4,086 research outputs found
Author Attributions in Medieval Text Collections: An Exploration
No full textThis article examines the role and function of author attributions in multi-text manuscripts containing Dutch, English, French or German short verse narratives. The findings represent one strand of the investigations undertaken by the cross-European project ‘The Dynamics of the Medieval Manuscript’, which analysed the dissemination of short verse narratives and the principles of organisation underlying the compilation of text collections. Whilst short verse narratives are more commonly disseminated anonymously, there are manuscripts in which authorship is repeatedly attributed to a text or corpus. Through six case studies, this article explores medieval concepts of authorship and how they relate to constructions of authority, whether regarding an empirical figure or a literary construction. In addition, it looks at how authorship plays a role in manuscript compilation, and at the effects of attributions (by author and/or compiler) on reception. The case studies include manuscripts from the thirteenth to fifteenth centuries, produced in a range of social and cultural contexts, and featuring some of the most important European authors of short verse narratives: Rutebeuf, Baudouin de Condé, Der Striker, Konrad von Würzberg, Willem of Hildegaersberch, and Geoffrey Chaucer. The preliminary findings contribute to our understanding of author attributions in text collections from across northern Europe and point towards future lines of enquiry into the role of authorship in medieval textual dissemination
Investigation of cyclist and pedestian impacts with motor vehicles using experimentation and simulation
No full textPhysical tests were performed with a bicycle and a dummy in a controlled laboratory
environment to reproduce cyclist accidents. The kinematics of 13 sled tests were used to
identify the cyclist head impact location, understand the interaction between the cyclist
and bicycle and to validate a mathematical model.
The finite element software code LS-DYNA was used to simulate 70 cyclist and
pedestrian accidents with motor vehicles with four different vehicle shapes which
supplemented the physical testing. The study has shown that when cyclists and
pedestrians were struck by any of the vehicles their whole body kinematics can be
distinguished into two phases, initially a rotation followed by a sliding action. The Sports
Utility Vehicle (SUV) vehicle produced more of a rotation action rather than sliding,
whereas the other vehicles produced a combination of the two.
The current pedestrian legislation does not cover all head impact locations for cyclists
and therefore needs to be extended to encompass the windscreen and A-Pillar regions of
the vehicles. The wrap around distance (WAD) for all the vehicles, apart from the SUV,
should be extended to encompass a larger region. For the SUV the current WAD region is
adequate in protecting cyclists and pedestrians and does not need to change. The
predicted head impactor angle for cyclists is 40 degrees which is lower than the current
legislative value of 65 degrees and the predicted pedestrian head impact angle is higher at
a value of 80 degrees for the MPV, SM and LFC. For the SUV the proposed impactor
angle increased to 100 degrees for cyclists and pedestrians.
This research has demonstrated significant differences in terms of input variables and
outcomes between cyclist and pedestrian accidents involving vehicles. It has used
mathematical models to obtain injury data from a human mathematical model and
physical testing to replicate real world cyclist accident scenarios. Recommendations have
been proposed for future legislative testing techniques for cyclists, based on existing
pedestrian legislation. These recommendations to alter legislation will improve vehicle
design and make future vehicles more cyclist-friendly
Batch Bayesian Learning of Large-Scale LS-SVMs Based on Low-rank Tensor Networks
No full textLeast Squares Support Vector Machines (LS-SVMs) are state-of-the-art learning algorithms that have been widely used for pattern recognition. The solution for an LS-SVM is found by solving a system of linear equations, which involves the computational complexity of O(N^3). When datasets get larger, solving LS-SVM problems with standard methods becomes burdensome or even unfeasible. The Tensor Train (TT) decomposition provides an approach to representing data in highly compressed formats without loss of accuracy. By converting vectors and matrices in the TT format, the storage and computational requirements can be greatly reduced. In this thesis, we develop a Bayesian learning method in the TT format to solve large-scale LS-SVM problems, which involves the computation of a matrix inverse. This method allows us to include the information we know about the model parameters in the prior distribution. As a result, we are able to obtain a probability distribution of the parameters, which enables us to construct confidence levels of the predictions. In the numerical experiment, we show that the developed method performs competitively with the current methods.Mechanical Engineering | Systems and Contro
