Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ensemble Model of Word-based and Character-based Models for Japanese and Chinese Input Method

Since Japanese and Chinese languages have too many characters to be input directly using a standard keyboard, input methods for these languages that enable users to input the characters are required. Recently, input methods based on statistical models have become popular because of their accuracy and ease of maintenance. Most of them adopt word-based models because they utilize word-segmented corpora to train the models. However, such word-based models suffer from unknown words because they cannot convert words correctly which are not in corpora. To handle this problem, we propose a character-based model that enables input methods to convert unknown words by exploiting character-aligned corpora automatically generated by a monotonic alignment tool. In addition to the character-based model, we propose an ensemble model of both character-based and word-based models to achieve higher accuracy. The ensemble model combines these two models by linear interpolation. All of these models are based on joint source channel model to utilize rich context through higher order joint n-gram. Experiments on Japanese and Chinese datasets showed that the character-based model performs reasonably and the ensemble model outperforms the word-based baseline model. As a future work, the effectiveness of incorporating large raw data should be investigated.
Automatic plagiarism detection among term papers

Recently, plagiarized term papers have become a serious problem. Therefore, we propose, in this paper, a method to detect plagiarized parts between two term papers. Our method is based on the Smith-Waterman algorithm that can detect similar parts between two molecules. Moreover, we experimented on our method using a document set consisting of actually submitted term papers and artificially-produced ones that plagiarized a paper written on the same theme. Experimental results show that our method attains higher accuracy than conventional ones.
Demographic and Geographic Differences in Exposure to Secondhand Smoke in Missouri Workplaces, 2007-2008

Introduction African Americans, Hispanics, service and blue-collar workers, and residents of rural areas are among those facing higher rates of workplace secondhand smoke exposure in states without smokefree workplace laws. Consequently, these groups also experience more negative health effects resulting from secondhand smoke exposure. The objective of this study was to examine disparities in workplace secondhand smoke exposure in a state without a comprehensive statewide smokefree workplace law and to use this information in considering a statewide law. Methods We developed a logistic multilevel model by using data from a 2007-2008 county-level study to account for individual and county-level differences in workplace secondhand smoke exposure. We included sex, age, race, annual income, education level, smoking status, and rural or urban residence as predictors of workplace secondhand smoke exposure. Results Factors significantly associated with increased exposure to workplace secondhand smoke were male sex, lower education levels, lower income, living in a small rural or isolated area, and current smoking. For example, although the overall rate of workplace exposure in Missouri is 11.5%, our model predicts that among young white men with low incomes and limited education living in small rural areas, 40% of nonsmokers and 56% of smokers may be exposed to secondhand smoke at work. Conclusion Significant disparities exist in workplace secondhand smoke exposure across Missouri. A statewide smokefree workplace law would protect all citizens from workplace secondhand smoke exposure.
Statistical Analysis of Single-Molecule AFM Force Spectroscopy Curves

We develop a statistical framework for the quantitative characterization and analysis of force-extension curves obtained from single-molecule force spectroscopy (SMFS) measurements. We apply this methodology to force-extension data obtained for elastinlike polypeptides (ELPs) with precisely engineered molecular architectures, where we demonstrate that our approach enables SMFS to be used to study hydrophobic hydration in intrinsically unstructured biomacromolecules. Our results obtained for ELPs suggest that hydrophobic hydration, rather than local backbone conformational entropy, is the key contributor to modulating the molecular elasticity of ELPs under changes in amino acid sequence. As with previous analysis, we parametrize SMFS curves using models from polymer statistical mechanics; however, we introduce several statistical innovations that dramatically improve the precision of the estimated parameters. Our approach (i) accounts for increased thermal noise in the data at low forces, (ii) provides confidence intervals for fitted polymer-theory parameters obtained from nonparametric bootstrapping, (iii) treats multiple curves simultaneously to reduce variability in the fitted parameters, and (iv) treats noise in both force and extension measurements simultaneously. A key advantage of our approach is the ability to quantify uncertainty in the fitted parameters, allowing comparison of parameters obtained under different experimental conditions or for structural variants. Our approach is able to distinguish previously unresolvable small differences in the molecular architecture of ELPs from SMFS experiments, resolving differences in Kuhn segment lengths as small as 0.01nm, significantly smaller than previously possible. This approach is universally applicable to SMFS data and of general interest for the analysis of structure-property relations in polymers and biomacromolecules. ∗Corresponding author: Scott C. Schmidler, Department of Statistical Science, Duke University, Durham, NC 27708-0251. Tel: (919) 684-8064; Fax: (919) 684-8594; Email: schmidler@stat.duke. edu
Macrophage Depletion Disrupts Immune Balance and Energy Homeostasis

