representations have the property that “the whole is greater than the sum of its parts” (Kent et al., 2016). At this point in the thesis, we have discussed much research that indicates that PRC would contain these conjunctive representations, but no direct evidence existed that these representations are in fact highly conjunctive object-based representations and not separately represented features that are co-activated. In 2016, Erez and colleagues provided direct evidence using fMRI to support the hierarchical model and conjunctive representations in the ventral visual stream of humans. The Hierarchical model predicts that early VVS regions contain low-level features and as representations move through the visual stream these features are combined to create increasingly complex object representations. This is in contrast to a non-local binding mechanism where the features are represented independently and are bound by co- activation. In this study, Erez investigated whether the representations of whole objects differed from combined representations of its features. To do this participants viewed different combinations of three features (A, B, C) added to a common base object (similar to adding parts to a Mr. Potato Head toy) during a classic 1-back task where participants pressed a button when they saw an exact repeat. Importantly, a “conjunctive contrast” was performed to compare patterns of activation elicited by different conjunctions of features across two objects: 1 one-feature object + 1 two-feature object (i.e., A + BC versus B + AC versus C + AB). All combinations contained the same three features therefore controlling for visual and mnemonic characteristics. This allowed the
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Modifying the spatiochromatic and spatiotemporal properties of a stimulus so that it can selectively probe the operations of the three retinogeniculostriate processing streams at different hierarchical levels within the same migraineur, and then comparing these ﬁ ndings to non- migraine individuals provides a comprehensive analysis of visual de ﬁ cits and their pathway speci ﬁ city. Certainly, when it comes to behavioral measures, even psychophysi- cal ones, a cautionary note needs to be given with respect to the functional discreteness that is bound to the stimulus. 53,54 Psychophysics never completely isolates the operations of parallel streams. However, making infer- ences about stream biases along with localizing hierarch- ical process stagings based on research design are valid ones, particularly when the metrics are contextually pre- mised on system, and not unit-level analyses. 55 In the current study, the properties of the three visual streams (MC, PC, KC) in migraineurs with aura (MA) and migrai- neurs without aura (MWO) were psychophysically assessed in four separate experiments. In order to target different hierarchical processing loci, we measured four different sensitivities: S-cone retinal sensitivity using the SWAP (Experiment 1), postreceptoral chromatic spatio- temporal contrast sensitivity using heterochromatic, isolu- minant vertical 2-D Gabors presented steadily or counterphase ﬂ ickered for maximal PC- and KC-responsivity (Experiment 2), postreceptoral achro- matic spatiotemporal contrast sensitivity using heterolumi- nant Gabors presented steadily or counterphase ﬂ ickered for maximal PC- and MC-responsivity, respectively (Experiment 3), and postreceptoral/cortical color discri- minability along three cardinal chromatic axes using the Cambridge Trivector Colour Test (Experiment 4). Based on past research, we expected to ﬁ nd signi ﬁ cantly more migraine-related de ﬁ cits in measures that probed different hierarchical stages of the S-cone driving KC stream. The logic goes that if the pathophysiology of migraine is simi- lar to that of glaucoma, then the vulnerable S-cones should yield the greatest de ﬁ cits with the SWAP, with lesser effects showing up for early postreceptoral operations characterized by the contrast measurements and higher- end color discrimination perceptions due to cortical redun- dancies and gain compensations. Alternatively, if indeed migraine is strongly tied to an impairment of GABAergic
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It has been demonstrated (L˝orincz et al., 2002) that maximization of information trans- fer is an emerging constraint in reconstruction networks. Here, we note that the model provides straightforward explanation for the differences found between neurons of the deep and superficial layers of the entorhinal cortex (i.e., that deep layer neurons have sustained responses, whereas superficial layer neurons do not) (Egorov et al., 2002), which is the consequence of the sustained activities in the hidden layer. The model also explains the long and adaptive delays found recently in the dentate gyrus (Henze et al., 2002), which – according to the model – should be there but are not necessary anywhere else along the visual processing stream. Last but not least, the model makes falsifying predictions about the feedback connections between visual processing areas, which – according to the mapping to neocortical regions – correspond to the long-term memory of the model.
