To find out which component of DIALIGN is to blame for its unsatisfactory performance on some of the BAliBASE data, we applied our program to BAliBASE (a) using the non-anchored default version of the program and (b) using the core blocks as anchorpoints in order to enforce biologically correct alignments of the sequences. We then compared the numerical DIALIGN scores of the anchored alignments to the non-anchored default alignments. The results of these program runs are summarised in Table 3. The numerical alignment scores of the (biologically cor- rect) anchored alignments turned out to be slightly below the scores of the non-anchored default alignments. As an example, Figure 4 shows an alignment calculated by the non-anchored default version of DIALIGN for BAli- BASE reference set lr69. This sequence set consists of four DNA-binding proteins and is a challenging alignment example as there is only weak similarity at the primary sequence level. These proteins contain three core blocks for which a reliable multi-alignment is known based on 3D- structure information. As shown in Figure 4, most of the core blocks are misaligned by DIALIGN because of the low level of sequence similarity. With the BAliBASE scor- ing system for multiple alignments, the default alignment produced by DIALIGN has a sum-of-pairs score of only 33%, i.e. 33% of the amino-acid pairs in the core blocks are correctly aligned. The column score of this alignment 0%, i.e. there is not a single column of the core blocks cor- rectly aligned.
In this paper, we consider an approximation sequence of a common ﬁxed point generated by Halpern type iteration with a ﬁnite family of nonexpansive mappings in a Hadamard space. We propose another style of Halpern type iteration with multiple anchorpoints and prove that it converges strongly to a common ﬁxed point. MSC: 47H09
An anchor point in DEA is an extreme- efficient DMU lying on the intersection of some strongly and weakly efficient frontiers of the PPS. An anchor point is, therefore, an extreme-efficient DMU in which some inputs can increase and/or some outputs can decrease without passing through the interior of the PPS. Anchorpoints play a significant role in DEA theory and applications. The concept of anchor point was used in Thanassoulis and Allen  (1998) for the generation of unobserved DMUs in order to reduce appropriately the DEA-inefficient boundary of the PPS. Anchorpoints were first named and identified by Allen and Thanassoulis  (2004). They proposed a method for detecting anchorpoints of the constant returns to scale production possibility set (CRS-PPS) with one input and multiple outputs. However, their method is not applicable to multiple inputs and outputs. Thanassoulis et al. (2012) proposed another approach to identify anchorpoints, using the radial efficiency scores and slack variables at the optimal solution of envelopment models. They extended the proposed approach in Allen and Thanassoulis  (2004) to the multiple inputs and outputs case in variable returns to scale production possibility set (VRS-PPS) in order to improve envelopment by means of unobserved DMUs. Bougnol and Dulá  (2009) defined the anchor point for the VRS-PPS. They provided a specialized procedure to identify anchorpoints based on their geometrical properties. Rouse (2004) employed this idea in identifying prices for health care services. For more detail about the notion and applications of the anchor DMUs, see Bougnol  (2001) and Allen and Thanassoulis  (2004). Since the set of anchor DMUs is a subset of the set of extreme DMUs, the set of extreme DMUs must be obtained. For this aim, one can use the proposed algorithms in Charnes,
People tend to process graph relations using systematic visual routines, particularly when coordinating data with text (e.g., Michal et al., 2016). Here we show that people exhibited idiosyncratic but highly consistent feature pref- erences (“anchorpoints”) to guide visual routines when judging graph relations, even when there was no verbal component to the task. Most participants judged size con- figurations by attending to the taller bar first, whereas most participants judged contrast configurations by at- tending to the darker bar first. Importantly, these anchorpoints persisted in a task-dependent manner: When par- ticipants judged graphs that varied in both size and con- trast (orthogonal task), preference ratios defined by the task-relevant dimension were significantly stronger than preference ratios defined by the task-irrelevant dimension. Thus, when a graph can be interpreted in multiple ways, people attend to a single dimension (e.g., size) in a specific order (e.g., taller bar first), which may generate a specific interpretation of the graph (e.g., “the taller bar is on the left”). These results are consistent with the proposal that visual routines do not merely facilitate graph com- parisons by deploying attention to data points over time; rather, specific visual routines are associated with specific graph interpretations.
