Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding farmers’ adoption of conservation tillage in South Dakota: A modified application of the theory of planned behavior

The government and stakeholders in the US agricultural sector promote conservation farming practices, but voluntary adoption is still a challenge among agricultural producers at the farm level. This paper aims to investigate the factors that influence the adoption of conservation tillage (CT) among producers in eastern and central South Dakota. A modified application of the theory of planned behavior (TPB) including controls for socioeconomic and demographic indicators is used to understand farmers’ current usage of CT. Data were gathered from a probability sample of 486 commodity crop farming operations through an online and mail survey. We find that the three constructs of the TPB—attitude, subjective norms, and perceived behavioral control—are positively and significantly correlated with the adoption of CT. Binary logistic regression models also show that attitude and perceived behavioral control are related to farmers’ likelihood of adopting CT. Farm size and farmers’ level of education were also positively associated with CT adoption. The results indicate that a modified application of the TPB is useful for understanding the behavior of farmers regarding conservation practices, specifically the adoption of CT in South Dakota. This study contributes to knowledge regarding the role of social-psychological factors in farmers’ CT adoption and provides insights for policymakers and conservation practitioners promoting the adoption of CT in the region.
Do school-based smoking preventive interventions have unintended effects? Post hoc analysis of the Focus cluster randomised controlled trial

Objectives Public health interventions are designed to improve specific health-related outcomes; however, they may also produce negative side effects, such as substitution use, psychological or social harms. Knowledge about the unintended effects of school-based smoking preventive interventions is sparse. Hence, this study examined these potential unintended effects of the smoking-reducing intervention, Focus, among students in the vocational education and training setting. Design Cluster randomised controlled trial stratified by school type with 5 months follow-up. Setting and participants Across Denmark, eight schools were randomised to the intervention group (n=844 students, response proportion 76%) and six schools to the control group (n=815 students, response proportion 75%). This study focused solely on students who smoked at baseline (N=491). Interventions The intervention was developed systematically based on theory and a thoroughly mixed-methods needs assessment. Intervention components included a comprehensive school tobacco policy (smoke-free school hours) supported by a 3-day course for school staff and launched by an edutainment session for students; class-based lessons and a quit-and-win competition; and individual telephone smoking cessation support. Outcomes Alternative tobacco and nicotine products (regular use of smokeless tobacco, hookah and e-cigarettes), regular cannabis use, boredom and loneliness at school, stress and perceived stigmatisation among smokers. Results We found no statistically significant unintended effects of the intervention. Nonetheless, insignificant findings indicated that students in the intervention group were less likely to be bored during school hours (OR 0.59, 95% CI 0.32 to 1.10) and experience stress (OR 0.62, 95% CI 0.35 to 1.10), but more likely to report feeling stigmatised compared with the control group (OR 1.55, 95% CI 0.71 to 3.40). Conclusions Overall, findings suggested no unintended effects of the Focus trial with respect to substitution use, psychological, nor group or social harms. Future research is encouraged to report potential harmful outcomes of smoking preventive interventions, and interventions should be aware of the possible stigmatisation of smokers. Trial registration number ISRCTN16455577.
Maritime adaptations in Hokkaido

On the Pacific coast of Hokkaido, maritime adaptation was established as early as 7000 to 6000 BP. In areas like Tomakomai and Kushiro, where shallow inland bays were formed at the time of marine transgression, shell lenses were deposited by Initial Jomon sea mammal hunter-fishermen. On the Japan Sea coast, however, where rocky beaches prevail, shell middens were not deposited until the Middle Jomon stage. Thereafter, maritime-oriented economy became dominant all over Hokkaido, as evidenced by widely distributed large shell middens and their associated sea mammal and fish remains. Game hunted mainly by harpoons throughout the Jomon period consisted of fur seal, porpoise, Steller's sea lion, and other sea mammals. In the late Jomon stage, salmon and trout were the most preferred fish, as well as herring, which was popular from the onset of maritime adaptation. A flourishing economy of sea mammal hunting and marine fishing was carried over to the succeeding Epi-Jomon culture. Fish-shaped stone objects are characteristic of the earlier half of the Epi-Jomon phase in southern Hokkaido. The Okhotsk culture, unlike the contemporaneous Satsumon culture more widely distributed over Hokkaido, is also characterized by its high utilization of rich aquatic resources. Evidence shows that the Okhotsk people made use of net fishing and long-line fishing for migratory fish, and were also engaged in catching seals with refined toggle-head harpoons. Shell mounds of the Ainu period are relatively small, including the ones interpreted as sacred places to send off animal spirits. Salmon fishing by the Ainu was mainly undertaken in river valleys, a trait originated in the preceding Satsumon culture. The Ainu, however, inherited characteristic types of harpoon heads from the Okhotsk people.
Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

