Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Classifier with K-Nearest Neighbor Density Estimation for Slope Collapse Prediction

Heavy rainfall and typhoon oftentimes cause the collapse of hillslopes across mountain roads. Disastrous consequences of slope collapses necessitate the approach for predicting their occurrences. In practice, slope collapse prediction can be formulated as a deterministic classification problem with two class labels, namely “collapse” and “non-collapse”. Nevertheless, due to the criticality and the uncertainty of the problem, evaluating the collapse susceptibility of an area is a challenging task. This study proposes a novel Artificial Intelligence (AI) approach, named as K-Nearest Neighbor Based Bayesian Classifier (KNNBC), to deal with slope collapse assessment. In the proposed model, Bayesian inference is used as a framework to achieve probabilistic prediction of slope collapse. Meanwhile, K-Nearest Neighbor (K-NN) is employed as a density estimation technique. Equipped with probabilistic outputs, the K-NNBC is able to yield predictions with different levels of confidence and diminish misclassified cases. Experimental results point out that the proposed model is very helpful for decision-makers in slope collapse assessment and disaster prevention planning.
A Probabilistic Approach to Lexical Semantic Knowledge Acquisition and Structural Disambiguation

In this thesis, I address the problem of automatically acquiring lexical semantic knowledge, especially that of case frame patterns, from large corpus data and using the acquired knowledge in structural disambiguation. The approach I adopt has the following characteristics: (1) dividing the problem into three subproblems: case slot generalization, case dependency learning, and word clustering (thesaurus construction). (2) viewing each subproblem as that of statistical estimation and defining probability models for each subproblem, (3) adopting the Minimum Description Length (MDL) principle as learning strategy, (4) employing efficient learning algorithms, and (5) viewing the disambiguation problem as that of statistical prediction. Major contributions of this thesis include: (1) formalization of the lexical knowledge acquisition problem, (2) development of a number of learning methods for lexical knowledge acquisition, and (3) development of a high-performance disambiguation method.
Technology Development in the ARPA/RL Planning Initiative

Since its inception, ARPI has supported a number of technology development efforts focused on crisis action planning. In this article we provide a synopsis of the progress made in a selected subset of the technologies that will need to contribute to making the ARPI vision a reality. We focus on ARPl-supported research that is providing critical advances in the enabling technology areas of plan generation, scheduling and temporal reasoning, plan analysis, and overall supporting infrastructure. We conclude with a discussion of several important remaining technology challenges.
Learning Edge-Specific Kernel Functions For Pairwise Graph Matching

In this paper we consider the pairwise graph matching problem of finding correspondences between two point sets using unary and pairwise potentials, which analyze local descriptor similarity and geometric compatibility. Recently, it was shown that it is possible to learn optimal parameters for the features used in the potentials, which significantly improves results in supervised and unsupervised settings. It was demonstrated that even linear assignments (not considering geometry) with well learned potentials may improve over state-of-the-art quadratic assignment solutions. In this paper we extend this idea by directly learning edge-specific kernels for pairs of nodes. We define the pairwise kernel functions based on a statistical shape model that is learned from labeled training data. Assuming that the setting of graph matching is a priori known, the learned kernel functions allow to significantly improve results in comparison to general graph matching. We further demonstrate the applicability of game theory based evolutionary dynamics as effective and easy to implement approximation of the underlying graph matching optimization problem. Experiments on automatically aligning a set of faces and feature-point based localization of category instances demonstrate the value of the proposed method.
Robust position control of a pneumatic actuator

This paper proposes a position control strategy for a pneumatic actuation system, which demonstrates high accuracy while it is subjected to a varying external force. We use a novel approach for the mathematical modeling of the pneumatic actuator: based on energy methods, we combine the kinetic and potential energy to derive the Lagrangian of the actuator and then the Euler-Lagrange equation of motion. The nonlinear backstepping method is applied to derive the control law, and the derivative of the potential energy is used as the controlled parameter. Results from the experiment show that tracking a sine wave of 0.1[m] magnitude produces a maximum error of ±0.008[m], while the actuator is subjected to an external force with a magnitude of 570[N] to 1150[N].
MANIAC / 2013 / 03 ) Mobile Offloading in Wireless Ad Hoc Networks