Increased macrophage infiltration in tissues including white adipose tissue and skeletal muscle has been recognized as a pro-inflammatory factor that impairs insulin sensitivity in obesity. However, the relationship between tissue macrophages and energy metabolism under non-obese physiological conditions is not clear. To study a homeostatic role of macrophages in energy homeostasis, we depleted tissue macrophages in adult mice through conditional expression of diphtheria toxin (DT) receptor and DT-induced apoptosis. Macrophage depletion robustly reduced body fat mass due to reduced energy intake. These phenotypes were reversed after macrophage recovery. As a potential mechanism, severe hypothalamic and systemic inflammation was induced by neutrophil (NE) infiltration in the absence of macrophages. In addition, macrophage depletion dramatically increased circulating granulocyte colony-stimulating factor (G-CSF) which is indispensable for NE production and tissue infiltration. Our in vitro study further revealed that macrophages directly suppress G-CSF gene expression. Therefore, our study indicates that macrophages may play a critical role in integrating immune balance and energy homeostasis under physiological conditions.
Cross-country heterogeneity and time variation in the Euro-area economies : investigation using VAR methods

This thesis investigates the monetary transmission mechanism in the Euro area, for countries taken individually and as an aggregate. The focus of the thesis is on the effects of monetary policy shocks on the area as a whole, across countries and over time during the period of single monetary policy by the Eurosystem. Using the mostrecent empirical techniques such as factor-augmented vector autoregression (VAR), Bayesian Gibbs sampling, rolling windows, data pre-screening and panel VAR, the thesis investigates a novel (large) data set for the economies of the Euro area. According to our empirical analyses utilising these techniques, the thesis reaches the following main conclusions: First, time variation in the impulse responses of area-wide consumer prices and monetary aggregates to monetary policy shocks is stronger than that of other key macroeconomic indicators. The contractionary impact of the monetary tightening on real activity is the strongest when it hits the economy during the global financial crisis period (Chapter 1). Second, although the effects of the policy shocks on national real activities and price levels are homogeneous across countries, the transmission mechanism displays important cross-country heterogeneity with the national monetary aggregates responding most heterogeneously to common monetary policy shocks (Chapter 2). Finally, despite the responses of the Eurosystem to the global financial crisis with unconventional monetary measures, country-specific factors such as defaults risks and bailouts played a significant role in disrupting the transmission of the policy actions to individual economic activities (Chapter 3).
Implicit JointBoost for multiclass object detection under high intra-class variation

In this paper, we address the problem of using boosting to detect different classes of objects with significant intra-class variation. Current approaches generally require prior knowledge such as semantic information to obtain an explicit partition over positive samples. This can require a lot of manually labeled samples and can limit the performance of classifier due to subjective error. We present a novel JointBoost based learning method which can learn the variant appearances of targets without any prior assumption. With an implicit partition, the specific features for fractional samples can contribute to classification as well as generic features for the whole training set. By encouraging an intra-class and inter-class feature sharing between implicit sub-categories, our data-driven learning approach avoids a local optimum in the candidate weak classifier space. Experimental results on two popular tasks demonstrate the considerable improvements brought by the new approach. We hope that implicit JointBoost will extend the application of traditional boosting methods.
Regulation of action potential delays via voltage-gated potassium Kv1.1 channels in dentate granule cells during hippocampal epilepsy

Action potential (AP) responses of dentate gyrus granule (DG) cells have to be tightly regulated to maintain hippocampal function. However, which ion channels control the response delay of DG cells is not known. In some neuron types, spike latency is influenced by a dendrotoxin (DTX)-sensitive delay current (ID) mediated by unidentified combinations of voltage-gated K+ (Kv) channels of the Kv1 family Kv1.1–6. In DG cells, the ID has not been characterized and its molecular basis is unknown. The response phenotype of mature DG cells is usually considered homogenous but intrinsic plasticity likely occurs in particular in conditions of hyperexcitability, for example during temporal lobe epilepsy (TLE). In this study, we examined response delays of DG cells and underlying ion channel molecules by employing a combination of gramicidin-perforated patch-clamp recordings in acute brain slices and single-cell reverse transcriptase quantitative polymerase chain reaction (SC RT-qPCR) experiments. An in vivo mouse model of TLE consisting of intrahippocampal kainate (KA) injection was used to examine epilepsy-related plasticity. Response delays of DG cells were DTX-sensitive and strongly increased in KA-injected hippocampi; Kv1.1 mRNA was elevated 10-fold, and the response delays correlated with Kv1.1 mRNA abundance on the single cell level. Other Kv1 subunits did not show overt changes in mRNA levels. Kv1.1 immunolabeling was enhanced in KA DG cells. The biophysical properties of ID and a delay heterogeneity within the DG cell population was characterized. Using organotypic hippocampal slice cultures (OHCs), where KA incubation also induced ID upregulation, the homeostatic reversibility and neuroprotective potential for DG cells were tested. In summary, the AP timing of DG cells is effectively controlled via scaling of Kv1.1 subunit transcription. With this antiepileptic mechanism, DG cells delay their responses during hyperexcitation.
Super resolution based on scale invariant feature transform