In conversational AI research, there's a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT. While these expansive models tend to generate increasingly better chat responses, they demand significant computational resources and memory. This study explores a pertinent question: Can a combination of smaller models collaboratively achieve comparable or enhanced performance relative to a singular large model? We introduce an approach termed"blending", a straightforward yet effective method of integrating multiple chat AIs. Our empirical evidence suggests that when specific smaller models are synergistically blended, they can potentially outperform or match the capabilities of much larger counterparts. For instance, integrating just three models of moderate size (6B/13B paramaeters) can rival or even surpass the performance metrics of a substantially larger model like ChatGPT (175B+ paramaters). This hypothesis is rigorously tested using A/B testing methodologies with a large user base on the Chai research platform over a span of thirty days. The findings underscore the potential of the"blending"strategy as a viable approach for enhancing chat AI efficacy without a corresponding surge in computational demands.
Incontestable Assignments

In school districts where assignments are exclusively determined by a clearinghouse students can only appeal their assignment with a valid reason. An assignment is incontestable if it is appeal-proof. We study incontestability when students do not observe the other students' preferences and assignments. Incontestability is shown to be equivalent to individual rationality, non-wastefulness, and respect for top-priority sets (a weakening of justified envy). Stable mechanisms and those Pareto dominating them are incontestable, as well as the Top-Trading Cycle mechanism (but Boston is not). Under a mild consistency property, incontestable mechanisms are i-indinstiguishable (Li, 2017), and share similar incentive properties.
Understanding Strandings: 25 years of Humpback Whale (Megaptera novaeangliae) Strandings in Queensland, Australia

ABSTRACT Meynecke, J-O. and Meager, J., 2016. Understanding strandings: 25 years of humpback whale strandings (Megaptera novaeangliae) in Queensland, Australia In: Vila-Concejo, A. ; Bruce, E. ; Kennedy, D.M. , and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 897–901. Coconut Creek (Florida), ISSN 0749-0208. Cetacean strandings are well-known phenomena in all parts of the world. In most cases the underlying reasons are unknown. Incidents have been associated with factors including changes in magnetic fields, diseases, unusual weather events, vessel strikes, pollution and fishing. Quantitative analyses over large spatial and time scales can provide insights into stranding hotspots, trends and relationships with physical drivers. Here we collated and combined available information on humpback whale (Megaptera novaeangliae) incidents that occurred in Queensland, on the east coast of Australia between 1989 and 2014. This study provides a first overview of incidents for humpback whales for the east coast of Australia over a 25 year period. Incidents included reports of carcasses, vessel strikes, injured animals found stranded, entrapped in shallow water, entangled or floating offshore. The location, timing, age class and types of over 200 incidents were analysed for trends and compared with oceanic and physical processes. The majority of incidents involving calves or juvenile humpback whales occurred during the austral winter (peak in August). The rate of strandings was associated with the position of the East Australian Current, and lagged Southern Oscillation Index (SOI). Our study demonstrates the importance of long-term strandings records to allow for the investigation of trends over spatial and temporal scales relevant to migratory species. A comprehensive strandings program is an important conservation and monitoring tool to better understand long-term trends and to quantify key threats to cetaceans.
Unifying Graph Contrastive Learning via Graph Message Augmentation

Graph contrastive learning is usually performed by first conducting Graph Data Augmentation (GDA) and then employing a contrastive learning pipeline to train GNNs. As we know that GDA is an important issue for graph contrastive learning. Various GDAs have been developed recently which mainly involve dropping or perturbing edges, nodes, node attributes and edge attributes. However, to our knowledge, it still lacks a universal and effective augmentor that is suitable for different types of graph data. To address this issue, in this paper, we first introduce the graph message representation of graph data. Based on it, we then propose a novel Graph Message Augmentation (GMA), a universal scheme for reformulating many existing GDAs. The proposed unified GMA not only gives a new perspective to understand many existing GDAs but also provides a universal and more effective graph data augmentation for graph self-supervised learning tasks. Moreover, GMA introduces an easy way to implement the mixup augmentor which is natural for images but usually challengeable for graphs. Based on the proposed GMA, we then propose a unified graph contrastive learning, termed Graph Message Contrastive Learning (GMCL), that employs attribution-guided universal GMA for graph contrastive learning. Experiments on many graph learning tasks demonstrate the effectiveness and benefits of the proposed GMA and GMCL approaches.
QCD anomalies in electromagnetic processes: A solution to the $\gamma\to3\pi$ puzzle

