Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-Stream Fusion Network with ConvNeXtV2 for Pig Weight Estimation Using RGB-D Data in Aisles

Simple Summary In the realm of livestock management, accurately estimating the weight of pigs presents a critical yet challenging task, particularly in the dynamic environment of farms. Traditional methods often struggle due to the continuous movement of pigs and fluctuating conditions such as lighting. To address these challenges, our study focuses on developing a novel method that simplifies weight estimation while adapting to the constantly changing conditions of real-world pig farms. Our solution, the moving pig weight estimate algorithm based on deep vision (MPWEADV), marks a significant step in this direction. It employs advanced imaging technology to capture both the visual appearance and depth information of moving pigs. The central idea is to combine these two types of data for more accurate weight estimates than traditional methods could provide. To validate our proposed method, we replicated two recently published methods and demonstrated through experimental results that our pig weight estimation model could rapidly and accurately determine the weight of pigs in the challenging scenarios we constructed. This model operates in an unconstrained environment, providing real-time evaluation of pigs’ weight, thereby offering data support for grading and adjusting breeding plans, indicating a wide range of potential applications. Abstract In the field of livestock management, noncontact pig weight estimation has advanced considerably with the integration of computer vision and sensor technologies. However, real-world agricultural settings present substantial challenges for these estimation techniques, including the impacts of variable lighting and the complexities of measuring pigs in constant motion. To address these issues, we have developed an innovative algorithm, the moving pig weight estimate algorithm based on deep vision (MPWEADV). This algorithm effectively utilizes RGB and depth images to accurately estimate the weight of pigs on the move. The MPWEADV employs the advanced ConvNeXtV2 network for robust feature extraction and integrates a cutting-edge feature fusion module. Supported by a confidence map estimator, this module effectively merges information from both RGB and depth modalities, enhancing the algorithm’s accuracy in determining pig weight. To demonstrate its efficacy, the MPWEADV achieved a root-mean-square error (RMSE) of 4.082 kg and a mean absolute percentage error (MAPE) of 2.383% in our test set. Comparative analyses with models replicating the latest research show the potential of the MPWEADV in unconstrained pig weight estimation practices. Our approach enables real-time assessment of pig conditions, offering valuable data support for grading and adjusting breeding plans, and holds broad prospects for application.
Crowdsourcing functions of the living city from Twitter and Foursquare data

ABSTRACT Urban functions are closely related to people’s spatiotemporal activity patterns, transportation needs, and a city’s business distribution and development trends. Studies investigating urban functions have used different data sources, such as remotely sensed imageries, observation, photography, and cognitive maps. However, these data sources usually suffer from low spatial, temporal, and thematic resolution. This article attempts to investigate human activities to understand urban functions through crowdsourcing social media data. In this study, we mined Twitter and Foursquare data to extract and analyze six types of human activities. The spatiotemporal analysis revealed hotspots for different activity intensities at different temporal resolution. We also applied the classified model in a real-time system to extract information of various urban functions. This study demonstrates the significance and usefulness of social sensing in analyzing urban functions. By combining different platforms of social media data and analyzing people’s geo-tagged city experience, this article contributes to leverage voluntary local knowledge to better depict human dynamics, discover spatiotemporal city characteristics, and convey information about cities.
Real-timeautomaticinterpolationofambientgammadoseratesfromthe DutchRadioactivityMonitoringNetwork

Detection of radiological accidents and monitoring the spread of the contamination is of great importance. Following the Chernobyl accident many European countries have installed monitoring networks to perform this task. Real-time availability of automatically interpolated maps showing the spread of radioactivity during and after an accident would improve the capability of decision makers to accurately respond to a radiological accident. The objective of this paper is to present a real-time automatic interpolation system suited for natural background radioactivity. Interpolating natural background radiation allows us to better understand the natural variability, thus improving our ability to detect accidents. A real-time automatic interpolation system suited for natural background radioactivity presents a rst step towards a system that can deal with radiological accidents. The interpolated maps are produced using a combination of universal kriging and an automatic variogram tting procedure. The system provides a map of 1) the kriging prediction, 2) the kriging standard error and 3) the position of approximate prediction intervals relative to a threshold. The maps are presented through a Web Map Service (WMS) to ensure interoperability with existing Geographic Information Systems (GIS).
Time after Time: Narratives of the Longue Durée in the Anthropocene

