MRGazer: Decoding Eye Gaze Points from Functional Magnetic Resonance Imaging in Individual Space

Eye-tracking research has proven valuable in understanding numerous cognitive functions. Recently, Frey et al. provided an exciting deep learning method for learning eye movements from fMRI data. However, it needed to co-register fMRI into standard space to obtain eyeballs masks, and thus required additional templates and was time consuming. To resolve this issue, in this paper, we propose a framework named MRGazer for predicting eye gaze points from fMRI in individual space. The MRGazer consisted of eyeballs extraction module and a residual network-based eye gaze prediction. Compared to the previous method, the proposed framework skips the fMRI co-registration step, simplifies the processing protocol and achieves end-to-end eye gaze regression. The proposed method achieved superior performance in a variety of eye movement tasks than the co-registration-based method, and delivered objective results within a shorter time (~ 0.02 Seconds for each volume) than prior method (~0.3 Seconds for each volume)

Emulating Complex Synapses Using Interlinked Proton Conductors

In terms of energy efficiency and computational speed, neuromorphic electronics based on non-volatile memory devices is expected to be one of most promising hardware candidates for future artificial intelligence (AI). However, catastrophic forgetting, networks rapidly overwriting previously learned weights when learning new tasks, remains as a pivotal hurdle in either digital or analog AI chips for unleashing the true power of brain-like computing. To address catastrophic forgetting in the context of online memory storage, a complex synapse model (the Benna-Fusi model) has been proposed recently[1], whose synaptic weight and internal variables evolve following a diffusion dynamics. In this work, by designing a proton transistor with a series of charge-diffusion-controlled storage components, we have experimentally realized the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Different memory timescales for the complex synapse are engineered by the diffusion length of charge carriers, the capacity and number of coupled storage components. The advantage of the demonstrated complex synapse in both memory capacity and memory consolidation is revealed by neural network simulations of face familiarity detection. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning.Comment: 6 figure

Turnout Fault Diagnosis through Dynamic Time Warping and Signal Normalization

Turnout is one key fundamental infrastructure in the railway signal system, which has great influence on the safety of railway systems. Currently, turnout fault diagnoses are conducted manually in China; engineers are obliged to observe the signals and make problem solving decisions. Thus, the accuracies of fault diagnoses totally depend on the engineers’ experience although massive data are produced in real time by the turnout microcomputer-based monitoring systems. This paper aims to develop an intelligent diagnosis method for railway turnout through Dynamic Time Warping (DTW). We firstly extract the features of normal turnout operation current curve and normalize the collected turnout current curves. Then, five typical fault reference curves are ascertained through the microcomputer-based monitoring system, and DTW is used to identify the turnout current curve fault through test data. The analysis results based on the similarity data indicate that the analyzed five turnout fault types can be diagnosed automatically with 100% accuracy. Finally, the benefits of the proposed method and future research directions were discussed

Self-supervised pretraining improves the performance of classification of task functional magnetic resonance imaging

IntroductionDecoding brain activities is one of the most popular topics in neuroscience in recent years. And deep learning has shown high performance in fMRI data classification and regression, but its requirement for large amounts of data conflicts with the high cost of acquiring fMRI data.MethodsIn this study, we propose an end-to-end temporal contrastive self-supervised learning algorithm, which learns internal spatiotemporal patterns within fMRI and allows the model to transfer learning to datasets of small size. For a given fMRI signal, we segmented it into three sections: the beginning, middle, and end. We then utilized contrastive learning by taking the end-middle (i.e., neighboring) pair as the positive pair, and the beginning-end (i.e., distant) pair as the negative pair.ResultsWe pretrained the model on 5 out of 7 tasks from the Human Connectome Project (HCP) and applied it in a downstream classification of the remaining two tasks. The pretrained model converged on data from 12 subjects, while a randomly initialized model required 100 subjects. We then transferred the pretrained model to a dataset containing unpreprocessed whole-brain fMRI from 30 participants, achieving an accuracy of 80.2 ± 4.7%, while the randomly initialized model failed to converge. We further validated the model’s performance on the Multiple Domain Task Dataset (MDTB), which contains fMRI data of 26 tasks from 24 participants. Thirteen tasks of fMRI were selected as inputs, and the results showed that the pre-trained model succeeded in classifying 11 of the 13 tasks. When using the 7 brain networks as input, variations of the performance were observed, with the visual network performed as well as whole brain inputs, while the limbic network almost failed in all 13 tasks.DiscussionOur results demonstrated the potential of self-supervised learning for fMRI analysis with small datasets and unpreprocessed data, and for analysis of the correlation between regional fMRI activity and cognitive tasks

