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We introduce a novel dataset consisting of images depicting pink eggs that have been identified as Pomacea canaliculata eggs, accompanied by corresponding bounding box annotations. The purpose of this dataset is to aid researchers in the analysis of the spread of Pomacea canaliculata species by utilizing deep learning techniques, as well as supporting other investigative pursuits that require visual data pertaining to the eggs of Pomacea canaliculata. It is worth noting, however, that the identity of the eggs in question is not definitively established, as other species within the same taxonomic family have been observed to lay similar-looking eggs in regions of the Americas. Therefore, a crucial prerequisite to any decision regarding the elimination of these eggs would be to establish with certainty whether they are exclusively attributable to invasive Pomacea canaliculata or if other species are also involved. The dataset is available at https://www.kaggle.com/datasets/deeshenzhen/pinkeggs

The use of rewriting-based visual formalisms is on the rise. In the formal methods community, this is due also to the introduction of adhesive categories, where most properties of classical approaches to graph transformation, such as those on parallelism and confluence, can be rephrased and proved in a general and uniform way.E-graphs (EGGs) are a formalism for program optimisation via an efficient implementation of equality saturation. In short, EGGs can be defined as (acyclic) term graphs with an additional notion of equivalence on nodes that is closed under the operators of the signature. Instead of replacing the components of a program, the optimisation step is performed by adding new components and linking them to the existing ones via an equivalence relation, until an optimal program is reached. This work describes EGGs via adhesive categories. Besides the benefits in itself of a formal presentation, which renders the properties of the data structure precise, the description of the addition of equivalent program components using standard graph transformation tools offers the advantages of the adhesive framework in modelling, for example, concurrent updates.

Novel view synthesis (NVS) is crucial in computer vision and graphics, with wide applications in AR, VR, and autonomous driving. While 3D Gaussian Splatting (3DGS) enables real-time rendering with high appearance fidelity, it suffers from multi-view inconsistencies, limiting geometric accuracy. In contrast, 2D Gaussian Splatting (2DGS) enforces multi-view consistency but compromises texture details. To address these limitations, we propose Exchangeable Gaussian Splatting (EGGS), a hybrid representation that integrates 2D and 3D Gaussians to balance appearance and geometry. To achieve this, we introduce Hybrid Gaussian Rasterization for unified rendering, Adaptive Type Exchange for dynamic adaptation between 2D and 3D Gaussians, and Frequency-Decoupled Optimization that effectively exploits the strengths of each type of Gaussian representation. Our CUDA-accelerated implementation ensures efficient training and inference. Extensive experiments demonstrate that EGGS outperforms existing methods in rendering quality, geometric accuracy, and efficiency, providing a practical solution for high-quality NVS.

The soybean cyst nematode (SCN), Heterodera glycines, is the most damaging pathogen of soybeans in the United States. To assess the severity of nematode infestations in the field, SCN egg population densities are determined. Cysts (dead females) of the nematode must be extracted from soil samples and then ground to extract the eggs within. Sucrose centrifugation commonly is used to separate debris from suspensions of extracted nematode eggs. We present a method using OptiPrep as a density gradient medium with improved separation and recovery of extracted eggs compared to the sucrose centrifugation technique. Also, computerized methods were developed to automate the identification and counting of nematode eggs from the processed samples. In one approach, a high-resolution scanner was used to take static images of extracted eggs and debris on filter papers, and a deep learning network was trained to identify and count the eggs among the debris. In the second approach, a lensless imaging setup was developed using off-the-shelf components, and the processed egg samples were passed through a microfluidic flow chip made from double-sided adhesive tape. Holographic videos were recorded of

Insect pest control poses a global challenge, affecting public health, food safety, and the environment. Diseases transmitted by mosquitoes are expanding beyond tropical regions due to climate change. Agricultural pests further exacerbate economic losses by damaging crops. The Sterile Insect Technique (SIT) emerges as an eco-friendly alternative to chemical pesticides, involving the sterilization and release of male insects to curb population growth. This work focuses on the automation of the analysis of field ovitraps used to follow-up a SIT program for the Aedes albopictus mosquito in the Valencian Community, Spain, funded by the Conselleria de Agricultura, Agua, Ganaderia y Pesca. Previous research has leveraged deep learning algorithms to automate egg counting in ovitraps, yet faced challenges such as manual handling and limited analysis capacity. Innovations in our study include classifying eggs as hatched or unhatched and reconstructing ovitraps from partial images, mitigating issues of duplicity and cut eggs. Also, our device can analyze multiple ovitraps simultaneously without the need of manual replacement. This approach significantly enhances the accuracy of egg counting

