Miracle-dz/UniFAR-FTUs huggingface dataset:
Continued Evaluation of Gear Condition Indicator Performance on Rotorcraft Fleet

This paper details analyses of condition indicator performance for the helicopter nose gearbox within the U.S. Army's Condition-Based Maintenance Program. Ten nose gearbox data sets underwent two specific analyses. A mean condition indicator level analysis was performed where condition indicator performance was based on a 'batting average' measured before and after part replacement. Two specific condition indicators, Diagnostic Algorithm 1 and Sideband Index, were found to perform well for the data sets studied. A condition indicator versus gear wear analysis was also performed, where gear wear photographs and descriptions from Army tear-down analyses were categorized based on ANSI/AGMA 1010-E95 standards. Seven nose gearbox data sets were analyzed and correlated with condition indicators Diagnostic Algorithm 1 and Sideband Index. Both were found to be most responsive to gear wear cases of micropitting and spalling. Input pinion nose gear box condition indicators were found to be more responsive to part replacement during overhaul than their corresponding output gear nose gear box condition indicators.
ODOR CHARACTERIZATION AT OPEN-LOT BEEF CATTLE FEEDYARDS USING TRIANGULAR FORCED-CHOICE OLFACTOMETRY

Odor is a growing concern at concentrated animal feeding operations as residential houses encroach upon rural areas once occupied only by agriculture. A research project was conducted to determine baseline ambient odor characteristics at large open-lot beef cattle feedyards and to develop a better understanding of when and why odors occur at feedyards. Ambient odor samples were collected two to four times per month over a 12-month period in 2002-2003 at three large commercial open-lot beef cattle feedyards in the Texas panhandle. Ambient odor samples were collected upwind of the feedyard, downwind of the pens, and downwind of the runoff storage pond. Odor samples were also collected on five separate days covering four months in 2004 from a surface isolation flux chamber to estimate odor emission rates from the feedyard surface. All odor samples were collected in 10 L Tedlar bags and analyzed with trained human odor panelists for odor concentration (detection threshold, DT) by dynamic dilution forced-choice olfactometry, intensity by reference scaling, and hedonic tone. Manure moisture content and weather data were collected on-site at each of the feedyards. At two of the feedyards, mean DTs downwind of the pens and storage pond were statistically similar to upwind DTs, ranging from 33 to 45 OU m-3. At the third feedyard, mean DTs downwind of the pens (69 OU m-3) and pond (124 OU m-3) were statistically higher than the mean upwind DT (36 OU m-3) (p < 0.05). Odor emission rates ranged from 0.3 to 3.2 OU m-2 s-1 during a period when downwind DTs ranged from 17 to 132 OU m-3. A number of elevated DTs were explained by elevated manure moisture contents from recent precipitation. These results demonstrate that odor production from open-lot beef cattle feedyards is a complex phenomenon that depends at least partially on weather conditions. Thus, odor prediction and control will likely be difficult at these facilities.
Learning by Knowledge Exchange in Logical Agents

In this paper we introduce a form of cooperation among agents based on exchanging sets of rules. In principle, the approach extends to agent societies a feature which is proper of human societies, i.e., the cultural transmission of abilities. However, acquiring knowledge from untrustworthy agents should be avoided, and the new knowledge should be evaluated according to its usefulness. After discussing the general principles of our approach, we present a prototypical implementation.
Dual Recognition of Double-Stranded DNA by 2'-Aminoethoxy-Modified Oligonucleotides.

