Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Learning-Based Species Range Estimation

We propose a new active learning approach for efficiently estimating the geographic range of a species from a limited number of on the ground observations. We model the range of an unmapped species of interest as the weighted combination of estimated ranges obtained from a set of different species. We show that it is possible to generate this candidate set of ranges by using models that have been trained on large weakly supervised community collected observation data. From this, we develop a new active querying approach that sequentially selects geographic locations to visit that best reduce our uncertainty over an unmapped species' range. We conduct a detailed evaluation of our approach and compare it to existing active learning methods using an evaluation dataset containing expert-derived ranges for one thousand species. Our results demonstrate that our method outperforms alternative active learning methods and approaches the performance of end-to-end trained models, even when only using a fraction of the data. This highlights the utility of active learning via transfer learned spatial representations for species range estimation. It also emphasizes the value of leveraging emerging large-scale crowdsourced datasets, not only for modeling a species' range, but also for actively discovering them.
An interactive Web-based atlas for the Spokane Valley-Rathdrum Prairie aquifer

OF THE THESIS An Interactive Web-Based Atlas for the Spokane Valley-Rathdrum Prairie Aquifer by Joseph Saltenberger Master of Science in Geography San Diego State University, 2011 Effective water resource management is increasingly dependent on public involvement in management decisions. Public awareness and education are both integral parts of public involvement, and pre-requisites to public participation. This research is focused on the issue of providing information related to water resources to students in high school level courses through a Web-based mapping application. A user needs analysis was performed to guide decisions during the design and development stage of the prototype. After the prototype was developed, user testing helped identify the strengths and weaknesses of the application’s information content, user interface, and interactive map. Server-side log files were also examined to determine participant actions and tool usage. Results from the user needs analysis showed water resource professionals and educators wanted an application capable of displaying geographic information related to water use and quality. They also expressed the need for an intuitive and easy-to-use interface for a wide range of users. After the application was developed, the evaluation revealed that users were generally satisfied with the prototype. Participants felt the information content was appropriate, but would need to be expanded in the future. Participants liked the design and layout of the user interface, but had trouble using some of the more advanced functions. Lastly, participants found the map easy to use and intuitive, but thought it would be more engaging with additional interactive elements. Overall, participants stated the prototype was successful and had potential as a useful tool for teaching about water resources to local students.
Grounded Intuition of GPT-Vision's Abilities with Scientific Images

GPT-Vision has impressed us on a range of vision-language tasks, but it comes with the familiar new challenge: we have little idea of its capabilities and limitations. In our study, we formalize a process that many have instinctively been trying already to develop"grounded intuition"of this new model. Inspired by the recent movement away from benchmarking in favor of example-driven qualitative evaluation, we draw upon grounded theory and thematic analysis in social science and human-computer interaction to establish a rigorous framework for qualitative evaluation in natural language processing. We use our technique to examine alt text generation for scientific figures, finding that GPT-Vision is particularly sensitive to prompting, counterfactual text in images, and relative spatial relationships. Our method and analysis aim to help researchers ramp up their own grounded intuitions of new models while exposing how GPT-Vision can be applied to make information more accessible.
LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery

We introduce LOTUS, a continual imitation learning algorithm that empowers a physical robot to continuously and efficiently learn to solve new manipulation tasks throughout its lifespan. The core idea behind LOTUS is constructing an ever-growing skill library from a sequence of new tasks with a small number of human demonstrations. LOTUS starts with a continual skill discovery process using an open-vocabulary vision model, which extracts skills as recurring patterns presented in unsegmented demonstrations. Continual skill discovery updates existing skills to avoid catastrophic forgetting of previous tasks and adds new skills to solve novel tasks. LOTUS trains a meta-controller that flexibly composes various skills to tackle vision-based manipulation tasks in the lifelong learning process. Our comprehensive experiments show that LOTUS outperforms state-of-the-art baselines by over 11% in success rate, showing its superior knowledge transfer ability compared to prior methods. More results and videos can be found on the project website: https://ut-austin-rpl.github. io/Lotus/.
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization

Quantifying the difference between two probability density functions, $p$ and $q$, using available data, is a fundamental problem in Statistics and Machine Learning. A usual approach for addressing this problem is the likelihood-ratio estimation (LRE) between $p$ and $q$, which -- to our best knowledge -- has been investigated mainly for the offline case. This paper contributes by introducing a new framework for online non-parametric LRE (OLRE) for the setting where pairs of iid observations $(x_t \sim p, x'_t \sim q)$ are observed over time. The non-parametric nature of our approach has the advantage of being agnostic to the forms of $p$ and $q$. Moreover, we capitalize on the recent advances in Kernel Methods and functional minimization to develop an estimator that can be efficiently updated online. We provide theoretical guarantees for the performance of the OLRE method along with empirical validation in synthetic experiments.
Epidemic Decision-making System Based Federated Reinforcement Learning

Epidemic decision-making can effectively help the government to comprehensively consider public security and economic development to respond to public health and safety emergencies. Epidemic decision-making can effectively help the government to comprehensively consider public security and economic development to respond to public health and safety emergencies. Some studies have shown that intensive learning can effectively help the government to make epidemic decision, thus achieving the balance between health security and economic development. Some studies have shown that intensive learning can effectively help the government to make epidemic decision, thus achieving the balance between health security and economic development. However, epidemic data often has the characteristics of limited samples and high privacy. However, epidemic data often has the characteristics of limited samples and high privacy. This model can combine the epidemic situation data of various provinces for cooperative training to use as an enhanced learning model for epidemic situation decision, while protecting the privacy of data. The experiment shows that the enhanced federated learning can obtain more optimized performance and return than the enhanced learning, and the enhanced federated learning can also accelerate the training convergence speed of the training model. accelerate the training convergence speed of the client. At the same time, through the experimental comparison, A2C is the most suitable reinforcement learning model for the epidemic situation decision-making. learning model for the epidemic situation decision-making scenario, followed by the PPO model, and the performance of DDPG is unsatisfactory.
Balanced independent sets and colorings of hypergraphs

A $k$-uniform hypergraph $H = (V, E)$ is $k$-partite if $V$ can be partitioned into $k$ sets $V_1, \ldots, V_k$ such that every edge in $E$ contains precisely one vertex from each $V_i$. We call such a graph $n$-balanced if $|V_i| = n$ for each $i$. An independent set $I$ in $H$ is balanced if $|I\cap V_i| = |I|/k$ for each $i$, and a coloring is balanced if each color class induces a balanced independent set in $H$. In this paper, we provide a lower bound on the balanced independence number $\alpha_b(H)$ in terms of the average degree $D = |E|/n$, and an upper bound on the balanced chromatic number $\chi_b(H)$ in terms of the maximum degree $\Delta$. Our results match those of recent work of Chakraborti for $k = 2$.
Modeling the Uncertainty with Maximum Discrepant Students for Semi-supervised 2D Pose Estimation

Semi-supervised pose estimation is a practically challenging task for computer vision. Although numerous excellent semi-supervised classification methods have emerged, these methods typically use confidence to evaluate the quality of pseudo-labels, which is difficult to achieve in pose estimation tasks. For example, in pose estimation, confidence represents only the possibility that a position of the heatmap is a keypoint, not the quality of that prediction. In this paper, we propose a simple yet efficient framework to estimate the quality of pseudo-labels in semi-supervised pose estimation tasks from the perspective of modeling the uncertainty of the pseudo-labels. Concretely, under the dual mean-teacher framework, we construct the two maximum discrepant students (MDSs) to effectively push two teachers to generate different decision boundaries for the same sample. Moreover, we create multiple uncertainties to assess the quality of the pseudo-labels. Experimental results demonstrate that our method improves the performance of semi-supervised pose estimation on three datasets.
Optimistic Multi-Agent Policy Gradient for Cooperative Tasks

