Miracle-dz/UniFAR-FTUs huggingface dataset:
Demand for gasoline is more price-inelastic than commonly thought

eScholarship provides open access, scholarly publishing services to the University of California and delivers a dynamic research platform to scholars worldwide. Abstract: One of the most frequently examined statistical relationships in energy economics has been the price elasticity of gasoline demand. We conduct a quantitative survey of the estimates of elasticity reported for various countries around the world. Our meta-analysis indicates that the literature suffers from publication selection bias: insignificant or positive estimates of the price elasticity are rarely reported, although implausibly large negative estimates are reported regularly. In consequence, the aver- age published estimates of both short- and long-run elasticities are exaggerated twofold. Using mixed effects multilevel meta-regression, we show that after correction for publication bias the average long-run elasticity reaches -0:31 and the average short-run elasticity only -0:09. Abstract One of the most frequently examined statistical relationships in energy economics has been the price elasticity of gasoline demand. We conduct a quantitative survey of the estimates of elasticity reported for various countries around the world. Our meta-analysis indicates that the literature suﬀers from publication selection bias: insigniﬁcant or positive estimates of the price elasticity are rarely reported, although implausibly large negative estimates are reported regularly. In consequence, the average published estimates of both short- and long-run elasticities are exaggerated twofold. Using mixed-eﬀects multilevel meta-regression, we show that after correction for publication bias the average long-run elasticity reaches − 0 . 31 and the average short-run elasticity only − 0 . 09.
Finite element modeling of flexible pavements

E bem conhecido que a analise de pavimentos flexiveis e uma tarefa dificil, uma vez o sistema de pavimento e de multiplas camadas e tridimensional. Para fornecer informacoes precisas deslocamentos, deformacoes e tensoes, o sistema deve considerar as diferentes caracteristicas de cada camada. Camadas granulares, por exemplo, apresentam complexas relacoes tensao-deformacao nao-linear, enquanto a superficie (asfalto) apresenta uma camada de dependentes do tempo o comportamento viscoelastico. Alem disso, rachaduras e fadiga sao alguns dos problemas que a camada superficial pode apresentar. Este artigo aborda as tecnicas utilizadas na modelagem de elementos finitos de pavimentos flexiveis. Portanto, apropriada de modelos constitutivos e algoritmos numericos para representar o comportamento nao-linear resilientes das camadas unbound e as caracteristicas viscoelasticas da camada de HMA sao exaustivamente discutidas. Estas tecnicas sao aplicadas em um sistema de computador desenvolvido usando um Object-Oriented Programming abordagem (OOP). Ambos os modelos axissimetrico e tridimensionais estao incluidos. Varios exemplos numericos serao analisados ​​a fim de validar a implementacao e avaliar a importância da consideracao dos efeitos nao-lineares e dependentes do tempo. Os resultados obtidos serao comparados com solucoes analiticas disponiveis e computacional.
Community Mapping in Borneo - Issues of Accuracy in Traditional Boundary Delineation

This research examines the issue of boundary accuracy in community mapping projects in Indonesian and Malaysian Borneo. Many rural communities have adopted spatial information technologies and methods to challenge 'official' maps, and to gain recognition of their customary lands and resources. Concerns have been expressed, however, about potential difficulties and impacts of community mapping, and a key issue is accuracy evaluation. Boundaries, while apparently straightforward on a map, may symbolize many different social meanings and physical manifestations. I examine how both technical and social aspects of boundary accuracy emerge through three key phases of community mapping: field data collection, mapped representations, and applications. Several sources of data were accessed, including journal articles, manuals, technical reports, legal documents and key informant interviews. It was found that several methods were used to map boundaries in the field. Surprisingly, some methods involved the deliberate avoidance of the boundary concept altogether. Boundaries had multiple definitions at the field data collection level, including land use, land marks, and watershed boundaries. In addition, boundaries were often contemporary responses to external pressures. Large scale environmental changes, such as logging, also impacted the memory, negotiation and location of boundaries. The physical map as well as the mapping process was found to have a role not only in representing spatial perceptions of boundaries but also in producing them. In applications of community mapping, several modes of accuracy assessment were discovered, particularly in legal contexts. These include technical issues such as the evaluation of the map products and skills of the facilitators, and social issues such as how property rights are created and how they are demonstrated using maps. I suggest that community mapping is located somewhere in the translation between how the law and local people 'see' and claim property, and conclude that both of these views need to be considered in order to achieve effective levels of accuracy.
Electromagnetic waves' self-focusing in metamaterials having both LHM and RHM behavior: an application to filter design

