Miracle-dz/UniFAR-FTUs huggingface dataset:
Late sowing with high seeding rate increases wheat water use efficiency under deficit irrigation

In the North China Plain (NCP), drought and groundwater overdraft threaten winter wheat (Triticum aestivum L.) production. The objective of this study was to improve water use efficiency (WUE) under deficit irrigation. A three-year field experiment was conducted with four sowing dates (SD; SD1: October 10, SD2: October 20, SD3: October 30, and SD4: November 10) using two cultivars (Jimai22 [JM22] and Shimai15 [SM15]). As SD was delayed, seeding rate (SR) increased from 450 seeds m−2 in SD1 to 600, 700, and 800 seeds m−2 in SD2, SD3, and SD4, respectively. Relative to the normal SD (SD1), moderately delaying SD to SD2 with increased SR did not reduce yield (SD1: 8.79 Mg ha−1 and SD2: 8.72 Mg ha−1). Delaying SD to SD3 and SD4 reduced yield by 9% and 11.3% (SD3: 8 Mg ha−1 and SD4: 7.74 Mg ha−1). Delaying SD increased WUE from 1.83 kg m−3 in SD1 to 2.07 kg m−3 in SD4. The increase in WUE associated with delayed SD was attributed to shorter vegetative stage and decreased preanthesis evapotranspiration (ET). Under deficit irrigation, soil water availability and photosynthetic rate (Pn) during grain filling were higher as SD was delayed. As SD was delayed, the ratios of spike number to maximum stem number, postanthesis ET to seasonal ET, spike biomass to biomass at anthesis, and postanthesis biomass to total biomass all increased, resulting in higher harvest index and WUE. The yield reduction on SD4 was attributed to a reduced number of grains per spike due to decreased preanthesis biomass and thousand-grain weight (TGW). Comparing two cultivars, JM22 had higher Pn, postanthesis biomass, and TGW, but lower biomass remobilization (BMR) during grain filling. On SD4, JM22 had higher yield and WUE than SM15. In conclusion, under deficit irrigation in the NCP, moderately delaying sowing (SD2) with increased SR can obtain both high yield and high WUE. Extremely late sowing (SD4) with increased SR can maximize WUE and minimize yield loss, especially in cultivar JM22.
Enhancing Court View Generation with Knowledge Injection and Guidance

Court View Generation (CVG) is a challenging task in the field of Legal Artificial Intelligence (LegalAI), which aims to generate court views based on the plaintiff claims and the fact descriptions. While Pretrained Language Models (PLMs) have showcased their prowess in natural language generation, their application to the complex, knowledge-intensive domain of CVG often reveals inherent limitations. In this paper, we present a novel approach, named Knowledge Injection and Guidance (KIG), designed to bolster CVG using PLMs. To efficiently incorporate domain knowledge during the training stage, we introduce a knowledge-injected prompt encoder for prompt tuning, thereby reducing computational overhead. Moreover, to further enhance the model's ability to utilize domain knowledge, we employ a generating navigator, which dynamically guides the text generation process in the inference stage without altering the model's architecture, making it readily transferable. Comprehensive experiments on real-world data demonstrate the effectiveness of our approach compared to several established baselines, especially in the responsivity of claims, where it outperforms the best baseline by 11.87%.
D6 branes wrapped on a spindle and $Y^{p,q}$ manifolds

We present new solutions of 8d gauged supergravity which, upon uplift to type IIA, represent D6 branes wrapped on spindles. A further circle uplift gives 11d supergravity on a Calabi-Yau three-fold which is the cone over five-dimensional $Y^{p,q}$ manifolds. This highlights a connection between co-homogeneity one Sasaki-Einstein metrics in general dimension and the recently introduced spindle solutions in gauged supergravity. We find that a similar connection also exists for the small resolution of the Calabi-Yau cone over such manifolds.
Environmental Risk Explored Through Oil Spills and Hydraulic Fracturing: Attempting Environmental Protection through Policy

History demonstrates the dangers of ignoring the environmental risks that correspond to resource extraction, especially in precarious locations. This project analyzed three cases: (1) the 1989 Exxon Valdez oil spill, (2) the 2010 Deepwater Horizon oil spill, and (3) the current question of hydraulic fracturing in New York’s Marcellus Shale. The case studies assessed whether policies for protecting the environment from the risks of human activity have improved and whether such policies can be made both economically efficient and environmentally less risky. While the Exxon Valdez case demonstrated the dangers of not planning for disaster, it did result in the 1990 Oil Pollution Act [OPA]. Although OPA is requiring BP to fund cleanup of the Deepwater Horizon spill, OPA alone did little to prevent this event from happening. Using these historical examples, New York now has a chance to put better policies in place for the risk-laden technique of hydraulic fracturing. Economic pressures mean hydraulic fracturing is almost a certainty. This project argues that the State must require that extremely precautionary policies and strict regulations be implemented before drilling.
Moduli of K3 surfaces of degree 2 with four rational double points of type $D_4$

