Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of juniper removal on rainfall partitioning in the Edwards Aquifer region: large-scale rainfall simulation experiments

The Effects of Juniper Removal on Rainfall Partitioning in the Edwards Aquifer Region: Large-Scale Rainfall Simulation Experiments. (May 2006) Philip Isaiah Taucer, B.S., Texas A&M University Chair of Advisory Committee: Dr. Clyde Munster Two experimental rainfall simulation plots in the Edwards Aquifer region of Texas were established to measure the effects of brush clearing on surface and subsurface water movement pathways. Multi-stage rainfall simulations were carried out at a site with Juniperus ashei (ashe juniper) cover both before and after brush removal, with three replications of a particular rainfall event for each vegetation condition. Similar simulations were carried out on a plot with a longstanding grass cover. Both plots included trenches at their downhill ends for observation of shallow lateral subsurface flow. Canopy interception was found to represent a major water loss, with interception of 32.7 mm for an average 166 mm, 5.25 hr rainfall event. Brush clearing had little impact on surface runoff, with no overland flow occurring at the juniper plot for either vegetation condition, while 31.9 percent of applied rainfall moved as overland flow at the grass plot. This difference was attributed to differences in the structure and permeability of the epikarst. Brush removal caused significant (90 percent confidence level) reduction in shallow lateral subsurface flow into the trench after brush removal, with 56.7 percent of water reaching the surface entering the trench for the pre-cut condition and only 43.4 percent for the post-cut condition. However, subsurface water movement through other pathways increased from 31.0 to 54.1 percent after brush removal. This additional water, due to removal of canopy interception, could either move off-site through conduit and fracture flow or remain on site as storage in conduits, unconsolidated caliche/marl layers, or in soil pockets. Two tracer tests with fluorescent dyes were also conducted using simulated
Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks

Large Language Models (LLMs) have demonstrated considerable advances, and several claims have been made about their exceeding human performance. However, in real-world tasks, domain knowledge is often required. Low-resource learning methods like Active Learning (AL) have been proposed to tackle the cost of domain expert annotation, raising this question: Can LLMs surpass compact models trained with expert annotations in domain-specific tasks? In this work, we conduct an empirical experiment on four datasets from three different domains comparing SOTA LLMs with small models trained on expert annotations with AL. We found that small models can outperform GPT-3.5 with a few hundreds of labeled data, and they achieve higher or similar performance with GPT-4 despite that they are hundreds time smaller. Based on these findings, we posit that LLM predictions can be used as a warmup method in real-world applications and human experts remain indispensable in tasks involving data annotation driven by domain-specific knowledge.
Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning

In-Context Learning (ICL) is an emergent capability of Large Language Models (LLMs). Only a few demonstrations enable LLMs to be used as blackbox for new tasks. Previous studies have shown that using LLMs' outputs as labels is effective in training models to select demonstrations. Such a label is expected to estimate utility of a demonstration in ICL; however, it has not been well understood how different labeling strategies affect results on target tasks. This paper presents an analysis on different utility functions by focusing on LLMs' output probability given ground-truth output, and task-specific reward given LLMs' prediction. Unlike the previous work, we introduce a novel labeling method, incremental utility, which estimates how much incremental knowledge is brought into the LLMs by a demonstration. We conduct experiments with instruction-tuned LLMs on binary/multi-class classification, segmentation, and translation across Arabic, English, Finnish, Japanese, and Spanish. Our results show that (1) the probability is effective when the probability values are distributed across the whole value range (on the classification tasks), and (2) the downstream metric is more robust when nuanced reward values are provided with long outputs (on the segmentation and translation tasks). We then show that the proposed incremental utility further helps ICL by contrasting how the LLMs perform with and without the demonstrations.
Ghost Value Augmentation for k-ECSS and k-ECSM