Additive Manufacturing: Polymers Applicable for Laser Sintering (LS)
No full textAbstractAdditive Manufacturing (AM) is close to become a production technique changing the way of part fabrication in future. Enhanced complexity and personalized features are aimed. The expectations in AM for the future are enormous and betimes it is considered as kind of the next industrial revolution. Laser Sintering (LS) of polymer powders is one component of the AM production techniques. However materials successfully applicable to Laser Sintering (LS) are very limited today. The presentation picks up this topic and gives a short introduction on the material available today. Important factors of polymer powders, their significance for effective LS processing and analytical approaches to access those values are presented in the main part. Concurrently the exceptional position of polyamide 12 powders is this connection is outlined
THE LINEAR REGRESSION MODEL WITH AUTOCORRELATED ERRORS: JUST SAY NO TO ERROR AUTOCORRELATION
No full textThis paper focuses on the practice of serial correlation correcting of the Linear Regression Model (LRM) by modeling the error. Simple Monte Carlo experiments are used to demonstrate the following points regarding this practice. First, the common factor restrictions implicitly imposed on the temporal structure of yt and xt appear to be completely unreasonable for any real world application. Second, when one compares the Autocorrelation-Corrected LRM (ACLRM) model estimates with estimates from the (unrestricted) Dynamic Linear Regression Model (DLRM) encompassing the ACLRM there is no significant gain in efficiency! Third, as expected, when the common factor restrictions do not hold the LRM model gives poor estimates of the true parameters and estimation of the ACLRM simply gives rise to different misleading results! On the other hand, estimates from the DLRM and the corresponding VAR model are very reliable. Fourth, the power of the usual Durbin Watson test (DW) of autocorrelation is much higher when the common factor restrictions do hold than when they do not. But, a more general test of autocorrelation is shown to perform almost as well as the DW when the common factor restrictions do hold and significantly better than the DW when the restrictions do not hold. Fifth, we demonstrate how simple it is to, at least, test the common factor restrictions imposed and we illustrate how powerful this test can be.Research Methods/ Statistical Methods,
The Social Cost-of-Living: Welfare Foundations and Estimation
No full textWe present a new class of social cost-of-living indices and a nonparametric framework for estimating these and other social cost-of- living indices. Common social cost-of-living indices can be understood as aggregator functions of approximations of individual cost-of-living indices. The Consumer Price Index (CPI) is the expenditure-weighted average of first-order approximations of each individual’s cost-of-living index. This is troubling for three reasons. First, it has not been shown to have a welfare economic foundation for the case where agents are heterogeneous (as they clearly are.) Second, it uses an expenditure-weighted average which downweights the experience of poor households relative to rich households. Finally, it uses only first-order approximations of each individual’s cost-of-living index, and thus ignores substitution effects. We propose a “common-scaling” social cost-of-living index, which is defined as the single scaling to everyone’s expenditure which holds social welfare constant across a price change. Our approach has an explicit social welfare foundation and allows us to choose the weights on the costs of rich and poor households. We also give a unique solution for the welfare function for the case where the weights are independent of household expenditure. A first order approximation of our social cost-of- living index nests as special cases commonly used indices such as the CPI. We also provide a nonparametric method for estimating second- order approximations (which account for substitution effects).Inflation, Social cost-of-living, Demand, Average Derivatives
The Social Cost-of-Living: Welfare Foundations and Estimation
