Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of the Preload Force in Metal-Elastomer Torsion Springs

Abstract Neidhart type suspension units composed of metal-elastomer torsion springs can be a good alternative to steel helical springs in applications such as vibration absorbers or vehicle suspension systems. Assembling this type of spring requires initial preload of the elastomeric working elements, which determines their operating properties. The results of experimental tests and numerical simulations concerning the preload of elastomeric working elements in Neidhart type suspension units are presented in the paper. The performed research made it possible to propose a new calculation model for determining the preload force value acting on the elastomeric cylindrical elements applied in this type of suspension unit. The results obtained using the proposed model exhibit good convergence with FEM simulation results within the range of the tested geometrical and material properties.
Strain modes identification using cross-correlation function of measured dynamic strain

Strain modes of structures reflect the distribution rule of dynamic strain and can be of service to structural health monitoring. This article presents a new method to estimate the strain modes of beam structures based on the measured dynamic strain. Since the cross-correlation matrix of the modal coordinates is a diagonal matrix, thus, the cross-correlation matrix of the measured dynamic strain is also a diagonal matrix, and the strain modes can be obtained to find the eigenvectors of the strain cross-correlation matrix. The strain modes of a simply support beam and a continuous beam subjected to various impulsive excitations are identified from the numerical simulations using the proposed method. The noise effect is also investigated in this article. The numerical results show that the proposed method can effectively identify the strain modes even with noise effect. Finally, the method is verified through the experiment of a simply supported beam under hammering excitation. The test results also show that the proposed method can estimate the strain modes of beam structures with a high accuracy.
Approximation techniques for utilitarian mechanism design

This paper deals with the design of efficiently computable incentive compatible, or truthful, mechanisms for combinatorial optimization problems with multi-parameter agents. We focus on approximation algorithms for NP-hard mechanism design problems. These algorithms need to satisfy certain monotonicity properties to ensure truthfulness. Since most of the known approximation techniques do not fulfill these properties, we study alternative techniques.Our first contribution is a quite general method to transform a pseudopolynomial algorithm into a monotone FPTAS. This can be applied to various problems like, e.g., knapsack, constrained shortest path, or job scheduling with deadlines. For example, the monotone FPTAS for the knapsack problem gives a very efficient, truthful mechanism for single-minded multi-unit auctions. The best previous result for such auctions was a 2-approximation. In addition, we present a monotone PTAS for the generalized assignment problem with any bounded number of parameters per agent.The most efficient way to solve packing integer programs (PIPs) is LP-based randomized rounding, which also is in general not monotone. We show that primal-dual greedy algorithms achieve almost the same approximation ratios for PIPs as randomized rounding. The advantage is that these algorithms are inherently monotone. This way, we can significantly improve the approximation ratios of truthful mechanisms for various fundamental mechanism design problems like single-minded combinatorial auctions (CAs), unsplittable flow routing and multicast routing. Our approximation algorithms can also be used for the winner determination in CAs with general bidders specifying their bids through an oracle.
Application of functional feature extraction to the compression of network time series

Network management actions require the retention of data representing the temporal evolution of network state, mainly in the form of time series. Nonetheless, storing and exploiting those measurements is becoming a challenge as the production rate of such data is continuously increasing and data lasting for long time periods are used. To scale up the storage and improve both the analysis and visualization of network measurements, we apply Functional Principal Components Analysis (FPCA) to extract the most meaningful functional features for network time series, pruning those with low informational importance. We compare such algorithm with other state-of-the-art proposals, and show that it achieves lower error for the representation of atypical observations even with higher compression ratios.
Summarizing Review Scores of "Unequal" Reviewers

A frequently encountered problem in decision making is the following review problem: review a large number of objects and select a small number of the best ones. An example is selecting conference papers from a large number of submissions. This problem involves two sub-problems: assigning reviewers to each object, and summarizing reviewers’ scores into an overall score that supposedly reflects the quality of an object. In this paper, we address the score summarization sub-problem for the scenario where a small number of reviewers evaluate each object. Simply averaging the scores may not work as even a single reviewer could influence the average significantly. We recognize that reviewers are not necessarily on an equal ground and propose the notion of “leniency” to model this difference of reviewers. Two insights underpin our approach: (1) the “leniency” of a reviewer depends on how s/he evaluates objects as well as on how other reviewers evaluate the same set of objects, (2) the “leniency” of a reviewer and the “quality” of objects evaluated exhibit a mutual dependency relationship. These insights motivate us to develop a model that solves both “leniency” and “quality” simultaneously. We study the effectiveness of this model on a real-life dataset.
A Fast Incremental Spectral Clustering for Large Data Sets

