Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward integrating citizen science and radar data for migrant bird conservation

Migrating birds face an array of challenging conditions and hazards during their annual movements, including novel threats of mortality from anthropogenic structures, such as wind energy turbines and buildings. There is a need for new data sources that can be used to create risk assessments across a wide range of spatial and temporal scales, and weather radar data have shown promise in this regard. A primary limitation on the use of weather radar data in macroscale ecology has been the difficulty in data access and processing to make real‐time assessments, and the lack of species‐level information. We compared two weather radar products, Level II and UNQC‐CREF that differ in several aspects. We found the 2D UNQC‐CREF product provides similar information to Level II data regarding spatial and temporal patterns of bioscatter, although not without caveats. Due to its rapid availability and ease of use, we combined UNQC‐CREF data with eBird daily species counts to quantify autumnal migration along one of North America's major migratory routes. During their autumn migration southward, we predicted birds would use prevailing winds and favorable atmospheric conditions to time their migratory movements. Wind direction and ordinal date were strong predictors as were variables associated with inclement weather, including barometric pressure and ambient temperature. These patterns suggest that at a coarse scale, migratory birds avoid headwinds during their southerly movements during autumn, and more broadly indicate that local weather conditions can be used to predict pulses of migratory activity in the aerosphere. Our results suggest that similar methods could be used to measure the temporal and spatial components of avian migration at greater spatial scales – providing crucial information for conservation and management for migratory faunas. Specifically, future algorithms should focus on real‐time implementation for bird strike mitigation, by altering wind energy facility operation on nights of peak migratory flow, in addition to timing “lights‐out” events to minimize collisions with existing infrastructure such as buildings.
Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese

The aim of this quantitative risk assessment model is to estimate the risk of Haemolytic Uremic Syndrome (HUS) caused by Shiga-toxin producing Escherichia coli (STEC) in raw milk soft cheese and explore intervention strategies to minimise this risk. Building upon previous work from literature, the model considers microbial contamination of raw milk at the farm level, as well as STEC growth and survival during cheese production, ripening and storage, along with intervention strategies in both pre- and post-harvest scenarios. It allows for the assessment of intervention steps at the farm level or during cheese production. Besides estimating the risk of HUS, it also assesses the production losses associated with interventions.
Diffusion Creep and Grain Growth in Forsterite +20 vol% Enstatite Aggregates: 2. Their Common Diffusional Mechanism and Its Consequence for Weak‐Temperature‐Dependent Viscosity

We determined the rate‐controlling diffusivities for creep and grain growth of forsterite +20 vol% enstatite aggregates from the experimental results presented in Part 1 of this study. Both diffusivities exhibit similar values with similar temperature dependency, indicating that a common grain boundary diffusion process controls both creep and grain growth. This correspondence is due to the presence of pyroxene grains as a secondary phase in the olivine aggregate, which is the case for the majority of mantle‐derived rocks. As a consequence, the viscosity (η) of the material during deformation by grain boundary diffusion creep accompanied by grain growth changes with time (t) and temperature (T) as η∝t34expQGB4RT, where QGB is the activation energy for grain boundary diffusion and R is gas constant. This viscosity relationship is used to interpret the very low activation energy estimated for the viscosity of the oceanic mantle lithosphere.
Atmospheres Supporting Information for Brown and black carbon emitted by a marine engine operated on heavy fuel oil and distillate fuels : optical properties , size distributions , emission factors