Mobile Data Offloading permits users to use cheap communication media, whenever feasible, for delivering th eir personal data instead of using the infrastructure which is more expensive. Having good procedures to assess and compare the different possibilities a device has to send his data is cruc ial. In the following paper, we propose an evaluating approach that takes into consideration the topology changes history in or der to provide an efficient way to calculate the quality of a speci fic medium. In particular Bayesian Networksare used as basis to provide our solution to the Maniac Challenge 2013. Bayesian Network s is a statistical model for the generation of inferences star ting with very little information. In the next sections we will give a definition of the Bayesian Networks focusing on the simples t and computationally efficient of the Bayesian Networks vers ions, named Naı̈ve Bayes . Finally, we will show our method for the challenge to evaluate the medium and the bid strategy. I. BAYESIAN NETWORKS Bayesian Networks (BN) [3] aid the reasoning and decision making in an uncertain environment. BN are built on top of a graphical model representing a set of random variables and their conditional independence. The learning phase in B N consists in retrieving the probabilities and the inference phase consists in computing the probabilities a posteriori by app lying the Bayes rules. BN are powerful in terms of inference, they are able to produce a decision pattern with very little information. Very efficient algorithms exist for computing the probability a posteriori. In the simplest BN model, all the variables are only dependen t on the inferring variable. This model is called Naı̈ve Bayes (NB) and it is successfully applied to several domains, prov iding satisfactory results.
Toward Spinal Cord Injury Repair Strategies: Peptide Surface Modification of Expanded Poly(Tetrafluoroethylene) Fibers for Guided Neurite Outgrowth In Vitro

Expanded poly(tetrafluoroethylene) fibers were surface modified using an ultraviolet-activated mercury/ammonia reaction to yield amine-functional groups for the coupling of laminin-derived cell adhesive peptides CYIGSR, CDPGYIGSR, CIKVAV, and CQAASIKVAV. a Surface elemental composition, determined by X-ray photoelectron spectroscopy, and radiolabeling data indicated that the amount of peptide introduced was approximately equivalent regardless of peptide type, yet mixed peptide surfaces had ∼60% YIGSR and 40% IKVAV. The peptide-modified surfaces were compared in terms of the response of dorsal root ganglia with neurite length and number of cells attached to each fiber measured. All peptide-functionalized surfaces had a greater cellular response than the aminated ePTFE and ePTFE controls. Surfaces modified with extended peptide sequences CDPGYIGSR and CQAASIKVAV demonstrated a greater cellular response than those modified with the shorter peptide sequences CYIGSR and CIKVAV, respectively, likely because the extended peptides more closely mimic the three-dimensional conformation that the peptides maintain in laminin. Differences in neurite extension were evident among the peptide-functionalized surfaces, with the longest neurites observed on surfaces modified with both CQAASIKVAV and CDPGYIGSR. The “guidance capacity” of the fibers as a function of fiber diameter was investigated in terms of length and directionality of neurite outgrowth. As fiber diameter decreased (from 100+ to 10 &mgr;m), the neurites tended to grow to a greater degree down the length of the fiber. The thinnest fibers (with diameters <20 &mgr;m) extended shorter neurites than the fibers with a wider diameter. Combining neurite length with guidance indicated that of the fiber diameters investigated, the optimal fiber diameter for neurite guidance was between 30 and 50 &mgr;m.
More than One Way of Being a Moa: Differences in Leg Bone Robustness Map Divergent Evolutionary Trajectories in Dinornithidae and Emeidae (Dinornithiformes)

The extinct moa of New Zealand included three families (Megalapterygidae; Dinornithidae; Emeidae) of flightless palaeognath bird, ranging in mass from <15 kg to >200 kg. They are perceived to have evolved extremely robust leg bones, yet current estimates of body mass have very wide confidence intervals. Without reliable estimators of mass, the extent to which dinornithid and emeid hindlimbs were more robust than modern species remains unclear. Using the convex hull volumetric-based method on CT-scanned skeletons, we estimate the mass of a female Dinornis robustus (Dinornithidae) at 196 kg (range 155–245 kg) and of a female Pachyornis australis (Emeidae) as 50 kg (range 33–68 kg). Finite element analysis of CT-scanned femora and tibiotarsi of two moa and six species of modern palaeognath showed that P. australis experienced the lowest values for stress under all loading conditions, confirming it to be highly robust. In contrast, stress values in the femur of D. robustus were similar to those of modern flightless birds, whereas the tibiotarsus experienced the highest level of stress of any palaeognath. We consider that these two families of Dinornithiformes diverged in their biomechanical responses to selection for robustness and mobility, and exaggerated hindlimb strength was not the only successful evolutionary pathway.
Feature-based Inductive Transfer Learning through Minimum Encoding

This paper proposes an Extended Minimum Description Length Principle (EMDLP) for feature-based inductive transfer learning, in which both the source and the target data sets contain class labels and relevant features are transferred from the source domain to the target one. Despite numerous works on this topic, few of them have a solid theoretical framework and are parameter-free. Our EMDLP overcomes these flaws and allows us to evaluate the inferiority of the results of transfer learning with the add-sum of the code lengths of five components: the corresponding two hypotheses, the two data sets with the help of the hypotheses, and the set of the transferred features. We design a code book to build the connections between the source and the target tasks. Extensive experiments using both real and artificial data sets show that EMDLP is robust against noise and performs better on the classification accuracy than the state-of-the-art methods.
Comparison of changes in retentive force and wear pattern of two stud attachments for implant overdentures: An in vitro study