No full textWe present a new class of social cost-of-living indices and a nonparametric framework for estimating these and other social cost-of-living indices. Common social cost-of-living indices can be understood as aggregator functions of approximations of individual cost-of-living indices. The Consumer Price Index (CPI) is the expenditure-weighted average of first-order approximations of each individual’s cost-of-living index. This is troubling for three reasons. First, it has not been shown to have a welfare economic foundation for the case where agents are heterogeneous (as they clearly are.) Second, it uses an expenditure-weighted average which downweights the experience of poor households relative to rich households. Finally, it uses only first-order approximations of each individual’s cost-of-living index, and thus ignores substitution effects. We propose a “common-scaling” social cost-of-living index, which is defined as the single scaling to everyone’s expenditure which holds social welfare constant across a price change. Our approach has an explicit social welfare foundation and allows us to choose the weights on the costs of rich and poor households. We also give a unique solution for the welfare function for the case where the weights are independent of household expenditure. A first order approximation of our social cost-of-living index nests as special cases commonly used indices such as the CPI. We also provide a nonparametric method for estimating second-order approximations (which account for substitution effects).Inflation, Social cost-of-living, Demand, Average derivatives
Tell us our story: Understanding 'religion and violence' in multiple contexts of learning
No full textThis article raises the question about how definitions of religion and violence can be understood as links to the context in which they are formulated. The focus is on the context of academic learning. Understanding a definition as a micro-narrative that reflects the cultural 'archive', the author uses two academic contexts (i.e. Utrecht, The Netherlands and Jakarta, Indonesia) to show how religion and violence are differently understood. These differences are taken as significant information for understanding how the topic of 'religion and violence' is related to cultural understandings of the place of religion in society. The question is raised how 'narrative learning' can help as a strategy to raise awareness about the preconditioning of (academic) definitions of 'religion and violence'
Bilyjomyia Niitsuma & Watson, 2009, new genus
No full textBilyjomyia new genus Type species: Tanypus algens Coquillett, 1902, by present designation. Etymology. The new genus is named after Mr. Bohdan Bilyj, in recognition of his contributions to chironomid taxonomy and systematics. Diagnosis. Male: Anterior abdominal tergites pale, contrasting with dark brown posterior tergites and hypopygium. Antenna with 14 flagellomeres. Occasionally small scutal tubercle present; postnotals present. Wing banded. Foreleg without tibial comb; pulvilli large. Tergite IX with few setae confined to its posterior margin; gonostylus greater than 0.5 length of gonocoxite, with long and nearly parallel-sided distal arm ending in blunt apex bearing megaseta. Female: Abdominal tergites uniformly colored. Antenna with 14 flagellomeres. Antepronotals in two groups. Wing banding more extensive than in male. First tarsomere with sensilla chaetica on mid- and hind legs. Seminal capsule ovoid, with prominent neck placed symmetrically; coxosternapodeme nearly straight; Gp VIII broadly triangular, produced caudomesially. Pupa: Thoracic horn broad, flattened and spinulate on surface; plastron plate with 3 prominent aeropyles on basal margin. Tergite I with pigmented scar. Tergites II–VII with spiniform D 1; T.III–V with D 2 and D 3 taeniate and apically hooked; A.VII and VIII each with 5 LS-setae. Anal lobes longer than broad, with spines on inner margin. Larva: Labrum with small sclerite. Ventral cephalic seta S 9 anteromedial to S 10; S 9 and SSm multibranched, S 10 simple. M appendage with petiolate labial vesicles; pseudoradula with granulation in apical half only. Dorsomental plates with 6–8 moderately sized teeth; inner margin rounded, not extending to pseudoradula. Pecten hypopharyngis with innermost tooth broadened. Posterior parapod with two small claws depressed and expanded basally. Description. Male. Body length 4.8–6.4 mm; wing length 3.2–4.4 mm. Coloration. Head pale to light brown. Thorax pale to brown with darker vittae and pleural sclerites. Wing banded by black macrotrichiae. Legs mostly pale. Abdomen distinctively patterned; anterior tergites pale, contrasting with mostly brown posterior tergites. Gonostylus and gonocoxite brown. Head. Antenna with 14 flagellomeres; apical