object recognition

Recently, the multiscale problem in computer vision has gradually attracted people's attention. This article focuses on multiscale representation for object detection and recognition, comprehensively introduces the development of multiscale deep learning, and constructs an easy-to-understand, but powerful knowledge structure. First, we give the definition of scale, explain the multiscale mechanism of human vision, and then lead to the multiscale problem discussed in computer vision. Second, advanced multiscale representation methods are introduced, including pyramid representation, scale-space representation, and multiscale geometric representation...

Negative flips are errors introduced in a classification system when a legacy model is updated. Existing methods to reduce the negative flip rate (NFR) either do so at the expense of overall accuracy by forcing a new model to imitate the old models, or use ensembles, which multiply inference cost prohibitively. We analyze the role of ensembles in reducing NFR and observe that they remove negative flips that are typically not close to the decision boundary, but often exhibit large deviations in the distance among their logits...

Viewers use contextual information to visually explore complex scenes. Object recognition is facilitated by exploiting object-scene relations (which objects are expected in a given scene) and object-object relations (which objects are expected because of the occurrence of other objects). Semantically inconsistent objects deviate from these expectations, so they tend to capture viewers' attention (the semantic-inconsistency effect ). Some objects fit the identity of a scene more or less than others, yet semantic inconsistencies have hitherto been operationalized as binary (consistent vs...

(1) Background: Mindfulness-based interventions (MBIs) have shown interesting preliminary effects on self-regulation processes in early adolescence. However, programs have typically combined different types of interventions with no understanding of the specific effect of each intervention type on attentional, emotional, and behavioral regulation. The objective of this research was to evaluate the effect of two MBIs-one focused on classic attentional practices and another focused on the recognition and expression of emotions-on attentional, emotional, and behavioral self-regulation in early adolescents...

Dopamine neurons signal the salience of environmental stimuli and influence learning, although it is less clear if these neurons also determine the salience of memories. Ventral tegmental area (VTA) dopamine neurons increase their firing in the presence of new objects and reduce it upon repeated, inconsequential exposures, marking the shift from novelty to familiarity. This study investigates how dopamine neuron activity during repeated familiar object exposure affects an animal's preference for new objects in a subsequent novel object recognition (NOR) test...

BACKGROUND: Point-of-care ultrasound (POCUS) is becoming increasingly crucial in the Pediatric Emergency Department for objective patient examination. However, despite its growing interest and wide-ranging applications, POCUS remains relatively unexplored in general pediatric training and education. Many physicians still find it challenging to comprehend and implement. METHODS: A theoretical-practical POCUS course for pediatric residents was conducted at the University of Catania, Italy...

The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process...

Hearing loss is a prevalent, chronic condition in the United States; it is often gradual and progressive and is underreported by patients and undertreated by physicians. The impaired ability to effectively hear and communicate may result in negative emotional, cognitive, economic, and social consequences for individuals and may pose a safety risk. Questionnaires and smartphone apps are available to help identify and evaluate self-perceived hearing loss. Physicians should assess for objective hearing impairment when the patient or family member raises a concern or if cognitive or mood symptoms are present that could be influenced by hearing loss...

BACKGROUND: Medical documentation plays a crucial role in clinical practice, facilitating accurate patient management and communication among health care professionals. However, inaccuracies in medical notes can lead to miscommunication and diagnostic errors. Additionally, the demands of documentation contribute to physician burnout. Although intermediaries like medical scribes and speech recognition software have been used to ease this burden, they have limitations in terms of accuracy and addressing provider-specific metrics...

Machine vision and artificial intelligence hold promise across healthcare applications. In this paper, we focus on the emerging research direction of infant action recognition, and we specifically consider the task of reaching which is an important developmental milestone. We develop E-babyNet, a lightweight yet effective neural-network-based framework for infant action recognition that leverages the spatial and temporal correlation of bounding boxes of infants' hands and objects to reach for to determine the onset and offset of the reaching action...

Accurate recognition of fetal anatomical structure is a pivotal task in ultrasound (US) image analysis. Sonographers naturally apply anatomical knowledge and clinical expertise to recognizing key anatomical structures in complex US images. However, mainstream object detection approaches usually treat each structure recognition separately, overlooking anatomical correlations between different structures in fetal US planes. In this work, we propose a Fetal Anatomy Reasoning Network (FARN) that incorporates two kinds of relationship forms: a global context semantic block summarized with visual similarity and a local topology relationship block depicting structural pair constraints...