Spectral clustering is an emerging research topic that has numerous applications, such as data dimension reduction and image segmentation. In spectral clustering, as new data points are added continuously, dynamic data sets are processed in an on-line way to avoid costly re-computation. In this paper, we propose a new representative measure to compress the original data sets and maintain a set of representative points by continuously updating Eigen-system with the incidence vector. According to these extracted points we generate instant cluster labels as new data points arrive. Our method is effective and able to process large data sets due to its low time complexity. Experimental results over various real evolutional data sets show that our method provides fast and relatively accurate results.
The future of microprocessors

Outlines technology and business issues in today's microprocessor industry. From their humble beginnings 25 years ago, microprocessors have proliferated into an astounding range of chips, powering devices ranging from telephones to supercomputers. Today, microprocessors for personal computers get widespread attention-and have enabled Intel to become the world's largest semiconductor maker. In addition, embedded microprocessors are at the heart of a diverse range of devices that have become staples of consumers worldwide. Microprocessors have become specialized in many ways. Those for desktop computers fall into classes based on their instruction set architectures: either x86, the primary surviving complex instruction set computing (CISC) architecture, or one of the five major reduced instruction set computing (RISC) architectures-PA-RISC, Mips, Spare, Alpha, and PowerPC. Such chips typically integrate few functions other than cache memory and bus interfaces with the processor but usually include a floating-point unit and memory management unit. Embedded microprocessors, on the other hand, typically do not have floating-point or memory management units but often integrate various peripheral functions with the processor to reduce system cost.
Omnivory as a Stabilizing Feature of Natural Communities

Omnivory—defined broadly as feeding on more than one trophic level—occupies a prominent position in discussions of food web architecture and dynamics, due in large part to an enduring conflict regarding omnivory's role in community dynamics. According to classical results from mathematical food web theory, omnivory destabilizes ecological communities, whereas more recent conceptual syntheses suggest that omnivory should be a strongly stabilizing factor in food webs. Working with an arthropod assemblage at Mount Saint Helens, I experimentally addressed this controversy using a two‐way factorial design that crossed a manipulation of the degree of omnivory with another “disturbance” manipulation that targeted a specific component of the assemblage. In this statistical design, significant interaction effects (i.e., how the community impacts of the disturbance varied with the degree of omnivory) identified key stabilizing or destabilizing influences of omnivory. Overall, my experimental results indicated that increasing the degree of omnivory stabilized community dynamics, in keeping with recent conceptual syntheses.
Improved Large Vocabulary Mandarin Speech Recognition Using Prosodic and Lexical Information in Maximum Entropy Framework

Tone plays an important role in distinguishing ambiguous words in Chinese Mandarin speech recognition. In this paper, we make full use of pitch information. On the one hand, we interpolate F0 contour to make the F0 contour continuous between voiced and unvoiced segments in order to embed F0 into speech recognition system in two streams, which cepstrum and its first and second order derivatives constitute one stream , and F0 and its first and second order derivatives make up the other stream; On the other hand, we use prosodic and lexical features, as well as syllable context information under maximum entropy framework to build explicit tone modeling in rescoring the first-pass outputting lattice. Experimental results show that pitch information and the tonal cues can reduce substitution error greatly and achieve a 3.65% absolute Chinese character error rate (CER) reduction on widely used Mandarin speech recognition tasks-863 test.
Common Garden and Natural Selection Experiments Support Ecotypic Differentiation in the Dominican Anole (Anolis oculatus)

The theory behind ecotypic differentiation and ecological speciation assumes a predominant role for natural selection working on characteristics with genetic variance, but experimental support for these assumptions is limited. Lesser Antillean anoles show marked ecotypic variation within islands and the potential for ecological speciation. Common garden rearing experiments on the Dominican anole (Anolis oculatus) suggest that the characters showing geographic variation have genetic variance and are not primarily determined by environmental plasticity. Replicated natural selection experiments using large‐scale enclosures show that translocated montane samples experience significant (multivariate) directional selection in both wet and dry seasons in both males and females. The targets of selection appear to be spread among the various character systems. An experiment on 12 geographically segregated populations along a coastal xeric‐montane rainforest gradient (four replicate enclosures) clearly showed that the magnitude of the directional selection intensity is positively related to the position along this gradient. The results of the common garden and natural selection experiments support the interpretation that the geographic differentiation is primarily driven by natural selection and are compatible with the potential for ecological speciation in this system.
Registration of Challenging Image Pairs: Initialization, Estimation, and Decision