In this paper, SIFT (scale invariant feature transform) algorithm is used for the image registration of super resolution to ensure a more stable and accurate registration result, and thus improve the result of super-resolution which will be realized by least squares minimization. The advantage of this approach is that the super-resolution process will have a stable result even under severe transformation conditions. SIFT method is compared with Kerenpsilas method to prove its accuracy and robustness. Fine reconstruction results are also given to show the effectiveness of this approach. Simultaneous registration method can be introduced in future work to further improve the registration accuracy.
Reval: A Tool for Real-time Evaluation of DDoS Mitigation Strategies

There is a growing number of DDoS attacks on the Internet, resulting in significant impact on users. Network operators today have little access to scientific means to effectively deal with these attacks in real time. The need of the hour is a tool to accurately assess the impact of attacks and more importantly identify feasible mitigation responses enabling real-time decision making. We designed and implemented Reval, a tool that reports DDoS attack impact in real time, scaling to large networks. This is achieved by modeling resource constraints of network elements and incorporating routing information. We demonstrate the usefulness of the tool on two real network topologies using empirical traffic data and examining real attack scenarios. Using data from a tier-1 ISP network (core, access and customer router network) of size in excess of 60000 nodes, Reval models network conditions with close to 0.4 million traffic flows in about 11 seconds, and evaluates a given mitigation deployment chosen from a sample set in about 35 seconds. Besides real-time decision support, we show how the simulator can also be used in longer term network planning to identify where and how to upgrade the network to improve network resilience. The tool is applicable for networks of any size and can be used to analyze other network anomalies like flash crowds.
Evenly-distributed clustering for wireless sensor network: A scalable, energy-efficient approach

Users can accurately monitor a remote field conveniently with a wireless sensor network (WSN), whereas the sensor nodes deployed in WSN are extremely power-constrained and are nearly impossible to recharge due to the harsh conditions of monitoring areas. Thus, lifetime of the network becomes the most significant factor in WSN designment. Clustering is an energy-effective network management technique, since its hierarchical structure reduces the total energy consumption of the whole network. In this paper, we develop and analyze the Evenly Distributed Adaptive Clustering Hierarchy (EDACH), a clustering algorithm devoted to maximizing lifetime of WSN by periodically creating proper new clusters evenly distributed in the monitoring field, which exploits distributed method to create clusters and uses level-based flooding technology to exchange data with sink node so that centralized management and long range data transmission are not required. Moreover, EDACH selects cluster heads with high residual energy which will not be too close or too distant to other cluster heads. EDACH can balance the energy consumption of each single node in WSN and thus avoids any nodes depleting its energy too early. Experimental results show that EDACH can effectively prolong the lifetime of network.
KNOWLEDGE DISCOVERY FOR IDENTIFICATION OF ENZYME WITH A PRIORY SPECIFIED PROPERTIES

Development of new products with the given properties from the known raw materials is one of the common research tasks in process engineering. Usually the first step of research is a literature survey based on the search for the specific keywords. Nowadays there exist many vast databases of articles and patents, and the traditional, keywords-based, searching tools are not always sufficient to find the desired information. The main objective of this paper is to develop methodology for identification of new materials, based on knowledge discovery. As an example, the proposed methodology is applied for identification of new enzyme of microbial origin capable of polymerizing lactose in aqueous solution, with the number of required criteria.
Contrasting trends in North Atlantic deep‐water formation in the Labrador Sea and Nordic Seas during the Holocene