The investigation on the representation of non-accidental properties by neurons in the ventral visual stream (VisNet) showed not only that they can arise in a relatively simple network modelling many aspects of processing in the ventral visual cortical stream, but also showed how non-accidental properties could arise, and could show insensitivity, that is, in fact, invariance, with respect to metric properties. The mechanism underlying the encoding of non-accidental properties in VisNet, and we propose in the ventral cortical visual stream, is that as an object transforms into different views over short times, slow learning implemented by for example the temporal trace synaptic learning rule in VisNet results in different metric properties such as the degree of curvature described above to be associated together. This invariance learning of metric properties then enables the neurons to generalize to other objects with the same non-accidental property (e.g. concave curvature), but different metric properties (e.g. degree of curvature), because some of the metric properties of the two objects overlap. This is illustrated in Fig. 4, in which there was no associative learning between the concave-sided objects 5–7, yet the individual neurons, and the population of selective neurons as a whole, responded to all of the concave- sided objects 5–7. This investigation thus shows how invariance can be learned for metric properties of objects, and at the same time how non-accidental properties such as concave vs convex edges which are present for many views of an object become what neurons are tuned to.
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inferior frontal gyrus has been linked to controlled retrieval of semantic information, while the pars triangularis (Brodmann area 45) has been linked to post-retrieval verbal response selection (Badre and Wagner, 2007). Studies of language lateralization in schizophrenia (including paranoid schizophrenia) have identified bilateral cortical activations during linguistic tasks driven primarily by enhanced right hemisphere activations (Sommer et al., 2003; Li et al., 2007). Increased bilateral anterior language network activations to neutral words may mediate enhanced (dysfunctional) retrieval and processing of semantic information and potentially accounts for the previously characterized neutral word classification delay seen in patients with persecutory delusions (Holt et al., 2006b). In response to emotionally valenced words, subjects also showed modulation of ventral visual stream and limbic/paralimbic regions correlated with persecutory delusion severity. As P6 scores increased, threat and neutral valence words each increased ventral visual stream activations in lingual, inferior temporal and fusiform gyri. In healthy subjects, linguistic threat has been shown to modulate limbic/paralimbic (e.g., amygdala, parahippocampus, medial OFC, ACC), semantic processing (vlPFC), and lingual gyrus activations (Isenberg et al., 1999; Lewis et al., 2007; Citron, 2012); negatively valenced pictures commonly increase occipital-temporal ventral visual stream (“what” pathway) activations (Taylor et al., 2000; Sabatinelli et al., 2005; Sabatinelli et al., 2009). Visual processing deficits in schizophrenia have increasingly been characterized as involving predominantly dorsal stream (“where” pathway) abnormalities (Butler et al., 2001; Braus et al., 2002; Chen et al., 2004; Butler et al., 2007a; Seymour et al., 2013). However, increased ventral stream activations were found in paranoid schizophrenic patients compared to healthy subjects during performance of a dual working memory, affectively valenced face- viewing task (Wolf et al., 2011). The ventral visual stream is implicated in object and face perception (Kanwisher and Yovel, 2006), and the left middle portion of the fusiform gyrus (VWFA) is specifically attuned for reading (although not exclusively) (Cohen et al., 2000; Dehaene and Cohen, 2011). Our cohort exhibited increased VWFA (McCandliss et al., 2003) activation in the threat word and neutral word vs. baseline contrasts. These results suggest a potential modality-specific threat (and neutral) bias for visual word and color (instructed-fear) perceptual processing associated with persecutory delusion severity in schizophrenia.
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Previous study on this topic is limited to Lucey et al. [13,14], who projected the final visual speech features of complete profile images to a frontal viewpoint with a linear transform. However, the authors do not justify the use of a linear transform between the visual speech features of different poses, are limited to the extreme cases of completely frontal and profile views and their audiovisual experiments are not conclusive. Compared to these studies, we introduce other projection techni- ques applied in face recognition to the lipreading task and discuss and justify their use in the different feature spaces involved in the lipreading system: the images themselves, a smooth and compact representation of the images in the frequency domain or the final features used in the classifier. We also analyze the effects of pose normalization in the audio-visual fusion strategy in terms of the weight associated to the visual stream. Lucey et al.  propose an audio-visual system based on the concatenation of audio and visual features in a single stream, which is later processed in the speech classifier neglecting the multi-modal nature of speech and the possibility to assign different weights to the audio and visual streams. The main contributions of this study, partially presented in , are the adaptation of pose-invariant methods used in face recognition to the
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were incorporated from LANDSAT ETM+, October, 22, 2000 (www.landsat.org). The ordering of drainage reached to the fifth order as highest order in both sub-catchments (Fig. 3). The software package which was used is ArcGIS, GIS software. The symbols of the parameters used are of standard usages. The parameters computed include stream orders and stream numbers, cumulative length and stream orders, mean cumulative length and stream orders, stream length ratio, bifurcation ratio, drainage density, stream frequency, form factor, circulatory ratio, elongation ratio, constant of channel maintenance, relative relief, percentage of slope, and relief ratio. The statistical methods were also applied to validate data and to obtain further precise results. The various aspects were studied for their inter-relationship which helps to depict the nature of the sub-catchments.