Simultaneous interaction of the 2'-aminoethoxy-modified oligonucleotides with the phosphodiester backbone (shown on the right, A) and with the bases through Hoogsteen base contacts (B) is seen at each base-pair step of the duplex DNA target. The electrostatic interaction between the protonated amino group and the negatively charged phosphate group provides for a dramatic increase in the binding affinity and the association rate constant.
Recommender Systems Evaluation: A 3D Benchmark

Recommender systems add value to vast content resources by matching users with items of interest. In recent years, immense progress has been made in recommendation techniques. The evaluation of these has however not been matched and is threatening to impede the further development of recommender systems. In this paper we propose an approach that addresses this impasse by formulating a novel evaluation concept adopting aspects from recommender systems research and industry. Our model can express the quality of a recommender algorithm from three perspectives, the end consumer (user), the service provider and the vendor (business and technique for both). We review current benchmarking activities and point out their shortcomings, which are addressed by our model. We also explain how our 3D benchmarking framework would apply to a specific use case.
Recursive calculation of one-loop QCD integral coefficients

We present a new procedure, using on-shell recursion, to determine coefficients of integral functions appearing in the one-loop scattering amplitudes of gauge theories, including QCD. With this procedure, coefficients of integrals, including bubbles and triangles, can be determined without resorting to integration. We give criteria for avoiding spurious singularities and boundary terms that would invalidate the recursion. As an example where the criteria are satisfied, we obtain all cut-constructible contributions to the one-loop n-gluon scattering amplitude, Anone−loop(.−−−+++.), with split-helicity from an = 1 chiral multiplet and from a complex scalar. Using the supersymmetric decomposition, these are ingredients in the construction of QCD amplitudes with the same helicities. This method requires prior knowledge of amplitudes with sufficiently large numbers of legs as input. In many cases, these are already known in compact forms from the unitarity method.
An Integrated Machine Learning and Case-Based Reasoning Approach to Answer Validation

We propose a case-based reasoning (CBR) approach to answer validation/answer scoring and reranking in question answering (QA) systems, where annotated answer candidates for known questions provide evidence for validating answer candidates for new questions. The use of CBR promises a continuous increase in answer quality, given user feedback that extends the case base. In the paper, we present the complete approach, emphasizing the use of CBR techniques, namely the structural case base, built with annotated MultiNet graphs, and corresponding graph similarity measures. We cover a priori relations to experienced answer candidates for former questions. We describe the adequate structuring of the case base and develop appropriate similarity measures. Finally we integrate CBR into an existing framework for answer validation and reranking that also includes logical answer validation and a shallow linguistic validation, using a learning-to-rank approach for the final answer ranking based on CBR-related features. In our experiments on QA@CLEF questions, the best learned models make heavy use of CBR features. The advantage already achieved by CBR will increase with time due to the automatic improvement with new user annotations given by relevance feedback.
An automated mechanism to characterize wheelchair user performance

This paper proposes a mechanism to derive quantitative descriptions of wheelchair usage as a tool to aid Occupational Therapist with their performance assesment of mobility platform users. This is accomplished by analysing data computed from a standalone sensor package fitted on an wheelchair platform. This work builds upon previous propositions where parameters that could assist in the assessment were recommended to the authors by a qualified occupational therapist (OT). In the current scheme however the task-specific parameters that may provide the most relevant user information for the assessment are automatically revealed through a machine learning approach. Data mining techniques are used to reveal the most informative parameters, and results from three typical classifiers are presented based on learnings from manual labelling of the training data. Trials conducted by healthy volunteers gave classifications with an 81% success rate using a Random Forest classifier, a promising outcome that sets the scene for a potential clinical trial with a larger user pool.
SIMILAR SHAPED HANDWRITTEN HINDI CHARACTERS USING MACHINE LEARNING

The growing need of handwritten Hindi character recognition in Indian offices such as passport, railway etc, has made it a vital area of research. Similar shaped characters are more prone to misclassification. In this paper four Machine Learning (ML) algorithms namely Bayesian Network, Radial Basis Function Network (RBFN), Multilayer Perceptron (MLP), and C4.5 Decision Tree are used for recognition of Similar Shaped Handwritten Hindi Characters (SSHHC) and their performance is compared. A novel feature set of 85 features is generated on the basis of character geometry. Due to the high dimensionality of feature vector, the classifiers can be computationally complex. So, its dimensionality is reduced to 11 and 4 using Correlation-Based (CFS) and Consistency-Based (CON) feature selection techniques respectively. Experimental results show that Bayesian Network is a better choice when used with CFS while C4.5 gives better performance with CON features.
Knowledge Representation in pAninI Framework Using Neural Network Model