J. C.Corbin 1,∗, S. M.Pieber , H.Czech , M.Zanatta 1,∗∗, G.Jakobi , D. Massabò , J.Orasche , I.El Haddad , A. A.Mensah , B.Stengel , L.Drinovec , G. Močnik , R.Zimmermann , A. S. H. Prévôt , and M.Gysel 1 Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, CH-6232 Villigen, Switzerland Joint Mass Spectrometry Centre, Chair of Analytical Chemistry, Institute of Chemistry, University of Rostock, Dr.-Lorenz-Weg 1, 190610 Rostock, Germany Joint Mass Spectrometry Centre, Cooperation Group Comprehensive Molecular Analytics, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 96875 Neuherberg, Germany HICE – Helmholtz Virtual Institute for Complex Molecular Systems in Environmental Health, Germany Department of Physics, University of Genoa, Via Dodecaneso 33, 17157 Genova, Italy INFN, Sezione di Genova, Via Dodecaneso 22, 17157 Genova, Italy Institute for Atmospheric and Climate Science, ETH Zurich, 90102 Zurich, Switzerland Department of Piston Machines and Internal Combustion Engines, University of Rostock, Albert-Einstein-Str. 2, 190610 Rostock, Germany Aerosol d.o. o. , 1000 Ljubljana, Slovenia Condensed Matter Physics Department, Jožef Stefan Institute, Ljubljana, Slovenia ∗Now at: National Research Council Canada, Ottawa, Canada ∗∗Now at: Alfred Wegener Institute, Bussestrasse 27, 27570 Bremerhaven, Germany
Perceptions from Member-Consumers of a University Community for Sustainable and Healthy Eating: Evidence from Greece

Healthy eating is a vital component of a sustainable daily life, especially after the COVID-19 pandemic. In this study, we investigated the perceptions and attitudes of the members of a Greek university community, especially the young members, on: (i) health itself, (ii) healthy eating, and (iii) healthy eating campaigns today. The research was conducted electronically in the last three months of 2023 via a questionnaire survey and 1046 member-consumers participated. Statistical analysis, including descriptive and cluster analysis to group respondents into homogenous segments, was performed by employing a one-way ANOVA. The highlights of the results indicate that consumers perceive physical and mental health and energy efficiency as health preconditions, while they perceive a balanced and sustainable diet as the main parameter of healthy eating. They are influenced mostly by doctors and health providers and use information mostly based on common sense and their upbringing for their healthy eating choices. The cluster analysis revealed two distinctive groups of consumers categorized by this study as the “approachables” (54.5%), affected by today’s sustainable knowledge and concepts on healthy eating, and the “conservatives” (45.5%), who are reluctant to accepted these as such.
The Efficacy and Analytical Importance of Manual Feature Extraction Using Lidar Datasets

ABSTRACT The availability of lidar datasets has led to several advances in archaeology, notably in the process of site prospection. Some remote sensing practitioners have aimed to create automated feature extraction (AFE) techniques that increase the efficiency and efficacy of identification and analysis. While these advances have been successful, many archaeological professionals who might have an interest in lidar-derived products do not have the technical experience to modify or create AFE techniques for particular regions or environments. Additionally, some features are not appropriate for AFE. Instead, the most widely used technique is still likely to be visually based manual feature identification. Using authors of different experience levels, we seek to evaluate the use of manual techniques for feature identification and subsequent analysis by implementing a publicly available lidar-derived digital elevation model (DEM). We demonstrate that manual feature extraction (MFE) can be accurate when more than one researcher is involved in a sort of “checks and balances” process. We also show that the use of confidence ratings can be an important part of this process if those ratings have some systematic and clearly defined underpinning. Finally, we argue, using a case study from American Samoa, that manually identified features can be analytically important as part of larger landscape studies. La disponibilidad de conjuntos de datos lídar ha permitido varios avances en arqueología, notablemente en el proceso de prospección de sitios. Algunos profesionales de teledetección han apuntado a crear técnicas de extracción de características automatizadas (AFE por sus siglas en inglés) que aumentan la eficiencia y eficacia de la identificación y análisis. Aun cuando estos avances han sido exitosos, muchos arqueólogos interesados en el conjunto de datos lídar no tienen la experiencia técnica para modificar o crear técnicas AFE para su uso en regiones o ambientes particulares. Adicionalmente, algunos rasgos podrían no ser apropiados para el uso de AFE. Por lo tanto, es probable que la técnica mayormente usada continúe siendo la identificación manual de características por medio visual. Usando tres autores con diferentes niveles de experiencia, buscamos evaluar el uso de técnicas manuales para la identificación de rasgos y análisis subsecuentes usando un modelo de elevación digital de acceso público derivado de datos lídar. Demostramos que la extracción manual de características (MFE por sus siglas en inglés) puede ser precisa cuando más de un investigador participa en una especie de sistema de controles y balances. Demostramos que el uso de índices de confianza puede ser una parte importante de este proceso si las clasificaciones tienen bases claramente definidas y sistemáticas. Finalmente, usando el estudio de un caso de Samoa Estadounidense, argumentamos que la identificación manual de características puede ser analíticamente importante como parte de estudios de paisaje más amplios.
The sources and transport of iron in the North Pacific and its impact on marine ecosystems