ABSTRACT Self-focusing, also known as Talbot’s effect had been widely studied by the scientific community. In this paper we show self-focusing as a coherence propagation phenomenon, in particular we studied self-focusing for a pulse propagating in a metamaterial with both behaviors: left handed material (LHM) and right handed material (RHM). These dual behaviors allow the construction of frequency filters in the range of frequency in which the metamaterial stops behaving as a LHM (negative refraction index) and begins an RHM behavior (positive refraction index). The coherence length (CL) for pulses propagating in these metamaterials seems to be larger when its frequencies are in the LHM region. Finally, we show that the cell’s size turns out to be critical in designing the filter, and as an example we present here four metamaterial cells.
The Effect of Geometry Box Media on the Ability to Know the Concept of Numbers and Geometric Shapes

This research aims to know the influence of geometry box media against the ability to know the concept of numbers and geometric shapes in the group A. This research is quantitative approach with the types of experimental quasy research using non-equivalent control group design. Media of geometry box includes one media that can facilitate the learning of cognitive development of the child with regard to geometric shapes and numbers. Therefore, it can be concluded that the influence of the media affects students to know the concept of numbers and geometric shapes in the group A.
Differential neutron production cross sections for 800-MeV protons

In this paper, differential (p,xn) cross sections are measured for 800-MeV protons incident on thin targets of depleted uranium and of natural beryllium, boron, carbon, nitrogen, oxygen, aluminum, iron, cadmium, tungsten, and lead. Measurements for neutron energies from 0.3 to 800 MeV are made at angles of 30, 60, 120, and 150 deg. Time-of-flight techniques are used to determine the neutron energy spectra, and particular effort is made to identify and discriminate against background contributions. Comparisons of the experimental data with calculations using the high-energy transport code (HETC) intranuclear-cascade evaporation model show good agreement for the heaviest elements (tungsten, lead, and uranium), but significant discrepancies exist for the light elements, especially in the evaporation region.
Renminbi Trade Settlement as a Catalyst to Hong Kong's Development as an Offshore Renminbi Center

Renminbi settlement has become the preferred mode of settlement for mainland Chinese imports, with most of the imports settled this way coming from Hong Kong. As a result, renminbi deposits in Hong Kong have increased sharply, as has the issuance of dim sum bonds. It will be interesting to see what routes are opened up to enable this money to find its way back from Hong Kong's offshore renminbi market to the mainland and whether Japanese companies take advantage of the market to issue dim sum bonds.
An Analysis of Transport, Dispersion, and Deposition from Two Above-Ground Nuclear Tests in China

Abstract : There has been recent international interest on where nuclear fallout occurred from above ground test events conducted at the Chinese Lop Nor test site. This study pertains to detonations presumed to occur during the morning hours of June 17, 1967 and June 27, 1973. The purpose of this study is to determine whether it is likely that significant fallout from the two aboveground nuclear weapons tests fell near the cities of Makanchi, Urdzhar, and Taskesken in Kazakhstan. To accomplish this it was necessary to reconstruct the regional meteorological conditions (winds, temperatures, and humidity), then perform nuclear weapon source term, transport, dispersion, and deposition calculations based on the best available information.
Airborne laser scanning for forested landslides investigation in temperate and tropical environments

Landslide hazard and risk have increased over the last decades and pose a significant threat to modern society. Despite remarkable efforts of compiling and updating landslide maps at regional, national or global scales, the number of landslide events is often underestimated, especially in forested areas where the vegetation obscures the geomorphic features indicative of landsliding. The primary objective of this study was to investigate the suitability of an active remote sensing technique, airborne laser scanning (ALS), for mapping and classifying landslides in temperate and tropical forest environments. The methods were developed in two study areas: (1) the Bois Noir area in France (Southern French Alps), (2) the Cameron Highlands in Malaysia (tropical rainforest region). In conclusion, the emergence of ALS for investigating geomorphic processes and activities has improved our ability to map, monitor and model the topographic terrain signature and landslide-induced vegetation anomalies. This study explicitly proved that ALS can be a very important new data source and mapping tool to characterize landslides even in a complex environment. The increased prevalence of modern ALS system and advanced point cloud processing had led the ways to improve future landslide maps and subsequently reduce landslide risk. Given the complexity of the terrain, automating the inventorization will still be challenging in the tropics with extensive anthropogenic activity, and differentiating the vegetative reaction in response to different earth surface processes requires further research. Airborne remote sensing is a critical and supportive tool for better understanding of landslide geomorphology in a changing environment.
Seeing the Inside: Bark Painting in Western Arnhem Land

1. Introduction 2. The development of the market for bark paintings in western Arnhem Land 3. Kunwinjku social organization and land ownership 4. Apprenticeship and the social identity of artists 5. Themes and paintings in ceremonies 6. Formal components of bark paintings 7. Iconic representation 8. Transforming figures 9. The meaning of x-ray paintings 10. Metaphors and socialization Conclusion: Innovation and social reproduction Appendix: Glossary of Kunwinjku words
DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics

Synthesizing realistic human movements, dynamically responsive to the environment, is a long-standing objective in character animation, with applications in computer vision, sports, and healthcare, for motion prediction and data augmentation. Recent kinematics-based generative motion models offer impressive scalability in modeling extensive motion data, albeit without an interface to reason about and interact with physics. While simulator-in-the-loop learning approaches enable highly physically realistic behaviors, the challenges in training often affect scalability and adoption. We introduce DROP, a novel framework for modeling Dynamics Responses of humans using generative mOtion prior and Projective dynamics. DROP can be viewed as a highly stable, minimalist physics-based human simulator that interfaces with a kinematics-based generative motion prior. Utilizing projective dynamics, DROP allows flexible and simple integration of the learned motion prior as one of the projective energies, seamlessly incorporating control provided by the motion prior with Newtonian dynamics. Serving as a model-agnostic plug-in, DROP enables us to fully leverage recent advances in generative motion models for physics-based motion synthesis. We conduct extensive evaluations of our model across different motion tasks and various physical perturbations, demonstrating the scalability and diversity of responses.
Beyond Traditional Teaching: The Potential of Large Language Models and Chatbots in Graduate Engineering Education

In the rapidly evolving landscape of education, digital technologies have repeatedly disrupted traditional pedagogical methods. This paper explores the latest of these disruptions: the potential integration of large language models (LLMs) and chatbots into graduate engineering education. We begin by tracing historical and technological disruptions to provide context and then introduce key terms such as machine learning and deep learning and the underlying mechanisms of recent advancements, namely attention/transformer models and graphics processing units. The heart of our investigation lies in the application of an LLM-based chatbot in a graduate fluid mechanics course. We developed a question bank from the course material and assessed the chatbot's ability to provide accurate, insightful responses. The results are encouraging, demonstrating not only the bot's ability to effectively answer complex questions but also the potential advantages of chatbot usage in the classroom, such as the promotion of self-paced learning, the provision of instantaneous feedback, and the reduction of instructors' workload. The study also examines the transformative effect of intelligent prompting on enhancing the chatbot's performance. Furthermore, we demonstrate how powerful plugins like Wolfram Alpha for mathematical problem-solving and code interpretation can significantly extend the chatbot's capabilities, transforming it into a comprehensive educational tool. While acknowledging the challenges and ethical implications surrounding the use of such AI models in education, we advocate for a balanced approach. The use of LLMs and chatbots in graduate education can be greatly beneficial but requires ongoing evaluation and adaptation to ensure ethical and efficient use.
Beginner's guide to Aggregation-Diffusion Equations

The aim of this survey is to serve as an introduction to the different techniques available in the broad field of aggregation-diffusion equations. We aim to provide historical context, key literature, and main ideas in the field. We start by discussing the modelling and famous particular cases: heat equation, Fokker–Plank, Porous medium, Keller–Segel, Chapman–Rubinstein–Schatzman, Newtonian vortex, Caffarelli–Vázquez, McKean–Vlasov, Kuramoto, and one-layer neural networks. In Sect. 4 we present the well-posedness frameworks given as PDEs in Sobolev spaces, and gradient-flow in Wasserstein. Then we discuss the asymptotic behaviour in time, for which we need to understand minimisers of a free energy. We then present some numerical methods which have been developed. We conclude the paper mentioning some related problems.
GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph

Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning of vision-language models (VLMs) under the low-data regime, where only a few additional parameters are introduced to excavate the task-specific knowledge based on the general and powerful representation of VLMs. However, most adapter-style works face two limitations: (i) modeling task-specific knowledge with a single modality only; and (ii) overlooking the exploitation of the inter-class relationships in downstream tasks, thereby leading to sub-optimal solutions. To mitigate that, we propose an effective adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual adapter by explicitly modeling the dual-modality structure knowledge (i.e., the correlation of different semantics/classes in textual and visual modalities) with a dual knowledge graph. In particular, the dual knowledge graph is established with two sub-graphs, i.e., a textual knowledge sub-graph, and a visual knowledge sub-graph, where the nodes and edges represent the semantics/classes and their correlations in two modalities, respectively. This enables the textual feature of each prompt to leverage the task-specific structure knowledge from both textual and visual modalities, yielding a more effective classifier for downstream tasks. Extensive experimental results on 11 benchmark datasets reveal that our GraphAdapter significantly outperforms previous adapter-based methods. The code will be released at https://github.com/lixinustc/GraphAdapter
Related Rhythms: Recommendation System To Discover Music You May Like