flagellomere weakly separated, with subapical seta; AR 1.9 –2.0. Eye with dorsal extension. Temporals multiserial. Thorax. Antepronotum well developed, with broad medial notch. Occasionally scutum with small dorsomedial tubercle. Antepronotals in one lateral group; pteropleurals, mesosternals and postnotals present. Wing. Membrane with dense macrotrichiae. Costa produced well beyond R 4 + 5; MCu joining M 3 + 4 just distal of FCu. Legs. Tibial spurs bearing 15–20 side teeth. Foreleg without tibial comb; hind leg with well-developed tibial comb. Tarsal claws pointed, with 2–5 short spines arising from basoventral margin; pulvilli well developed. Abdomen. Tergite IX not enlarged, with 1–8 setae inserted on posterior margin. Gonocoxite simple, evenly setose. Gonostylus greater than 0.5 length of gonocoxite, sharply angled near base; distal arm nearly parallel sided for most of its length, with fine longitudinal ridges, ending in blunt apex. Phallapodeme with or without strong bend near mesial end; membranous aedeagal lobe compact with group of spines. Female. Body length 3.8–4.7 mm; wing length 3.3–4.4 mm. Coloration. Abdominal tergites uniformly colored, pale or brown. Wing banding more extensive than in male. Head. Antenna with 14 flagellomeres; AR 0.23–0.27. Thorax. Antepronotal setae in two groups, dorsolateral and lateral. Legs. First tarsomeres of mid- and hind legs with sensilla chaetica in apical 0.05–0.10. Abdomen. Seminal capsule ovoid, widest near middle, with prominent neck placed symmetrically, partially or completely infuscated. Coxosternapodeme mostly straight, curved only near union with ramus. Segment X sometimes with few setae at corners; Gp VIII broadly triangular, moderately produced caudomesially. Pupa. Body length 6.0–9.0 mm. Coloration. Brown. Abdominal segments lighter laterally and posteriorly. Cephalothorax. Thorax rugose dorsally. Thoracic seta Dc 1 robust with pointed apex, granulose in distal half; Dc 2 very short; Sa longer than Dc 1. Thoracic horn flattened, broadening rapidly from base, with numerous spines on surface. Plastron plate broadly oval to trapezoidal, often with distal margin concave, occupying distal 0.4–0.5 of horn; base of plastron plate with 3 aeropyles, smallest located medially, larger one on either side. Tracheae usually visible, extending through horn sac to base of plastron plate; internal rods absent. FIGURES 6–21. Bilyjomyia fontana new genus, new species, pupa (6–10) and larva (11–21). 6, thoracic horn; 7, thoracic setae; 8, abdomen, dorsal view; 9, abdominal segment I, dorsal view; 10, shagreen on posteromedial part of abdominal tergite IV; 11, head with chaetotaxy, dorsal view (R) and ventral view (L); 12, labral region, dorsal view; 13, showing distance between two labral setae S 2 (D) and width of labral sclerite (W); 14, antenna; 15, apex of antenna; 16, mandible; 17, maxillary palp with apical stylets; 18, dorsomental plate; 19, ligula and paraligula; 20, pecten hypopharyngis; 21, claws of posterior parapod. Abbreviations: CP, coronal sensory pore; Dc1, 2, dorsocentral setae 1, 2; DP, dorsal sensory pore; S 1–11, cephalic setae 1–11; Sa, supraalar seta; SSm, seta submenti; VP, ventral sensory pore. Abdomen. Scar on T.I more or less distinct. Shagreen mainly consisting of serial rows of 2–5 spinules. D 1 - setae on T.II–VII spiniform, 0.2–0.4 times as long as segment, arising from small tubercles; D 2 - and D 3 -setae on T.III–V taeniate, apically hooked, 0.4–0.7 times as long as segment, arising from small tubercles; remaining D- setae short and hair-like. Segment VII with 5 LS-setae inserted laterally on about posterior 0.5 of segment; LS 1 -seta set off from others located equidistantly from each other. Segment VIII with 5 LS- setae inserted laterally on posterior 0.3–0.4 of segment. Anal lobe 2.5 –3.0 times as long as broad; inner margin with fine spines; outer margin with 2 macrosetae, and fringe of fine setae gradually shortening towards apex. Fourth instar larva. Total length 6.2–11.6 mm. Coloration. Head capsule with light brown occipital margin. Head. Cephalic index 0.70–0.80. Cephalic setae S 1 –S 9 and SSm multi-branched, S 10 simple and 2.0– 2.5 times as long as S 9, S 11 not discernable; S 8 anterolateral to dorsal sensory pore and near to S 7; S 9 anteromedial to S 10. Labral region with small sclerite anterior to labral rod, nearly circular to amoebiform, occasionally fragmented, and with characteristically irregular surface. Antenna with 4 segments, 1.2–1.4 times as long as mandible; AR 6.8–8.7. First segment with ring organ located 0.77–0.82 from base; blade shorter than flagellum. Second segment with style about 1.5–1.9 times as long as peg sensilla, these arising from its side. Third segment slightly longer