We give a poly-time algorithm for the $k$-edge-connected spanning subgraph ($k$-ECSS) problem that returns a solution of cost no greater than the cheapest $(k+10)$-ECSS on the same graph. Our approach enhances the iterative relaxation framework with a new ingredient, which we call ghost values, that allows for high sparsity in intermediate problems. Our guarantees improve upon the best-known approximation factor of $2$ for $k$-ECSS whenever the optimal value of $(k+10)$-ECSS is close to that of $k$-ECSS. This is a property that holds for the closely related problem $k$-edge-connected spanning multi-subgraph ($k$-ECSM), which is identical to $k$-ECSS except edges can be selected multiple times at the same cost. As a consequence, we obtain a $\left(1+O\left(\frac{1}{k}\right)\right)$-approximation algorithm for $k$-ECSM, which resolves a conjecture of Pritchard and improves upon a recent $\left(1+O\left(\frac{1}{\sqrt{k}}\right)\right)$-approximation algorithm of Karlin, Klein, Oveis Gharan, and Zhang. Moreover, we present a matching lower bound for $k$-ECSM, showing that our approximation ratio is tight up to the constant factor in $O\left(\frac{1}{k}\right)$, unless $P=NP$.
The Effects of Coral Bleaching in The Northern Caribbean and Western Atlantic

introduCtion Coral bleaching during the hot summer of 2005 hit the reefs of the Northern Caribbean (Bahamas, Bermuda, Cayman Islands, Cuba, Dominican Republic, Haiti, Jamaica, Turks and Caicos) just as they were recovering from many damaging stresses of the past 30 years. In the 1970s, the coral reefs generally had more than 50% coral cover, with large stands of branching corals; Acropora palmata on the reef crest, and A. cervicornis on the shallow fore-reef slopes. The first signs of over-fishing of groupers and large parrotfish were evident, but the reefs were predominantly healthy.
Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition

Recognizing fallacies is crucial for ensuring the quality and validity of arguments across various domains. However, computational fallacy recognition faces challenges due to the diverse genres, domains, and types of fallacies found in datasets. This leads to a highly multiclass, and even multi-label, setup with substantial class imbalance. In this study, we aim to enhance existing models for fallacy recognition by incorporating additional context and by leveraging large language models to generate synthetic data, thus increasing the representation of the infrequent classes. We experiment with GPT3.5 to generate synthetic examples and we examine the impact of prompt settings for this. Moreover, we explore zero-shot and few-shot scenarios to evaluate the effectiveness of using the generated examples for training smaller models within a unified fallacy recognition framework. Furthermore, we analyze the overlap between the synthetic data and existing fallacy datasets. Finally, we investigate the usefulness of providing supplementary context for detecting fallacy types that need such context, e.g., diversion fallacies. Our evaluation results demonstrate consistent improvements across fallacy types, datasets, and generators.
How Far Can We Extract Diverse Perspectives from Large Language Models? Criteria-Based Diversity Prompting!

Collecting diverse human opinions is costly and challenging. This leads to a recent trend in collaborative efforts between humans and Large Language Models (LLMs) for generating diverse data, offering potential scalable and efficient solutions. However, the extent of LLMs' capability to generate diverse perspectives on subjective topics remains an unexplored question. In this study, we investigate LLMs' capacity for generating diverse perspectives and rationales on subjective topics, such as social norms and argumentative texts. We formulate a new problem of maximum diversity extraction from LLMs. Motivated by how humans develop their opinions through their values, we propose a criteria-based prompting technique to ground diverse opinions. To see how far we can extract diverse perspectives from LLMs, or called diversity coverage, we employ a step-by-step recall prompting for generating more outputs from the model in an iterative manner. As we apply our methods to various tasks, indeed we find that LLMs can generate diverse opinions according to the degree of task subjectivity
Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change Detection

Autonomous driving is a challenging task that has gained broad attention from both academia and industry. Current solutions using convolutional neural networks require large amounts of computational resources, leading to high power consumption. Spiking neural networks (SNNs) provide an alternative computation model to process information and make decisions. This biologically plausible model has the advantage of low latency and energy efficiency. Recent work using SNNs for autonomous driving mostly focused on simple tasks like lane keeping in simplified simulation environments. This project studies SNNs on photo-realistic driving scenes in the CARLA simulator, which is an important step toward using SNNs on real vehicles. The efficacy and generalizability of the method will be investigated.
Correlation networks: Interdisciplinary approaches beyond thresholding

