julilili42/papers huggingface dataset:
Potential Precursors for Terminal Methylidene Rare‐Earth‐Metal Complexes Supported by a Superbulky Tris(pyrazolyl)borato Ligand
2019-09-06

Abstract A series of solvent‐free heteroleptic terminal rare‐earth‐metal alkyl complexes stabilized by a superbulky tris(pyrazolyl)borato ligand with the general formula [Tp t Bu,Me LnMeR] have been synthesized and fully characterized. Treatment of the heterobimetallic mixed methyl/tetramethylaluminate compounds [Tp t Bu,Me LnMe(AlMe 4 )] (Ln=Y, Lu) with two equivalents of the mild halogenido transfer reagents SiMe 3 X (X=Cl, I) gave [Tp t Bu,Me LnX 2 ] in high yields. The addition of only one equivalent of SiMe 3 Cl to [Tp t Bu,Me LuMe(AlMe 4 )] selectively afforded the desired mixed methyl/chloride complex [Tp t Bu,Me LuMeCl]. Further reactivity studies of [Tp t Bu,Me LuMeCl] with LiR or KR (R=CH 2 Ph, CH 2 SiMe 3 ) through salt metathesis led to the monomeric mixed‐alkyl derivatives [Tp t Bu,Me LuMe(CH 2 SiMe 3 )] and [Tp t Bu,Me LuMe(CH 2 Ph)], respectively, in good yields. The SiMe 4 elimination protocols were also applicable when using SiMe 3 X featuring more weakly coordinating moieties (here X=OTf, NTf 2 ). X‐ray structure analyses of this diverse set of new [Tp t Bu,Me LnMeR/X] compounds were performed to reveal any electronic and steric effects of the varying monoanionic ligands R and X, including exact cone‐angle calculations of the tridentate tris(pyrazolyl)borato ligand. Deeper insights into the reactivity of these potential precursors for terminal alkylidene rare‐earth‐metal complexes were gained through NMR spectroscopic studies.
Social calls of Myotis nattereri during swarming: Call structure mirrors the different behavioral context
2019-09-06

Swarming is a characteristic behavior of bats that occurs in different social contexts. We studied the swarming behavior of Myotis nattereri at a maternity colony and at an autumn swarming site in South-West Germany by using synchronized sound and video recordings. Swarming was always associated with social vocalizations consisting of four frequently occurring call types. Call type A was a short call with a broadband steep-shallow-steep downward frequency modulation. Call type B consisted of two elements beginning with a broadband upward hooked element followed by a steep frequency modulated element. Call type C showed a characteristic rapid downward-upward-downward frequency modulation. Call type D was a long sinusoidal trill-like call with high variability in signal structure. All call types were recorded at the maternity colony, as well as at the autumn swarming site, but the incidence of each call type differed distinctly between the study sites. At the maternity roost, type A calls were most commonly produced. We found evidence for an individual signature in this call type and suggest that this social call has the function of a contact call in Natterer's bats. At the autumn swarming site, type D calls were the most common social calls; in contrast, this call type was recorded only twice at the maternity roost. The occurrence of trills mainly at the autumn swarming site and their high variability suggests that trills function as male advertisement calls in M. nattereri.
Inducing a human-like shape bias leads to emergent human-level distortion robustness in CNNs
2019-09-06

Convolutional neural networks (CNNs) have been proposed as computational models for (rapid) human object recognition and the (feedforward-component) of the primate ventral stream. The usefulness of CNNs as such models obviously depends on the degree of similarity they share with human visual processing. Here we investigate two major differences between human vision and CNNs, first distortion robustness---CNNs fail to cope with novel, previously unseen distortions---and second texture bias---unlike humans, standard CNNs seem to primarily recognise objects by texture rather than shape. During our investigations we discovered an intriguing connection between the two: inducing a human-like shape bias in CNNs makes them inherently robust against many distortions. First we show that CNNs cope with novel distortions worse than humans even if many distortion-types are included in the training data. We hypothesised that the lack of generalisation in CNNs may lie in fundamentally different classification strategies: Humans primarily use object shape, whereas CNNs may rely more on (easily distorted) object texture. Thus in a second set of experiments we investigated the importance of texture vs. shape cues for human and CNN object recognition using a novel method to create texture-shape cue conflict stimuli. Our results, based on 49K human psychophysical trials and eight widely used CNNs, reveal that CNNs trained with typical “natural” images indeed depend much more on texture than on shape, a result in contrast to the recent literature claiming human-like object recognition in CNNs. However, both differences between humans and CNNs can be overcome: training CNNs on a suitable dataset induces a human-like shape bias. This resulted in an emerging human-level distortion robustness in CNNs. Taken together, our experiments highlight how key differences between human and machine vision can be harnessed to improve CNN robustness---and thus make them more similar to the human visual system---by inducing a human-like bias.
Meaning maps and deep neural networks are insensitive to meaning when predicting human fixations
2019-09-06