Site tracing experiment on the diffusion range of regional grouting renovation under the coal seam floor aquifer

In recent years, to liberate coal resources from high pressure limestone water on the coal seam floor, North China Coalfields have generally adopted surface directional drilling technology to carry out regional grouting reinforcement and transformation (commonly known as “floor regional treatment”) on the thin-layer limestone of the Taiyuan Formation in order to comprehensively seal karst cracks in limestone and block vertical guide water channels. In this technology, the design of the spacing between “horizontal branching holes” closely related to the diffusion range (radius) of the slurry has been widely studied by academia and industry. There is a large amount of grouting work in the bottom plate area of the Anhui North mining area, especially in the mining of deep resources, which will cost billions of yuan. It is necessary to verify the true data of the diffusion range of the grout. Therefore, based on the Hengyuan Coal Mine in the northern Anhui mining area as the research base, relying on the II63 mining area floor area treatment project, the slurry diffusion range tracing test was designed and implemented. The fluorescent agent (tracer) was added to the horizontal branch hole (Z8-7) in the middle, and the rock debris samples were taken from the horizontal branch holes (Z8-6, Z8-8) and cross branch detection holes (Z8JC) on both sides to identify fluorescent cement and obtain the diffusion range of the slurry. Furthermore, based on the analysis of the influencing factors of slurry diffusion, a formula for calculating the diffusion range of slurry in the grouting treatment of the bottom plate area of the Hengyuan Coal Mine was constructed. The results show that: ① Based on the analysis of on-site and indoor identification results of rock debris, the diffusion range of grouting slurry under the coal seam floor area of the Hengyuan Coal Mine II63 mining area was 38.3−44.0 m, and the cement distribution was dense within the diffusion range of horizontal branch hole slurry within 30 meters. The grouting effect was the best in this area. ② Through a rapid identification of on-site rock cuttings and precise identification of indoor rock cuttings, the diffusion range of the slurry obtained was basically consistent, proving the effectiveness of fluorescence tracing of the diffusion range of the slurry. ③ Through comparative analysis, it was believed that under actual grouting conditions such as calculation parameters and boundary constraints, the theoretical calculation and numerical simulation results of the slurry diffusion range were close to the measured results of on-site tracing experiments. ④ Using the data from water pressure tests, grouting parameters, drilling structures, and hydrogeological responses during the tracer test process, taking into account the factors such as gravity, structure, and groundwater runoff, and using SPSS nonlinear fitting software, a formula for calculating the diffusion range of grouting slurry in the bottom plate area of the Hengyuan Coal Mine II63 mining area was obtained. ⑤ Based on the actual geological and hydrogeological conditions of the injection layer in the Hengyuan Coal Mine, using the fitted slurry diffusion range calculation formula, the slurry diffusion range of Z8 site in the II63 mining area was obtained to be 37.8−42.9 m, which was similar to the measured results of the slurry diffusion range tracer test. The calculation formula could be promoted and applied under similar conditions. The on-site tracing engineering test of the diffusion range of grouting slurry in the coal mine floor area not only obtained real data on the diffusion range of slurry, but also clarified the inherent relationship between slurry diffusion and various geological and hydrogeological factors. The diffusion mechanism of grouting slurry for ultra deep and ultra long directional drilling was revealed, and a formula for calculating the diffusion range of slurry was constructed, providing a reference basis for the reasonable design of horizontal branch hole spacing in bottom plate area treatment projects under similar conditions

Ophiopogonin B suppresses the growth and epithelialmesenchymal transition in laryngeal cancer cells by inhibiting FAK/AKT signaling pathway

