Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.Furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNF).A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

YOLOv8 for Fire and Smoke Recognition Algorithm Integrated with the Convolutional Block Attention Module

The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network’s bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods.

Role of Cannabinoid CB1 Receptor in Object Recognition Memory Impairment in Chronically Rapid Eye Movement Sleep-deprived Rats

Objective We aimed to investigate whether antagonism of the cannabinoid CB1 receptor(CB1R)could affect novel object recognition(NOR)memory in chronically rapid eye movement sleep-deprived(RSD)rats.Methods The animals were examined for recognition memory following a 7-day chronic partial RSD paradigm using the multiple platform technique.The CB1R antagonist rimonabant(1 or 3 mg/kg,i.p.)was administered either at one hour prior to the sample phase for acquisition,or immediately after the sample phase for consolidation,or at one hour before the test phase for retrieval of NOR memory.For the reconsolidation task,rimonabant was administered immediately after the second sample phase.Results The RSD episode impaired acquisition,consolidation,and retrieval,but it did not affect the reconsolidation of NOR memory.Rimonabant administration did not affect acquisition,consolidation,and reconsolidation;however,it attenuated impairment of the retrieval of NOR memory induced by chronic RSD.Conclusions These findings,along with our previous report,would seem to suggest that RSD may affect different phases of recognition memory based on its duration.Importantly,it seems that the CB1R may,at least in part,be involved in the adverse effects of chronic RSD on the retrieval,but not in the acquisition,consolidation,and reconsolidation,of NOR memory.

An object detection approach with residual feature fusion and second-order term attention mechanism

Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging research.Since the boundary box location is not sufficiently accurate and it is difficult to distinguish overlapping and occluded objects,the authors propose a network model with a second-order term attention mechanism and occlusion loss.First,the backbone network is built on CSPDarkNet53.Then a method is designed for the feature extraction network based on an item-wise attention mechanism,which uses the filtered weighted feature vector to replace the original residual fusion and adds a second-order term to reduce the information loss in the process of fusion and accelerate the convergence of the model.Finally,an objected occlusion regression loss function is studied to reduce the problems of missed detections caused by dense objects.Sufficient experimental results demonstrate that the authors’method achieved state-of-the-art performance without reducing the detection speed.The mAP@.5 of the method is 85.8%on the Foggy_cityscapes dataset and the mAP@.5 of the method is 97.8%on the KITTI dataset.

Enhanced Object Detection and Classification via Multi-Method Fusion

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

Redundant discrete wavelet transforms based moving object recognition and tracking

A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform （RDWT） based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.

Optimizing Deep Learning Parameters Using Genetic Algorithm for Object Recognition and Robot Grasping

The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.

Adaptive key SURF feature extraction and application in unmanned vehicle dynamic object recognition

A new method based on adaptive Hessian matrix threshold of finding key SRUF （ speeded up robust features） features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navigation. First, the object recognition algorithm based on SURF feature matching for unmanned vehicle guided navigation is introduced. Then, the standard local invariant feature extraction algorithm SRUF is analyzed, the Hessian Metrix is especially discussed, and a method of adaptive Hessian threshold is proposed which is based on correct matching point pairs threshold feedback under a close loop frame. At last, different dynamic object recognition experi- ments under different weather light conditions are discussed. The experimental result shows that the key SURF feature abstract algorithm and the dynamic object recognition method can be used for un- manned vehicle systems.

Human-Object Interaction Recognition Based on Modeling Context

This paper proposes a method to recognize human-object interactions by modeling context between human actions and interacted objects.Human-object interaction recognition is a challenging task due to severe occlusion between human and objects during the interacting process.Since that human actions and interacted objects provide strong context information,i.e.some actions are usually related to some specific objects,the accuracy of recognition is significantly improved for both of them.Through the proposed method,both global and local temporal features from skeleton sequences are extracted to model human actions.In the meantime,kernel features are utilized to describe interacted objects.Finally,all possible solutions from actions and objects are optimized by modeling the context between them.The results of experiments demonstrate the effectiveness of our method.

Exploring Local Regularities for 3D Object Recognition

In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles（L-MSDA）, localized minimizing standard deviation of segment magnitudes（L-MSDSM）, localized minimum standard deviation of areas of child faces （L-MSDAF）, localized minimum sum of segment magnitudes of common edges （L-MSSM）, and localized minimum sum of areas of child face （L-MSAF）. Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities： L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness.

Real-Time Recognition and Location of Indoor Objects

Object recognition and location has always been one of the research hotspots in machine vision.It is of great value and significance to the development and application of current service robots,industrial automation,unmanned driving and other fields.In order to realize the real-time recognition and location of indoor scene objects,this article proposes an improved YOLOv3 neural network model,which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network,which is applied to the detection and recognition of objects in indoor scenes.In this article,RealSense D415 RGB-D camera is used to obtain the RGB map and depth map,the actual distance value is calculated after each pixel in the scene image is mapped to the real scene.Experiment results proved that the detection and recognition accuracy and real-time performance by the new network are obviously improved compared with the previous YOLOV3 neural network model in the same scene.More objects can be detected after the improvement of network which cannot be detected with the YOLOv3 network before the improvement.The running time of objects detection and recognition is reduced to less than half of the original.This improved network has a certain reference value for practical engineering application.