In contrast, in our proposed solution, the SenCar only visits a subset of selected sensor nodes (anchorpoints) and collects data through multi-hop transmissions, which can enhance data collection fairness, reduce data collection latency, and avoid stopping at unnecessary sensor locations for battery recharge. In wireless rechargeable sensor networks, as each sensor has different energy status at different time, it is desirable to recharge as many sensors with low energy as possible to ensure the perpetual operation of sensors. Accordingly, the sensors located at the selected anchorpoints should be those with the most urgent needs of energy supplement. In the meanwhile, to better enjoy benefit of energy from the SenCar, more anchorpoints should be selected. However, this would prolong the travelling tour length and increase the data gathering latency. Thus, it is an inherent tradeoff between the number of sensors to be recharged in a tour and the data gathering latency. Based on these observations, when determining the sequence of anchorpoints to visit, we jointly consider the remaining energy levels of sensors and the travelling tour length of the SenCar.
The main difference of anchorpoints (control points) be-tween radial basis functions warping and piecewise polyno- mial warping is that, it must at least three anchorpoints ex-ists in piecewise polynomial warping if we have to handle local distortions (three points form a triangle), while only one anchor point is needed in radial basis warping. Fur-thermore, due to the computation complexity, the anima-tion speed can achieve in real-time with piecewise polyno-mial method. Traditionally, warping was applied to correct geometric distortions such as the distortion of viewing ge-ometry. In this paper, we applied warping for human fa-cial expressions. The facial warping techniques are also re quired for various future applications, such as face recog-nition, criminal identification, authentication in secure sys-tem, dynamic imagery, speech (action) semantic animation, multimedia and virtual reality construction, low-bandwidth video conference image transmission, and intelligent man-machine interface.
Our software simulation and physical modeling experi- ments provide a new approach to measuring topo- graphic data uncertainty where legacy data from a variety of data sources can be integrated with HRT data to expand the time-scales of topographic change detec- tion. As anticipated, the difference in the actual and ex- pected errors of our HRT physical model experiment was quite small (< 2 mm). Current instrumentation and field methods often have a higher minimal level of detec- tion, so this value is quite acceptable for HRT data sources and represents a value comparable to or lower than most uncertainty measures found in current research exploring topographic change detection. The uncertainty model, associated anchorpoints, and stochastic estimator was further applied to a software simulation whereby a variety of remote-sensing data sources were used to simu- late data capture from legacy data sources. Our findings show the estimated error coincides with the actual error using certain sensors (Kinematic GNSS, ALS, TLS, and SfM-MVS). Data from 2D imagery and static GNSS did not perform as well at the time the sensor is integrated into estimator. Nevertheless, the software simulation shows the approach can be used to estimate the error as- sociated with all elevation values in the legacy and HRT data over the time-period of the simulation.
This paper proposed a novel methodology for general fish classification based on significant combined features that have been extracted using Gabor filter, anchorpoints detection, statistical measurements from texture and shape measurements. Where 4 features were extracted using Gabor filter, 24 features were extracted using angle and distance tools and 2 features were extracted using statistical measurements. Then; the combined extracted feature are used for the recognition of fish images by the hybrid meta- heuristic algorithms (genetic algorithm with iterated local search) with back propagation classifier (GAILS-BPC), to classify the fish images into dangerous and non-dangerous, and to recognize the dangerous fish families into predatory and poison fish family, and recognize the non-dangerous fish families into garden and food fish family. The proposed features extracting methods and the meta- heuristic algorithm significantly improved the recognition accuracy of the BPC by enhancing and optimizing the weights that will be used in the training and testing process of the BPC.
