Neural network methods for one-to-many multi-valued mapping problems

An investigation of the applicability of neural network-based methods in predicting the values of multiple parameters, given the value of a single parameter within a particular problem domain is presented. In this context, the input parameter may be an important source of variation that is related with a complex mapping function to the remaining sources of variation within a multivariate distribution. The definition of the relationship between the variables of a multivariate distribution and a single source of variation allows the estimation of the values of multiple variables given the value of the single variable, addressing in that way an ill-conditioned one-to-many mapping problem. As part of our investigation, two problem domains are considered: predicting the values of individual stock shares, given the value of the general index, and predicting the grades received by high school pupils, given the grade for a single course or the average grade. With our work, the performance of standard neural network-based methods and in particular multilayer perceptrons (MLPs), radial basis functions (RBFs), mixture density networks (MDNs) and a latent variable method, the general topographic mapping (GTM), is compared. According to the results, MLPs and RBFs outperform MDNs and the GTM for these one-to-many mapping problems

Removing pose from face images

This paper proposes a novel approach to pose removal from face images based on the inherent symmetry that is present in faces. In order for face recognition systems and expression classification systems to operate optimally, subjects must look directly into the camera. The removal of pose from face images after their capture removes this restriction. To obtain a pose-removed face image, the frequency components at each position of the face image, obtained through a wavelet transformation, are examined. A cost function based on the symmetry of this wavelet transformed face image is minimized to achieve pose removal.Experimental results are presented that demonstrate that the proposed algorithm improves upon existing techniques in the literature

Type II enteropathy-associated T-cell lymphoma features a unique genomic profile with highly recurrent SETD2 alterations.

Enteropathy-associated T-cell lymphoma (EATL), a rare and aggressive intestinal malignancy of intraepithelial T lymphocytes, comprises two disease variants (EATL-I and EATL-II) differing in clinical characteristics and pathological features. Here we report findings derived from whole-exome sequencing of 15 EATL-II tumour-normal tissue pairs. The tumour suppressor gene SETD2 encoding a non-redundant H3K36-specific trimethyltransferase is altered in 14/15 cases (93%), mainly by loss-of-function mutations and/or loss of the corresponding locus (3p21.31). These alterations consistently correlate with defective H3K36 trimethylation. The JAK/STAT pathway comprises recurrent STAT5B (60%), JAK3 (46%) and SH2B3 (20%) mutations, including a STAT5B V712E activating variant. In addition, frequent mutations in TP53, BRAF and KRAS are observed. Conversely, in EATL-I, no SETD2, STAT5B or JAK3 mutations are found, and H3K36 trimethylation is preserved. This study describes SETD2 inactivation as EATL-II molecular hallmark, supports EATL-I and -II being two distinct entities, and defines potential new targets for therapeutic intervention

Local endothelial complement activation reverses endothelial quiescence, enabling t-cell homing, and tumor control during t-cell immunotherapy.

Cancer immunotherapy relies upon the ability of T cells to infiltrate tumors. The endothelium constitutes a barrier between the tumor and effector T cells, and the ability to manipulate local vascular permeability could be translated into effective immunotherapy. Here, we show that in the context of adoptive T cell therapy, antitumor T cells, delivered at high enough doses, can overcome the endothelial barrier and infiltrate tumors, a process that requires local production of C3, complement activation on tumor endothelium and release of C5a. C5a, in turn, acts on endothelial cells promoting the upregulation of adhesion molecules and T-cell homing. Genetic deletion of C3 or the C5a receptor 1 (C5aR1), and pharmacological blockade of C5aR1, impaired the ability of T cells to overcome the endothelial barrier, infiltrate tumors, and control tumor progression in vivo, while genetic chimera mice demonstrated that C3 and C5aR1 expression by tumor stroma, and not leukocytes, governs T cell homing, acting on the local endothelium. In vitro, endothelial C3 and C5a expressions were required for endothelial activation by type 1 cytokines. Our data indicate that effective immunotherapy is a consequence of successful homing of T cells in response to local complement activation, which disrupts the tumor endothelial barrier

Recognising facial expressions in video sequences

We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base

Lasting impressions: archaeology and community engagement in the Xeros River Valley (Cyprus)

This volume brings together specialists from a broad demographic and professional range – academics, museum curators, students, and content creators – to discuss case studies, challenges, and potential future avenues for public scholarship on the history, archaeology, and cultures of the ancient Mediterranean, North Africa, and Western Asia.Together, the contributions promote the creation of inclusive methods of knowledge mobilization and communication in public spheres across three main areas: cultural heritage, pedagogy, and public-facing scholarship. These areas have all been directly affected by Eurocentric structures that have claimed ownership of ancient Mediterranean cultural heritage and have dictated how it has been taught in schools and communicated to the broader public. The volume is divided into three sections – Museums, Teaching and Learning, and Global and Local Projects – each addressing pressing challenges faced within these interrelated fields and offering ways for us to overcome the exclusionary narratives that plague them.Ancient Pasts for Modern Audiences provides an invaluable resource for those interested in public history, from academics to lay audiences, in the fields of Ancient Mediterranean, North African, and Western Asian Studies. The book also appeals to professionals and researchers whose interests lie in public-facing scholarship, pedagogy, digital humanities, decolonization studies, museum studies, and popular media

TaxaNet:Harnessing a Hierarchical Loss Function for Insect Classification Using Deep Learning