Our goal is an automated 2D-image-pair registration algorithm capable of aligning images taken of a wide variety of natural and man-made scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and physical changes in the scene. An important component of this is the ability to automatically reject pairs that have no overlap or have too many differences to be aligned well. We propose a complete algorithm including techniques for initialization, for estimating transformation parameters, and for automatically deciding if an estimate is correct. Keypoints extracted and matched between images are used to generate initial similarity transform estimates, each accurate over a small region. These initial estimates are rank-ordered and tested individually in succession. Each estimate is refined using the Dual-Bootstrap ICP algorithm, driven by matching of multiscale features. A three-part decision criteria, combining measurements of alignment accuracy, stability in the estimate, and consistency in the constraints, determines whether the refined transformation estimate is accepted as correct. Experimental results on a data set of 22 challenging image pairs show that the algorithm effectively aligns 19 of the 22 pairs and rejects 99.8 percent of the misalignments that occur when all possible pairs are tried. The algorithm substantially out-performs algorithms based on keypoint matching alone.
Korean Hand Acupressure for Motion Sickness in Prehospital Trauma Care: A Prospective, Randomized, Double-Blinded Trial in a Geriatric Population

Patients with trauma or medical illnesses transported to the hospital by ambulance have a frequent incidence of motion sickness. Because the administration of drugs in the ambulance is prohibited by law in Austria, the noninvasive Korean hand acupressure point at K-K9 may be an alternative against nausea and vomiting. We enrolled 100 geriatric patients with minor trauma, randomizing them into a K-K9 group and a sham acupressure group. We recorded visual analog scores (VAS) for nausea and for the patient’s overall satisfaction with the treatment, hemodynamic variables, and peripheral vasoconstriction. In the K-K9 group, a significant (P < 0.01) increase in nausea was recorded in all cases: from VAS of 0 mm to 25 ± 6 mm. A similarly significant (P < 0.01) increase was registered in the sham group: from VAS of 0 mm to 83 ± 8 mm. However, at the time of arrival in the hospital, nausea scores were significantly different between the K-K9 group and the sham group (P < 0.01). Although all patients had been vasoconstricted at the emergency site before treatment, there was a significant difference (P < 0.01) between groups with regard to the number of vasoconstricted patients at the hospital (4 and 46 constricted and dilated, respectively, in the K-K9 group versus 48 and 2 constricted and dilated, respectively, in the sham group). On arrival in the hospital, a significant difference (P < 0.01) in heart rate was noted between the K-K9 group and the sham group (65 ± 6 bpm versus 98 ± 8 bpm). The patients’ overall satisfaction with the provided care was significantly higher (P < 0.01) in the K-K9 group (19 ± 9 mm VAS) than in the sham group (48 ± 12 mm VAS). Neither group experienced a significant change in blood pressure. K-K9 stimulation was an effective and simple treatment for nausea during emergency care and significantly improved patient satisfaction.
Rapid modeling of 3D faces for animation using an efficient adaptation algorithm

This paper presents a new efficient method for the reconstruction of a personalized 3D facial model for animation from range data. Our method adapts a generic control model with anatomical structure to the geometry of a specific person's face with minimum manual intervention. The face adaptation algorithm starts with the specification of a small set of anthropometric landmarks on the 2D images of both the generic control model and individual face. 3D positions of landmarks are recovered automatically by using a projection-mapping approach. A global adaptation is then carried out to adapt the size, position and orientation of the generic model in the 3D space based on a series of measurements between the recovered 3D landmarks. After the global adaptation, a local adaptation deforms the generic model to fit all of its vertices to the scan data-set. The underlying muscle structure is automatically adapted as well, such that the reconstructed model not only resembles the individual face in shape and color but also reflects the structure of human face including skin and muscles, therefore can be animated immediately with the given muscle parameters.
Fusing heterogeneous traffic data by Kalman filters and Gaussian mixture models

Heterogeneous traffic data collected from different types of sensors are fused for estimating traffic states more accurately. Data quality and fusion method are two key issues required to be solved in the traffic state estimation. In this paper, we propose a fusion method of heterogeneous traffic data based on the Kalman filters (KF) and Gaussian mixture models (GMM). The noise in collected raw data is reduced by the KF in order to improve the quality of input data for fusion. The vectors of historical data from global positioning system (GPS) and remote traffic microwave sensors (RTMS) in different traffic states are modeled with multiple multi-variate GMM respectively. Finally, the estimated traffic state can be obtained by computing the posterior probabilities with the vector data and GMM. Performance of our work is examined by series of experiments, and the results show that the proposed method is effective for improving the precision of traffic state estimation.
Functions of Silences towards Information Flow in Spoken Conversation