than wide, subequal to segment 4, and subtended by membranous stalk. Basal segment of maxillary palp with ring organ basally. Mandible evenly curved, with basal tooth appressed and apically bifid; ventrolateral seta 1 simple, ventrolateral setae 2 and 3 multi-branched. Dorsomental plate with 6–9 teeth; outermost tooth usually much smaller than others; inner margin of plate rounded, not reaching pseudoradula. M appendage with petiolate lateral vesicles; pseudoradula with granulation in distal half only, not expanded apically. Ligula with 5 teeth, toothed margin concave; paraligula bifid, with inner tooth small, about as long as wide, often appressed to outer tooth and difficult to see. Pecten hypopharyngis with innermost tooth, and sometimes next, broadened. Body. Abdominal segments I–VII with lateral fringes of sparse setae. Procercus bearing 8 anal setae. Two pairs of conical anal tubules present; dorsolateral pair about 3 times as long as its basal width, ventrolateral pair about 2 / 3 times as long as dorsolateral pair. Posterior parapods with 11 large and 5 smaller claws; some large claws with fine spines along inner margin, two small claws distinctly depressed with expanded bases. Remarks. Because of the depressed posterior parapod claws with expanded bases, the larva will not key past couplet 13 in Fittkau & Roback (1983). The pupa will key to Macropelopia in Fittkau & Murray (1986). Couplet 12 in the Murray & Fittkau key (1989) to adult males presents a problem, due to the variable scutal tubercle. If a scutal tubercle is discernable, the male of Bilyjomyia will key to Bethbilbeckia; if absent or obscured, the male will key to couplet 16, but will not fit either alternative (Apsectrotanypus or Radotanypus Fittkau et Murray). The female will key to Natarsia Fittkau and Macropelopia (part) in Saether (1977). The basal segment of the larval maxillary palp with a ring organ near the base, and the pseudoradula with granulation restricted to the distal half, are also found in Bethbilbeckia and Macropelopia, and set the larvae of these genera apart from those of other Macropelopiini. However, Bilyjomyia larvae differ from all other known Macropelopiini in the distinctive labral sclerite, and the relative positions and state of the S 9 - and S 10 - setae. The S 9 -seta multi-branched and anteromedial to a simple S 10 is unique among Macropelopiini. Most Macropelopiini have the S 9 -seta simple and directly anterior or anterolateral to a simple S 10 (Kowalyk 1985). Apsectrotanypus johnsoni (Coquillett), Apsectrotanypus yoshimurai (Tokunaga), Alotanypus kuroberobustus (Sasa et Okazawa) and Brundiniella yagukiensis Niitsuma, have S 10 multi-branched. However, the S 9 is simple in Brundiniella yagukiensis (Niitsuma 2003) and multi-branched in the other three species (Niitsuma 2004, 2005; Watson, personal observation). The two depressed posterior parapod claws with expanded bases will separate Bilyjomyia larvae from all other known Macropelopiini except Brundiniella Roback, and an Australian species, Apsectrotanypus pallipes (Freeman) (Horne & Pettigrove 1989). The inner margins of the dorsomental plates are rounded, contrasting with those of Bethbilbeckia and Brundiniella, which have an inner projection reaching the pseudoradula. In the general form of the thoracic horn, the shape and armature of the anal lobes, the scar of T.I, abdominal setation, and shagreen, the pupa is identical to those of the Macropelopia notata group. Bilyjomyia pupae can only be distinguished from them by the prominent aeropyles at the base of the plastron plate. The adult males of Bilyjomyia can be separated from all other known Macropelopiini by the distinctive color pattern of the abdomen, the small number of setae on T.IX, and the restriction of those setae to the posterior margin of the tergite. Unlike the immatures, there is little in the morphology of the adult male to suggest a close relationship to Bethbilbeckia and Macropelopia. The form of the gonostylus is unlike that of those genera. It more closely resembles the gonostylus of Brundiniella, Derotanypus Roback, Fittkauimyia Karunakaran, Psectrotanypus Kieffer and Radotanypus. Bilyjomyia algens males may have a very small scutal tubercle. It is easily obscured by setae or distortion of the thorax in mounting. To date, it has not been observed in Bilyjomyia fontana. The male lacks the foreleg tibial comb found in Macropelopia and Alotanypus Roback. Bilyjomyia adults have prominent pulvilli, a feature shared with Apsectrotanypus, Brundiniella, Fittkauimyia, Psectrotanypus and Radotanypus, but not found in Bethbilbeckia or Macropelopia. Bilyjomyia females can only be separated from those of other Macropelopiini by a combination of characters. Among the