- Knowledge representation is base for expressing semantic content of input in intelligent information retrieval systems. Identification of semantic requires processing of input language at various levels. To make system understand text or speech is a challenging task as it involves extracting semantics of the language which itself is a complex problem. At the same time languages posses with multiple ambiguities and uncertainty which needs to be resolved at various phases of language processing. Level of understandability depends upon the grammar, syntactic and semantic representation of the language and methods employed for these analysis. Processing depends on the type of language, grammar of the language, ambiguities present and size of corpus available. Order free language posses different features as compared to rigid order language. Most of the Indian languages are order free; hence mechanism for such language needs to be formulated. One of the ancient Indian Sanskrit grammarians, pAninI has defined grammar of Sanskrit language in such a way that it is suitable for computational analysis. Six main semantic class identified under this theory is a baseline model for knowledge representation. This paper exploits the features of the language, applicability of rules and resolving ambiguities using neural network model. A hybrid model incorporating the features of rules based and neural network the is designed and implemented for pAninI based semantic analysis, generating case frames as output.
Kähler-Einstein metrics on log Del Pezzo surfaces in weighted projective 3-spaces

Nous determinons toutes les surfaces de del Pezzo logarithmiques quasi-lisses dans les espaces projectifs a poids de dimension 3, qui sont plongees par leur morphisme anticanonique. Beaucoup de ces surfaces admettent une metrique de Kahler-Einstein, et la plupart de celles-ci n'ont pas de tigres.
I/O-Efficient Algorithms for Bounded Treewidth Graphs

We present an I/O-efficient algorithm for the single source shortest path (SSSP) problem for graphs of bounded treewidth. For sparse graphs in general SSSP seems to be extremely hard to solve. We show how to solve SSSP in O sort N I/Os for the above class of graphs. An important ingredient to our solution is an O sort N algorithm to compute a tree-decomposition of width k of the given graph. Given this decomposition, the algorithm for solving SSSP is rather simple. In order to construct the treedecomposition, we present an I/O-efficient algorithm for finding a maximal matching of a graph, and introduce flippable DAGs as an interesting new concept that may be useful for other applications as well. We also show how to realize the well-known time-forward processing technique in O scan N I/Os if the processed graph is a tree whose vertices are sorted in preorder. This does not give any asymptotic improvements, but is interesting from a practical point of view, as the used techniques are extremely simple.
Improving the speech recognition performance of beginners in spoken conversational interaction for language learning

The provision of automatic systems that can provide conversational practice for beginners would make a valuable addition to existing aids for foreign language teaching. To achieve this goal, the SCILL (Spoken Conversational Interaction for Language Learning) project is developing a spoken dialogue system that is capable of maintaining interactive dialogues with non-native students in the target language. However, the effective realisation of the intelligent language understanding and dialogue management needed for such a system, requires robust recognition of poorly articulated non-native speech. This paper studies several popular techniques for robust acoustic modelling including HLDA, MAP and CMLLR on non-native speech data within a specific dialogue domain. In addition, a novel approach for using cross language speech data to adapt the acoustic models is described and shown to be useful when very limited non-native adaptation data is available. The experimental results provide a clear story of how to improve recognition performance on non-native speech for a specific task, and this will be of interest more generally for those developing multi-lingual spoken dialogue systems.
CrowdInside: automatic construction of indoor floorplans