Considering the pivotal role of eggs in the food industry and their nutritional significance, this study employed micro-Raman spectroscopy of eggs, examining both shells and yolks to assess the quality and freshness of eggs. Raman spectra were collected at different temperatures and time intervals to investigate temperature and time effects, potentially indicating Raman peak reduction due to Maillard reaction and oxidation of proteins and lipids and carotenoid depletion, respectively. By calculating the ratio of Raman peaks, lipids, fatty acids, and choline methyl were introduced as biomarkers of temperature and time. Notable correlations were identified between Raman peaks and egg quality coefficients, including egg coefficient and peak 1002 cm$^{-1}$ (protein), total weight and 1301 cm$^{-1}$ (Lipids), yolk weight and 2934 and 3057 cm$^{-1}$, total weight with peak 710 cm$^{-1}$, and egg shape index and peak 3057 cm$^{-1}$. Analysis of eggshells at different time intervals revealed Raman peak reduction during time, demonstrating Raman's effectiveness in assessing egg quality from its shell. Using the PLS-DA method, the classification of eggs at different temperatures and storage

Competition among gametes for fertilization imposes strong selection. For external fertilizers, this selective pressure extends to eggs for which spawning conditions can range from sperm limitation (competition among eggs) to sexual conflict (overabundance of competing sperm toxic to eggs). Yet existing fertilization models ignore dynamics that can alter the functional nature of gamete interactions. These factors include attraction of sperm to eggs, egg crowding effects or other nonlinearities in per capita rates of sperm-egg interaction. Such processes potentially allow egg concentrations to drastically affect viable fertilization probabilities. I experimentally tested whether such egg effects occur using the urchin $\textit{Strongylocentrotus purpuratus}$ and parameterized a newly derived model of fertilization dynamics and existing models modified to include such interactions. The experiments revealed that at low sperm concentrations, eggs compete for sperm while at high sperm concentrations eggs cooperatively reduce abnormal fertilization (a proxy for polyspermy). I show that these observations are consistent with declines in the per capita rate at which sperm and eggs interact

Inspired by the connection between ovoids and unitals arising from the Buekenhout construction in the André/Bruck-Bose representation of translation planes of dimension at most two over their kernel, and since eggs of PG(4m-1,q), m>=1, are a generalization of ovoids, we explore the relation between eggs and unitals in translation planes of higher dimension over their kernel. By investigating such a relationship, we construct a unital in the Dickson semifield plane of order 3^{10}, which is represented in PG(20,3) by a cone whose base is a set of points constructed from the dual of the Penttila-Williams egg in PG(19,3). This unital is not polar; so, up to the knowledge of the authors, it seems to be a new unital in such a plane.

We give examples of situations -- stochastic production, military tactics, corporate merger -- where it is beneficial to concentrate risk rather than to diversify it, that is, to put all eggs in one basket. Our examples admit a dual interpretation: as optimal strategies of a single player (the `principal') or, alternatively, as dominant strategies in a non-cooperative game with multiple players (the `agents'). The key mathematical result can be formulated in terms of a convolution structure on the set of increasing functions on a Boolean lattice (the lattice of subsets of a finite set). This generalizes the well-known Harris inequality from statistical physics and discrete mathematics; we give a simple self-contained proof of this result, and prove a further generalization based on the game-theoretic approach.

Soybeans are an important crop for global food security. Every year, soybean yields are reduced by numerous soybean diseases, particularly the soybean cyst nematode (SCN). It is difficult to visually identify the presence of SCN in the field, let alone its population densities or numbers, as there are no obvious aboveground disease symptoms. The only definitive way to assess SCN population densities is to directly extract the SCN cysts from soil and then extract the eggs from cysts and count them. Extraction is typically conducted in commercial soil analysis laboratories and university plant diagnostic clinics and involves repeated steps of sieving, washing, collecting, grinding, and cleaning. Here we present a robotic instrument to reproduce and automate the functions of the conventional methods to extract nematode cysts from soil and subsequently extract eggs from the recovered nematode cysts. We incorporated mechanisms to actuate the stage system, manipulate positions of individual sieves using the gripper, recover cysts and cyst-sized objects from soil suspended in water, and grind the cysts to release their eggs. All system functions are controlled and operated by a touchscree

Culling newly hatched male chicks in industrial hatcheries poses a serious ethical problem. Both laying and broiler breeders need males, but it is a problem because they are produced more than needed. Being able to determine the sex of chicks in the egg at the beginning or early stage of incubation can eliminate ethical problems as well as many additional costs. When we look at the literature, the methods used are very costly, low in applicability, invasive, inadequate in accuracy, or too late to eliminate ethical problems. Considering the embryo's development, the earliest observed candidate feature for sex determination is blood vessels. Detection from blood vessels can eliminate ethical issues, and these vessels can be seen when light is shined into the egg until the first seven days. In this study, sex determination was made by morphological analysis from embryonic vascular images obtained in the first week when the light was shined into the egg using a standard camera without any invasive procedure to the egg.