An important aspect of vision – control of eye-movements in scene viewing – is intensely debated, with many studies suggesting that people look at scene regions rich in meaning. A recent proposal suggests that the distribution of meaning can be quantified by ‘Meaning Maps’ (MMs). To create MMs, images are segmented into partially overlapping patches, which are rated for their meaningfulness by multiple observers. These ratings are combined into a smooth distribution over the image. If MMs capture the distribution of meaning, and if the deployment of eye-movements in humans is guided by meaning, two predictions arise: first, MMs should be better predictors of gaze position than saliency models, which use image features rather than meaning to predict fixations; second, differences in eye movements that result from changes in meaning should be reflected in equivalent differences in MMs. Here, we tested these predictions. Results show that MMs performed better than the simplest saliency model (GBVS), were similar to a more advanced model (AWS), and were outperformed by DeepGaze II – a model using features from a deep neural network. These data suggest that, similar to saliency models, MMs might not measure meaning but index the distribution of features. Using the SCEGRAM database, we tested this notion directly by comparing scenes containing consistent object-context relationships with identical images, in which one object was contextually inconsistent, thus changing its meaning (e.g., a kitchen with a mug swapped for a toilet roll). Replicating previous studies, regions containing inconsistencies attracted more fixations from observers than the same regions in consistent scenes. Crucially, however, MMs of the modified scenes did not attribute more ‘meaning’ to these regions. DeepGaze II exhibited the same insensitivity to meaning. Both methods are thus unable to capture changes in the deployment of eye-movements induced by changes of an image’s meaning.
Introducing the Mosaic of Integration Theory
2019-09-06

This introductory chapter of the third edition of European Integration Theory (OUP 2018) addresses the rationale for a book on European integration theory and introduces the contributions to the book. It begins by addressing the question of Why Study Integration Theory; it then defines the terms ‘integration’ and ‘theory’ and introduces the ‘mosaic of European integration theory’ as the book’s central concept. The chapter also offers an overview of European integration as a process which has been studied for several decades now. To that end, the chapter recalls distinct phases of integration and the respective parts of the mosaic which have been developed to understand and explain them based on descriptive, analytical and constructive theorising. Each phase is distinguished by historical context, leading questions and relevant theoretical reference points. The book’s extensive section on Studying European Integration by taking account of ‘contexts of theoretical development’ and addressing the question of ‘competing or complementary theoretical approaches’ which also identifies the functions and areas of theory. In concluding, the chapter details the concept of the ‘Mosaic of Integration Theory’ and introduces the chapter structure of the book’s contributions.
Decoding the Viewpoint and Identity of Faces and Bodies
2019-09-06

Our visual system allows us to recognize familiar individuals across different viewpoints, despite large differences in low-level visual information. Previous neuroimaging research has shown that there is a hierarchical organisation across face-responsive brain regions, with lower-level regions representing head viewpoint and higher-level regions representing face identity. In this study, we investigated whether a similar hierarchy is present in body-responsive brain regions, as we also see bodies from many different viewpoints and psychological research has shown we also use information from the body for identification. Furthermore, we investigated whether representations of viewpoint and identity are face and body specific, or generalise to a common representation. We trained participants to recognize three individuals from images of their face and body. We then recorded their brain activity using fMRI while they viewed images of the face and body (shown separately) of the individuals from different viewpoints. Participants responded to the identity or viewpoint, revealing differences in neural representation depending on which feature participants attended to. We found that the occipital face area and extrastriate body area contain representations of face and body viewpoint, and that these viewpoint representations generalize across the face and body (e.g. a classifier trained to distinguish viewpoint of faces could decode viewpoint of bodies). Furthermore, we found that the fusiform body area (FBA) represents body identity in a viewpoint-invariant manner. We decoded face identity in the FBA, and also found a trend in the anterior temporal face area, that has previously been shown to represent face identity. In total, our results show that lower-level face- and body-responsive regions represent viewpoint, and these representations are not driven by low-level visual similarity. We show that the FBA represents body identity, indicating that a similar hierarchy is present for body identity representations in occipi-totemporal cortex as has been previously identified for faces.
The history of the elements influences object correspondence in the Ternus display
2019-09-06

