Miracle-dz/UniFAR-FTUs huggingface dataset:
Use Q-RTM to Image Beneath Gas Hydrates In Alaminos Canyon, Gulf of Mexico

Gas hydrates buried in the shallow sediments below the ocean cause amplitude attenuation, wavelet phase distortion, and frequency loss in the seismic reflection data beneath them. This can make reservoir evaluation based on the deeper seismic image difficult. The distortion can be compensated by using pre-stack inverse Q-filtering, or by a more sophisticated Q-migration. While inverse Q-filtering (amplitude and phase) is a traditional method to correct these problems, it is not sufficient to compensate for localized image distortion resulting from complex overburden structures. A dataset in Alaminos Canyon, Gulf of Mexico was used to perform a 3D Q-tomographic inversion. The derived Q-model of the gas hydrates was used in Q-Reverse Time Migration (Q-RTM) (Zhang et al, 2010) to improve the imaging around complex geological structures. The results show that Q-tomography can generate a reliable Q-model from the surface seismic data, and that Q-RTM can reduce the energy loss and image distortion caused by the shallow gas hydrates. Altogether, Q-tomography and Q-RTM provide an improved structural definition and more uniform amplitude distribution for a more accurate and interpretable seismic section.
Hydrological and water quality modeling of agricultural fields in Quebec

Two tile-drained agricultural fields in the Pike River watershed of Southern Quebec were instrumented in October 2000 to monitor phosphorus and nitrate concentrations in surface runoff and tile drainage. Data collected from these sites were used as the primary input to test a GIS-based hydrological and water quality simulation model (ArcView SWAT2000) at the field scale. Surface runoff, subsurface flow, sediment yield, nitrate loads and phosphorus loads were the principal parameters evaluated by the model. The SWAT model was calibrated using data collected in the year 2002 while 2003 data was used for validating the model. Particulate phosphorus and total dissolved phosphorus loads in streamflow were also simulated using SWAT and compared with field measurements. %%%%A sensitivity analysis showed that curve number, available soil water content and soil evaporation factors significantly influenced water yield simulations while model performance for water quality parameters was governed mainly by the accuracy of simulating field operations such as fertilization and tillage. The monthly coefficients of performance after calibration ranged from being very good for some parameters (0.27 to 0.66 for total water yield; 0.38 to 0.67 for total phosphorus; and 0.23 to 0.89 for sediments) to being inconsistent for others (0.44 to 2.28 for subsurface flow; 0.63 to 4.36 for surface runoff; and 0.66 to 1.35 for total nitrate loads). Overall, it was found that SWAT results on a seasonal scale were generally more reliable whereas daily or monthly simulations could be improved by using a longer calibration period or incorporating model changes. Short-term impacts of implementing different best management practices for tillage, crop rotation and fertilization were also evaluated using the validated SWAT model. It was found that conservation tillage of corn coupled with pasture or soybean rotations can reduce total phosphorus loads in the range of 25-50% over conventional tillage with corn.
Machine learning detecting Majorana Zero Mode from Zero Bias Peak measurements

Majorana zero modes (MZMs), emerging as exotic quasiparticles that carry non-Abelian statistics, hold great promise for achieving fault-tolerant topological quantum computation. A key signature of the presence of MZMs is the zero-bias peaks (ZBPs) from tunneling differential conductance. However, the identification of MZMs from ZBPs has faced tremendous challenges, due to the presence of topological trivial states that generate spurious ZBP signals. In this work, we introduce a machine-learning framework that can discern MZM from other signals using ZBP data. Quantum transport simulation from tight-binding models is used to generate the training data, while persistent cohomology analysis confirms the feasibility of classification via machine learning. In particular, even with added data noise, XGBoost classifier reaches $85\%$ accuracy for 1D tunneling conductance data and $94\%$ for 2D data incorporating Zeeman splitting. Tests on prior ZBP experiments show that some data are more likely to originate from MZM than others. Our model offers a quantitative approach to assess MZMs using ZBP data. Furthermore, our results shed light on the use of machine learning on exotic quantum systems with experimental-computational integration.
Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity

Current deep-learning models for object recognition are known to be heavily biased toward texture. In contrast, human visual systems are known to be biased toward shape and structure. What could be the design principles in human visual systems that led to this difference? How could we introduce more shape bias into the deep learning models? In this paper, we report that sparse coding, a ubiquitous principle in the brain, can in itself introduce shape bias into the network. We found that enforcing the sparse coding constraint using a non-differential Top-K operation can lead to the emergence of structural encoding in neurons in convolutional neural networks, resulting in a smooth decomposition of objects into parts and subparts and endowing the networks with shape bias. We demonstrated this emergence of shape bias and its functional benefits for different network structures with various datasets. For object recognition convolutional neural networks, the shape bias leads to greater robustness against style and pattern change distraction. For the image synthesis generative adversary networks, the emerged shape bias leads to more coherent and decomposable structures in the synthesized images. Ablation studies suggest that sparse codes tend to encode structures, whereas the more distributed codes tend to favor texture. Our code is host at the github repository: \url{https://github.com/Crazy-Jack/nips2023_shape_vs_texture}
Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies

Thematic analysis and other variants of inductive coding are widely used qualitative analytic methods within empirical legal studies (ELS). We propose a novel framework facilitating effective collaboration of a legal expert with a large language model (LLM) for generating initial codes (phase 2 of thematic analysis), searching for themes (phase 3), and classifying the data in terms of the themes (to kick-start phase 4). We employed the framework for an analysis of a dataset (n=785) of facts descriptions from criminal court opinions regarding thefts. The goal of the analysis was to discover classes of typical thefts. Our results show that the LLM, namely OpenAI's GPT-4, generated reasonable initial codes, and it was capable of improving the quality of the codes based on expert feedback. They also suggest that the model performed well in zero-shot classification of facts descriptions in terms of the themes. Finally, the themes autonomously discovered by the LLM appear to map fairly well to the themes arrived at by legal experts. These findings can be leveraged by legal researchers to guide their decisions in integrating LLMs into their thematic analyses, as well as other inductive coding projects.
An efficient modeling workflow for high-performance nanowire single-photon avalanche detector.

Single-photon detector (SPD), an essential building block of the quantum communication system, plays a fundamental role in developing next-generation quantum technologies. In this work, we propose an efficient modeling workflow of nanowire SPDs utilizing avalanche breakdown at reverse-biased conditions. The proposed workflow is explored to maximize computational efficiency and balance time-consuming drift-diffusion simulation with fast script-based post-processing. Without excessive computational effort, we could predict a suite of key device performance metrics, including breakdown voltage, dark/light avalanche built-up time, photon detection efficiency, dark count rate, and the deterministic part of timing jitter due to device structures. Implementing the proposed workflow onto a single InP nanowire and comparing it to the extensively studied planar devices and superconducting nanowire SPDs, we showed the great potential of nanowire avalanche SPD to outperform their planar counterparts and obtain as superior performance as superconducting nanowires, i.e., achieve a high photon detection efficiency of 70% with a dark count rate less than 20 Hz at non-cryogenic temperature. The proposed workflow is not limited to single-nanowire or nanowire-based device modeling and can be readily extended to more complicated two-/three dimensional structures.
Subspace projection regularization for large-scale Bayesian linear inverse problems

The Bayesian statistical framework provides a systematic approach to enhance the regularization model by incorporating prior information about the desired solution. For the Bayesian linear inverse problems with Gaussian noise and Gaussian prior, we propose a new iterative regularization algorithm that belongs to subspace projection regularization (SPR) methods. By treating the forward model matrix as a linear operator between the two underlying finite dimensional Hilbert spaces with new introduced inner products, we first introduce an iterative process that can generate a series of valid solution subspaces. The SPR method then projects the original problem onto these solution subspaces to get a series of low dimensional linear least squares problems, where an efficient procedure is developed to update the solutions of them to approximate the desired solution of the original problem. With the new designed early stopping rules, this iterative algorithm can obtain a regularized solution with a satisfied accuracy. Several theoretical results about the algorithm are established to reveal the regularization properties of it. We use both small-scale and large-scale inverse problems to test the proposed algorithm and demonstrate its robustness and efficiency. The most computationally intensive operations in the proposed algorithm only involve matrix-vector products, making it highly efficient for large-scale problems.
AllTogether: Investigating the Efficacy of Spliced Prompt for Web Navigation using Large Language Models

Large Language Models (LLMs) have emerged as promising agents for web navigation tasks, interpreting objectives and interacting with web pages. However, the efficiency of spliced prompts for such tasks remains underexplored. We introduces AllTogether, a standardized prompt template that enhances task context representation, thereby improving LLMs' performance in HTML-based web navigation. We evaluate the efficacy of this approach through prompt learning and instruction finetuning based on open-source Llama-2 and API-accessible GPT models. Our results reveal that models like GPT-4 outperform smaller models in web navigation tasks. Additionally, we find that the length of HTML snippet and history trajectory significantly influence performance, and prior step-by-step instructions prove less effective than real-time environmental feedback. Overall, we believe our work provides valuable insights for future research in LLM-driven web agents.
Handling the Conflicting Dip Problem in the CRS/i-CRS Methods