This article suggests that new potential areas of collaboration between historians and literary scholars have emerged around a nascent but galloping interest in two fields of scholarship, the Anthropocene and the longue duree. Building on the tradition of the Annales school, and in particular the contributions of Fernand Braudel, this article argues that while there is a growing consensus about the necessity of trans-national and trans-temporal history, the critical implications of this enterprise for thinking historical and literary narrative have remained highly unsatisfying. Any return to the longue duree necessitates a deep reconsideration of the social scientific foundations of the longue duree in its previous manifestations, especially its conception of the nature-culture relationship, and an acknowledgement of the limitations of these earlier formulations for thinking about longer time scales and beyond national boundaries today. Following recent work in anthropology that has attempted to break down the nature-culture barrier, this article suggests that the Anthropocene has generated a new arrangement of temporal scales and therefore a poignant rearticulation of the long and short duree as well as the agency that drives action within these two realms.
Beyond Integer Quantization: Approximate Arithmetic for Machine Learning Accelerators : (Invited Paper)

There is an insatiable need for compute in AI/ML as the largest models in use today require thousands of GPU years to train. As these models increase in size, mitigating the power consumption while increasing compute performance is essential. Quantization to smaller number formats such as 8-bit floating point for training and 8-bit/4-bit integer for inference are common methods for reducing complexity, but simply reducing the number of bits is not the only method for approximation. We introduce a method for approximate computer arithmetic for AI/ML based on a hybrid floating point-logarithmic (FPLNS) system. Our arithmetic approximation algorithms are demonstrated to have 4.36x power reduction and 4.21x latency reduction for 32-bit floating point multiplication in exchange for a worst-case relative error of 12%. We further demonstrate that this relative error leads to negligible loss in several popular AI/ML algorithms due to their resilience to error. We finally demonstrate that power/performance benefits from FPLNS approximate arithmetic compound with quantization.
Current status and future of gene engineering in livestock

The application of gene engineering in livestock is necessary for various reasons, such as increasing productivity and producing disease resistance and biomedicine models. Overall, gene engineering provides benefits to the agricultural and research aspects, and humans. In particular, productivity can be increased by producing livestock with enhanced growth and improved feed conversion efficiency. In addition, the application of the disease resistance models prevents the spread of infectious diseases, which reduces the need for treatment, such as the use of antibiotics; consequently, it promotes the overall health of the herd and reduces unexpected economic losses. The application of biomedicine could be a valuable tool for understanding specific livestock diseases and improving human welfare through the development and testing of new vaccines, research on human physiology, such as human metabolism or immune response, and research and development of xenotransplantation models. Gene engineering technology has been evolving, from random, time-consuming, and laborious methods to specific, time-saving, convenient, and stable methods. This paper reviews the overall trend of genetic engineering technologies development and their application for efficient production of genetically engineered livestock, and provides examples of technologies approved by the United States (US) Food and Drug Administration (FDA) for application in humans.
Recovery Potential Modelling of Hickorynut (Obovaria olivaria) in Canada

iv RESUME iv INTRODUCTION 1 METHODS 1 The model 1 Hickorynut 1 Lake Sturgeon 2 Host-Dependence 2 Population Sensitivity 5 Host-independent model 5 Host-dependent model 5 Allowable harm 6 RESULTS 6 Population sensitivity 6 Host-independent model 6 Host-dependent model 7 DISCUSSION 11 Uncertainties 12 REFERENCES 13
Sorl1 knockout inhibits expression of brain-derived neurotrophic factor: involvement in the development of late-onset Alzheimer's disease

Cerebral palsy is a diagnostic term utilized to describe a group of permanent disorders affecting movement and posture. Patients with cerebral palsy are often only capable of limited activity, resulting from non-progressive disturbances in the fetal or neonatal brain. These disturbances severely impact the child’s daily life and impose a substantial economic burden on the family. Although cerebral palsy encompasses various brain injuries leading to similar clinical outcomes, the understanding of its etiological pathways remains incomplete owing to its complexity and heterogeneity. This review aims to summarize the current knowledge on the genetic factors influencing cerebral palsy development. It is now widely acknowledged that genetic mutations and alterations play a pivotal role in cerebral palsy development, which can be further influenced by environmental factors. Despite continuous research endeavors, the underlying factors contributing to cerebral palsy remain are still elusive. However, significant progress has been made in genetic research that has markedly enhanced our comprehension of the genetic factors underlying cerebral palsy development. Moreover, these genetic factors have been categorized based on the identified gene mutations in patients through clinical genotyping, including thrombosis, angiogenesis, mitochondrial and oxidative phosphorylation function, neuronal migration, and cellular autophagy. Furthermore, exploring targeted genotypes holds potential for precision treatment. In conclusion, advancements in genetic research have substantially improved our understanding of the genetic causes underlying cerebral palsy. These breakthroughs have the potential to pave the way for new treatments and therapies, consequently shaping the future of cerebral palsy research and its clinical management. The investigation of cerebral palsy genetics holds the potential to significantly advance treatments and management strategies. By elucidating the underlying cellular mechanisms, we can develop targeted interventions to optimize outcomes. A continued collaboration between researchers and clinicians is imperative to comprehensively unravel the intricate genetic etiology of cerebral palsy.
THE FRAME-SLOT MODEL OF THE CONCEPT OF MOTIVATION ON THE BASIS OF MOTIVATIONAL SPEECHES BY PUBLIC FIGURES