Context: Edentulous patients often complain about the instability of mandibular denture. To overcome that, implant-supported overdentures (IODs) have been applied as a good prosthetic option. Aims: The current study compared the changes in retentive force and patterns of surface wear of nylon rings of two stud attachments (ball and locator) upon cyclic loading. Subjects and Methods: Two implant analogs were fixed parallel to each other, 22 mm apart, in custom-made rectangular blocks for each attachment system. Ten nylon inserts of each attachment system were tested. Universal testing machine was used to measure the retentive force, and a low-value fatigue testing machine was used for cyclic loading of 2500 insertion–removal cycles. Surface changes of the components were evaluated by scanning electron microscope (SEM). Statistical Analysis Used: Paired Student's t-test was used to determine groups that were statistically significant. Results: The greatest retention loss rate, i.e., the difference between the initial retentive force and final retentive force was observed in the ball attachment (6.20 N) followed by locator attachment (3.70 N). The results were found to be statistically significant (d ≤ 0.001**). Upon SEM analysis, the nylon inserts of ball attachment revealed more surface wear compared to that of locator attachment. Conclusions: For greater retention and longer function, locator overdenture attachment should be preferred for implant overdentures over ball attachment.
Znf202 Affects High Density Lipoprotein Cholesterol Levels and Promotes Hepatosteatosis in Hyperlipidemic Mice

Background The zinc finger protein Znf202 is a transcriptional suppressor of lipid related genes and has been linked to hypoalphalipoproteinemia. A functional role of Znf202 in lipid metabolism in vivo still remains to be established. Methodology and Principal Findings We generated mouse Znf202 expression vectors, the functionality of which was established in several in vitro systems. Next, effects of adenoviral znf202 overexpression in vivo were determined in normo- as well as hyperlipidemic mouse models. Znf202 overexpression in mouse hepatoma cells mhAT3F2 resulted in downregulation of members of the Apoe/c1/c2 and Apoa1/c3/a4 gene cluster. The repressive activity of Znf202 was firmly confirmed in an apoE reporter assay and Znf202 responsive elements within the ApoE promoter were identified. Adenoviral Znf202 transfer to Ldlr−/− mice resulted in downregulation of apoe, apoc1, apoa1, and apoc3 within 24 h after gene transfer. Interestingly, key genes in bile flux (abcg5/8 and bsep) and in bile acid synthesis (cyp7a1) were also downregulated. At 5 days post-infection, the expression of the aforementioned genes was normalized, but mice had developed severe hepatosteatosis accompanied by hypercholesterolemia and hypoalphalipoproteinemia. A much milder phenotype was observed in wildtype mice after 5 days of hepatic Znf202 overexpression. Interestingly and similar to Ldl−/− mice, HDL-cholesterol levels in wildtype mice were lowered after hepatic Znf202 overexpression. Conclusion/Significance Znf202 overexpression in vivo reveals an important role of this transcriptional regulator in liver lipid homeostasis, while firmly establishing the proposed key role in the control of HDL levels.
“People over Profits”: Retailers Who Voluntarily Ended Tobacco Sales

Background Tobacco retailers are key players in the ongoing tobacco epidemic. Tobacco outlet density is linked to a greater likelihood of youth and adult smoking and greater difficulty quitting. While public policy efforts to address the tobacco problem at the retail level have been limited, some retailers have voluntarily ended tobacco sales. A previous pilot study examined this phenomenon in California, a state with a strong tobacco program focused on denormalizing smoking and the tobacco industry. We sought to learn what motivated retailers in other states to end tobacco sales and how the public and media responded. Methods We conducted interviews with owners, managers, or representatives of six grocery stores in New York and Ohio that had voluntarily ended tobacco sales since 2007. We also conducted unobtrusive observations at stores and analyzed media coverage of each retailer’s decision. Results Grocery store owners ended tobacco sales for two reasons, alone or in combination: health or ethics-related, including a desire to send a consistent health message to employees and customers, and business-related, including declining tobacco sales or poor fit with the store’s image. The decision to end sales often appeared to resolve troubling contradictions between retailers’ values and selling deadly products. New York retailers attributed declining sales to high state tobacco taxes. All reported largely positive customer reactions and most received media coverage. Forty-one percent of news items were letters to the editor or editorials; most (69%) supported the decision. Conclusion Voluntary decisions by retailers to abandon tobacco sales may lay the groundwork for mandatory policies and further denormalize tobacco. Our study also suggests that high tobacco taxes may have both direct and indirect effects on tobacco use. Highlighting the contradictions between being a responsible business and selling deadly products may support voluntary decisions by retailers to end tobacco sales.
A web mining based approach for automatic student model discovery