We perceive an object, e.g., a frog jumping around, as a continuous entity, even if the object temporarily disappears behind other objects on its path. In order to solve this correspondence problem our visual system has to connect the right instances of an image across space and time. It has been suggested that this process can depend on image-based information, i.e., the retinal size of an object, as well as on object-based information, i.e., the perceived size of an object. Manipulating object-based information, however, often leads to changes of image-based information, e.g., luminance contrast, as well. Our aim was to clearly distinguish between these two levels by manipulating the temporal history of objects without changing the objects themselves. We used a Ternus display, an ambiguous apparent motion display. Depending on how correspondence is solved, the Ternus elements are perceived either as moving together (group motion) or as one element jumping across two others (element motion). We manipulated the temporal history of the Ternus elements by showing a short movie prior to presenting the Ternus display itself. In the movie, the Ternus elements either moved together along the same trajectory (common history) or separately (separate history), in order to manipulate how much the elements were perceived as connected to each other. If the object-based information about the history of the Ternus elements plays a role for establishing correspondence, we expected more group motion percepts in the common compared to the separate history condition. We found the expected result, but only if the motion in the movie was similar to the Ternus motion. In addition, this history effect could be generalized to another type of history, i.e. synchronized and desynchronized luminance changes. Our findings confirm that object-based information can influence object correspondence strengthening the evidence in favor of an object-based correspondence mechanism.
Behavioural evidence for the existence of a spatiotopic free-viewing saliency map
2019-09-06

Humans gather high-resolution visual information only in the fovea, therefore they must make eye movements to explore the visual world. Inspired by results in attention research (Treisman 1980), it has been proposed that free-viewing fixations are driven by a spatial priority or “saliency” map. Whether this is the case has been debated for decades in neuroscience and psychology. One hypothesis states that priority values are assigned locally to image locations, independent of saccade history, and are only later combined with saccade history and other constraints to select the next fixation location. A second hypothesis is that there are interactions between saccade history and image content that cannot be summarised by a single value. For example, if after long saccades different content drives the next fixation than after short saccades, then it is impossible to assign a single saliency value to image locations. Here we discriminate between these possibilities in a data-driven manner. We extend the DeepGaze II model (Kümmerer et al., 2017) to a new model of scanpath prediction. First, we extract features from the VGG deep neural network that are used in a small “readout network” to predict one or multiple saliency maps. These saliency maps are then processed in a second readout network together with information on the scanpath history to predict upcoming saccade landing positions. We train the model using human free-viewing scan path data and achieve state-of-the-art performance compared to previous scanpath models. We find that using multiple saliency maps gives no advantage in scanpath prediction compared to a single saliency map. Since the number of saliency maps the network can use imposes strong qualitative constraints on what the model is able to predict, this suggests that for free-viewing a single saliency map may exist that does not depend on either current or previous gaze locations.
The influence of observer lapses on maximum-likelihood difference scaling
2019-09-06

The psychometric function relates an observer’s performance to some physical stimulus quantity in a psychophysical task. Performance is characterized by several parameters of the psychometric function such as the point of subjective equality or the slope. Apart from these primary parameters of interest, two other parameters have been modelled to increase goodness-of-fit: guesses and lapses. Lapses refer to mistakes that are independent of the stimulus level. For example, when an observer mixes up response buttons or lapses in attention. Here, we explore the question whether an explicit modelling of the lapse rate would also improve the estimation of perceptual scales in procedures such as Maximum Likelihood Difference Scaling (MLDS). MLDS is a psychophysical method to derive perceptual scales from forced-choice judgments of suprathreshold stimulus differences. It was tested for its robustness against violations of several model assumptions (Maloney and Yang, 2003), but the influence of lapses on estimated scales has not yet been studied systematically. We run computer simulations to test how a stimulus-independent error rate influences scale estimates in MLDS. We simulated data from different statistical models: we include the classical implementation of MLDS as a generalized linear model (GLM), a Bayesian implementation of the same GLM, as well as two models that explicitly model the lapse rate. We also used the models to reanalyse data from a previous study (Wiebel, Aguilar, and Maertens, 2017), to test the effect of modelling the lapse rate in actual data. In the simulations, lapses lead to an overestimation of the internal noise. In the reanalysis of the experimental data we found that for experienced observers with a low noise estimate the different models did not differ much. For observers with a higher internal noise estimate, models that considered the lapse rate resulted in scales with a smaller internal noise estimate.
A divisive model of midget and parasol ganglion cells explains the contrast sensitivity function
2019-09-06