\textit{Relative overgeneralization} (RO) occurs in cooperative multi-agent learning tasks when agents converge towards a suboptimal joint policy due to overfitting to suboptimal behavior of other agents. In early work, optimism has been shown to mitigate the \textit{RO} problem when using tabular Q-learning. However, with function approximation optimism can amplify overestimation and thus fail on complex tasks. On the other hand, recent deep multi-agent policy gradient (MAPG) methods have succeeded in many complex tasks but may fail with severe \textit{RO}. We propose a general, yet simple, framework to enable optimistic updates in MAPG methods and alleviate the RO problem. Specifically, we employ a \textit{Leaky ReLU} function where a single hyperparameter selects the degree of optimism to reshape the advantages when updating the policy. Intuitively, our method remains optimistic toward individual actions with lower returns which are potentially caused by other agents' sub-optimal behavior during learning. The optimism prevents the individual agents from quickly converging to a local optimum. We also provide a formal analysis from an operator view to understand the proposed advantage transformation. In extensive evaluations on diverse sets of tasks, including illustrative matrix games, complex \textit{Multi-agent MuJoCo} and \textit{Overcooked} benchmarks, the proposed method\footnote{Code can be found at \url{https://github.com/wenshuaizhao/optimappo}. } outperforms strong baselines on 13 out of 19 tested tasks and matches the performance on the rest.
Asymptotic behavior of continuous weak measurement and its application to real-time parameter estimation

The asymptotic quantum trajectory of weak continuous measurement for the magnetometer is investigated. The magnetometer refers to a setup where the field-to-estimate and the measured moment are orthogonal, and the quantum state is governed by the stochastic master equation which, in addition to a deterministic part, depends on the measurement outcomes. We find that the asymptotic behavior is insensitive to the initial state in the following sense: given one realization, the quantum trajectories starting from arbitrary initial states asymptotically converge to the {\em same} realization-specific {\em pure} state. For single-qubit systems, we are able to prove this statement within the framework of Probability Theory by deriving and analyzing an effective one-dimensional stochastic equation. Numerical simulations strongly indicate that the same statement holds for multi-qubit systems. Built upon this conclusion, we consider the problem of real-time parameter estimation whose feasibility hinges on the insensitivity to the initial state, and explicitly propose and test a scheme where the quantum state and the field-to-estimate are updated simultaneously.
A cast of thousands: How the IDEAS Productivity project has advanced software productivity and sustainability

Computational and data-enabled science and engineering are revolutionizing advances throughout science and society, at all scales of computing. For example, teams in the U.S. DOE Exascale Computing Project have been tackling new frontiers in modeling, simulation, and analysis by exploiting unprecedented exascale computing capabilities-building an advanced software ecosystem that supports next-generation applications and addresses disruptive changes in computer architectures. However, concerns are growing about the productivity of the developers of scientific software, its sustainability, and the trustworthiness of the results that it produces. Members of the IDEAS project serve as catalysts to address these challenges through fostering software communities, incubating and curating methodologies and resources, and disseminating knowledge to advance developer productivity and software sustainability. This paper discusses how these synergistic activities are advancing scientific discovery-mitigating technical risks by building a firmer foundation for reproducible, sustainable science at all scales of computing, from laptops to clusters to exascale and beyond.
Optimisation of Active Space Debris Removal Missions With Multiple Targets Using Quantum Annealing