Many empirical networks originate from correlational data, arising in domains as diverse as psychology, neuroscience, genomics, microbiology, finance, and climate science. Specialized algorithms and theory have been developed in different application domains for working with such networks, as well as in statistics, network science, and computer science, often with limited communication between practitioners in different fields. This leaves significant room for cross-pollination across disciplines. A central challenge is that it is not always clear how to best transform correlation matrix data into networks for the application at hand, and probably the most widespread method, i.e., thresholding on the correlation value to create either unweighted or weighted networks, suffers from multiple problems. In this article, we review various methods of constructing and analyzing correlation networks, ranging from thresholding and its improvements to weighted networks, regularization, dynamic correlation networks, threshold-free approaches, and more. Finally, we propose and discuss a variety of key open questions currently confronting this field.
Cubic surfaces failing the integral Hasse principle

We study the integral Brauer--Manin obstruction for affine diagonal cubic surfaces, which we employ to construct the first counterexamples to the integral Hasse principle in this setting. We then count in three natural ways how such counterexamples are distributed across the family of affine diagonal cubic surfaces and how often such surfaces satisfy integral strong approximation off $\infty$.
Connectivity threshold for superpositions of Bernoulli random graphs. II

Let $G_1,\dots, G_m$ be independent Bernoulli random subgraphs of the complete graph ${\cal K}_n$ having variable sizes $X_1,\dots, X_m\in \{0,1,2,\dots\}$ and densities $Q_1,\dots, Q_m\in [0,1]$. Letting $n,m\to+\infty$ we establish the connectivity threshold for the union $\cup_{i=1}^mG_i$ defined on the vertex set of ${\cal K}_n$. Assuming that $(X_1,Q_1), (X_2,Q_2),\dots, (X_m,Q_m)$ are independent identically distributed bivariate random variables and $\ln n -\frac{m}{n}E\bigl(X_1(1-(1-Q_1)^{|X_1-1|}\bigr)\to c$ we show that $P\{\cup_{i=1}^mG_i$ is connected$\}\to e^{-e^c}$. The result extends to the case of non-identically distributed random variables $(X_1,Q_1),\dots, (X_m,Q_m)$ as well.
Source Prompt: Coordinated Pre-training of Language Models on Diverse Corpora from Multiple Sources

Pre-trained language models (PLMs) have established the new paradigm in the field of NLP. For more powerful PLMs, one of the most popular and successful way is to continuously scale up sizes of the models and the pre-training corpora. These large corpora are generally obtained by converging smaller ones from multiple sources, they are thus growing increasingly diverse. However, the side-effects of these colossal converged corpora remain understudied. In this paper, we identify the disadvantage of heterogeneous corpora from multiple sources for pre-training PLMs. Towards coordinated pre-training on diverse corpora, we further propose source prompts (SP), which explicitly prompt the model of the data source at the pre-training and fine-tuning stages. Results of extensive experiments demonstrate that PLMs pre-trained with SP on diverse corpora gain significant improvement in various downstream tasks.
Prospects for detecting the circum- and intergalactic medium in X-ray absorption using the extended intracluster medium as a backlight