Machine Learning models are being utilized extensively to drive recommender systems, which is a widely explored topic today. This is especially true of the music industry, where we are witnessing a surge in growth. Besides a large chunk of active users, these systems are fueled by massive amounts of data. These large-scale systems yield applications that aim to provide a better user experience and to keep customers actively engaged. In this paper, a distributed Machine Learning (ML) pipeline is delineated, which is capable of taking a subset of songs as input and producing a new subset of songs identified as being similar to the inputted subset. The publicly accessible Million Songs Dataset (MSD) enables researchers to develop and explore reasonably efficient systems for audio track analysis and recommendations, without having to access a commercialized music platform. The objective of the proposed application is to leverage an ML system trained to optimally recommend songs that a user might like.
An Optimal Control Framework for Influencing Human Driving Behavior in Mixed-Autonomy Traffic

As autonomous vehicles (AVs) become increasingly prevalent, their interaction with human drivers presents a critical challenge. Current AVs lack social awareness, causing behavior that is often awkward or unsafe. To combat this, social AVs, which are proactive rather than reactive in their behavior, have been explored in recent years. With knowledge of robot-human interaction dynamics, a social AV can influence a human driver to exhibit desired behaviors by strategically altering its own behaviors. In this paper, we present a novel framework for achieving human influence. The foundation of our framework lies in an innovative use of control barrier functions to formulate the desired objectives of influence as constraints in an optimal control problem. The computed controls gradually push the system state toward satisfaction of the objectives, e.g. slowing the human down to some desired speed. We demonstrate the proposed framework's feasibility in a variety of scenarios related to car-following and lane changes, including multi-robot and multi-human configurations. In two case studies, we validate the framework's effectiveness when applied to the problems of traffic flow optimization and aggressive behavior mitigation. Given these results, the main contribution of our framework is its versatility in a wide spectrum of influence objectives and mixed-autonomy configurations.
Accelerating Large Batch Training via Gradient Signal to Noise Ratio (GSNR)

As models for nature language processing (NLP), computer vision (CV) and recommendation systems (RS) require surging computation, a large number of GPUs/TPUs are paralleled as a large batch (LB) to improve training throughput. However, training such LB tasks often meets large generalization gap and downgrades final precision, which limits enlarging the batch size. In this work, we develop the variance reduced gradient descent technique (VRGD) based on the gradient signal to noise ratio (GSNR) and apply it onto popular optimizers such as SGD/Adam/LARS/LAMB. We carry out a theoretical analysis of convergence rate to explain its fast training dynamics, and a generalization analysis to demonstrate its smaller generalization gap on LB training. Comprehensive experiments demonstrate that VRGD can accelerate training ($1\sim 2 \times$), narrow generalization gap and improve final accuracy. We push the batch size limit of BERT pretraining up to 128k/64k and DLRM to 512k without noticeable accuracy loss. We improve ImageNet Top-1 accuracy at 96k by $0.52pp$ than LARS. The generalization gap of BERT and ImageNet training is significantly reduce by over $65\%$.
Cardiac Troponins Used as Diagnostic and Prognostic Tests in Patients With Kidney Disease

Troponin is a protein complex of three subunits (T, I, and C) that is involved in the contractile process of skeletal and cardiac muscle. Both cardiac and skeletal muscle express troponin C; whereas troponin T and I are generally thought to be cardiac-specific. *1 When cardiac injury occurs (from ischemia or various other causes), cardiomyocytes release cardiac troponin into the blood in proportion to the degree of damage.2 Troponin levels increase within 3 to 4 hours after the onset of damage and remain high for up to 4 to 7 days (troponin I) or 10 to 14 days (troponin T). However, blood from healthy individuals with no evidence of cardiac disease also contains very low amounts of cardiac troponin.3 Some of the newer highsensitivity assays may be able to measure troponin in normal individuals; although many of the commercially available assays cannot detect troponin at all or cannot quantify it at levels below the measuring range of the assay.
Rethinking Social Robot Navigation: Leveraging the Best of Two Worlds

Empowering robots to navigate in a socially compliant manner is essential for the acceptance of robots moving in human-inhabited environments. Previously, roboticists have developed geometric navigation systems with decades of empirical validation to achieve safety and efficiency. However, the many complex factors of social compliance make geometric navigation systems hard to adapt to social situations, where no amount of tuning enables them to be both safe (people are too unpredictable) and efficient (the frozen robot problem). With recent advances in deep learning approaches, the common reaction has been to entirely discard these classical navigation systems and start from scratch, building a completely new learning-based social navigation planner. In this work, we find that this reaction is unnecessarily extreme: using a large-scale real-world social navigation dataset, SCAND, we find that geometric systems can produce trajectory plans that align with the human demonstrations in a large number of social situations. We, therefore, ask if we can rethink the social robot navigation problem by leveraging the advantages of both geometric and learning-based methods. We validate this hybrid paradigm through a proof-of-concept experiment, in which we develop a hybrid planner that switches between geometric and learning-based planning. Our experiments on both SCAND and two physical robots show that the hybrid planner can achieve better social compliance compared to using either the geometric or learning-based approach alone.