A strategy for the analysis of active debris removal missions targeting multiple objects from a set of objects in near-circular orbit with similar inclination is presented. Algebraic techniques successfully reduce the orbital mechanics regarding specific inter-debris transfer and disposal methods to simple computations, which can be used as the coefficients of a quadratic unconstrained binary optimisation (QUBO) problem formulation which minimises the total propellant used in the mission whilst allowing for servicing time and meeting the mission deadline. The QUBO is validated by solving artificial small problems (from 2 to 11 debris) using classical computational methods and the weaknesses in using these methods are examined prior to solution using quantum annealing hardware. The quantum processing unit (QPU) and quantum-classical hybrid solvers provided by D-Wave are then used to solve the same small problems, with attention paid to evident strengths and weaknesses of each approach. Hybrid solvers are found to be significantly more effective at solving larger problems. Finally, the hybrid method is used to solve a large problem using a real dataset. From a set of 79 debris objects resulting from the destruction of the Kosmos-1408 satellite, an active debris removal mission starting on 30 September 2023 targeting 5 debris objects for disposal within a year with 20 days servicing time per object is successfully planned. This plan calculates the total propellant cost of transfer and disposal to be 0.87km/s and would be complete well within the deadline at 241 days from the start date. This problem uses 6,478 binary variables in total and is solved using around 25s of QPU access time.
Closure property of contraction-depth of matroids

The contraction$^*$-depth is the matroid depth parameter analogous to tree-depth of graphs. We establish the matroid analogue of the classical graph theory result asserting that the tree-depth of a graph $G$ is the minimum height of a rooted forest whose closure contains $G$ by proving the following for every matroid $M$ (except the trivial case when $M$ consists of loops and bridges only): the contraction$^*$-depth of $M$ plus one is equal to the minimum contraction-depth of a matroid containing $M$ as a restriction.
Depth-guided Free-space Segmentation for a Mobile Robot

Accurate indoor free-space segmentation is a challenging task due to the complexity and the dynamic nature that indoor environments exhibit. We propose an indoors free-space segmentation method that associates large depth values with navigable regions. Our method leverages an unsupervised masking technique that, using positive instances, generates segmentation labels based on textural homogeneity and depth uniformity. Moreover, we generate superpixels corresponding to areas of higher depth and align them with features extracted from a Dense Prediction Transformer (DPT). Using the estimated free-space masks and the DPT feature representation, a SegFormer model is fine-tuned on our custom-collected indoor dataset. Our experiments demonstrate sufficient performance in intricate scenarios characterized by cluttered obstacles and challenging identification of free space.
The Metallicity Variations Along the Chromosome Maps: The Globular Cluster 47 Tucanae

The “chromosome maps” (ChMs) of globular clusters (GCs) have revealed that these ancient structures are inhomogeneous in metallicity in various ways and in different natures. Type II GCs generally display larger variations, sometimes coupled with slow neutron-capture (s) element enrichments on the ChMs redder sequences, which have been interpreted as due to multiple generations of stars. On the other hand, most GCs have inhomogeneous first populations (1P) in the form of large ranges in the ΔF275W,F814W values, pointing toward a not fully mixed pristine molecular cloud. We analyze the chemical composition of GC 47 Tucanae, which shows both inhomogeneous 1P stars and, although not formally a Type II GC, hosts a small number of stars distributed on the red side of the main stream of ChM stars. Our results suggest that 1P stars are inhomogeneous in the overall metallicity, with variations on the order of ∼0.10 dex in all the chemical species. The anomalous stars distributed on a redder sequence of the ChM are further enriched in metals but have no evidence for a significant enrichment in the s elements. Our three second population stars located on the normal component of the map have metallicities similar to those of the metal-richer 1P oup, suggesting that this population formed from these stars. Although three stars is a too-small sample to draw strong conclusions, the low spread in metals of these objects might point toward formation in a fully mixed medium, possibly after a cooling flow phase.
Active Reasoning in an Open-World Environment