A long standing proposition in psychophysics is that there are two temporal channels in spatial vision: One temporal lowpass filter which is spatially bandpass and one temporal bandpass filter which is spatially lowpass. Here we equate these two channels with the midget and parasol ganglion cells of the primate retina respectively. Parasol cells show a frequency doubling response at high spatial frequencies, i.e. they respond to both polarities of high spatial frequency gratings. This is usually thought to result from them pooling over smaller units. If true, this explanation predicts that signals with both high spatial and high temporal frequency should be detectable, but their spatial orientation and phase should not be perceivable, i.e. parasol cells act not as labelled lines in this scenario. We confirm this prediction by finding a difference between detection and orientation discrimination thresholds at high spatial and temporal frequency, not observed at other spatial-temporal frequency combinations. We model midget and parasol cells using a standard divisive model dividing a generalized Gaussian center by a larger surround. When scaling cells proportional to their known separation in the retina, we can make predictions for the perception performance of human observers assuming optimal decoding after a single quadratic non-linearity. To confirm the predictions of our model we measured contrast sensitivity functions (CSFs) under diverse conditions and fit data from the literature. Our model fits the CSF under different temporal conditions and changes in size with fixed spatial parameters and can thus replace previous CSF models which require new parameters for each condition and have no mechanistic interpretation. Finally, our model provides a counter hypothesis to the textbook explanation for the CSF which describes it as the envelope of the spatial frequency tuned channels in V1; instead, we believe its shape to result from properties of retinal cells.
Eye Movements Are Required to Process Spatial Configurations in Visual Working Memory
2019-09-06

[Introduction] Previous research showed that object locations are not memorized independently but in relation to the global spatial configuration formed by the objects. It is possible to subset a global configuration into a task relevant partial one while encoding. With the present experiments, we investigated the flexibility of the memory representation underlying this spatial configuration effect. Thus, we studied the following research question: Is it possible that spatial configurations can be reorganized to a subset of objects by shifting attention to these objects only during encoding or also in visual working memory (VWM)? [Methods] Participants encoded the locations of six objects (first experiment) or twelve objects (second experiment) and performed a location change detection task for one object probed during retrieval. This object was displaced in half of the trials. We cued the side (left/right) of the object probed either during encoding or afterwards (retro-cue), thus allowing for the reorganization of spatial configurations either during encoding or in VWM. During retrieval, either a complete (all objects), a congruent (cued objects), an incongruent (non-cued objects) or no (probed object only) configuration was shown. In a third experiment, we manipulated eye movements and presented a retro-cue only. One group had to fixate the center of screen during a trial while the other group could move their eyes. [Results] The successful reorganization of spatial configurations was indicated by a higher change detection performance for both the complete and the congruent configuration condition as compared with the no configuration condition. We observed a reliable reorganization both when cued during encoding and also under some retro-cue conditions. Importantly, configuration effects disappeared with enforced fixation. [Conclusion] Our findings provide evidence for the reorganization of spatial configurations and indicate the requirement of eye movements for processing spatial configurations in VWM, which is contradictory to previous snapshot hypotheses.
Hole-in-the-wall: Perception of 3D shape and affordances from static images in humans and machines
2019-09-06

One popular toy for toddlers involves sorting block shapes into their respective holes. While toddlers require trial-and-error actions to sort blocks correctly, adults can rapidly see the appropriate solution through visual inspection alone. This feat requires an understanding of 3D shape and mental rotation. We study this task in a simplified vision-only setting by generating “shapes” of varying complexity using square matrices filled with connected binary regions, and “holes” by taking the negative region. “Fits” and “doesn’t fit” conditions are created while ensuring that shapes do not match exactly and that the total filled area is the same in both conditions. These matrices are rendered into black-and-white images (“bw”) and into more realistic rendered scenes. Human observers performed a single-interval fits / doesn’t fit task for two complexity levels for bw and rendered scenes. Performance was high for both bw (average d’ high complexity = 2.6, low complexity = 3.1) and rendered scenes (d’ high = 2.7, low = 3.1), showing that indeed humans can perform this task well. To assess whether current machine vision systems can learn this task, we finetuned the weights of a convolutional neural network (CNN; ResNet-50) on 250k bw images at four complexity levels. The network achieved a (test) accuracy of 94% (same complexity) and 87% (generalisation on higher complexity). For the same images seen by humans, the network performs better than humans at both complexity levels (d’ high = 3.5, low = 4.4), but there was no correlation between human response time and network logit. This suggests that the network is solving the task in a non-humanlike way. While this CNN can learn to exceed human performance at this particular task, we expect the model to fail further tests of generalisation because it does not understand the physical properties of the hole-in-the-wall task.