This article introduces the outcomes of the conceptual analysis of motivational speeches delivered by prominent public figures, obtained by means of the UAM Corpus Tool – a cutting-edge software renowned for conducting both automated and manual text annotations. Furthermore, the study is enhanced by a conceptual metaphor analysis, revealing that ontological and structural components are the predominant and efficacious elements in constructing a frame-slot model. In accordance with Minsky’s theoretical framework, the frame-slot model consists of the main concept, and frames as complex structures, resembling multi-layered networks. It is figured out that these intricate structures are formed by slots and subplots [17]. Accordingly, the concept of MOTIVATION comprises a combination of frames reflecting embodied speakers’ experiences such as A LIVING BEING, DIFFICULTIES, PURPOSE, MATERIAL OBJECT, STATE, COGNITION, MOVEMENT, INSPIRATION, LIFE, WORLD, EDUCATION, and LANGUAGE. These components collectively serve as a comprehensive reflection of the perceptual paradigms employed by the selected public speakers, whose speeches serve as the primary corpus for this research. Consequently, the corpus comprises 20 speeches delivered by eminent male and female public figures, featuring Michelle Obama, Oprah Winfrey, Madonna, Kamala Harris, Melinda Gates, Angelina Jolie Voight, Arianna Huffington, Condoleezza Rice, Sheryl Sandberg, Hillary Clinton, Warren Buffett, Steve Jobs, Jeff Bezos, Elon Musk, Bill Gates, Barack Obama, David Beckham, Mark Zuckerberg, Jay Shetty, and Steven Spielberg. This research underscores the significance of conceptual metaphors in understanding how motivational speeches shape our perception of the world and influence our actions. By dissecting the linguistic elements that construct these metaphors, the insights into the strategies employed by public figures to inspire and motivate their audiences are received and elaborated.
Programming Assignment Grading Through Control Statement and Program Features

Recently, automatic programming assessment has grown in importance as a technique for supporting lecturers and instructors of programming courses in automatically marking and grading students programming exercises and giving helpful feedback on the students programming answers. In this paper, we propose a method for evaluating C programs. Static and dynamic analysis methods are two distinct strategies that are distinguished in this context. In contrast to dynamic analysis, which must assess an executable program, static analysis can analyse a program even if it is not totally executed. The proposed approach is based on the static analysis of programs. This paper introduces two metrics to grade student programs by using (i) Control Statement Features (ii) Program Features like different types of statements, operators and operands.It consists of comparing the student program with the program offered by the evaluator through feature metrics.Dealing with the number of solutions that are available for the same programming problem presents a significant difficulty in this situation. To address this flaw, we suggest a novel similarity metric that contrasts two programs based on how they execute semantically.In the area of automated program evaluation, the experiments findings point to a promising realisation. They further prove that the proposed method guarantees a close approximation to the human program evaluation.
Accident Prediction Model Using Divergence Between Visual Attention and Focus of Expansion in Vehicle-Mounted Camera Images

Recently, accident prediction models, which predict the occurrence of traffic accidents through deep learning algorithms have been proposed. The application of these models demands both high precision and visualization of the decision basis applied. Current models use the motion features of objects in the surrounding environment, but they do not predict well when the motion feature of the risk factor is small. Meanwhile, drivers can avoid accidents because they utilize visual attention functions. This study focuses on the divergence between visual attention and focus of expansion (FOE), which are highly correlated in normal driving situations, as the basis for an accident prediction method. The proposed model can visualize decision basis with high accuracy, even when the motion feature of the risk factors is small, by combining it with Dynamic-Spatial-Attention, a deep-learning-based accident prediction method. In this experiment, we classified data from the Dashcam Accident Dataset, a widely used accident dataset, into categories of accidents. Using the Dashcam Accident Dataset, the proposed method achieves higher accident prediction performance in categories for which the motion feature of risk factors tends to be small while maintaining the same accident prediction performance as achieved by the baseline Dynamic-Spatial-Attention method in categories for which the motion feature of risk factors tends to be large. In addition, the proposed method visualizes the risk factors using visual attention and FOE to provide a visual explanation of the decision basis.
Identification of Unreliable Data in in-VR Surveys using Biosignal Sensors