The warm-hot plasma in cosmic web filaments is thought to comprise a large fraction of the gas in the local Universe. So far, the search for this gas has focused on mapping its emission, or detecting its absorption signatures against bright, point-like sources. Future, non-dispersive, high spectral resolution X-ray detectors will, for the first time, enable absorption studies against extended objects. Here, we use the Hydrangea cosmological hydrodynamical simulations to predict the expected properties of intergalactic gas in and around massive galaxy clusters, and investigate the prospects of detecting it in absorption against the bright cores of nearby, massive, relaxed galaxy clusters. We probe a total of 138 projections from the simulation volumes, finding 16 directions with a total column density $N_{{\rm O\, \small {VII}}} > 10^{14.5}$ cm−2. The strongest absorbers are typically shifted by ±1000 km s−1 with respect to the rest frame of the cluster they are nearest to. Realistic mock observations with future micro-calorimeters, such as the Athena X-ray Integral Field Unit or the proposed Line Emission Mapper (LEM) X-ray probe, show that the detection of cosmic web filaments in ${\rm O\, \small {VII}}$ and ${\rm O\, \small {VIII}}$ absorption against galaxy cluster cores will be feasible. An ${\rm O\, \small {VII}}$ detection with a 5σ significance can be achieved in 10 − 250 ks with Athena for most of the galaxy clusters considered. The ${\rm O\, \small {VIII}}$ detection becomes feasible only with a spectral resolution of around 1 eV, comparable to that envisioned for LEM.
Dwarf–Dwarf Interactions Can Both Trigger and Quench Star Formation

It is exceedingly rare to find quiescent low-mass galaxies in the field at low redshift. UGC 5205 is an example of such a quenched field dwarf (M ⋆ ∼ 3 × 108 M ⊙). Despite a wealth of cold gas (M HI ∼ 3.5 × 108 M ⊙) and UV emission that indicates significant star formation in the past few hundred megayears, there is no detection of Hα emission—star formation in the last ∼10 Myr—across the face of the galaxy. Meanwhile, the near equal-mass companion of UGC 5205, PGC 027864, is starbursting (which has an Hα equivalent width > 1000 Å). In this work, we present new Karl G. Jansky Very Large Array 21 cm line observations of UGC 5205, showing that the lack of star formation is caused by an absence of H i in the main body of the galaxy. The H i of UGC 5205 is highly disturbed; the bulk of the H i resides in several-kiloparsec–long tails, while the H i of PGC 027864 is dominated by ordered rotation. We model the stellar populations of UGC 5205 to show that, as indicated by the UV and Hα emission, the galaxy underwent a coordinated quenching event ∼100–300 Myr ago. The asymmetry of outcomes for UGC 5205 and PGC 027864 demonstrate that major mergers can both quench and trigger star formation in dwarfs. However, because the gas remains bound to the system, we suggest that such mergers only temporarily quench star formation. We estimate a total quenched time of ∼560 Myr for UGC 5205, consistent with established upper limits on the quenched fraction of a few percent for dwarfs in the field.
Investigating the Impact of Weight Sharing Decisions on Knowledge Transfer in Continual Learning

Continual Learning (CL) has generated attention as a method of avoiding Catastrophic Forgetting (CF) in the sequential training of neural networks, improving network efficiency and adaptability to different tasks. Additionally, CL serves as an ideal setting for studying network behavior and Forward Knowledge Transfer (FKT) between tasks. Pruning methods for CL train subnetworks to handle the sequential tasks which allows us to take a structured approach to investigating FKT. Sharing prior subnetworks' weights leverages past knowledge for the current task through FKT. Understanding which weights to share is important as sharing all weights can yield sub-optimal accuracy. This paper investigates how different sharing decisions affect the FKT between tasks. Through this lens we demonstrate how task complexity and similarity influence the optimal weight sharing decisions, giving insights into the relationships between tasks and helping inform decision making in similar CL methods. We implement three sequential datasets designed to emphasize variation in task complexity and similarity, reporting results for both ResNet-18 and VGG-16. By sharing in accordance with the decisions supported by our findings, we show that we can improve task accuracy compared to other sharing decisions.
Scalable Sequential Optimization Under Observability Don't Cares