Object Recognition Algorithm Based on an Improved Convolutional Neural Network

In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition.

Melatonin Enhances Object Recognition Memory through Melatonin MT1 and MT2 Receptor-Mediated and Non-Receptor-Mediated Mechanisms in Male Mice

Melatonin (MEL) has been reported to have acute enhancing effects on some aspects of cognition. Recently, we revealed that N1-acetyl-5-methoxyquinuramine (AMK), a brain metabolite of MEL, is much more potent than MEL in converting short-term memory (STM) to long-term memory (LTM) with a single administration immediately after the acquisition trial of the novel object recognition (NOR) task. These data suggest that the memory-enhancing effects of MEL may be mediated by mechanisms independent of the activation of MEL MT1 and MT2 receptors. In the present study, we examined the contribution of MT1 and MT2 receptor-mediated and non-receptor-mediated mechanisms to the acute memory-enhancing effects of MEL using NOR task. Mice were administered with either MEL, AMK, or a highly selective MT1/MT2 receptor agonist ramelteon (RAM) immediately after the acquisition trial and the effects of varying doses of these drugs on both STM and LTM performance were compared. We found that both AMK and RAM were more potent than MEL in both facilitating STM and promoting LTM formation. We also found that pretreatment with luzindole, a MT1/MT2 receptor antagonist, markedly suppressed only the effects of RAM. These results suggest that acutely administered MEL enhances NOR memory through both MT1 and MT2 receptor-mediated and non-receptor-mediated mechanisms.

An accurate detection algorithm for time backtracked projectile-induced water columns based on the improved YOLO network

During a sea firing training,the intelligent detection of projectile-induced water column targets in a firing video is the prerequisite for and critical to the automatic calculation of miss distance,while the correct and precise calculation of miss distance is directly affected by the accuracy,false alarm rate and time delay of detection.After analyzing the characteristics of projectile-induced water columns,an accurate detection algorithm for time backtracked projectile-induced water columns based on the improved you only look once(YOLO)network is put forward.The capability and accuracy of detecting projectileinduced water column targets with the conventional YOLO network are improved by optimizing the anchor box through K-means clustering and embedding the squeeze and excitation(SE)attention module.The detection area is limited by adopting a sea-sky line detection algorithm based on gray level co-occurrence matrix(GLCM),so as to effectively eliminate such disturbances as ocean waves and ship wakes,and lower the false alarm rate of projectile-induced water column detection.The improved algorithm increases the mAP50 of water column detection by 30.3%.On the basis of correct detection,a time backtracking algorithm is designed with mean shift to track images containing projectile-induced water column in reverse time sequence.It accurately detects a projectile-induced water column at the time of its initial appearance as well as its pixel position in images,and considerably reduces detection delay,so as to provide the support for the automatic,accurate,and real-time calculation of miss distance.

Key-Part Attention Retrieval for Robotic Object Recognition

The ability to recognize novel objects with a few visual samples is critical in the robotic applications.Existing methods mainly concern the recognition of inter-category objects,however,the object recognition fromdifferent sub-classes within the same category remains challenging due to their similar appearances.In thispaper,we propose a key-part attention retrieval solution to distinguish novel objects of different sub-classesaccording to a few samples without re-training.Especially,an object encoder,including convolutional neuralnetwork with attention and key-part aggregation,is designed to generate object attention map and extract theobject-level embedding,where object attention map from the middle stage of the backbone is used to guide thekey-part aggregation.Besides,to overcome the non-differentiability drawback of key-part attention,the objectencoder is trained in a two-step scheme,and a more stable object-level embedding is obtained.On this basis,the potential objects are located from a scene image by mining connected domains of the attention map.Bymatching the embedding of each potential object and embeddings from support data,the recognition of thepotential objects is achieved.The effectiveness of the proposed method is verified by experiments.

《释量论》第三品之“所量有二故,能量唯二种”注疏比较研究

Placing reasons,coverage of the reasons,and subjects and similar examples found in classical root texts of Logic such as Pramana-Samucchaya(tshad ma kun btus in Tibetan)and Pramana-Vartika(tshad ma rnam vgrel in Tibetan)have been highly debated and explained in accordance with how different scholars and masters understood.Refencing to the classical root text and the commentaries as well as the books written by goloje(sgo blo rje in Tibetan),under the guidance of seeking truth from the facts,the numerals of the praman are discussed in this article.