The existence of a worldwide indoor floorplans database can lead to significant growth in location-based applications, especially for indoor environments. In this paper, we present CrowdInside: a crowdsourcing-based system for the automatic construction of buildings floorplans. CrowdInside leverages the smart phones sensors that are ubiquitously available with humans who use a building to automatically and transparently construct accurate motion traces. These accurate traces are generated based on a novel technique for reducing the errors in the inertial motion traces by using the points of interest in the indoor environment, such as elevators and stairs, for error resetting. The collected traces are then processed to detect the overall floorplan shape as well as higher level semantics such as detecting rooms and corridors shapes along with a variety of points of interest in the environment. Implementation of the system in two testbeds, using different Android phones, shows that CrowdInside can detect the points of interest accurately with 0.2% false positive rate and 1.3% false negative rate. In addition, the proposed error resetting technique leads to more than 12 times enhancement in the median distance error compared to the state-of-the-art. Moreover, the detailed floorplan can be accurately estimated with a relatively small number of traces. This number is amortized over the number of users of the building. We also discuss possible extensions to CrowdInside for inferring even higher level semantics about the discovered floorplans.
Isolation Forest for Anomaly Detection in Raw Vehicle Sensor Data

A vehicle generates data describing its condition and the driver’s behavior. Sending data from many vehicles to a backend costs money and therefore needs to be reduced. The limitation to relevant data is inescapable. When using data collected from a vehicle fleet, the normality can be learned and deviations from it identified as abnormal and thus relevant. The idea of learning the normality with the Replicator Neural Network and the Isolation Forest is applied to the identification of anomalies and the reduction of data transfer. It is compared how good the methods are in detecting anomalies and what it means for the traffic between vehicle and backend. It can be shown that the Isolation Forest beats the Replicator Neural Network. When reducing the transfered amount of data to 7%, in average more than 80.63% of the given anomalies are included.
Extended fast fixed-order RLS adaptive filters

The existing derivations of conventional fast RLS adaptive filters are intrinsically dependent on the shift structure in the input regression vectors. This structure arises when a tapped-delay line (FIR) filter is used as a modeling filter. We show, unlike what original derivations may suggest, that fast fixed-order RLS adaptive algorithms are not limited to FIR filter structures. We show that fast recursions in both explicit and array forms exist for more general data structures, such as orthonormally based models. One of the benefits of working with orthonormal bases is that fewer parameters can be used to model long impulse responses.
Correlation of the multifocal visual evoked potential and standard automated perimetry in compressive optic neuropathies.

PURPOSE To evaluate the relationship between abnormalities detected by the multifocal visual-evoked potential (mfVEP) compared with those detected by static achromatic automated perimetry in patients with compressive optic neuropathy. METHODS Fifteen patients of mean age 50.8 years, with known compressive optic neuropathy from chiasmal lesions, underwent monocular mfVEP and 24-2 SITA-standard Humphrey visual field (HVF; Carl Zeiss Meditec, Dublin CA) testing in each eye. Visual field spatial agreement and extent of involvement were analyzed by assigning a severity score to each quadrant, based on pattern deviation and amplitude deviation probability plots. RESULTS HVF mean deviation (MD) was -6.54 +/- 7.43 dB (mean), and the mfVEP mean AccuMap Severity Index (ASI; ObjectiVision Pty. Ltd., Sydney, Australia) score was 81 +/- 74. MD and ASI correlated significantly (r = -0.55, P = 0.024). Although both mfVEP and HVF reported approximately the same proportion of visual fields as abnormal (70%, 21/30, and 87%, 26/30, respectively), 19% (5/26) with abnormal HVF were labeled normal or borderline by mfVEP. The agreement for field quadrants between instruments was 69% (kappa = 0.33). mfVEP severity scores for quadrants and hemifields were higher than scores for HVF in the same eyes. The superotemporal quadrant showed the strongest correlation between techniques (r = 0.73, P = 0.002). CONCLUSIONS In the first study to compare mfVEP to HVF in patients with compressive optic neuropathy, there was good qualitative and quantitative agreement between tests, though findings were in only modest agreement in some areas. The injury caused by compressive optic neuropathy may be usefully identified by mfVEP. Improved methods of analysis may increase the diagnostic utility of the method.
The Long-Term Impact of Systemic Student Support in Elementary School: Reducing High School Dropout