genera with prominent pulvilli, Apsectrotanypus, Brundiniella and Psectrotanypus also have banded wings, but the pattern of markings cannot be confused with that of Bilyjomyia species (see Cheng & Wang 2006; Niitsuma 2003, 2004; Roback 1971). Bilyjomyia females have sensilla chaetica confined to the apical 0.05–0.10 of the first tarsomeres of the mid- and hind legs. Females of Brundiniella eumorpha (Sublette), Alotanypus aris Roback, Apsectrotanypus johnsoni and Radotanypus florens (Johannsen) are similar to those of Bilyjomyia in having sensilla chaetica confined to the apical 0.1 or less of the first tarsomeres of the mid- and hind legs (Watson, personal observation). The Japanese species Apsectrotanypus yoshimurai, Alotanypus kuroberobustus and Brundiniella yagukiensis are similar in this respect (Niitsuma 2004, 2005 in the former two species, personal observation in the last species). Allowing for variation in mounting, there do not appear to be significant differences in the form of Gp VIII between females of Bilyjomyia, and those of Apsectrotanypus, Bethbilbeckia, and Macropelopia. The Gp VIII of North American Psectrotanypus species is more rounded and not as strongly produced. The coxosternapodeme usually appears more evenly curved in the latter genera, but the difference is subtle and influenced by variation in mounting (Watson, personal observation).Published as part of Niitsuma, Hiromi & Watson, Charles N., 2009, Bilyjomyia, a new genus of the tribe Macropelopiini from the Holarctic (Diptera: Chironomidae), pp. 57-68 in Zootaxa 2166 on pages 58-62, DOI: 10.5281/zenodo.18911
Epileptic Seizure Detection using a Tensor-Network Kalman Filter for LS-SVMs
No full textEpilepsy is one of the most common neurological conditions, affecting nearly 1% of the global population. It is defined by the seemingly random occurrence of spontaneous seizures. Anti-epileptic drugs provide adequate treatment for about 70% of patients. The remaining 30%, on the other hand, continue to have seizures, which has a significant impact on their quality of life as they are constantly unsure when these seizures will occur. Reliable seizure detection methods would thus have a significant impact on the lives of these patients. Despite ongoing research efforts involving academia and industry in large international collaborations, epileptic seizure detection and especially prediction is still an unsolved problem. The key to the solution could lie within ultralong-term, reallife datasets that are currently being generated using wearable sensors. However, due to the size of these datasets, conventional learning techniques such as least-square support vector machines (LS-SVMs) can become intractable. Therefore, this work proposes the use of a recently developed tensor network Kalman filtering approach for LS-SVMs (TNKFLSSVM) to detect epileptic seizures [1]. In the TNKF-LSSVM algorithm, the dual problem of the LS-SVM is solved using a recursive Bayesian filtering approach. This way the least-square problem can be solved row-by-row using a Kalman filter, thereby avoiding explicit matrix inversions, while also being able to provide confidence bounds on the estimates. By making use of the tensor-train format [2] to represent the matrices and vectors in the Kalman equations, it is even possible to avoid the construction of the (N + 1) × (N + 1) covariance matrix1. To be able to apply the TNKF-LSSVM algorithm for seizure detection there are still some issues that need to be tackled. One such problem is that the TNKF-LSSVM only performs well when the dataset is properly balanced, which is generally not the case for seizure datasets. Furthermore, for the TNKF-LSSVM to work efficiently for large scale problems the modes of the tensortrains representing the matrices and vectors should be as small as possible, thus it must hold that N + 1 = Q i ni, such that ni is ‘small’ for all i. To overcome both of these challenges we propose using the SMOTE method to oversample the seizure class, such that a balanced training set can be generated that has good factorization properties. Some preliminary results using a small subset of data from a public EEG dataset [3] show that taking the above considerations into account, the TNKF-LSSVM method can have performance that is competitive with a regular LS-SVM. Where the TNKFLSSVM method has the benefit of scaling log-linearly with the size of the dataset (in terms of memory usage) and can provide an uncertainty estimate of the detection. Future work will need 1N is the number of data points in the training set and 1 is added for the bias. to show whether this scaling up works as expected for the entire dataset.Signal Processing System
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