DiffMatch: Diffusion Model for Dense Matching

The objective for establishing dense correspondence between paired images consists of two terms: a data term and a prior term. While conventional techniques focused on defining hand-designed prior terms, which are difficult to formulate, recent approaches have focused on learning the data term with deep neural networks without explicitly modeling the prior, assuming that the model itself has the capacity to learn an optimal prior from a large-scale dataset. The performance improvement was obvious, however, they often fail to address inherent ambiguities of matching, such as textureless regions, repetitive patterns, and large displacements. To address this, we propose DiffMatch, a novel conditional diffusion-based framework designed to explicitly model both the data and prior terms. Unlike previous approaches, this is accomplished by leveraging a conditional denoising diffusion model. DiffMatch consists of two main components: conditional denoising diffusion module and cost injection module. We stabilize the training process and reduce memory usage with a stage-wise training strategy. Furthermore, to boost performance, we introduce an inference technique that finds a better path to the accurate matching field. Our experimental results demonstrate significant performance improvements of our method over existing approaches, and the ablation studies validate our design choices along with the effectiveness of each component. Project page is available at https://ku-cvlab.github.io/DiffMatch/.Comment: Project page is available at https://ku-cvlab.github.io/DiffMatch

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Comparison of visual quantities in untrained neural networks

Summary: The ability to compare quantities of visual objects with two distinct measures, proportion and difference, is observed even in newborn animals. However, how this function originates in the brain, even before visual experience, remains unknown. Here, we propose a model in which neuronal tuning for quantity comparisons can arise spontaneously in completely untrained neural circuits. Using a biologically inspired model neural network, we find that single units selective to proportions and differences between visual quantities emerge in randomly initialized feedforward wirings and that they enable the network to perform quantity comparison tasks. Notably, we find that two distinct tunings to proportion and difference originate from a random summation of monotonic, nonlinear neural activities and that a slight difference in the nonlinear response function determines the type of measure. Our results suggest that visual quantity comparisons are primitive types of functions that can emerge spontaneously before learning in young brains

Solid Phase-Mediated Asymmetric Palladium-Catalyzed Hydroalkoxylation of Alkoxyallene

The Pd-catalyzed asymmetric addition reaction of solid phase-supported alcohol with ene-alkoxyallenes is reported. Combined with the subsequent metal catalysis, this reaction gave access to various monosaccharides with no need for isolation of the intermediates. Potential utility of the method in the oligosaccharide synthesis was demonstrated by the synthesis of (α)-mannose disaccharide. (Figure presented.). © 2022 Wiley-VCH GmbH.11Nsciescopu

Mineralogy of the Mudeungsan Tuff (Republic of Korea) Using Synchrotron X-ray Powder Diffraction and Rietveld Quantitative Analysis

Mudeungsan (Mount Mudeung) is an extinct volcano located in the southwestern part of South Korea that was formed in the Late Cretaceous period. This mountain, 1187 m above sea level, is adjacent to Gwangju Metropolitan City, which has a large population (about 1.4 million) and volcanic rocks, including columnar joints, which form various types of outcrops. Although this mountain was listed as a national geopark in 2014 and a UNESCO Global Geopark in 2018, much basic research has yet to be carried out. In particular, there are no mineralogical studies of volcanic rock samples despite the well-preserved variety of volcanic rocks. For this study, X-ray diffraction analysis was conducted using rock samples from Mudeungsan columnar joints known as tuff. We report that the rocks are mostly dacite, mainly composed of quartz, plagioclase, and sanidine through Rietveld quantitative analysis. In particular, alpha-cristobalite, a crystalline polymorph of silica, appears in the columnar joint rocks, indicating that Mudeungsan experienced an explosive eruption during the formation of the mountain.N

Robust Deep-learning Based Autofocus Score Prediction for Scanning Electron Microscope

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Deep Learning-Based Autonomous Scanning Electron Microscope

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Development of a Privacy-Preserving UAV System With Deep Learning-Based Face Anonymization

In this paper, we develop a privacy-preserving UAV system that does not infringe on the privacy of people in the videos taken by UAVs. Instead of blurring or masking the face parts of the videos, we want to exquisitely modify only the face parts so that the people in the modified videos still look like humans, but they become anonymous. Doing so, the semantic information of the videos can be preserved even with the anonymization. Specifically, based on the latest generative adversarial network architecture, we propose a deep learning-based face-anonymization scheme so that each modified face part looks like the face of a person who does not actually exist. The trained face-anonymizer is then mounted on the UAV system we have implemented. Through experiments, we confirm that the developed privacy-preserving UAV system anonymizes UAV's first-person videos so that the people in the video are not recognized as anyone in the dataset used. In addition, we show that even with such anonymized videos, the perception performance required for performing UAV's essential functions such as simultaneous localization and mapping is not degraded