Surveys that rely on self-reporting are often prone to error. Therefore, measures to identify meaningless, careless, or fraudulent responses in surveys can improve data quality. In the past, various indicators have been proposed to identify data with implausible responses in questionnaires, e.g. by including attention check questions or by applying statistical techniques. Immersive virtual reality (VR) environments are equipped with several biosignal sensors. We propose and investigate whether sensory data already provided by the headset or additional biosignal sensors can be useful for automatically detecting unreliable data in in-VR surveys. Our results show that biosensor signals, such as eye tracking and electrocardiography, can provide useful cues, and that this information can be combined with statistical techniques (namely the validity score) to further improve classification accuracy.
Artificial intelligence applications in computed tomography in gastric cancer: a narrative review

Background and Objective Artificial intelligence (AI) is a revolutionary technique which is deeply impacting and reshaping clinical practice in oncology. This review aims to summarize the current status of the clinical application of AI-based computed tomography (CT) for gastric cancer (GC), focusing on diagnosis, genetic status detection and risk prediction of metastasis, prognosis and treatment efficacy. The challenges and prospects for future research will also be discussed. Methods We searched the PubMed/MEDLINE database to identify clinical studies published between 1990 and November 2022 that investigated AI applications in CT in GC. The major findings of the verified studies were summarized. Key Content and Findings AI applications in CT images have attracted considerable attention in various fields such as diagnosis, prediction of metastasis risk, survival, and treatment response. These emerging techniques have shown a high potential to outperform clinicians in diagnostic accuracy and time-saving. Conclusions AI-powered tools showed great potential to increase diagnostic accuracy and reduce radiologists’ workload. However, the goal of AI is not to replace human ability but to help oncologists make decisions in their practice. Therefore, radiologists should play a predominant role in AI applications and decide the best ways to integrate these complementary techniques within clinical practice.
Exploring the therapeutic potential of edible vegetables, fruits, and spices against cancer in various cell lines

Cancer is rapidly becoming the leading cause of death globally. This study aimed to identify edible foods with cytotoxic and/or antioxidant activities that can prevent cancer when consumed in a regular diet. Sixty-eight edible foods were purchased from the local market, and the materials were extracted with 80% methanol. The cytotoxic activity of the extracts was evaluated using MTT on HeLa, H2228, HEK293, and H3122 cell lines. To study apoptosis, triple fluorescence labeling with DAPI, Annexin V, and propidium iodide was used. The phenolic content, antioxidant capacity, and free radical scavenging capabilities were studied using conventional spectrophotometric techniques. Among the edible foods, carrot, pointed gourd, wax gourd, ficus, apple, lemon, cumin seed, and white peppercorn showed moderate cytotoxicity in HeLa cells. The growth of HeLa cells was significantly inhibited dose-dependently by tomato, banana, Indian spinach, guava, lemon peel, and coriander (IC50, 24.54, 17.89, 13.18, 9.33, 1.23, and 2.96 µg/mL, respectively). Tomato, Indian spinach, lemon peel, and coriander exerted significant dose-dependent inhibition of H2228, HEK293, and H3122 cell proliferation. The tomato, Indian spinach, lemon peel, and coriander extracts induced HeLa cell apoptosis. White peppercorn, amaranth, apple, wax gourd, cumin seed, taro, and lemon peel contained significant amounts of polyphenols and showed high antioxidant activity. White peppercorn, apple, coriander, lemon peel, and ficus significantly scavenged DPPH free radicals (IC50 values of 10.23, 12.02, 13.49, 13.8, and 14.0 µg/mL, respectively). The overall results suggest that the daily intake of these antioxidant-rich cytotoxic foods can prevent or reduce the risk of cancer.
Weather-Induced Power Outage Prediction: A Comparison of Machine Learning Models

This study investigates power outage duration and occurrence for the state of California, Los Angeles County, and Alpine County using historical weather data. With extreme weather events on the rise, many areas have seen a subsequent increase in weather-related power outages. To better understand the relationship between these two variables, generalizable frameworks using publicly available data are investigated, using outage data down to a single customer. These models aim to predict power outage duration and occurrence, which would aid in power system preparedness and restoration efforts. The results of the models are also assessed not only in terms of prediction accuracy, but also in terms of reliability metrics. Out of the models considered to predict power outage duration (regression), the Multi-Layer Feed-Forward Neural Network (MFFNN) showed significantly better results for large regions, but showed mixed results for a small region. When considering power outage occurrence predictions (classification), models that incorporate boosting outperformed the more simplistic models. The results indicate that the optimal model is highly dependent on the characteristics of the region, and that these models can prove valuable for identifying regions that will have weather-related reliability impacts in the future.