We show that the Satake-Baily-Borel compactification of the moduli space of lattice polarized K3 surfaces parametrizing K3 surfaces of degree 2 with four rational double points of type $D_4$ is the projective 3-space. We also show that the corresponding graded ring of automorphic forms is generated by four elements of weight 2 and one element of weight 11 with one relation of weight 22.
Generalizing Cooperative Eco-driving via Multi-residual Task Learning

Conventional control, such as model-based control, is commonly utilized in autonomous driving due to its efficiency and reliability. However, real-world autonomous driving contends with a multitude of diverse traffic scenarios that are challenging for these planning algorithms. Model-free Deep Reinforcement Learning (DRL) presents a promising avenue in this direction, but learning DRL control policies that generalize to multiple traffic scenarios is still a challenge. To address this, we introduce Multi-residual Task Learning (MRTL), a generic learning framework based on multi-task learning that, for a set of task scenarios, decomposes the control into nominal components that are effectively solved by conventional control methods and residual terms which are solved using learning. We employ MRTL for fleet-level emission reduction in mixed traffic using autonomous vehicles as a means of system control. By analyzing the performance of MRTL across nearly 600 signalized intersections and 1200 traffic scenarios, we demonstrate that it emerges as a promising approach to synergize the strengths of DRL and conventional methods in generalizable control.
Cooperative Task Execution in Multi-Agent Systems

We propose a multi-agent system that enables groups of agents to collaborate and work autonomously to execute tasks. Groups can work in a decentralized manner and can adapt to dynamic changes in the environment. Groups of agents solve assigned tasks by exploring the solution space cooperatively based on the highest reward first. The tasks have a dependency structure associated with them. We rigorously evaluated the performance of the system and the individual group performance using centralized and decentralized control approaches for task distribution. Based on the results, the centralized approach is more efficient for systems with a less-dependent system $G_{18}$, while the decentralized approach performs better for systems with a highly-dependent system $G_{40}$. We also evaluated task allocation to groups that do not have interdependence. Our findings reveal that there was significantly less difference in the number of tasks allocated to each group in a less-dependent system than in a highly-dependent one. The experimental results showed that a large number of small-size cooperative groups of agents unequivocally improved the system's performance compared to a small number of large-size cooperative groups of agents. Therefore, it is essential to identify the optimal group size for a system to enhance its performance.
Improved Focus on Hard Samples for Lung Nodule Detection

Recently, lung nodule detection methods based on deep learning have shown excellent performance in the medical image processing field. Considering that only a few public lung datasets are available and lung nodules are more difficult to detect in CT images than in natural images, the existing methods face many bottlenecks when detecting lung nodules, especially hard ones in CT images. In order to solve these problems, we plan to enhance the focus of our network. In this work, we present an improved detection network that pays more attention to hard samples and datasets to deal with lung nodules by introducing deformable convolution and self-paced learning. Experiments on the LUNA16 dataset demonstrate the effectiveness of our proposed components and show that our method has reached competitive performance.
Alternative Method for Estimating Betti Numbers

Topological data analysis (TDA) is a fast-growing field that utilizes advanced tools from topology to analyze large-scale data. A central problem in topological data analysis is estimating the so-called Betti numbers of the underlying simplicial complex. While the difficulty of this problem has been established as NP-hard, previous works have showcased appealing quantum speedup. In this article, we provide an alternative method for estimating Betti numbers of given simplicial complex, based on some recent results on quantum algorithm. Our method can be faster than the best-known classical method for finding Betti numbers, and interestingly, it can also find the Betti numbers of the complement graph to our original one.
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series

This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models. The study involves a comprehensive evaluation across three different datasets: MSL, SMAP, and SWaT. Each dataset poses unique challenges, allowing for a robust assessment of the models' capabilities in varied contexts. The dimensionality reduction techniques examined include PCA, UMAP, Random Projection, and t-SNE, each offering distinct advantages in simplifying high-dimensional data. Our findings reveal that dimensionality reduction not only aids in reducing computational complexity but also significantly enhances anomaly detection performance in certain scenarios. Moreover, a remarkable reduction in training times was observed, with reductions by approximately 300\% and 650\% when dimensionality was halved and minimized to the lowest dimensions, respectively. This efficiency gain underscores the dual benefit of dimensionality reduction in both performance enhancement and operational efficiency. The MUTANT model exhibits notable adaptability, especially with UMAP reduction, while the Anomaly-Transformer demonstrates versatility across various reduction techniques. These insights provide a deeper understanding of the synergistic effects of dimensionality reduction and anomaly detection, contributing valuable perspectives to the field of time series analysis. The study underscores the importance of selecting appropriate dimensionality reduction strategies based on specific model requirements and dataset characteristics, paving the way for more efficient, accurate, and scalable solutions in anomaly detection.
GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models

