Carmen Soto, Lazaro P. Orihuela, Grego Apostol
et al.
ABSTRACT Chronic stress increases susceptibility to anxiety and depression disorders, recurrent and common psychiatric conditions. Current antidepressant medications have varying degrees of efficacy and often have multiple side effects limiting treatment adherence. Physical exercise has beneficial effects on stress‐related mental disorders. However, the underlying mechanisms are unclear. Dentate gyrus granule cells (GCs) excitability may mediate stress resilience. Here, we expose young adult C57Bl6 mice to chronic restraint stress (CRS) for 14 days followed by 30 days of running treatment. Behavioural evaluation before and after treatment showed that the behavioural alterations elicited by CRS were mitigated by running. Next, we evaluated serotonergic modulation of GC excitability, as a potential mechanism underlying running‐induced stress resilience. Electrophysiological recordings indicate that CRS alters serotonergic modulation of GC excitability. Utilising (S)‐WAY 100135 and Tropisetron, antagonists of 5‐HT 1A and 5‐HT 3 receptors respectively, we show that running recovers 5‐HT 1A receptor activity lost by CRS. Additionally, running promotes the indirect modulation of GCs through 5‐HT 3 receptor activation. Thus, 5‐HT 1A and 5‐HT 3 receptors may be potential targets for the treatment of stress‐related psychiatric disorders.
A declining honeybee population could pose a threat to a food resources of the whole world one of the latest trend in beekeeping is an effort to monitor a health of the honeybees using various sensors and devices. This paper participates on a development on one of these devices. The aim of this paper is to make an upgrades and improvement of an in-development bee health monitoring device and propose a remote data logging solution for a continual monitoring of a beehive.
Priya Martin, Lucylynn Lizarondo, Geoff Argus
et al.
The COVID-19 pandemic has caused significant disruptions to healthcare student placements worldwide, including already challenged rural areas in Australia. While accounts are emerging of student experiences in larger centers and from a student perspective, there is a need for in-depth exploration of student supervisor experiences in rural areas at the onset of the pandemic. This study aims to address this gap through 23 individual, semi-structured interviews with healthcare workers from ten health professions who were either direct student supervisors or in roles supporting student supervisors A reflexive thematic analysis approach was used to develop four themes, namely compounding stress, negative impacts on student learning, opportunity to flex and innovate, and targeted transitioning support strategies. The findings indicate that healthcare workers with student supervision responsibilities at the onset of the pandemic experienced high levels of stress and wellbeing concerns. This study sheds light on the importance of supporting student supervisors in rural areas, and the need for implementing targeted support strategies for new graduates whose placements were impacted by the pandemic. This is not only essential for supporting the rural healthcare workforce but is also imperative for addressing inequalities to healthcare access experienced in rural communities.
We investigate how well CLIP understands texture in natural images described by natural language. To this end, we analyze CLIP's ability to: (1) perform zero-shot learning on various texture and material classification datasets; (2) represent compositional properties of texture such as red dots or yellow stripes on the Describable Texture in Detail(DTDD) dataset; and (3) aid fine-grained categorization of birds in photographs described by color and texture of their body parts.
To safely navigate in various complex traffic scenarios, autonomous driving systems are generally equipped with a motion forecasting module to provide vital information for the downstream planning module. For the real-world onboard applications, both accuracy and latency of a motion forecasting model are essential. In this report, we present an effective and efficient solution, which ranks the 3rd place in the Argoverse 2 Motion Forecasting Challenge 2022.
The goal of this thesis is to present my research contributions towards solving various visual synthesis and generation tasks, comprising image translation, image completion, and completed scene decomposition. This thesis consists of five pieces of work, each of which presents a new learning-based approach for synthesizing images with plausible content as well as visually realistic appearance. Each work demonstrates the superiority of the proposed approach on image synthesis, with some further contributing to other tasks, such as depth estimation.
Lane detection is a long-standing task and a basic module in autonomous driving. The task is to detect the lane of the current driving road, and provide relevant information such as the ID, direction, curvature, width, length, with visualization. Our work is based on CNN backbone DLA-34, along with Affinity Fields, aims to achieve robust detection of various lanes without assuming the number of lanes. Besides, we investigate novel decoding methods to achieve more efficient lane detection algorithm.
This report presents the results of a multi-scale wavelet based scheme for single image de-hazing and underwater image enhancement. The scheme is fast and highly localized in addition to global enhancement of hazy images. A PDE-based formulation enables additional versatility as the iterative nature allows more flexibility for various types of images. Visual and objective results from experiments indicate that the proposed approach competes favourably or surpasses most of the state-of-the-art approaches.