The Holocene North Atlantic deep‐water formation is studied in a 9,000‐year long simulation with a coupled climate model of intermediate complexity, forced by changes in orbital forcing and atmospheric trace gas concentrations. During the experiment, deep‐water formation in the Nordic Seas is reduced due to an enhanced influx of sea ice from the Central Arctic, decreasing both surface salinity and density, whereas deep‐water formation in the Labrador Sea increases due to surface cooling. This leads to changes in the distribution of oceanic heat transported northwards by the Atlantic Ocean, with less heat released (−120 Wm−2 in February) in the Nordic Seas, amplifying the surface cooling and increasing the sea‐ice cover. In the Labrador Sea, the oceanic heat release increases slightly (+14 Wm−2), thus dampening locally the cooling trend. The overall Atlantic overturning strength remains constant throughout the experiment. Over the Nordic Seas, reduced evaporation contributes to the surface freshening.
Selective Integration of GNSS, Vision Sensor, and INS Using Weighted DOP Under GNSS-Challenged Environments

Accurate and precise navigation solution can be obtained by integrating multiple sensors such as global navigation satellite system (GNSS), vision sensor, and inertial navigation system (INS). However, accuracy of position solutions under GNSS-challenged environment occasionally degrades due to poor distributions of GNSS satellites and feature points from vision sensors. This paper proposes a selective integration method, which improves positioning accuracy under GNSS-challenged environments when applied to the multiple navigation sensors such as GNSS, a vision sensor, and INS. A performance index is introduced to recognize poor environments where navigation errors increase when measurements are added. The weighted least squares method was applied to derive the performance index, which measures the goodness of geometrical distributions of the satellites and feature points. It was also used to predict the position errors and the effects of the integration, and as a criterion to select the navigation sensors to be integrated. The feasibility of the proposed method was verified through a simulation and an experimental test. The performance index was examined by checking its correlation with the positional error covariance, and the performance of the selective navigation was verified by comparing its solution with the reference position. The results show that the selective integration of multiple sensors improves the positioning accuracy compared with nonselective integration when applied under GNSS-challenged environments. It is especially effective when satellites and feature points are posed in certain directions and have poor geometry.
Isolation and characterization of porcine Müller cells. Myofibroblastic dedifferentiation in culture.

PURPOSE To characterize phenotypic and antigenic changes in isolated Müller cells during proliferation in extended culture. METHODS Müller cells were isolated from porcine retina by sequential papain and DNase digestion, trituration, and density gradient centrifugation. The identity of the isolated cells was confirmed by immunodetection of carbonic anhydrase II (CA-II), cellular retinaldehyde-binding protein (CRALBP), glial fibrillary acidic protein (GFAP), vimentin, and delta smooth muscle actin (alpha SMA). Continuously proliferating cells established in culture were examined for changes in the expression of these antigens. RESULTS Primary cultures of purified Müller cells, incubated under routine culture conditions, were proliferative and lost immunodetectable CRALBP within 2 weeks. The expression of CA-II also diminished with time, but at an apparently lower rate than that of CRALBP. Loss of GFAP expression was even more gradual and was complete by passage 5. De novo expression of alpha SMA was detectable in some cells within 12 days in culture and by all cells by passage 5. During this period, vimentin expression remained qualitatively unchanged. CONCLUSIONS Isolated porcine Muller cells in culture undergo a phenotypic dedifferentiation to a fibroblast-like cell, which includes loss of detectable CRALBP, CA-II, and GFAP, and they acquire expression of the myoid marker alpha SMA.
A Challenge to the Ownership Society: Does Home Ownership Alone Improve Relative Neighborhood Quality?

In this paper we use agent-based modeling to explore what we identify as the housing policy paradox: that individuals are encouraged to use ownership as a mechanism for escaping low quality neighborhoods while policy designs depend upon ownership as a catalyst to improve neighborhoods and communities under the logic of the ownership society. This apparent contradiction would suggest that if policy depends upon ownership to drive quality improvement in communities, then it is likely that high-quality areas will improve at greater rates than lowquality areas thereby widening the gap. We specify our model in order to understand this policy construct and find that, indeed, the gap widens when individuals seek to own in higher quality areas and when renters are considered to be detrimental to the quality of the community. We explore further by limiting the information available to agents in the model to determine if this has any influence upon relative quality change. While limiting the options for agents does reduce the difference in relative change between high and low quality communities, it does not close the gap in neighborhood quality between high status and low status communities.
An analysis and improvement of 3GPP SAE AKA protocol based on strand space model

In this paper, we first analyze the novel 3GPP SAE AKA protocol proposed by Deng et al., and point out the flaws of protocol's authentication, and then we give an attack to the protocol. Moreover, we propose an improved 3GPP SAE AKA protocol, which can keep the frame of the 3GPP's protocol and overcome the security shortages of the D-AKA protocol. Finally, the security of the improved protocol is formally proved with the strand space model and authentication tests.