ABSTRACT The limitation of iron (Fe) makes the North Pacific a typically high-nitrate, low-chlorophyll (HNLC) region in comparison with other oceanic regions of the world. Iron inputs from land via river discharge and atmospheric dust deposition are the primary processes introducing Fe into the ocean. Also, subsequent physical processes are crucial in transporting biologically available Fe into the upper ocean. As anthropogenic dust increases, the Fe from anthropogenic activities is expected to become more important in terms of impacting marine ecosystems. To investigate the Fe cycle and its impact on ecosystems, a project entitled ‘The sources and transport of Fe in the North Pacific and its impact on marine ecosystems’ has been funded by the National Natural Science Foundation of China. The project will focus on three major scientific questions: (1) What are the major sources of Fe in the North Pacific? (2) What is the influence of the Fe-binding ligand cycle on marine ecosystems? (3) What is the likely influence of global change in the future? The distribution of Fe and its corresponding impact on the marine ecosystem in current and future environmental conditions will be investigated. The results of the project are expected to improve our understanding of the marine ecosystem in the North Pacific. Graphical Abstract
South Sinai Growth Charts of Children and Adolescents

Data from a cross-sectional study of 5245 healthy children in South Sinai were used to construct standard growth charts of weight, stature and BMI for each sex. The sample was collected during the project funded by the SSRDP which belong to the European Union commission (project 238). Weight and stature were obtained under standard conditions for each year cohort for children aged 1-16 years, BMI was calculated. The values were compared with those of the Egyptian and WHO growth curves. The constructed growth standards are suitable for growth monitoring programmes throughout South Sinai. (Journal of American Science. 2010;6(10):232-240). (SSN: 1545-1003).
Epigenome Defines Aberrant Brain Laterality in Major Mental Illnesses

Brain-hemisphere asymmetry/laterality is a well-conserved biological feature of normal brain development. Several lines of evidence, confirmed by the meta-analysis of different studies, support the disruption of brain laterality in mental illnesses such as schizophrenia (SCZ), bipolar disorder (BD), attention-deficit/hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), and autism. Furthermore, as abnormal brain lateralization in the planum temporale (a critical structure in auditory language processing) has been reported in patients with SCZ, it has been considered a major cause for the onset of auditory verbal hallucinations. Interestingly, the peripheral counterparts of abnormal brain laterality in mental illness, particularly in SCZ, have also been shown in several structures of the human body. For instance, the fingerprints of patients with SCZ exhibit aberrant asymmetry, and while their hair whorl rotation is random, 95% of the general population exhibit a clockwise rotation. In this work, we present a comprehensive literature review of brain laterality disturbances in mental illnesses such as SCZ, BD, ADHD, and OCD, followed by a systematic review of the epigenetic factors that may be involved in the disruption of brain lateralization in mental health disorders. We will conclude with a discussion on whether existing non-pharmacological therapies such as rTMS and ECT may be used to influence the altered functional asymmetry of the right and left hemispheres of the brain, along with their epigenetic and corresponding gene-expression patterns.
Dysarthric Speech Recognition Using Pseudo-Labeling, Self-Supervised Feature Learning, and a Joint Multi-Task Learning Approach