We study distributed training of deep learning models in time-constrained environments. We propose a new algorithm that periodically pulls workers towards the center variable computed as a weighted average of workers, where the weights are inversely proportional to the gradient norms of the workers such that recovering the flat regions in the optimization landscape is prioritized. We develop two asynchronous variants of the proposed algorithm that we call Model-level and Layer-level Gradient-based Weighted Averaging (resp. MGRAWA and LGRAWA), which differ in terms of the weighting scheme that is either done with respect to the entire model or is applied layer-wise. On the theoretical front, we prove the convergence guarantee for the proposed approach in both convex and non-convex settings. We then experimentally demonstrate that our algorithms outperform the competitor methods by achieving faster convergence and recovering better quality and flatter local optima. We also carry out an ablation study to analyze the scalability of the proposed algorithms in more crowded distributed training environments. Finally, we report that our approach requires less frequent communication and fewer distributed updates compared to the state-of-the-art baselines.
Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving

Machine Learning (ML) has replaced traditional handcrafted methods for perception and prediction in autonomous vehicles. Yet for the equally important planning task, the adoption of ML-based techniques is slow. We present nuPlan, the world's first real-world autonomous driving dataset, and benchmark. The benchmark is designed to test the ability of ML-based planners to handle diverse driving situations and to make safe and efficient decisions. To that end, we introduce a new large-scale dataset that consists of 1282 hours of diverse driving scenarios from 4 cities (Las Vegas, Boston, Pittsburgh, and Singapore) and includes high-quality auto-labeled object tracks and traffic light data. We exhaustively mine and taxonomize common and rare driving scenarios which are used during evaluation to get fine-grained insights into the performance and characteristics of a planner. Beyond the dataset, we provide a simulation and evaluation framework that enables a planner's actions to be simulated in closed-loop to account for interactions with other traffic participants. We present a detailed analysis of numerous baselines and investigate gaps between ML-based and traditional methods. Find the nuPlan dataset and code at nuplan.org.
Tight general bounds for the extremal numbers of 0-1 matrices

A zero-one matrix $M$ is said to contain another zero-one matrix $A$ if we can delete some rows and columns of $M$ and replace some $1$-entries with $0$-entries such that the resulting matrix is $A$. The extremal number of $A$, denoted $\operatorname{ex}(n,A)$, is the maximum number of $1$-entries that an $n\times n$ zero-one matrix can have without containing $A$. The systematic study of this function for various patterns $A$ goes back to the work of F\"uredi and Hajnal from 1992, and the field has many connections to other areas of mathematics and theoretical computer science. The problem has been particularly extensively studied for so-called acyclic matrices, but very little is known about the general case (that is, the case where $A$ is not necessarily acyclic). We prove the first asymptotically tight general result by showing that if $A$ has at most $t$ $1$-entries in every row, then $\operatorname{ex}(n,A)\leq n^{2-1/t+o(1)}$. This verifies a conjecture of Methuku and Tomon. Our result also provides the first tight general bound for the extremal number of vertex-ordered graphs with interval chromatic number $2$, generalizing a celebrated result of F\"uredi, and Alon, Krivelevich and Sudakov about the (unordered) extremal number of bipartite graphs with maximum degree $t$ in one of the vertex classes.
Testing scale-invariant inflation against cosmological data

There is solid theoretical and observational motivation behind the idea of scale-invariance as a fundamental symmetry of Nature. We consider a recently proposed classically scale-invariant inflationary model, quadratic in curvature and featuring a scalar field non-minimally coupled to gravity. We go beyond earlier analytical studies, which showed that the model predicts inflationary observables in qualitative agreement with data, by solving the full two-field dynamics of the system -- this allows us to corroborate previous analytical findings and set robust constraints on the model's parameters using the latest Cosmic Microwave Background (CMB) data from Planck and BICEP/Keck. We demonstrate that scale-invariance constrains the two-field trajectory such that the effective dynamics are that of a single field, resulting in vanishing entropy perturbations and protecting the model from destabilization effects. We derive tight upper limits on the non-minimal coupling strength, excluding conformal coupling at high significance. By explicitly sampling over them, we demonstrate an overall insensitivity to initial conditions. We argue that the model \textit{predicts} a minimal level of primordial tensor modes set by $r \gtrsim 0.003$, well within the reach of next-generation CMB experiments. These will therefore provide a litmus test of scale-invariant inflation, and we comment on the possibility of distinguishing the model from Starobinsky and $\alpha$-attractor inflation. Overall, we argue that scale-invariant inflation is in excellent health, and possesses features which make it an interesting benchmark for tests of inflation from future CMB data.
Chirped pulse control over the melting of superconductors