Results for the audio, visual and audiovisual models un- der noisy conditions are shown in Fig. 3. The video-only classifier (blue solid line) is not affected by the addition of the audio noise and therefore its performance remains con- stant over all noise levels. On the other hand, as expected, the performance of the audio classifier (red dashed line) is sig- nificantly affected. Similarly, the performance of the MFCC classifier (purple solid line) is also significantly affected by noise. It is interesting to point out that although the MFCC and end-to-end audio models result in the same performance when audio is clean or under low levels of noise (10 to 20 dB), the end-to-end audio model results in much better per- formance under high levels of noise (-5 dB to 5 dB). It results
Available Online at www.ijpret.com 167 stream order in the study area was computed as fifth.To evaluate the basin morphometry, various parameters like stream number, stream order, stream length, stream length ratio, bifurcation ratio have been analyzed using the standard mathematical formulae.
The study area covers 732.95 Km2 in suke sub-watershed of Tawa reservoir catchment area of Hoshangabad, Bhopal (M.P.). the drainage network of suke sub watershed and measurement of Linear, Aereal and Relief aspects of basin by digitized using remote sensing and GIS techniques. The drainage network shows that the terrain exhibits dendritic drainage pattern. Stream order ranges from one to sixth order. The drainage density in the area 2.06km/km.2 belong to moderate category.Stream frequency in the area 2.82 and texture ratio 4.08 is range to belong moderate condition. The form factor indicate the sub watershed are less elongated in shape. The high value of circulatory ration the sub watershed is characterize by high to moderate relief and drainage system structurally controlled but the study area Rc is less than .50 indicating they are less elongated in shape.
In Table 1, we present the statistics of the AVSD dataset. Given the fact that the lengths of the I3D- RGB frame feature sequences are more than 20 times longer than the questions, using a recurrent neural network to encode the visual feature se- quences will be very time consuming, as the vi- sual frames are processed sequentially. Our pro- posed question-guided video representation mod- ule summarizes the video sequence efficiently - aggregating the visual features by question-guided attention and weighted summation and performing gating with a question-guided gate vector, both of which can be done in parallel across all frames. 4.2 Experimental Setup
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Individual counting of the streams in the river basin reveals the total number of the streams. According to the stream number of streams in a watershed can easily be segregated and measured. The entire Thadayampatti drainage basin has total of 44 streams. The maximum number of streams 32 is found in watershed 1 (WS1) and the minimum number of streams 3 is found in watershed 3 (WS3). Among the 2 watershed, the WS1 has produced more tributaries indicating good runoff characteristics of soil. The bilinear diagram has been drawn using Excel Sheet to indicate correlation between stream order and stream length. It shows that the stream number decreases when the stream order increases
Linear aspects of the basins are related to the channel patterns of the drainage network wherein the topological characteristics of the stream segments in terms of open links of the network system are analyzed. The morphometric investigation of the linear parameters of the basins includes stream order (Sμ), Stream number (Nμ), Stream Length (Lμ), Mean Stream Length (Lsm), Stream Length Ratio (RL), Bifurcation Ration (Rb), Mean Bifurcation Ratio (Rbm), Drainage Density (Dd), Drainage texture (Dt), Stream Frequency (Sf), Elongation ratio (Re), Circularity Ratio (Rc), Form Factor Ratio (Rf), Length of Overland Flow (Lg), Relief (R) and Relief ratio (Rr). Some of the important linear aspects have been computed as shown in (Table 2 and 3).
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The total number of stream segments divided by perimeter of that area is known as drainage texture. Hard rock terrain represents the coarse texture while the soft and weak rock terrain with sparse vegetation represents fine texture. In 1950, Smith classified drainage texture into five categories on the basis of drainage density. Drainage density < 2.0 reveals very coarse drainage texture, whereas 2.0 to 4.0 show coarse in nature and 4.0 to 6.0 shows moderate. The values recovered from the study for fine texture ranges from 6.0 to 8.0, and the value which shows > 8.0 represents very fine texture. Figure 5 depicts the drainage texture for the entire catchment and the value shows 2.21, which represents coarse texture while the sub-basin texture of the catchment ranges from 0.41 to 1.16, which is less than 2.0, indicating very coarse grain drainage texture.
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The morphometric analysis of Lower penna sub-basins using GIS retrieved that, Geographical Information System helps the researchers to analysis the drainage basin easily and accurately. The study of linear aspects of drainage basin result shows that, the basin has been formed in dendritic pattern with fourth order stream, plotting the logarithm of number of streams against stream order shows a straight line which states the number of streams usually decreases as the stream order increases. The result of relief aspect shows the study area is extremely rugged with high relief and high stream density, the result of arial aspect shows the texture of drainage is less and the result of elongation ratio indicates the drainage is high relief and steep ground slope. Integration of the thematic maps with hydro-morphology resulted in better result to delineate the groundwater potential zones in the study area.