Sequential logic synthesis can provide better Power-Performance-Area (PPA) than combinational logic synthesis since it explores a larger solution space. As the gate cost in advanced technologies keeps rising, sequential logic synthesis provides a powerful alternative that is gaining momentum in the EDA community. In this work, we present a new scalable algorithm for don't-care-based sequential logic synthesis. Our new approach is based on sequential k-step induction and can apply both redundancy removal and resubstitution transformations under Sequential Observability Don't Cares (SODCs). Using SODC-based optimizations with induction is a challenging problem due to dependencies and alignment of don't cares among the base case and the inductive case. We propose a new approach utilizing the full power of SODCs without limiting the solution space. Our algorithm is implemented as part of an industrial tool and achieves 6.9% average area improvement after technology mapping when compared to state-of-the-art sequential synthesis methods. Moreover, all the new sequential optimizations can be verified using state-of-the-art sequential verification tools.
Kirigami Sheets in Fluid Flow

Kirigami patterned materials have found several applications in recent years due to their ability to assume complicated shapes and exhibit emergent physical properties when exposed to external forces. Consisting of an array of cuts in a thin material, fabrication of these patterns can be quite simple. Here we show that when they are placed in fluid flow, kirigami cut sheets with various patterns produce a verity of flow patterns in the wake. Through several sets of experiments, we show that the kirigami sheets placed in flow can undergo static or dynamic flow-induced instabilities as a result of which they can buckle or undergo limit cycle oscillations, or they can remain stable while undergoing very large elongations. These structural responses produce several different types of fluid patterns in the wake. We show that vortices both at small scales (scales comparable with the size of the individual kirigami cuts) and large scale (scales comparable with the size of the sheet) are formed in the wake of kirigami sheets. We also show that jets at different sizes can be formed and directed passively using kirigami sheets. These results show the potentials of kirigami sheets for passive flow control.
Constructing interpretable principal curve using Neural ODEs

The study of high dimensional data sets often rely on their low dimensional projections that preserve the local geometry of the original space. While numerous methods have been developed to summarize this space as variations of tree-like structures, they are usually non-parametric and"static"in nature. As data may come from systems that are dynamical such as a differentiating cell, a static, non-parametric characterization of the space may not be the most appropriate. Here, we developed a framework, the principal flow, that is capable of characterizing the space in a dynamical manner. The principal flow, defined using neural ODEs, directs motion of a particle through the space, where the trajectory of the particle resembles the principal curve of the dataset. We illustrate that our framework can be used to characterize shapes of various complexities, and is flexible to incorporate summaries of relaxation dynamics.
Spectrally distinguishing symmetric spaces I

We prove that the irreducible symmetric space of complex structures on $\mathbb R^{2n}$ (resp.\ quaternionic structures on $\mathbb C^{2n}$) is spectrally unique within a $2$-parameter (resp.\ $3$-parameter) family of homogeneous metrics on the underlying differentiable manifold. Such families are strong candidates to contain all homogeneous metrics admitted on the corresponding manifolds. The main tool in the proof is an explicit expression for the smallest positive eigenvalue of the Laplace-Beltrami operator associated to each homogeneous metric involved. As a second consequence of this expression, we prove that any non-symmetric Einstein metric in the homogeneous families mentioned above are $\nu$-unstable.
Sol-gel transition by evaporation in porous media

Historical monuments, outdoor stone sculptures and artworks made of porous materials are exposed to chemical and physical degradation over time. Presently, the most promising route for consolidation of weakened porous materials is the injection of viscoelastic solutions of polymerizing compounds. Those compounds, after injection, undergo a sol-gel transition inside the porous media through evaporation of the solvent. Finding a suitable gelifying solution as a consolidant calls for understanding the drying kinetics of viscoelastic fluids in porous media. Here, we present a multiscale study of the drying kinetics of solutions during the sol-gel transition. We investigate the drying dynamics and subsequent water uptake of a gel using NMR and X-ray microtomography techniques in porous materials. We find that during drying, a distinct front develops which separates the liquid region from the gelled region. This front advances from the free surface of evaporation towards the inner parts of the stone. We identify different drying periods which appear to be dependent on the intrinsic properties of the porous medium influencing strongly the homogeneity of the final gel distribution within a treated stone. Our findings not only are relevant for the consolidation of porous artworks but also for civil and soil engineering processes where the fluids considered are generally more complex than water.