In this paper, we investigate the use of the spontaneous speech of dysarthric people for training an automatic speech recognition (ASR) model for them. Although the spontaneous speech of dysarthric people can be collected relatively easily compared to script-reading speech, which is obtained by having them read a prepared script, labeling the spontaneous speech of dysarthric people is very difficult and costly. For training an ASR model using unlabeled speech data, pseudo-labeling and self-supervised feature learning have been studied as effective approaches; however, the effectiveness of these approaches has not been clear when they are applied to the unlabeled dysarthric speech. In addition, pseudo-labeling may not be effective since the pseudo-labels of dysarthric speech include many errors and are not reliable. In this paper, we evaluate the above two approaches for the dysarthric speech recognition, and we propose a multi-task learning approach, which combines these approaches to train an ASR model that is robust against the errors in the pseudo-labels. Experimental results using Japanese and English datasets demonstrated that all approaches are effective, but among them, the proposed multi-task learning approach showed the best performance.
The reduction of building energy demand by a living wall system in a Central European climate

Green façades can be applied to reduce building energy demands. However, their effect has hardly been reported in the literature. Since Green Wall Models (GWM) are not yet integrated into Building Energy Modeling (BEM) tools, this study modified the Green Roof Model (GRM) of EnergyPlus for the application on a vertical surface and, subsequently, validated it as a ventilated Green Wall (GW). Afterwards, a sensitivity analysis was conducted to investigate the energy-saving potential for different GW orientations and coverage percentages for a residential building in Weimar, Germany. Finally, the achieved energy savings of the GW were compared with those of a conventional façade renovation. The results showed that greening the south-east façade was the most efficient case, which reduced the cooling and heating energy demand by 10.6% and 0.2%, respectively. Additionally, the results showed that the ventilated GW had a significantly better cooling effect compared to the conventional façade renovation measure.
RESPONSE OF CHICKPEA (Cicer arietinum L.) VARIETIES TO IRRIGATION, NITROGEN AND PHOSPHORUS UNDER MIDDLE GUJARAT CONDITIONS

RESPONSE OF CHICKPEA {Cicer arietimm L. ) VARIETIES TO IRRIGATION, NITROGEN AND PHOSPHORUS UNDER MIDDLE GUJARAT CONDITIONS Name of s t u d e n t R.K. CHAUDHARI Major Advisor DR. T . D . PATEL DEPARTMENT OF AGRONOMY B. A. COLLEGE OF AGRICULTURE GUJARAT AGRICULTURAL UNIVERSITY ANAIJD CAMPUS, ANAND
Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models

Hallucinations in large language models (LLMs) refer to the phenomenon of LLMs producing responses that are coherent yet factually inaccurate. This issue undermines the effectiveness of LLMs in practical applications, necessitating research into detecting and mitigating hallucinations of LLMs. Previous studies have mainly concentrated on post-processing techniques for hallucination detection, which tend to be computationally intensive and limited in effectiveness due to their separation from the LLM's inference process. To overcome these limitations, we introduce MIND, an unsupervised training framework that leverages the internal states of LLMs for real-time hallucination detection without requiring manual annotations. Additionally, we present HELM, a new benchmark for evaluating hallucination detection across multiple LLMs, featuring diverse LLM outputs and the internal states of LLMs during their inference process. Our experiments demonstrate that MIND outperforms existing state-of-the-art methods in hallucination detection.
A Formal Analysis of SCTP: Attack Synthesis and Patch Verification