In this study, the description of the directions of shifts was always topographically relative, i.e. it focussed on the position of a selected POI in relation to the other two POIs forming one virtual triangle. Intrinsically, the change in the position of such a tri- angle as a whole within the skull’s outline remained unclear, as there was a lack of knowledge about a biologically (phylogenetically) truly stable centre point of the skull. The concept of using centroid size and Procrustes superimposition, as employed by Drake and Klingenberg , Drake , and Ge et al.  when comparing skulls of dogs and lagomorphs all in their adult state, was not adopted in our study in order not to neglect the age-related differences in size of the skulls. Consequently, the choice of an appropriate, i.e. topographically stable reference point was most desirable, but had to be attempted by a different approach. An ideal reference point (called anchor point in this study) would remain in its position, with the (Neuro-)Cranium growing around it. This prob- lem was addressed by hypothetically choosing two alternatives of anchorpoints in the median plane of the Basis cranii, i.e. POI a (on the Crista orbitosphe- noidalis) and POI b (on the Crista sphenooccipitalis), respectively (both with a strong clinical relevance due to their topographical relation to the pituitary gland and to most significant intracranial blood vessels).
After brainstorming and weighing all of our original design ideas, we decided to move forward with a prosthetic design that used hinges in order to drive the motion of the thumb. The hinge uses four points and four linkages in order to do so. The distal points are anchored distal of the IP joint while the proximal two points are anchored proximal to the IP joint. Depending on how much movement the patient has of the residual thumb distal to the MCP joint, the proximal anchorpoints of the prosthetic can be shifted back to sit over the MCP joint. However, upon meeting with our project sponsor, he informed us that there was most likely not enough residual left to drive that motion, and so our project decided to change to a mechanical wire driven prosthetic. A wire will connect to a wrist strap, and sit atop the proximal piece. The wire will be inserted inside the prosthetic, and wrap around an internal cam that will thus drive the forward and backwards motion of the prosthetic. As the proximal piece is moved forward by the motion of the residual, the cable will get shorter pulling the lever down, and pushing the distal piece down. This wire was incorporated into a design that resembled the human thumb by taking a 3D scan of the patients non-injured hand, mirroring it, and then cutting away the excess pieces to create a prosthetic that will match patients form. By doing so, we were able to have a body-powered prosthesis that does not have a mechanical appearance and roughly resembles the patients normal thumb.
structures; h: height; ϕ, λ: geographic coordinates, t: Uni- versal Time. There are no restrictions to the nature of the large scale model provided it takes height and horizontal co- ordinates as input. Examples are models of the “profiler” type which use large scale “maps” for profile anchorpoints (e.g., E, F1, F2 peak properties) like the International Ref- erence Ionosphere (IRI). Typical examples for smaller scale structures are ridges, troughs and wavelike disturbances. The advantage of modulation by multiplication is that there is no danger to get zero or negative values of electron density as long as the background and modulations are > 0 everywhere. For each modulation model, unity means “undisturbed”.