In this work, the $\gamma\to3\pi$ form factor is calculated within the Dyson-Schwinger equations framework using a contact interaction model within the so-called modified rainbow ladder truncation. The present calculation takes into account the pseudovector component in the pion Bethe-Salpeter amplitude (BSA) and $\pi-\pi$ scattering effects, producing a $\gamma\to3\pi$ anomaly which is $1+6\mathcal{R}_\pi^2$ larger than the low energy prediction. Here $\mathcal{R_\pi}$ is the relative ratio of the pseudovector and pseudoscalar components in the pion BSA; with our parameters input, this correction raises the $\gamma\to3\pi$ anomaly by around $10\%$. The main outcome of this work is the unveiling of the origin of such correction, which could be a possible explanation of the discrepancy between the existing experimental data and the low energy prediction. Moreover, it is highlighted how the magnitude of the anomaly is affected in effective theories that require an irremovable ultraviolet cutoff. We find that for both the anomalous processes $\pi\to2\gamma$ and $\gamma\to 3\pi$, the missing contribution to the anomaly can be compensated by the additional structures related with the quark anomalous magnetic moment.
Data-Dependent Stability Analysis of Adversarial Training

Stability analysis is an essential aspect of studying the generalization ability of deep learning, as it involves deriving generalization bounds for stochastic gradient descent-based training algorithms. Adversarial training is the most widely used defense against adversarial example attacks. However, previous generalization bounds for adversarial training have not included information regarding the data distribution. In this paper, we fill this gap by providing generalization bounds for stochastic gradient descent-based adversarial training that incorporate data distribution information. We utilize the concepts of on-average stability and high-order approximate Lipschitz conditions to examine how changes in data distribution and adversarial budget can affect robust generalization gaps. Our derived generalization bounds for both convex and non-convex losses are at least as good as the uniform stability-based counterparts which do not include data distribution information. Furthermore, our findings demonstrate how distribution shifts from data poisoning attacks can impact robust generalization.
Data-driven Dynamic Event-triggered Control

This paper revisits the event-triggered control problem from a data-driven perspective, where unknown continuous-time linear systems subject to disturbances are taken into account. Using data information collected off-line instead of accurate system model information, a data-driven dynamic event-triggered control scheme is developed in this paper. The dynamic property is reflected by that the designed event-triggering function embedded in the event-triggering mechanism (ETM) is dynamically updated as a whole. Thanks to this dynamic design, a strictly positive minimum inter-event time (MIET) is guaranteed without sacrificing control performance. Specifically, exponential input-to-state stability (ISS) of the closed-loop system with respect to disturbances is achieved in this paper, which is superior to some existing results that only guarantee a practical exponential ISS property. The dynamic ETM is easy-to-implement in practical operation since all designed parameters are determined only by a simple data-driven linear matrix inequality (LMI), without additional complicated conditions as required in relevant literature. As quantization is the most common signal constraint in practice, the developed control scheme is further extended to the case where state transmission is affected by a uniform or logarithmic quantization effect. Finally, adequate simulations are performed to show the validity and superiority of the proposed control schemes.
Weak Correlations as the Underlying Principle for Linearization of Gradient-Based Learning Systems

Deep learning models, such as wide neural networks, can be conceptualized as nonlinear dynamical physical systems characterized by a multitude of interacting degrees of freedom. Such systems in the infinite limit, tend to exhibit simplified dynamics. This paper delves into gradient descent-based learning algorithms, that display a linear structure in their parameter dynamics, reminiscent of the neural tangent kernel. We establish this apparent linearity arises due to weak correlations between the first and higher-order derivatives of the hypothesis function, concerning the parameters, taken around their initial values. This insight suggests that these weak correlations could be the underlying reason for the observed linearization in such systems. As a case in point, we showcase this weak correlations structure within neural networks in the large width limit. Exploiting the relationship between linearity and weak correlations, we derive a bound on deviations from linearity observed during the training trajectory of stochastic gradient descent. To facilitate our proof, we introduce a novel method to characterise the asymptotic behavior of random tensors.
Heffter Spaces

The notion of a Heffter array, which received much attention in the last decade, is equivalent to a pair of orthogonal Heffter systems. In this paper we study the existence problem of a set of $r$ mutually orthogonal Heffter systems for any $r$. Such a set is equivalent to a resolvable partial linear space of degree $r$ whose parallel classes are Heffter systems: this is a new combinatorial design that we call a Heffter space. We present a series of direct constructions of Heffter spaces with block size odd and arbitrarily large degree $r$ obtained with the crucial use of finite fields. Among the applications we establish, in particular, the existence of $r$ mutually orthogonal $k$-cycle systems of order a prime power $q=2kw+1$ whenever $kw$ is odd and $w>4k^4\lceil{r\over k}\rceil$.
T-FREX: A Transformer-based Feature Extraction Method from Mobile App Reviews