Insects have the largest percentage of all living organisms worldwide, playing a pivotal role in maintaining essential ecosystem services such as pollination, pest control, nutrient cycling, and food provisioning. However, recent studies have reported alarming declines in insect populations globally, highlighting an urgent need for automated methods to estimate and quantify these populations, to better understand the reasons of their decline and to take proper measures. The ability to automatically estimate insect populations is crucial for shaping appropriate environmental policies. Advances in AI and computer vision techniques are revolutionizing the study of insects through non-invasive camera traps. However, the diversity of insects, close resemblances of many species, and multi-level taxa classification remain significant challenges in image-based insect monitoring. In this work, we propose TaxaNet a deep learning model for multi-level insect taxa classification, utilizing a pretrained EfficientNet as a feature extractor, followed by six classification blocks. Each block predicts one of the six taxonomic levels: Kingdom, Class, Order, Family, Genus, and Species. This hierarchical design and the loss function improves lower-level taxa predictions by leveraging the higher-level features available. A class-weighted hierarchical loss function, alongside the standard class-wise loss, allows the model to understand the relationships between taxonomic levels while maintaining classification accuracy. Trained on the Diopsis insect camera trap dataset containing 31,000 training images and 7,900 test images, the model achieved an average precision of 0.85 and a recall of 0.86 across five taxonomic levels. These results demonstrate the effectiveness of our approach in harnessing multi-level insect taxonomy to achieve multi-level insect classification.</p

Lasting impressions: archaeology and community engagement in the Xeros River Valley (Cyprus)

This volume brings together specialists from a broad demographic and professional range – academics, museum curators, students, and content creators – to discuss case studies, challenges, and potential future avenues for public scholarship on the history, archaeology, and cultures of the ancient Mediterranean, North Africa, and Western Asia.Together, the contributions promote the creation of inclusive methods of knowledge mobilization and communication in public spheres across three main areas: cultural heritage, pedagogy, and public-facing scholarship. These areas have all been directly affected by Eurocentric structures that have claimed ownership of ancient Mediterranean cultural heritage and have dictated how it has been taught in schools and communicated to the broader public. The volume is divided into three sections – Museums, Teaching and Learning, and Global and Local Projects – each addressing pressing challenges faced within these interrelated fields and offering ways for us to overcome the exclusionary narratives that plague them.Ancient Pasts for Modern Audiences provides an invaluable resource for those interested in public history, from academics to lay audiences, in the fields of Ancient Mediterranean, North African, and Western Asian Studies. The book also appeals to professionals and researchers whose interests lie in public-facing scholarship, pedagogy, digital humanities, decolonization studies, museum studies, and popular media

The turbulent structure of the jet in cross-flow

In this thesis the structure of the jet in cross flow in the far field was investigated experimentally using time-resolved, multi-scale and statistically independent Stereoscopic Particle Image Velocimetry measurements to reveal the mean and instantaneous three-dimensional (3D) structures. All of the measurements were performed in the Counter-rotating Vortex Pair (CVP) plane for a high velocity ratio and jet Reynolds number. Statistical measurements at various downstream locations and velocity ratios are presented. Probability density functions of the streamwise vorticity field showed that each CVP core is instantaneously made of a number of small vortex tubes rather than a single vortex core. The characteristic ‘kidney’ shape was illustrated in the rms velocity profiles and the Reynolds stress profiles exhibited a high level of organisation which showed an evolving shape with downstream distance and persisted well into the far field. Two point spatial correlations pointed to a common structure for all conditions whose mean shape generates the ‘kidney’ shape, as well as evidence of wake structures. Time-resolved measurements were carried out in a moving and stationary frame of reference, converted to 3D measurements via the use of Taylor’s hypothesis. The origin of the ‘kidney’ shape and large degree of spatial order in the far field was found to be a result of an organised ‘train’ of consecutive hairpin, roller and wake structures. Together, these structures provide a physical explanation that reconciles the statistical and instantaneous structure of the CVP.This work was supported by the EPSR

The Be-Hive Project—Counting Bee Traffic Based on Deep Learning and Pose Estimation

Beekeeping is an important practice for ensuring the abundance of pollinators and for honey production. Traditionally, beekeepers inspect hives regularly to monitor their bees’ populations, but this method is invasive and can cause stress to the bees. It is also impractical, for beekeepers having hundreds or thousands of beehives. In recent decades, various attempts have been made to automate the monitoring of bee colonies using emerging technologies. These technologies include sensors that collect micro-climate parameters, photos, video and audio from inside the hives and the nearby environment, which are then analyzed using automatic or manual methods. The beehive project aims a range of sensing technologies (image, sound, temperature, humidity, weight), together with state-of-the-art computer vision technologies and remote-sensing imagery to create a smart beehive system and monitor beehive on real-time. In this paper, we present the preliminary results of the BE-HIVE, a smart beehive monitoring system. We present the monitoring system developed and the deep learning algorithm used to count bee traffic using the image from the camera placed at the entrance of the hive. For bee traffic estimation, we employ a counting algorithm that predicts the pose of individual bees and tracks them in subsequent frames. To reduce the annotation overhead of the key-points for pose estimation, we generate synthetic data to train our algorithm. The results show that the key-point detection model achieves an Intersection Over Union (IOU) of 86% when trained only on synthetic data and a traffic count mean absolute error of 5.7. These results indicate that the proposed approach can be used to monitor the bee activity remotely, increasing convenience and productivity.</p