In this report, we present and compare the results of an improved fractional and integer order partial differential equation (PDE)-based binarization scheme. The improved model incorporates a diffusion term in addition to the edge and binary source terms from the previous formulation. Furthermore, logarithmic local contrast edge normalization and combined isotropic and anisotropic edge detection enables simultaneous bleed-through elimination with faded text restoration for degraded document images. Comparisons of results with state-of-the-art PDE methods show improved and superior results.
In this paper, we propose a block-wise image transformation method with a secret key for support vector machine (SVM) models. Models trained by using transformed images offer a poor performance to unauthorized users without a key, while they can offer a high performance to authorized users with a key. The proposed method is demonstrated to be robust enough against unauthorized access even under the use of kernel functions in a facial recognition experiment.
The objective of the study is to evaluate the efficiency of a multi layer neural network models built by combining Recurrent Neural Network(RNN) and Convolutional Neural Network(CNN) for solving the problem of classifying different types of White Blood Cells. This can have applications in the pharmaceutical and healthcare industry for automating the analysis of blood tests and other processes requiring identifying the nature of blood cells in a given image sample. It can also be used in the diagnosis of various blood-related diseases in patients.
Localizing stereo boundaries is difficult because matching cues are absent in the occluded regions that are adjacent to them. We introduce an energy and level-set optimizer that improves boundaries by encoding the essential geometry of occlusions: The spatial extent of an occlusion must equal the amplitude of the disparity jump that causes it. In a collection of figure-ground scenes from Middlebury and Falling Things stereo datasets, the model provides more accurate boundaries than previous occlusion-handling techniques.
Our research topic is Human segmentation with static camera. This topic can be divided into three sub-tasks, which are object detection, instance identification and segmentation. These sub-tasks are three closely related subjects. The development of each subject has great impact on the other two fields. In this literature review, we will first introduce the background of human segmentation and then talk about issues related to the above three fields as well as how they interact with each other.
In this paper we present a fast minimal solver for absolute camera pose estimation from four known points that lie in a plane. We assume a perspective camera model with unknown focal length and unknown radial distortion. The radial distortion is modelled using the division model with one parameter. We show that the solutions to this problem can be found from a univariate six-degree polynomial. This results in a very fast and numerically stable solver.
This paper proposes using sketch algorithms to represent the votes in Hough transforms. Replacing the accumulator array with a sketch (Sketch Hough Transform - SHT) significantly reduces the memory needed to compute a Hough transform. We also present a new sketch, Count Median Update, which works better than known sketch methods for replacing the accumulator array in the Hough Transform.
In general it requires at least 7 point correspondences to compute the fundamental matrix between views. We use the cross ratio invariance between corresponding epipolar lines, stemming from epipolar line homography, to derive a simple formulation for the relationship between epipoles and corresponding points. We show how it can be used to reduce the number of required points for the epipolar geometry when some information about the epipoles is available and demonstrate this with a buddy search app.
Recent machine learning techniques can be modified to produce creative results. Those results did not exist before; it is not a trivial combination of the data which was fed into the machine learning system. The obtained results come in multiple forms: As images, as text and as audio. This paper gives a high level overview of how they are created and gives some examples. It is meant to be a summary of the current work and give people who are new to machine learning some starting points.
El artículo presenta la llegada del nuevo milenio, un número cada vez mayor de empresarios se unieron a la aplicación del diseño sostenible que comenzó a replantearse en las empresas y el rol que juegan con el desarrollo del medio ambiente, el planeta y en la sociedad. Podemos decir que el diseño sostenible busca generar soluciones a través de servicios y estilos de vida, pero no exclusivamente a través de objetos. Con el fin de introducir una definición elaborada de diseño sostenible es necesario mencionar los sistemas sostenibles, que básicamente, se refieren a cualquier tipo de red o servicio social que puede existir y replicarse. Además de sistemas sostenibles hay otros principios dentro del diseño sostenible. Por último, cualquier tipo de resultado obtenido para satisfacer la necesidad debe ser sostenible a largo plazo entendiéndose como un proceso que permita una comunidad lograr un resultado a través de estrategias de diseño.
This paper studies efficient means for dealing with intra-category diversity in object detection. Strategies for occlusion and orientation handling are explored by learning an ensemble of detection models from visual and geometrical clusters of object instances. An AdaBoost detection scheme is employed with pixel lookup features for fast detection. The analysis provides insight into the design of a robust vehicle detection system, showing promise in terms of detection performance and orientation estimation accuracy.
Vladimir Kolmogorov, Christoph Lampert, Emilie Morvant
et al.
The 38th Annual Workshop of the Austrian Association for Pattern Recognition (ÖAGM) will be held at IST Austria, on May 22-23, 2014. The workshop provides a platform for researchers and industry to discuss traditional and new areas of computer vision. This year the main topic is: Pattern Recognition: interdisciplinary challenges and opportunities.