Silence is an integral part of the most frequent turn-taking phenomena in spoken conversations. Silence is sized and placed within the conversation flow and it is coordinated by the speakers along with the other speech acts. The objective of this analytical study is twofold: to explore the functions of silence with duration of one second and above, towards information flow in a dyadic conversation utilizing the sequences of dialog acts present in the turns surrounding the silence itself; and to design a feature space useful for clustering the silences using a hierarchical concept formation algorithm. The resulting clusters are manually grouped into functional categories based on their similarities. It is observed that the silence plays an important role in response preparation, also can indicate speakers’ hesitation or indecisiveness. It is also observed that sometimes long silences can be used deliberately to get a forced response from another speaker thus making silence a multi-functional and an important catalyst towards information flow.
Effect of air blowing on the morphology and nanofiber properties of blowing-assisted electrospun polycarbonates

Polycarbonate (PC) nanofibers are prepared using the air blowing-assisted electrospinning process. The effects of air blowing pressure and PC solution concentration on the physical properties of fibers and the filtration performance of the nanofiber web are investigated. The air blowing-assisted electrospinning process produces fewer beads and smaller nanofiber diameters compared with those obtained without air blowing. Uniform PC nanofibers with an average fiber diameter of about 0.170 μm are obtained using an applied voltage of 40 kV, an air blowing pressure of 0.3 MPa, a PC solution concentration of 16%, and a tip-to-collection-screen distance (TCD) of 25 cm. The filtration efficiency improvement of the air blowing-assisted electrospun web can be attributed to the narrow distribution of fiber diameter and small mean flow pore size of the electrospun web. Performance results show that the air blowing-assisted electrospinning process can be applied to produce PC nanofiber mats with high-quality filtration. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
A Short Note on Social-Semiotic Networks from the Point of View of Quantitative Semantics

Summary. In this extended abstract we discuss four related characteristics of semantic spaces as the standard model of meaning representation in quantitative semantics. We argue that these characteristics are challenged from the point of view of social web communities and the possibilities which they oer in terms of exploring semantic and pragmatic data. More specically, we plead for a reconstruction of the weak contextual hypothesis in order to account for non-linguistic, pragmatic aspects of context. Finally, we mention two consequences of such a pragmatic turn, that is, in the area of named entity recognition and of language evolution. Predominant models of quantitative semantics are based on assumptions which are challenged from the point of view of the possibilities oered by ex
Biologia Plantarum Xx (x): Xxx-xxx, 20xx Two Novel Wrky Genes from Juglans Regia Jrwrky6 and Jrwrky53 Are Involved in Aba-dependent Stress Responses

Genes encoding plant WRKY transcription factors (TFs) are important for stress response. In the current study, two WRKY TF genes (JrWRKY6 and JrWRKY53) were identified from walnut (Juglans regia L.), and their function and involvement in stress responses were characterized. Under NaCl stress, JrWRKY6 and JrWRKY53 were upregulated in a short time (within 6 h of seedling exposure to salt), except in the roots, in which the highest induction occurred at 24 and 48 h of salt exposure. The gene expression patterns under polyethylene glycol stress were similar to those under NaCl stress. Under heat stress, both genes were induced in all tissues, except for JrWRKY6 in leaf tissue of seedlings treated for 24 and 48 h. Both genes were also mainly induced in all plants exposed to cold stress, except for JrWRKY6 in root tissue of seedlings exposed for 6 h and JrWRKY53 in root tissue exposed for 48 h. JrWRKY6 and JrWRKY53 also showed varied responses to abscisic acid (ABA), with the maximum expression being for JrWRKY6 in the roots of plants treated for 1 h, and JrWRKY53 in the leaves of plants treated for 3 h. Furthermore, under NaCl, sorbitol, heat, cold, and ABA treatments, yeast cells transformed with JrWRKY6 and JrWRKY53 showed improved growth activity and density relative to the empty-vector-containing control yeasts. Moreover, JrWRKY6 or JrWRKY53 could bind to the W-box motif. These results suggested that JrWRKY6 and JrWRKY53 can response positively to abiotic stressors and improve the plant's tolerance to salt, osmotic, and abnormal temperatures, in a mechanism that likely involves the ABA signalling pathway and W-box binding activity.
An Effective Hybrid Optimization Algorithm for HMM

Hidden Markov model (HMM) is currently the most popular approach to speech recognition. The problem of optimizing model parameters is of great interest to the researchers in this area. The Baum-Welch (BW) algorithm is very popular estimation method due to its reliability and efficiency. However, it is easily trapped in local optimum. particle swarm optimization (PSO) algorithm is a stochastic global optimization technique, but its convergence speed is comparatively slow. With the purpose of overcoming their drawbacks, a new training algorithm based on the PSO algorithm and the BW algorithm (PSOBW) is proposed to train the continuous HMM in continuous speech recognition. This algorithm not only overcomes the shortcoming of the slow convergence speed of the PSO algorithm but also helps the BW algorithm escape from local optimum. The experimental results show that the algorithm is superior to the BW algorithm in the recognition performance.