Most current implementations of the CRS operator suffer from the occurrence of conflicting dip situations in the acquired data. To address this properly we apply the idea of the CDS. We use the i-CRS operator that can be related to the CRS operator, and show, that conflicting dips can be resolved well in multi-parameter processing. The results are promising and reveal a lot of potential for further applications. This is shown by a diffraction separation technique applied to field data obtained in the Levantine Basin.
Maximum Independent Set: Self-Training through Dynamic Programming

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly constructs two smaller sub-graphs, predicts the one with the larger MIS, and then uses it in the next recursive call. To train our algorithm, we require annotated comparisons of different graphs concerning their MIS size. Annotating the comparisons with the output of our algorithm leads to a self-training process that results in more accurate self-annotation of the comparisons and vice versa. We provide numerical evidence showing the superiority of our method vs prior methods in multiple synthetic and real-world datasets.
Detangling the role of climate in vegetation productivity with an explainable convolutional neural network

Forests of the Earth are a vital carbon sink while providing an essential habitat for biodiversity. Vegetation productivity (VP) is a critical indicator of carbon uptake in the atmosphere. The leaf area index is a crucial vegetation index used in VP estimation. This work proposes to predict the leaf area index (LAI) using climate variables to better understand future productivity dynamics; our approach leverages the capacities of the V-Net architecture for spatiotemporal LAI prediction. Preliminary results are well-aligned with established quality standards of LAI products estimated from Earth observation data. We hope that this work serves as a robust foundation for subsequent research endeavours, particularly for the incorporation of prediction attribution methodologies, which hold promise for elucidating the underlying climate change drivers of global vegetation productivity.
Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles

Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the AV to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modeling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modeling, encompassing cognitive methods, machine‐learning approaches, and game‐theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration.
A Novel Skip Orthogonal List for Dynamic Optimal Transport Problem

Optimal transport is a fundamental topic that has attracted a great amount of attention from the optimization community in the past decades. In this paper, we consider an interesting discrete dynamic optimal transport problem: can we efficiently update the optimal transport plan when the weights or the locations of the data points change? This problem is naturally motivated by several applications in machine learning. For example, we often need to compute the optimal transport cost between two different data sets; if some changes happen to a few data points, should we re-compute the high complexity cost function or update the cost by some efficient dynamic data structure? We are aware that several dynamic maximum flow algorithms have been proposed before, however, the research on dynamic minimum cost flow problem is still quite limited, to the best of our knowledge. We propose a novel 2D Skip Orthogonal List together with some dynamic tree techniques. Although our algorithm is based on the conventional simplex method, it can efficiently find the variable to pivot within expected O(1) time, and complete each pivoting operation within expected O(|V|) time where V is the set of all supply and demand nodes. Since dynamic modifications typically do not introduce significant changes, our algorithm requires only a few simplex iterations in practice. So our algorithm is more efficient than re-computing the optimal transport cost that needs at least one traversal over all |E|=O(|V|^2) variables, where |E| denotes the number of edges in the network. Our experiments demonstrate that our algorithm significantly outperforms existing algorithms in the dynamic scenarios.
SBMLtoODEjax: Efficient Simulation and Optimization of Biological Network Models in JAX

Advances in bioengineering and biomedicine demand a deep understanding of the dynamic behavior of biological systems, ranging from protein pathways to complex cellular processes. Biological networks like gene regulatory networks and protein pathways are key drivers of embryogenesis and physiological processes. Comprehending their diverse behaviors is essential for tackling diseases, including cancer, as well as for engineering novel biological constructs. Despite the availability of extensive mathematical models represented in Systems Biology Markup Language (SBML), researchers face significant challenges in exploring the full spectrum of behaviors and optimizing interventions to efficiently shape those behaviors. Existing tools designed for simulation of biological network models are not tailored to facilitate interventions on network dynamics nor to facilitate automated discovery. Leveraging recent developments in machine learning (ML), this paper introduces SBMLtoODEjax, a lightweight library designed to seamlessly integrate SBML models with ML-supported pipelines, powered by JAX. SBMLtoODEjax facilitates the reuse and customization of SBML-based models, harnessing JAX's capabilities for efficient parallel simulations and optimization, with the aim to accelerate research in biological network analysis.
A long-wave model for film flow inside a tube with slip

