1991) is incorporated into the algorithm. At the same time, it tries to connect this technical work with linguistics by presenting an example of the problem resulting from HPSG approaches to linguistics (Hinrichs and Nakasawa 1994, Richter and Sailer 1995) and with computational linguistics by drawing connections from this approach to systems implementing HPSG, especially the TROLL system, Gerdemann et al. (forthcoming).
A robust latch-type sense amplifier using adaptive latch resistance

A latch-type sense amplifier (SA) utilizing adaptive resistance technique is proposed. With adaptively adjusted resistance in a latch path, the proposed SA can compensate for an erroneous voltage drop in bit-lines induced by bit-cell leakage current. The simulation shows that the sense amplifier margin (SM) is improved in the presence of mismatches. The SA test chip is fabricated in a 0.18-μm CMOS technology showing the SM improvement of 6% to 15% at various supply voltages.
Efficient Surface Detection for Augmented Reality on 3D Point Clouds

Surfaces are now where the augmented reality comes true. In this paper, we propose an efficient, learning-free and reliable way to detect for not only planar but also curved surfaces. Furthermore, our approach combines both advantages of local region growing and top-down refinements to retrieve wholesome surfaces in both organized point clouds and prebuilt 3D models. First, we obtain a down-sampled graph by supervoxel segmentation. Afterward, a recursive bottom-up agglomerative hierarchical clustering approach will iteratively merge the supervoxels into surfaces. Finally, top-down refinements on noisy and occluded planes will correct over-segmentations. To sum up, this is a model- and learning-free approach with experimental efficiency --- around 80 seeds and 3000 supervoxels are calculated for a 640x480 test point cloud, costing less than 0.2 sec on an ordinary laptop with 2.6GHz Intel core i5, 8GB RAM and without GPU aided.