Learner models represent a basic knowledge asset that can be used to ensure personalization within e-learning systems. These models can be built only based on learners activities, tracked and gathered on the web server side. In this paper, we propose to outline the general principles of an entirely automated web mining based approach for modeling learners in learning management systems. So, we consider a learner model with three components: the learner's profile, the learner's knowledge, and the learner's educational preferences. These learner's model components are inferred automatically from usage data, based on web mining techniques. Then, a hierarchical multi-level model based collaborative filtering approach is applied for modeling learners into groups.
Airway inflammation in patients with chronic non-asthmatic cough

Introduction Chronic non-asthmatic cough (CC) is a clinical challenge and underlying pathophysiological mechanisms remain still not completely understood. One of the most common comorbidities in CC is gastro-oesophageal reflux disease (GORD). Airway epithelium damage can contribute to airway inflammation in CC. Aims We studied airway inflammation in patients with CC compared to healthy controls. Patients with GORD were treated with proton pump inhibitors (PPI) and cough response to PPI was evaluated. Patients and methods Sputum was induced in 41 adults with CC and 20 healthy non-smokers who were age and sex matched. We compared sputum differential cell count by cytospin and cytokine and chemokine production at the mRNA and/or protein levels by real-time (RT)-PCR and cytokine bead array (CBA), between patients with CC and healthy subjects. Furthermore we studied airway inflammation in patients with different comorbidities. Results No differences in sputum differential cell counts were observed between patients with CC and healthy subjects. Sputum monocyte chemoattractant protein-1 (MCP-1) protein levels were significantly higher in patients when compared to controls. Thymic stromal lymphopoietin (TSLP) mRNA was significantly more often expressed in sputum of patients with CC than from healthy controls. Sputum transforming growth factor (TGF)-β levels did not differ between patients and controls, but were significantly lower in the PPI responders compared to the non-responders; p=0.047. There is no evidence for impaired T helper cell (Th)1/Th2/Th17 balance in CC. Patients with reflux oesophagitis (RO) have significantly more sputum eosinophils than patients without RO. Conclusions CC is a condition presenting with different disease phenotypes. High sputum MCP-1 levels are present in a large group of patients with CC and a majority of these patients with CC have increased sputum TSLP levels, most likely produced by damaged airway epithelial cells.
A lightweight multistroke recognizer for user interface prototypes

With the expansion of pen- and touch-based computing, new user interface prototypes may incorporate stroke gestures. Many gestures comprise multiple strokes, but building state-of-the-art multistroke gesture recognizers is nontrivial and time-consuming. Luckily, user interface prototypes often do not require state-of-the-art recognizers that are general and maintainable, due to the simpler nature of most user interface gestures. To enable easy incorporation of multistroke recognition in user interface prototypes, we present $N, a lightweight, concise multistroke recognizer that uses only simple geometry and trigonometry. A full pseudocode listing is given as an appendix. $N is a significant extension to the $1 unistroke recognizer, which has seen quick uptake in prototypes but has key limitations. $N goes further by (1) recognizing gestures comprising multiple strokes, (2) automatically generalizing from one multistroke to all possible multistrokes using alternative stroke orders and directions, (3) recognizing one-dimensional gestures such as lines, and (4) providing bounded rotation invariance. In addition, $N uses two speed optimizations, one with start angles that saves 79.1% of comparisons and increases accuracy 1.3%. The other, which is optional, compares multistroke templates and candidates only if they have the same number of strokes, reducing comparisons further to 89.5% and increasing accuracy another 1.7%. These results are taken from our study of algebra symbols entered in situ by middle and high schoolers using a math tutor prototype, on which $N was 96.6% accurate with 15 templates.
Using event segmentation to improve indexing of consumer photographs

Automatic albuming --- the automatic organization of photographs, either as an end in itself or for use in other applications -- is an application that promises to be of great assistance to photographers. Relatively sophisticated image content analysis techniques have been used for image indexing, organization and retrieval. In this paper, we describe a method of organizing photographs into events using spoken photograph captions. The results of this process can be used to improve image indexing and retrieval.
The Design and Evaluation of an Ion-Drag Dielectric Pump to Enhance Cooling in a Small Oil-Filled Transformer

An ion-drag dielectric pump consisting of a closed loop of glass tubing has been used to investigate the pumping of transformer oil. The velocity of the oil has been measured with a multiplicity of design variables: electrode spacing and density of field enhancing points, cofield temperature gradients, waveshape, and pressure heads. The observed velocities under our experimental conditions are severalfold higher (-10x) than the flow rates computed for convective oil flow in a 25-kVA distribution transformer (=0.6 cm/s).