ages is long, and so a full automatic selection of a targeted area may introduce a large registration error. Howev- er, the interactive operation in our method requires only the decision of seven positions around the upper or lower teeth, and so this operation is not burdensome to the dentist. In the selection of five anchorpoints along a teeth row, we need not worry about the accuracy of positioning between two acquisitions, because the curve of a teeth row is used for forming the targeted area approximately, and an accurate registration process is performed in the local registration. To make a well smoothed curve, we used the Lagrange polynomial. In our method, we pay attention to the length of a segmented curve such as P P i ′ ′ i + 1 . This length may change if the center of the head
In 1962, the HSRS (Health-Sickness Rating Scale) was published. Studies of the HSRS resulted in a proposal for a new scoring system in the 1970s, the Global Assessment Scale (GAS). Further development led to GAF in 1987. The split version of GAF proposed in 1992 had separate scales for symptoms (GAF-S) and functioning (GAF-F) [3,4,9,10,14,15,17-21]. Internationally, both single-scale and dual-scale systems are in use. In both the single-scale version and the separate GAF-S and GAF-F scales, there are 100 scoring possibilities (1-100). The 100-point scales are divided into intervals, or sections, each with 10 points (for example 31-40 and 51-60). The 10-point intervals have anchorpoints (verbal instructions) describing symptoms and functioning that are relevant for scoring. The anchorpoints represent hierarchies of mental illness [3,10,22]. The anchorpoints for interval 1-10 describe the most severely ill and the anchorpoints for interval 91-100 describe the healthiest. The scale is provided with exam- ples of what should be scored in each 10-point interval. For example, patients with occasional panic attacks are given a symptom score in the interval 51-60 (moderate symptoms), and patients with conflicts with peers or coworkers and few friends, a functioning score in the interval 51-60 (moderate difficulty in social, occupational or school functioning) [14,23]. The finer grading within intervals provides the possibility of distinguishing between nuances , but there are no verbal instruc- tions for this grading found on either of the two scales.
The Moscow architect M. Savchenco wrote a book “Architecture as a Science” where he explained the co-relation between such elements of architectural space as tópos and locus. The ‘topos 1 ’ combine the system of loci bringing the sense of place. Macro-space as a topos of urban life – is a recognized territory with locus and flows. The ‘archetype’ of a place includes the expected social activities of a person or a social group.  The mental map of a city is built of archetypes in a context of relationships, functions, symbolic meaning, time, experience, and other parameters of a particular place. In referring to “locus” we would allude to any space having center and boundaries, while by referring to “flows” we allude to a place of transit with anchorpoints ant nodes.
In controlled experimental studies, as can be seen in Figure 1, neither of the two syllabic parses, simplex or complex, has been observed to show the left edge to anchor interval as more stable than the center to anchor and right edge to anchor intervals. At high levels of variability, however, the probabilistic model developed in our work can produce patterns whereby the left edge to anchor interval is more stable than the other two intervals. This occurs regardless of the syllable parse when the anchor index is high (e.g. 15), which represents a high degree of variability in the intervals (the reason why high interval va- riability results in this pattern is explained in Shaw et al. 2009). Under these conditions of high variability, both values of the essential vari- able (simplex and complex onset parses) gener- ate a pattern whereby the left edge to anchor in- terval has a lower RSD than the center to anchor interval and the right edge to anchor interval. Thus, at this level of variability, stability-based phonetic heuristics, i.e., center to anchor stability implies a complex onset parse, are rendered inef- fective in distinguishing syllabic parses.
We observe that although people were asked to mark all anchors for every item they thought was an- chored, they actually produced only 1.86 anchors per anchored item. Thus, people were most con- cerned with finding an anchor, i.e. making sure that something they think is easily accommodatable is given at least one preceding item to blame for that; they were less diligent in marking up all such items. This is also understandable processing-wise; after a scrupulous read of the text, coming up with one or two anchors can be done from memory, only occa- sionally going back to the text; putting down all an- chors would require systematic scanning of the pre- vious stretch of text for every item on the list; the latter task is hardly doable in 70 minutes.
Wireless sensors networks are immediately forward its packets to organizer with seed node present in network. Seed node perform vital role in wireless sensor network, for each and every movement in communication is controlled by seed node but some attacks occurred for transmission attacker node gather the information from one node forward data packet to another node. It causes network depletion during packet transmission. In proposed Enhanced packet covering and stitching algorithm (EPCSA) method covers the data packet and stitched before packet transmission. Intruder present in the network not fetch the information during communication, so network lifetime is improved and end to end delay is minimized. Seed node collects all the information from cluster head and any other node present in network. Cluster head act as anchor node to organize data such node position and coverage and connectivity to neighbor that kind of information ’ s are forwarded to seed node.