SCTP is a transport protocol offering features such as multi-homing, multi-streaming, and message-oriented delivery. Its two main implementations were subjected to conformance tests using the PacketDrill tool. Conformance testing is not exhaustive and a recent vulnerability (CVE-2021-3772) showed SCTP is not immune to attacks. Changes addressing the vulnerability were implemented, but the question remains whether other flaws might persist in the protocol design. We study the security of the SCTP design, taking a rigorous approach rooted in formal methods. We create a formal Promela model of SCTP, and define 10 properties capturing the essential protocol functionality based on its RFC specification and consultation with the lead RFC author. Then we show using the Spin model checker that our model satisfies these properties. We define 4 attacker models - Off-Path, where the attacker is an outsider that can spoof the port and IP of a peer; Evil-Server, where the attacker is a malicious peer; Replay, where an attacker can capture and replay, but not modify, packets; and On-Path, where the attacker controls the channel between peers. We modify an attack synthesis tool designed for transport protocols, Korg, to support our SCTP model and four attacker models. We synthesize 14 unique attacks using the attacker models - including the CVE vulnerability in the Off-Path attacker model, 4 attacks in the Evil-Server attacker model, an opportunistic ABORT attack in the Replay attacker model, and eight connection manipulation attacks in the On-Path attacker model. We show that the proposed patch eliminates the vulnerability and does not introduce new ones according to our model and protocol properties. Finally, we identify and analyze an ambiguity in the RFC, which we show can be interpreted insecurely. We propose an erratum and show that it eliminates the ambiguity.
The Impact of Climate Variability on Wheat Growth and Yield

ii Acknowledgments . v List of Tables. viii List of Illustrations . x List of Abbreviations . xii I. Literature Review . 1 Planting Date and Variety Selection on Winter Production . 1 Crop Simulation Modeling . 5 References . 10 II. Effect of Planting Date and Varieties Selection on Wheat Yield and Yield Components. 20 Abstract . 20 Introduction . 21 Materials and Methods . 24 Results and Discussion . 26 Conclusion . 37 References . 39
PCLD: Point Cloud Layerwise Diffusion for Adversarial Purification

Point clouds are extensively employed in a variety of real-world applications such as robotics, autonomous driving and augmented reality. Despite the recent success of point cloud neural networks, especially for safety-critical tasks, it is essential to also ensure the robustness of the model. A typical way to assess a model's robustness is through adversarial attacks, where test-time examples are generated based on gradients to deceive the model. While many different defense mechanisms are studied in 2D, studies on 3D point clouds have been relatively limited in the academic field. Inspired from PointDP, which denoises the network inputs by diffusion, we propose Point Cloud Layerwise Diffusion (PCLD), a layerwise diffusion based 3D point cloud defense strategy. Unlike PointDP, we propagated the diffusion denoising after each layer to incrementally enhance the results. We apply our defense method to different types of commonly used point cloud models and adversarial attacks to evaluate its robustness. Our experiments demonstrate that the proposed defense method achieved results that are comparable to or surpass those of existing methodologies, establishing robustness through a novel technique. Code is available at https://github.com/batuceng/diffusion-layer-robustness-pc.
Decoupled Contrastive Learning for Long-Tailed Recognition

Supervised Contrastive Loss (SCL) is popular in visual representation learning. Given an anchor image, SCL pulls two types of positive samples, i.e., its augmentation and other images from the same class together, while pushes negative images apart to optimize the learned embedding. In the scenario of long-tailed recognition, where the number of samples in each class is imbalanced, treating two types of positive samples equally leads to the biased optimization for intra-category distance. In addition, similarity relationship among negative samples, that are ignored by SCL, also presents meaningful semantic cues. To improve the performance on long-tailed recognition, this paper addresses those two issues of SCL by decoupling the training objective. Specifically, it decouples two types of positives in SCL and optimizes their relations toward different objectives to alleviate the influence of the imbalanced dataset. We further propose a patch-based self distillation to transfer knowledge from head to tail classes to relieve the under-representation of tail classes. It uses patch-based features to mine shared visual patterns among different instances and leverages a self distillation procedure to transfer such knowledge. Experiments on different long-tailed classification benchmarks demonstrate the superiority of our method. For instance, it achieves the 57.7% top-1 accuracy on the ImageNet-LT dataset. Combined with the ensemble-based method, the performance can be further boosted to 59.7%, which substantially outperforms many recent works. Our code will be released.