Currently prevalent multi-modal 3D detection methods rely on dense detectors that usually use dense Bird's-Eye-View (BEV) feature maps. However, the cost of such BEV feature maps is quadratic to the detection range, making it not scalable for long-range detection. Recently, LiDAR-only fully sparse architecture has been gaining attention for its high efficiency in long-range perception. In this paper, we study how to develop a multi-modal fully sparse detector. Specifically, our proposed detector integrates the well-studied 2D instance segmentation into the LiDAR side, which is parallel to the 3D instance segmentation part in the LiDAR-only baseline...

Combining information from multiple senses is essential to object recognition, core to the ability to learn concepts, make new inferences, and generalize across distinct entities. Yet how the mind combines sensory input into coherent crossmodal representations - the crossmodal binding problem - remains poorly understood. Here, we applied multi-echo fMRI across a four-day paradigm, in which participants learned 3-dimensional crossmodal representations created from well-characterized unimodal visual shape and sound features...

Background The care provided to the mother and child from delivery to six weeks after is defined as postnatal care. The postnatal period is both a happy and critical phase for the mother and the newborn. However, the provision of high-quality care services is often ignored during this time. The objective of this study was to assess postnatal care services quality by Accredited Social Health Activist (ASHA) workers and associated factors such as newborn care in rural tribal areas of Gujarat, India. Methodology An ethnographic approach was adopted...

OBJECTIVE: To develop an artificial intelligence vaginal secretion analysis system based on deep learning and to evaluate the accuracy of automated microscopy in the clinical diagnosis of aerobic vaginitis (AV). METHODS: In this study, the vaginal secretion samples of 3769 patients receiving treatment at the Department of Obstetrics and Gynecology, West China Second Hospital, Sichuan University between January 2020 and December 2021 were selected. Using the results of manual microscopy as the control, we developed the linear kernel SVM algorithm, an artificial intelligence (AI) automated analysis software, with Python Scikit-learn script...

Deep learning models for medical image segmentation are usually trained with voxel-wise losses, e.g., cross-entropy loss, focusing on unary supervision without considering inter-voxel relationships. This oversight potentially leads to semantically inconsistent predictions. Here, we propose a contextual similarity loss (CSL) and a structural similarity loss (SSL) to explicitly and efficiently incorporate inter-voxel relationships for improved performance. The CSL promotes consistency in predicted object categories for each image sub-region compared to ground truth...

BACKGROUND: Deep brain stimulation (DBS) of the nucleus basalis of Meynert (NBM) has been preliminarily investigated as a potential treatment for dementia. The degeneration of NBM cholinergic neurons is a pathological feature of many forms of dementia. Although stimulation of the NBM has been demonstrated to improve learning, the ideal parameters for NBM stimulation have not been elucidated. This study assesses the differential effects of varying stimulation patterns and duration on learning in a dementia rat model...

Alcohol use disorders (AUDs) impose an enormous societal and financial burden, and world-wide, alcohol misuse is the 7th leading cause of premature death1. Despite this, there are currently only 3 FDA approved pharmacological treatments for the treatment of AUDs in the United States. The neurotensin (Nts) system has long been implicated in modulating behaviors associated with alcohol misuse. Recently, a novel compound, SBI-553, that biases the action of Nts receptor 1 (NTSR1) activation, has shown promise in preclinical models of psychostimulant misuse...

AIMS: Ventral pathway circuits are constituted by the interconnected brain areas that are distributed throughout the brain. These brain circuits are primarily involved in processing of object related information in brain. However, their role in object recognition memory (ORM) enhancement remains unknown. Here, we have studied on the implication of these circuits in ORM enhancement and in reversal of ORM deficit in aging. METHODS: The brain areas interconnected to ventral pathway circuits in rat brain were activated by an expression of a protein called regulator of G-protein signaling 14 of 414 amino acids (RGS14414 )...

Objective: To investigate perspectives and changes in treatment selection by Chinese surgeons since introduction of the watch-and-wait approach after neoadjuvant therapy for rectal cancer. Methods: A cross-sectional survey was conducted using a questionnaire distributed through the "Wenjuanxing" online survey platform. The survey focused on the recognition and practices of Chinese surgeons regarding the strategy of watch-and-wait after neoadjuvant therapy for rectal cancer and was disseminated within the China Watch-and-Wait Database (CWWD) WeChat group...