Purpose: To investigate the effect of ophiopogonin B on laryngeal cancer cells, and whether it is related to epithelial mesenchymal transition (EMT). Methods: Human laryngeal cancer cells (AMC-HN-8) were used as tool cells in vitro. Cell growth was characterized by cell viability and proliferation while cell apoptosis was analyzed using Annexin V/PI staining. Cell invasion and migration were assessed Transwell assay. Results: Ophiopogonin B inhibited the viability and proliferation of AMC-HN-8 cells at a concentration > 10 μM. Cell apoptosis was enhanced after treatment with ophiopogonin B. The fraction of apoptotic cells for 5, 10 and 20 μM groups were 6.25, 16.16, 28.3 and 39 %, respectively. Transwell assay data showed that ophiopogonin B inhibited the invasion and migration of laryngeal cancer cells. In addition, the expression of N-cad and snail (EMT inducer) was inhibited, while the expression of E-cad was enhanced. These results also indicate that ophiopogonin B inhibited the migration and EMT of laryngeal cancer cells. Besides, ophiopogonin B inhibited the phosphorylation of FAK and AKT. Conclusion: These results indicate that ophiopogonin B suppresses the growth, migration and EMT in laryngeal cancer cell by inhibiting FAK/AKT signaling pathway. These results provide some ideas for improved treatment of laryngeal cancer

Preparation and Performances of Polyether Polytriazole Elastomers Based on Click Chemistry

Since the polyurethane elastomer synthesis process is susceptible to moisture, polytriazole polyethylene oxide-tetrahydrofuran (PTPET) elastomer was used as a replacement owing to its mild production environment. In contrast to the conventional flask-synthesis method, the twin-screw reactor instrument could provide more meaningful data in the synthesis. In this study, PTPET elastomer was prepared by the MiniLab twin-screw reactor method for the first time, and the activation energy of the PTPET elastomer was calculated using the torque variation obtained from the MiniLab twin-screw reactor during the synthesis process at two different temperatures. The addition of flame retardants could endow the composites with more useful properties. The PTPET composites poly (phenylsilsesquioxane) (PTPET-PPSQ), octaphenyl polyhedral oligomeric silsesquioxane (PTPET-OPS) and PTPET-PhVPOSS (phenyl/vinyl polysilsesquioxane) were synthesized by using the MiniLab twin-screw reactor. The prepared PTPET elastomer and composites were fully characterized by FT-IR, TG, DSC, swelling test, mechanical test, SEM and combustion test. The characterization results show that the addition of the flame retardants has little influence on the original structure and properties of PTPET elastomer. The flame retardancy was characterized by the combustion test showing that all PTPET composites form a certain thickness of char layer during the burning process. These results indicate that the addition of flame retardants maintains the outstanding properties of PTPET elastomer and also endows the materials with a certain extent of flame retardancy; thus, it is believed to be a good engineering material that could be applied in many realms

Video Stabilization Using Scale-Invariant Features

Video Stabilization is one of those important video processing techniques to remove the unwanted camera vibration in a video sequence. In this paper, we present a practical method to remove the annoying shaky motion and reconstruct a stabilized video sequence with good visual quality. Here, the scale invariant(SIFT) features, proved to be invariant to image scale and rotation, is applied to estimate the camera motion. The unwanted vibrations are separated from the intentional camera motion with the combination of Gaussian kernel filtering and parabolic fitting. It is demonstrated that our method effectively removes the high frequency ‘noise ’ motion, but also minimize the missing area as much as possible. To reconstruct the undefined areas, resulting from motion compensation, we adopt the mosaicing method with Dynamic Programming. The proposed method has been confirmed to be effective over a widely variety of videos. 1

Turnout Fault Diagnosis through Dynamic Time Warping and Signal Normalization

Turnout is one key fundamental infrastructure in the railway signal system, which has great influence on the safety of railway systems. Currently, turnout fault diagnoses are conducted manually in China; engineers are obliged to observe the signals and make problem solving decisions. Thus, the accuracies of fault diagnoses totally depend on the engineers’ experience although massive data are produced in real time by the turnout microcomputer-based monitoring systems. This paper aims to develop an intelligent diagnosis method for railway turnout through Dynamic Time Warping (DTW). We firstly extract the features of normal turnout operation current curve and normalize the collected turnout current curves. Then, five typical fault reference curves are ascertained through the microcomputer-based monitoring system, and DTW is used to identify the turnout current curve fault through test data. The analysis results based on the similarity data indicate that the analyzed five turnout fault types can be diagnosed automatically with 100% accuracy. Finally, the benefits of the proposed method and future research directions were discussed