Mobile app reviews are a large-scale data source for software-related knowledge generation activities, including software maintenance, evolution and feedback analysis. Effective extraction of features (i.e., functionalities or characteristics) from these reviews is key to support analysis on the acceptance of these features, identification of relevant new feature requests and prioritization of feature development, among others. Traditional methods focus on syntactic pattern-based approaches, typically context-agnostic, evaluated on a closed set of apps, difficult to replicate and limited to a reduced set and domain of apps. Meanwhile, the pervasiveness of Large Language Models (LLMs) based on the Transformer architecture in software engineering tasks lays the groundwork for empirical evaluation of the performance of these models to support feature extraction. In this study, we present T-FREX, a Transformer-based, fully automatic approach for mobile app review feature extraction. First, we collect a set of ground truth features from users in a real crowdsourced software recommendation platform and transfer them automatically into a dataset of app reviews. Then, we use this newly created dataset to fine-tune multiple LLMs on a named entity recognition task under different data configurations. We assess the performance of T-FREX with respect to this ground truth, and we complement our analysis by comparing T-FREX with a baseline method from the field. Finally, we assess the quality of new features predicted by T-FREX through an external human evaluation. Results show that T-FREX outperforms on average the traditional syntactic-based method, especially when discovering new features from a domain for which the model has been fine-tuned.
Almost commuting self-adjoint operators and measurements

We study the problem when an almost commuting $n$-tuple self-adjoint operators in an infinite dimensional separable Hilbert space $H$ is close to an $n$-tuple of commuting self-adjoint operators on $H. $ We give an affirmative answer to the problem when the synthetic-spectrum and the essential synthetic-spectrum are close. Examples are also exhibited that, in general, the answer to the problem when $n\ge 3$ is negative even the associated Fredholm index vanishes. In the case that $n=2,$ we show that a pair of almost commuting self-adjoint operators in an infinite dimensional separable Hilbert space is close to a commuting pair of self-adjoint operators if and only if a corresponding Fredholm index vanishes outside of an essential synthetic-spectrum. This is an attempt to solve a problem proposed by David Mumford related to quantum theory and measurements.
Long-term Safe Reinforcement Learning with Binary Feedback

Safety is an indispensable requirement for applying reinforcement learning (RL) to real problems. Although there has been a surge of safe RL algorithms proposed in recent years, most existing work typically 1) relies on receiving numeric safety feedback; 2) does not guarantee safety during the learning process; 3) limits the problem to a priori known, deterministic transition dynamics; and/or 4) assume the existence of a known safe policy for any states. Addressing the issues mentioned above, we thus propose Long-term Binary-feedback Safe RL (LoBiSaRL), a safe RL algorithm for constrained Markov decision processes (CMDPs) with binary safety feedback and an unknown, stochastic state transition function. LoBiSaRL optimizes a policy to maximize rewards while guaranteeing long-term safety that an agent executes only safe state-action pairs throughout each episode with high probability. Specifically, LoBiSaRL models the binary safety function via a generalized linear model (GLM) and conservatively takes only a safe action at every time step while inferring its effect on future safety under proper assumptions. Our theoretical results show that LoBiSaRL guarantees the long-term safety constraint, with high probability. Finally, our empirical results demonstrate that our algorithm is safer than existing methods without significantly compromising performance in terms of reward.
Inverse-Like Antagonistic Scene Text Spotting via Reading-Order Estimation and Dynamic Sampling

Scene text spotting is a challenging task, especially for inverse-like scene text, which has complex layouts, e.g., mirrored, symmetrical, or retro-flexed. In this paper, we propose a unified end-to-end trainable inverse-like antagonistic text spotting framework dubbed IATS, which can effectively spot inverse-like scene texts without sacrificing general ones. Specifically, we propose an innovative reading-order estimation module (REM) that extracts reading-order information from the initial text boundary generated by an initial boundary module (IBM). To optimize and train REM, we propose a joint reading-order estimation loss ( $\mathcal {L}_{RE} $ ) consisting of a classification loss, an orthogonality loss, and a distribution loss. With the help of IBM, we can divide the initial text boundary into two symmetric control points and iteratively refine the new text boundary using a lightweight boundary refinement module (BRM) for adapting to various shapes and scales. To alleviate the incompatibility between text detection and recognition, we propose a dynamic sampling module (DSM) with a thin-plate spline that can dynamically sample appropriate features for recognition in the detected text region. Without extra supervision, the DSM can proactively learn to sample appropriate features for text recognition through the gradient returned by the recognition module. Extensive experiments on both challenging scene text and inverse-like scene text datasets demonstrate that our method achieves superior performance both on irregular and inverse-like text spotting.