Abstract A long-wave asymptotic model is developed for the flow of an axisymmetric viscous film lining the interior of a tube for the case where slip occurs at the tube wall. Both the case of a falling film with a passive air core and that of a film driven up the tube by pressure-driven airflow are considered. The impact of slip on the net liquid volume flux is discussed, and linear stability analysis of the evolution equation is conducted to identify the impact of slip on the phase speed and growth rates of disturbances in each case. The presence of slip enhances the growth rates, though its impact on phase speed depends on the film thickness and the strength of the core airflow. For some parameter combinations, slip can modify the phase speed without altering the base flow. The nonlinear evolution of the free surface is then studied numerically. For falling films, increasing the slip length reduces the critical thickness required for plug formation to occur. Families of travelling wave solutions are found via continuation and are used to derive a simple formula for the dependence of this critical thickness on the slip length; this formula is shown to hold for small slip length. For air-driven films, the topology of streamlines in the film can be altered by slip at the wall; if the slip length is large enough, it can prevent regions of recirculation from forming at the wave crest.
Mites (Acari) infesting stored animal feed products in northern Thailand

Abstract Storage mites are serious pests of stored animal feeds and animals that consume mite-infested feed could reduce feed intake and eventually cause weight loss. To understand the mite species and infestation rates of them in animal feeds, we conducted a survey of mite infestations in samples of animal feeds from animal feed shops, feed mills, or chicken farms in eight provinces in northern Thailand. High mite infestation rates were observed in chicken feed (42.9%), followed by pig feed (26.7%). No mites were observed in fish and frog feeds. Mites were common in pelleted feeds (n = 96) or raw materials (n = 3), but was never observed in extruded feeds (n = 33). The mite species identified include three storage mites: Suidasia pontifica, Dermatophagoides farinae, and Aleuroglyphus chinensis, as well as the predatory mites Cheyletus malaccensis, Blattisocius keegani, and B. everti. Among them, A. chinensis and B. everti, are newly recorded in Thailand. Suidasia pontifica was dominant in chicken and pig feeds, and could be the major storage mite pest in northern Thailand. Figures and identification keys are provided.
Acupuncture in the Treatment of a Series of Patients with Chronic Pain Associated with Hip Osteoarthritis

Resumo Objetivo A osteoartrite (OA) de quadril causa dor e perda da funcionalidade. O tratamento cirúrgico é a procedimento de escolha nos casos graves, mas as comorbidades clínicas, a idade e a longa lista de espera podem comprometer a qualidade de vida. Este estudo teve como objetivo descrever os resultados da acupuntura no controle da dor e melhora da funcionalidade em indivíduos com OA de quadril. Método Doze pacientes com OA de quadril grave foram tratados com dez sessões semanais de um protocolo padronizado de pontos de acupuntura. A intensidade da dor foi avaliada pela Escala Visual Analógica de Dor (EVA) e a qualidade de vida pelo Índice WOMAC. Resultados A intensidade da dor (EVA) reduziu de 75,8 ± 18,8mm para 20,0 ± 22,6mm após 10 sessões de acupuntura e 48,3 ± 26,6mm no período de seguimento (ANOVA F = 7,99; p < 0,001). Os valores do Índice WOMAC reduziram de 74,7 ± 12,7 para 45,7 ± 22,1 e 54,6 ± 22,9 nos mesmos momentos. Conclusão A acupuntura é uma estratégia de reabilitação conservadora eficaz para reduzir a dor e melhorar a qualidade de vida em indivíduos com OA de quadril grave. Abstract Objective Hip osteoarthritis (HO) causes pain and deranges functioning. Surgical treatment is the preferred approach in severe cases, but clinical comorbidities, age and the long waiting list may compromise quality of life. This study aimed to describe the results of acupuncture for the control pain and improvement of functioning in subjects with HO. Method Twelve severe HO patients were treated with ten weekly sessions of a standardized acupuncture point protocol. Pain intensity was assessed with the Visual Analog Pain Scale (VAS) and quality of life with WOMAC Index. Results Pain intensity (VAS) reduced from 75.8 ± 18.8 mm to 20.0 ± 22.6 mm after 10 acupuncture sessions and 48.3 ± 26.6mm in the follow-up (ANOVA F = 7.99; p < 0.001). WOMAC Index values reduced from 74.7 ± 12.7 to 45.7 ± 22.1 and 54.6 ± 22.9 at the same timepoints. Conclusion Acupuncture is an effective conservative rehabilitation strategy to reduce pain and improve quality of life in subjects with severe HO.