Image format pipeline and instrument diagram recognition method based on deep learning

In this study,we proposed a recognition method based on deep artificial neural networks to identify various elements in pipelines and instrumentation diagrams(P&ID)in image formats,such as symbols,texts,and pipelines.Presently,the P&ID image format is recognized manually,and there is a problem with a high recognition error rate;therefore,automation of the above process is an important issue in the processing plant industry.The China National Offshore Petrochemical Engineering Co.provided the image set used in this study,which contains 51 P&ID drawings in the PDF.We converted the PDF P&ID drawings to PNG P&IDs with an image size of 8410×5940.In addition,we used labeling software to annotate the images,divided the dataset into training and test sets in a 3:1 ratio,and deployed a deep neural network for recognition.The method proposed in this study is divided into three steps.The first step segments the images and recognizes symbols using YOLOv5+SE.The second step determines text regions using character region awareness for text detection,and performs character recognition within the text region using the optical character recognition technique.The third step is pipeline recognition using YOLOv5+SE.The symbol recognition accuracy was 94.52%,and the recall rate was 93.27%.The recognition accuracy in the text positioning stage was 97.26%and the recall rate was 90.27%.The recognition accuracy in the character recognition stage was 90.03%and the recall rate was 91.87%.The pipeline identification accuracy was 92.9%,and the recall rate was 90.36%.

Multimodal fusion recognition for digital twin

The digital twin is the concept of transcending reality,which is the reverse feedback from the real physical space to the virtual digital space.People hold great prospects for this emerging technology.In order to realize the upgrading of the digital twin industrial chain,it is urgent to introduce more modalities,such as vision,haptics,hearing and smell,into the virtual digital space,which assists physical entities and virtual objects in creating a closer connection.Therefore,perceptual understanding and object recognition have become an urgent hot topic in the digital twin.Existing surface material classification schemes often achieve recognition through machine learning or deep learning in a single modality,ignoring the complementarity between multiple modalities.In order to overcome this dilemma,we propose a multimodal fusion network in our article that combines two modalities,visual and haptic,for surface material recognition.On the one hand,the network makes full use of the potential correlations between multiple modalities to deeply mine the modal semantics and complete the data mapping.On the other hand,the network is extensible and can be used as a universal architecture to include more modalities.Experiments show that the constructed multimodal fusion network can achieve 99.42%classification accuracy while reducing complexity.

Magnesium acetyltaurate prevents retinal damage and visual impairment in rats through suppression of NMDA-induced upregulation of NF-κB,p53 and AP-1(c-Jun/c-Fos)

Magnesium acetyltaurate(MgAT)has been shown to have a protective effect against N-methyl-D-aspartate(NMDA)-induced retinal cell apoptosis.The current study investigated the involvement of nuclear factor kappa-B(NF-κB),p53 and AP-1 family members(c-Jun/c-Fos)in neuroprotection by MgAT against NMDA-induced retinal damage.In this study,Sprague-Dawley rats were randomized to undergo intravitreal injection of vehicle,NMDA or MgAT as pre-treatment to NMDA.Seven days after injections,retinal ganglion cells survival was detected using retrograde labelling with fluorogold and BRN3A immunostaining.Functional outcome of retinal damage was assessed using electroretinography,and the mechanisms underlying antiapoptotic effect of MgAT were investigated through assessment of retinal gene expression of NF-κB,p53 and AP-1 family members(c-Jun/c-Fos)using reverse transcription-polymerase chain reaction.Retinal phospho-NF-κB,phospho-p53 and AP-1 levels were evaluated using western blot assay.Rat visual functions were evaluated using visual object recognition tests.Both retrograde labelling and BRN3A immunostaining revealed a significant increase in the number of retinal ganglion cells in rats receiving intravitreal injection of MgAT compared with the rats receiving intravitreal injection of NMDA.Electroretinography indicated that pre-treatment with MgAT partially preserved the functional activity of NMDA-exposed retinas.MgAT abolished NMDA-induced increase of retinal phospho-NF-κB,phospho-p53 and AP-1 expression and suppressed NMDA-induced transcriptional activity of NF-κB,p53 and AP-1 family members(c-Jun/c-Fos).Visual object recognition tests showed that MgAT reduced difficulties in recognizing the visual cues(i.e.objects with different shapes)after NMDA exposure,suggesting that visual functions of rats were relatively preserved by pre-treatment with MgAT.In conclusion,pre-treatment with MgAT prevents NMDA induced retinal injury by inhibiting NMDA-induced neuronal apoptosis via downregulation of transcriptional activity of NF-κB,p53 and AP-1-mediated c-Jun/c-Fos.The experiments were approved by the Animal Ethics Committee of Universiti Teknologi MARA(UiTM),Malaysia,UiTM CARE No 118/2015 on December 4,2015 and UiTM CARE No 220/7/2017 on December 8,2017 and Ethics Committee of Belgorod State National Research University,Russia,No 02/20 on January 10,2020.