Dropping out of high school has adverse consequences, including negative effects on employment, lifetime earnings, and physical health. Students often fail to complete high school for complex reasons that often manifest long before they reach high school. This study examines the link between participation in a comprehensive elementary school student support intervention and high school dropout. In this study, students who attended intervention elementary schools in a large, urban, high-poverty district during 2001–2014 (N=894) were compared to students who did not attend intervention schools (N=10,200). Likelihood of dropping out in grades 9–12 was estimated using propensity score-weighted Discrete Event History Analysis. Intervention students had approximately half the odds of dropout (p<.001); the probability of dropout for intervention was 9.2%, compared to 16.6% for non-intervention students. Individually tailored student support interventions during elementary school can lead to lasting and meaningful effects.
The Tumor Suppressive Role of eIF3f and Its Function in Translation Inhibition and rRNA Degradation

Deregulated translation plays an important role in human cancer. We previously reported decreased eukaryotic initiation factor 3 subunit f (eIF3f) expression in pancreatic cancer. Whether decreased eIF3f expression can transform normal epithelial cells is not known. In our current study, we found evidence that stable knockdown of eIF3f in normal human pancreatic ductal epithelial cells increased cell size, nuclear pleomorphism, cytokinesis defects, cell proliferation, clonogenicity, apoptotic resistance, migration, and formation of 3-dimensional irregular masses. Our findings support the tumor suppressive role of eIF3f in pancreatic cancer. Mechanistically, we found that eIF3f inhibited both cap-dependent and cap-independent translation. An increase in the ribosomal RNA (rRNA) level was suggested to promote the generation of cancer. The regulatory mechanism of rRNA degradation in mammals is not well understood. We demonstrated here that eIF3f promotes rRNA degradation through direct interaction with heterogeneous nuclear ribonucleoprotein (hnRNP) K. We showed that hnRNP K is required for maintaining rRNA stability: under stress conditions, eIF3f dissociates hnRNP K from rRNA, thereby preventing it from protecting rRNA from degradation. We also demonstrated that rRNA degradation occurred in non-P body, non-stress granule cytoplasmic foci that contain eIF3f. Our findings established a new mechanism of rRNA decay regulation mediated by hnRNP K/eIF3f and suggest that the tumor suppressive function of eIF3f may link to impaired rRNA degradation and translation.
A Software Architecture for Distributed Geospatial Decision Support Systems

The National Agricultural Decision Support System (NADSS) is a joint project with the U.S. Department of Agriculture and the University of Nebraska-Lincoln. The initial focus of the NADSS project is to improve the quality and accessibility of drought related knowledge, information, and data for drought risk management. At the core of the NADSS project is a software architecture for distributed geospatial decision support systems that consists of four distributed layers: the data layer, the information layer, the knowledge layer, and the presentation layer. This work presents that architecture as well as the benefits and impact of the architecture and the NADSS project.
Adding Updates to XQuery: Semantics, Optimization, and Static Analysis

The need to extend XQuery to support updates has been recognized both in the research and the standards community. Several language proposals and prototype implementations have been put forward, and update language requirements are being deﬁned within the W3C. Most proposals center around the use of update primitives applied to XQuery expressions, along with a variant of the FLWR loop construct binding variables within a block of basic up-date statements. In deﬁning a precise semantics for such statements a number of issues arise: one must decide how conﬂicts among updates are to be resolved, and how query evaluation interacts with up-date application. In this work we provide a framework for deﬁning alternative semantics for updates, and identify within this space what is (thus far) the consensus choice: that semantics involves a two-stage execution process, in which query evaluation is performed ﬁrst, after which a generated sequence of concrete updates is applied in a ﬁxed order determined by query output. This results in a clean deterministic semantics which facilitates analysis. A drawback is that the evaluation of the language can be inefﬁcient. One would prefer to perform updates eagerly before further evaluation, or to re-order the update operations. We focus on an optimization of the “standard semantics”, in which updates are performed as soon as they are generated. We present a static analysis for determining