Increasing stimulus similarity drives nonmonotonic representational change in hippocampus

Studies of hippocampal learning have obtained seemingly contradictory results, with manipulations that increase coactivation of memories sometimes leading to differentiation of these memories, but sometimes not. These results could potentially be reconciled using the nonmonotonic plasticity hypothesis, which posits that representational change (memories moving apart or together) is a U-shaped function of the coactivation of these memories during learning. Testing this hypothesis requires manipulating coactivation over a wide enough range to reveal the full U-shape. To accomplish this, we used a novel neural network image synthesis procedure to create pairs of stimuli that varied parametrically in their similarity in high-level visual regions that provide input to the hippocampus. Sequences of these pairs were shown to human participants during high-resolution fMRI. As predicted, learning changed the representations of paired images in the dentate gyrus as a U-shaped function of image similarity, with neural differentiation occurring only for moderately similar images.

Qualitative head-to-head comparison of headlamp and microscope for visualizing 5-ALA fluorescence during resection of glioblastoma

Fluorescence-guided surgery (FGS) for high-grade gliomas using 5-aminolevulinic acid has become a new standard of care for neurosurgeons in several countries. In this video the authors present the case of a man with glioblastoma who underwent FGS in which similar images of the operative field were acquired alternating between the microscope and a new commercially available headlight, facilitating the comparison of visualization quality between the two devices. The authors also review some of the principles of fluorescence-guidance surgery that may explain the improved brightness and contrast that they observed when using the headlamp versus the microscope. The video can be found here: https://stream.cadmore.media/r10.3171/2021.10.FOCVID21181

Research on Energy Efficiency Management of Forklift Based on Improved YOLOv5 Algorithm

As an important tool for loading, unloading, and distributing palletized goods, forklifts are widely used in different links of industrial production process. However, due to the rapid increase in the types and quantities of goods, item statistics have become a major bottleneck in production. Based on machine vision, the paper proposes a method to count the amount of goods loaded and unloaded within the working time limit to analyze the efficiency of the forklift. The proposed method includes the data preprocessing section and the object detection section. In the data preprocessing section, through operations such as framing and clustering the collected video data and using the improved image hash algorithm to remove similar images, a new dataset of forklift goods was built. In the object detection section, the attention mechanism and the replacement network layer were used to improve the performance of YOLOv5. The experimented results showed that, compared with the original YOLOv5 model, the improved model is lighter in size and faster in detection speed without loss of detection precision, which could also meet the requirements for real-time statistics on the operation efficiency of forklifts.

Image Searching System

Finding similar images on a visual sample is a difficult AI task, to solve which many works are devoted. The problem is to determine the essential properties of images of low and higher semantic level. Based on them, a vector of features is built, which will be used in the future to compare pairs of images. Each pair always includes an image from the collection and a sample image that the user is looking for. The result of the comparison is a quantity called the visual relativity of the images. Image properties are called features and are evaluated by calculation algorithms. Image features can be divided into low-level and high-level. Low-level features include basic colors, textures, shapes, significant elements of the whole image. These features are used as part of more complex recognition tasks. The main progress is in the definition of high-level features, which is associated with understanding the content of images. In this paper, research of modern algorithms is done for finding similar images in large multimedia databases. The main problems of determining high-level image features, algorithms of overcoming them and application of effective algorithms are described. The algorithms used to quickly determine the semantic content and improve the search accuracy of similar images are presented. The aim: The purpose of work is to conduct comparative analysis of modern image retrieval algorithms and retrieve its weakness and strength.

An Image Encryption Technique Based on Logistic Sine Map and an Encrypted Image Retrieval Using DCT Frequency

In this digital era, most of the hospitals and medical labs are storing and sharing their medical data using third party cloud platforms for saving maintenance cost and storage and also to access data from anywhere. The cloud platform is not entirely a trusted party as the data is under the control of cloud service providers, which results in privacy leaks so that the data is to be encrypted while uploading into the cloud. The data can be used for diagnosis and analysis, for that the similar images to be retrieved as per the need of the doctor. In this paper, we propose an algorithm that uses discrete cosine transformation frequency and logistic sine map to encrypt an image, and the feature vector is computed on the encrypted image. The encrypted images are transferred to the cloud picture database, and feature vectors are uploaded to the feature database. Pearson’s Correlation Coefficient is calculated on the feature vector and is used as a measure to retrieve similar images. From the investigation outcomes, we can get an inference that this algorithm can resist against predictable attacks and geometric attacks with strong robustness.