Strong field terahertz pulses are increasingly used to excite and control quantum materials at the ultrafast timescale. They have found widespread application by enabling direct addressing of the superconducting gap or Josephson resonances and are essential in Higgs spectroscopy. Large non-linear optical signals can be induced by the strong coupling of the THz and superconducting degrees of freedom. However, far less attention has been paid to the strong bi-directional coupling between field and material this implies. Here, we use the framework of the time-dependent Ginzburg-Landau equations to study the full field and material evolution of a superconductor driven by strong field terahertz pulses. We find that at high field strengths, the backreaction of the superconductor induces large changes to the driving pulse, which in turn leads to a runaway melting of the superconducting condensate. This results in a surprisingly large sensitivity to the initial driving pulse chirp, enabling these purely dynamical changes to result in order of magnitude different levels of melting. We also find large-scale spectral shifting of the driving pulse to occur in just a few hundred nanometers of propagation through a superconductor. We attribute these effects to an inverse plasma redshift, in which the driving field breaks Cooper pairs and decreases the free-electron mobility, analogous to reducing the density of a plasma.
Processing Natural Language on Embedded Devices: How Well Do Transformer Models Perform?

This paper presents a performance study of transformer language models under different hardware configurations and accuracy requirements and derives empirical observations about these resource/accuracy trade-offs. In particular, we study how the most commonly used BERT-based language models (viz., BERT, RoBERTa, DistilBERT, and TinyBERT) perform on embedded systems. We tested them on four off-the-shelf embedded platforms (Raspberry Pi, Jetson, UP2, and UDOO) with 2 GB and 4 GB memory (i.e., a total of eight hardware configurations) and four datasets (i.e., HuRIC, GoEmotion, CoNLL, WNUT17) running various NLP tasks. Our study finds that executing complex NLP tasks (such as"sentiment"classification) on embedded systems is feasible even without any GPUs (e.g., Raspberry Pi with 2 GB of RAM). Our findings can help designers understand the deployability and performance of transformer language models, especially those based on BERT architectures.
Acceleron: A Tool to Accelerate Research Ideation

Several tools have recently been proposed for assisting researchers during various stages of the research life-cycle. However, these primarily concentrate on tasks such as retrieving and recommending relevant literature, reviewing and critiquing the draft, and writing of research manuscripts. Our investigation reveals a significant gap in availability of tools specifically designed to assist researchers during the challenging ideation phase of the research life-cycle. To aid with research ideation, we propose `Acceleron', a research accelerator for different phases of the research life cycle, and which is specially designed to aid the ideation process. Acceleron guides researchers through the formulation of a comprehensive research proposal, encompassing a novel research problem. The proposals motivation is validated for novelty by identifying gaps in the existing literature and suggesting a plausible list of techniques to solve the proposed problem. We leverage the reasoning and domain-specific skills of Large Language Models (LLMs) to create an agent-based architecture incorporating colleague and mentor personas for LLMs. The LLM agents emulate the ideation process undertaken by researchers, engaging researchers in an interactive fashion to aid in the development of the research proposal. Notably, our tool addresses challenges inherent in LLMs, such as hallucinations, implements a two-stage aspect-based retrieval to manage precision-recall trade-offs, and tackles issues of unanswerability. As evaluation, we illustrate the execution of our motivation validation and method synthesis workflows on proposals from the ML and NLP domain, given by 3 distinct researchers. Our observations and evaluations provided by the researchers illustrate the efficacy of the tool in terms of assisting researchers with appropriate inputs at distinct stages and thus leading to improved time efficiency.
Feedback-Generation for Programming Exercises With GPT-4

Ever since Large Language Models (LLMs) and related applications have become broadly available, several studies investigated their potential for assisting educators and supporting students in higher education. LLMs such as Codex, GPT-3.5, and GPT 4 have shown promising results in the context of large programming courses, where students can benefit from feedback and hints if provided timely and at scale. This paper explores the quality of GPT-4 Turbo's generated output for prompts containing both the programming task specification and a student's submission as input. Two assignments from an introductory programming course were selected, and GPT-4 was asked to generate feedback for 55 randomly chosen, authentic student programming submissions. The output was qualitatively analyzed regarding correctness, personalization, fault localization, and other features identified in the material. Compared to prior work and analyses of GPT-3.5, GPT-4 Turbo shows notable improvements. For example, the output is more structured and consistent. GPT-4 Turbo can also accurately identify invalid casing in student programs' output. In some cases, the feedback also includes the output of the student program. At the same time, inconsistent feedback was noted such as stating that the submission is correct but an error needs to be fixed. The present work increases our understanding of LLMs' potential, limitations, and how to integrate them into e-assessment systems, pedagogical scenarios, and instructing students who are using applications based on GPT-4.