IPIs caused by protozoan and helminth parasites are among the most common infections in humans in LMICs. They are regarded as a severe public health concern, as they cause a wide array of potentially detrimental health conditions. Researchers have been developing pattern recognition techniques for the automatic identification of parasite eggs in microscopic images. Existing solutions still need improvements to reduce diagnostic errors and generate fast, efficient, and accurate results. Our paper addresses this and proposes a multi-modal learning detector to localize parasitic eggs and categorize them into 11 categories. The experiments were conducted on the novel Chula-ParasiteEgg-11 dataset that was used to train both EfficientDet model with EfficientNet-v2 backbone and EfficientNet-B7+SVM. The dataset has 11,000 microscopic training images from 11 categories. Our results show robust performance with an accuracy of 92%, and an F1 score of 93%. Additionally, the IOU distribution illustrates the high localization capability of the detector.

In this note, we use the theory of Desarguesian spreads to investigate good eggs. Thas showed that an egg in $\mathrm{PG}(4n-1, q)$, $q$ odd, with two good elements is elementary. By a short combinatorial argument, we show that a similar statement holds for large pseudo-caps, in odd and even characteristic. As a corollary, this improves and extends the result of Thas, Thas and Van Maldeghem (2006) where one needs at least 4 good elements of an egg in even characteristic to obtain the same conclusion. We rephrase this corollary to obtain a characterisation of the generalised quadrangle $T_3(\mathcal{O})$ of Tits. Lavrauw (2005) characterises elementary eggs in odd characteristic as those good eggs containing a space that contains at least 5 elements of the egg, but not the good element. We provide an adaptation of this characterisation for weak eggs in odd and even characteristic. As a corollary, we obtain a direct geometric proof for the theorem of Lavrauw.

This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to suppress undesired parts of an image frame while allowing the desired parts resulting in efficient object detection. The efficacy of the framework is demonstrated on a critical plant science problem. In the United States, approximately $1 billion is lost per annum due to a nematode infection on soybean plants. Currently, plant-pathologists rely on labor-intensive and time-consuming identification of Soybean Cyst Nematode (SCN) eggs in soil samples via manual microscopy. The proposed framework attempts to significantly expedite the process by using a series of manually labeled microscopic images for training followed by automated high-throughput egg detection. The problem is particularly difficult due to the presence of a large population of non-egg particles (disturbances) in the image frames that are very similar to SCN eggs in shape, pose and illumination. Therefore, the selective autoencoder is trained to learn unique features related to the invariant shapes and sizes of the SCN eggs without handcraft

We examine the spinning behavior of egg-shaped axisymmetric bodies whose cross sections are described by several oval curves similar to real eggs with thin and fat ends. We use the gyroscopic balance condition of Moffatt and Shimomura and analyze the slip velocity of the bodies at the point of contact as a function of $θ$, the angle between the axis of symmetry and the vertical axis, and find the existence of the critical angle $θ_c$. When the bodies are spun with an initial angle $θ_{\rm initial}>θ_c$, $θ$ will increase to $π$, implying that the body will spin at the thin end. Alternatively, if $θ_{\rm initial}<θ_c$, then $θ$ will decrease. For some oval curves, $θ$ will reduce to 0 and the corresponding bodies will spin at the fat end. For other oval curves, a fixed point at $θ_f$ is predicted, where $0 <θ_f< θ_c$. Then the bodies will spin not at the fat end, but at a new stable point with $θ_f$. The empirical fact that eggs more often spin at the fat than at the thin end is explained.

In theories with extra dimensions, the cosmological hierarchy problem can be thought of as the unnaturally large radius of the observable universe in Kaluza-Klein units. We sketch a dynamical mechanism that relaxes this. In the early universe scenario we propose, three large spatial dimensions arise through tunneling from a 'cosmic egg', an effectively one-dimensional configuration with all spatial dimensions compact and of comparable, small size. If the string landscape is dominated by low-dimensional compactifications, cosmic eggs would be natural initial conditions for cosmology. A quantum cosmological treatment of a toy model egg predicts that, in a variant of the Hartle-Hawking state, cosmic eggs break to form higher dimensional universes with a small, but positive cosmological constant or quintessence energy. Hence cosmic egg cosmology yields a scenario in which the seemingly unnaturally small observed value of the vacuum energy can arise from natural initial conditions.

The egg-drop experiment introduced by Konhauser, Velleman, and Wagon, later generalized by Boardman, is further generalized to two additional types. The three separate types of egg-drop experiment under consideration are examined in the context of binary decision trees. It is shown that all three types of egg-drop experiment are binary decision problems that can be solved efficiently using a non-redundant algorithm -- a class of algorithms introduced here. The preceding theoretical results are applied to the three types of egg-drop experiment to compute, for each, the maximum height of a building that can be dealt with using a given number of egg-droppings.