Using Ultrasound-Based Multilayer Perceptron to Differentiate Early Breast Mucinous Cancer and its Subtypes From Fibroadenoma

ObjectivesMucinous breast cancer (MBC), particularly pure MBC (pMBC), often tend to be confused with fibroadenoma (FA) due to their similar images and firm masses, so some MBC cases are misdiagnosed to be FA, which may cause poor prognosis. We analyzed the ultrasonic features and aimed to identify the ability of multilayer perceptron (MLP) to classify early MBC and its subtypes and FA.Materials and MethodsThe study consisted of 193 patients diagnosed with pMBC, mMBC, or FA. The area under curve (AUC) was calculated to assess the effectiveness of age and 10 ultrasound features in differentiating MBC from FA. We used the pairwise comparison to examine the differences among MBC subtypes (pure and mixed types) and FA. We utilized the MLP to differentiate MBC and its subtypes from FA.ResultsThe nine features with AUCs over 0.5 were as follows: age, echo pattern, shape, orientation, margin, echo rim, vascularity distribution, vascularity grade, and tumor size. In subtype analysis, the significant differences were obtained in 10 variables (p-value range, 0.000–0.037) among pMBC, mMBC, and FA, except posterior feature. Through MLP, the AUCs of predicting MBC and FA were both 0.919; the AUCs of predicting pMBC, mMBC, and FA were 0.875, 0.767, and 0.927, respectively.ConclusionOur study found that the MLP models based on ultrasonic characteristics and age can well distinguish MBC and its subtypes from FA. It may provide a critical insight into MBC preoperative clinical management.

Prozodia, semantyka, styl – o hierarchii poziomów ekwiwalencji w tłumaczeniu piosenki kabaretowej (studium przypadku: polskojęzyczne warianty Money… Freda Ebba)

Prosody, Semantics and Style. On the Hierarchy of Levels of Equivalence in the Translation of Cabaret Songs (Case Study: Polish Versions of Fred Ebb's Money…) The article is a case study and contains a comparative analysis of four variants of the Polish translation of Fred Ebb and John Kander’s song Money… from the musical “Cabaret”. The author of the translation is Wojciech Młynarski, one of the most respected Polish songwriters of the second half of the twentieth century. In the study, an assumption is made that Młynarski, who repeatedly changed versions of his translation, sought to create the most faithful rendition of the songs from the musical for the needs of the Polish stage. His efforts can be observed at four levels of text organization. The translator aimed mainly for sound equivalence, i.e. conformity with the original song in terms of rhythm (word stress), rhyme (consonance) and voice instrumentation and, to a lesser extent, sound imitation. He also cared about pragmatic equivalence by rendering into Polish the original intentions, with particular emphasis on the modes of indirect communication, such as irony and satire. However, other aspects of equivalence remained in the background. Not everywhere the translator managed to keep the cognitive equivalence, i.e. convergence of imagery, by translating scenes and scenarios that were part of cultural knowledge into parallel ones and, more broadly, by trying to evoke similar images in the mind of the reader and listener. His efforts to achieve the effect of broadly understood stylistic equivalence were also noteworthy; only to a small extent they consisted in giving the right stylistic coloring to the individual lexical items which had their English equivalents, and they mainly boiled down to translating stylistic games that did not necessarily cover the same fragments of the song, though were usually based on the same mechanism (a clash between low and high style, absurdity). The analysis shows that the translator adopted tabular rather than linear approach to the original.

Bach Anxiety

A recent Twitter post by the composer Nico Muhly aligns with a recurring trope of “Bach-ness” that defines Bach’s public mythic profile. This chapter focuses on similar images of Bach, whether visual or aural. Bach has been most commonly imagined in the popular consciousness as representing not the human but the superhuman, the inhuman, the dehumanized, and the sublime. One can sense in recent writings on Bach an anxiety about how well these attributes can continue to resonate in our current moment of political or cultural relevance tests, and about which works by Bach are most likely to thrive in this new postmodern media world. I will wonder aloud, with some trepidation, whether Bach’s public mythic profile, long solidified along Modernist lines as the encyclopedic mathematical mystic, is undergoing a broad, gradual change; indeed, if it needs to in order for his music to survive in a twenty-first-century media environment and amid a postmodern audience sensibility.

Recovery of Natural Scenery Image by Content Using Wiener-Granger Causality: A Self-Organizing Methodology

One of the most important applications of data science and data mining is is organizing, classifying, and retrieving digital images on Internet. The current focus of the researchers is to develop methods for the content based exploration of natural scenery images. In this research paper, a self-organizing method of natural scenes images using Wiener-Granger Causality theory is proposed. It is achieved by carrying out Wiener-Granger causality for organizing the features in the time series form and introducing a characteristics extraction stage at random points within the image. Once the causal relationships are obtained, the k-means algorithm is applied to achieve the self-organizing of these attributes. Regarding classification, the k−NN distance classification algorithm is used to find the most similar images that share the causal relationships between the elements of the scenes. The proposed methodology is validated on three public image databases, obtaining 100% recovery results.

Multiple Ship Tracking in Remote Sensing Images Using Deep Learning

In remote sensing images, small target size and diverse background cause difficulty in locating targets accurately and quickly. To address the lack of accuracy and inefficient real-time performance of existing tracking algorithms, a multi-object tracking (MOT) algorithm for ships using deep learning was proposed in this study. The feature extraction capability of target detectors determines the performance of MOT algorithms. Therefore, you only look once (YOLO)-v3 model, which has better accuracy and speed than other algorithms, was selected as the target detection framework. The high similarity of ship targets will cause poor tracking results; therefore, we used the multiple granularity network (MGN) to extract richer target appearance information to improve the generalization ability of similar images. We compared the proposed algorithm with other state-of-the-art multi-object tracking algorithms. Results show that the tracking accuracy is improved by 2.23%, while the average running speed is close to 21 frames per second, meeting the needs of real-time tracking.