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The first step in the quantitative analysis of drainage basin is designation of stream order. The term “stream order” is a measure of the position of a stream in the hierarchy of tributaries. In the present study, the channel segment of the drainage basin has been ranked according to Strahler’s stream ordering system. According to Strahler (1964), the 1st order streams are those, which have no tributaries. The 2nd order streams are those, which have tributaries only of 1st order streams, where two 2nd order channels join, a segment of 3rd order is formed. When two 3rd order segments join, a 4th order channel is formed and so on. Morphometric analysis was carried out for the Halia drainage area. The parameters computed in the present study using GIS technique includes stream order, stream length, bifurcation ratio, drainage density, stream frequency, form factor, circulatory ratio, elongation ratio, relief ratio and ruggedness number. The input parameters for the present study such as area, perimeter, elevation, stream length etc. were obtained from digitized coverage of drainage network map in GIS environment. The morphometric parameters were computed using the standard formulae which are presented in Table 1.
Morphometric analysis of a drainage system requires delineation of all existing streams. The stream delineation was done digitally in GIS (Arcview 3.2a) system. All tributaries of different extents and patterns were digitized from survey of India toposheets 1961 (1:50,000 scale) and the Sub catchment boundary was also determined for Shaliganga Subcatchment. Similarly, two watersheds (D2A and D2B) were also delineated and measured for intensive study. Digitization work was carried out for entire analysis of drainage morphometry. The different morphometric parameters have been determined as shown in table1.1.
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Although with (Gallo et al., 2016) there is already one study employing neural network technology not only for DIC but also for PSS, their approach was not applicable to our project for two reasons. First, as mentioned earlier, they perform PSS only indirectly based on changing class labels of consecutive pages. Since we only have 17 docu- ment categories and a majority of them belong to one cate- gory (”letter”), we need to perform direct separation of the page stream by classifying each page into either SD or ND. Second, quality and layout of our data is extremely hetero- geneous due to the long time period of document creation. We expect a lowered performance by solely relying on vi- sual features for separation. Therefore, taking the previous work of (Gallo et al., 2016) as a starting point, we pro- pose our approach for direct PSS as a binary classification task combining textual features and visual features using deep neural networks. We compare this architecture against a baseline comprising an SVM classifier solely relying on textual features.
The original visual motion stimuli comprised 12 different 6-sec virtual tunnels (created with Open GL) consisting of a straight, a curved, and another straight segment (resolution = 550 × 549) with varying turn angles of 30°, 40°, 70°, 80°, 110°, and 120° to the left or right. These stimuli were Fourier-transformed using the discrete 3-D Fourier transform implemented in Matlab (MathWorks, Natick, MA). The phase components of all frequencies were then randomly exchanged, and the signal was back- transformed into the time domain, resulting in the phase- scrambled stimuli. Corresponding stimuli had the same amount of local flow, average contrast, and luminance, but phase-scrambled stimuli had larger frame-wise local luminance changes. As such phase scrambling maintained the spatial and temporal visual motion statistics relevant for low-level visual processing and eliminated the presence of any obvious form, edge, or structure. We therefore refer to phase-scrambled stimuli as indistinct stimuli and tunnel stimuli as meaningful. Stimulus presentation was followed by a 3-sec response interval in which participants were instructed to indicate, with a button press, the main di- rection of optic flow motion (left or right, Figure 1A). The duration between onset of response interval and button press is referred to as response time. The optic flow motion task will from now on be termed as direction task. Stimuli were presented in pairs of the same stimulus type such that one stimulus block was 18 sec, and the blocks
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We therefore aimed to identify a retinal phenotype by exam- ining low-level visual processing. This would also help explain our previous findings suggesting separable early (magnocellu- lar) and high-level (dorsal stream) visual deficits in WBS patients well characterized at the molecular level (3). We provide objective anatomical and physiological data, which we believe are novel, by means of noninvasive optical coherence tomography (OCT), confocal scanning laser tomographic imaging (Heidelberg retinal confocal tomography [HRT]), and multifocal electrophysiologi- cal (multifocal electroretinography [mfERG]) techniques that can explain an independent visual phenotype in WBS. Accord- ingly, our results demonstrate that the objectively identified neu- ral phenotype at the level of the retina predicts low-level visual deficits (3) and is independent of higher-level visual cortical–dor- sal stream damage.
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