Recent advances in vision-language learning have achieved notable success on complete-information question-answering datasets through the integration of extensive world knowledge. Yet, most models operate passively, responding to questions based on pre-stored knowledge. In stark contrast, humans possess the ability to actively explore, accumulate, and reason using both newfound and existing information to tackle incomplete-information questions. In response to this gap, we introduce $Conan$, an interactive open-world environment devised for the assessment of active reasoning. $Conan$ facilitates active exploration and promotes multi-round abductive inference, reminiscent of rich, open-world settings like Minecraft. Diverging from previous works that lean primarily on single-round deduction via instruction following, $Conan$ compels agents to actively interact with their surroundings, amalgamating new evidence with prior knowledge to elucidate events from incomplete observations. Our analysis on $Conan$ underscores the shortcomings of contemporary state-of-the-art models in active exploration and understanding complex scenarios. Additionally, we explore Abduction from Deduction, where agents harness Bayesian rules to recast the challenge of abduction as a deductive process. Through $Conan$, we aim to galvanize advancements in active reasoning and set the stage for the next generation of artificial intelligence agents adept at dynamically engaging in environments.
Tentative detection of cyanoformamide NCCONH2 in space

The peptide-like molecule cyanoformamide (NCCONH2) is the cyano (CN) derivative of formamide (NH2CHO). It is known to play a role in the synthesis of nucleic acid precursors under prebiotic conditions. In this paper, we present a tentative detection of NCCONH2 in the interstellar medium with the Atacama Large Millimeter/submillimeter Array (ALMA) archive data. 10 unblended lines of NCCONH2 were seen around 3σ noise levels toward Sagittarius B2(N1E), a position that is slightly offset from the continuum peak. The column density of NCCONH2 was estimated to be 2.4 × 1015 cm−2, and the fractional abundance of NCCONH2 toward Sgr B2(N1E) was 6.9 × 10−10. The abundance ratio between NCCONH2 and NH2CHO is estimated to be ∼0.01. We also searched for other peptide-like molecules toward Sgr B2(N1E). The abundances of NH2CHO, CH3NCO and CH3NHCHO toward Sgr B2(N1E) were about 1/10 of those toward Sgr B2(N1S), while the abundance of CH3CONH2 was only 1/20 of that toward Sgr B2(N1S).
Sterile Neutrinos at MAPP in the B-L Model

The possibility of searching for right-handed neutrinos at the MoEDAL's Apparatus for Penetrating Particles (MAPP) detector is investigated in this work. In particular, pair-production of right-handed (RH) neutrinos $N$ from either a $B-L$ gauge boson $Z'$, as well as Standard Model (SM) $Z$ boson are considered. Under a no-background assumption, we show that the MAPP detector can be sensitive to active-sterile neutrino mixing strengths as low as $V_{\mu N}^2 \approx 10^{-12}$ for multiple choices of $m_N / m_{Z'}$ values, when taking the $B-L$ gauge coupling $g_{B-L} = 10^{-3}$ near its current limit. The SM $Z$ boson portal can reach a similar sensitivity, when the effective mixing between the $B-L$ and SM gauge boson is $\alpha \approx 0.01$.
A Theoretical Case Study of the Generalisation of Machine-learned Potentials

Machine-learned interatomic potentials (MLIPs) are typically trained on datasets that encompass a restricted subset of possible input structures, which presents a potential challenge for their generalization to a broader range of systems outside the training set. Nevertheless, MLIPs have demonstrated impressive accuracy in predicting forces and energies in simulations involving intricate and complex structures. In this paper we aim to take steps towards rigorously explaining the excellent observed generalisation properties of MLIPs. Specifically, we offer a comprehensive theoretical and numerical investigation of the generalization of MLIPs in the context of dislocation simulations. We quantify precisely how the accuracy of such simulations is directly determined by a few key factors: the size of the training structures, the choice of training observations (e.g., energies, forces, virials), and the level of accuracy achieved in the fitting process. Notably, our study reveals the crucial role of fitting virials in ensuring the consistency of MLIPs for dislocation simulations. Our series of careful numerical experiments encompassing screw, edge, and mixed dislocations, supports existing best practices in the MLIPs literature but also provides new insights into the design of data sets and loss functions.