Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice

The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To realize this complementarity potential, humans need to exercise discretion in following AI 's advice, i.e., appropriately relying on the AI's advice. While previous work has focused on building a mental model of the AI to assess AI recommendations, recent research has shown that the mental model alone cannot explain appropriate reliance. We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP. In this study, we demonstrate the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making.
Risk Factors for Fractures in Renal Transplantation: A Population-Based Cohort Study

Introduction: Kidney transplant recipients are at an increased risk of fractures, and targeted preventive strategies are needed. Therefore, in this retrospective cohort study, we investigated a large population-based cohort to identify the transplant recipient-specific risk factors for fractures in Taiwanese kidney transplant recipients. Methods: We conducted a retrospective cohort study using the National Health Insurance Research Database. Patients who underwent renal transplantation between 2003 and 2015 were identified and followed until December 31, 2015, to observe the development of fractures. Variables associated with the development of post-transplant fractures were identified by calculating hazard ratios in a Cox regression model. Results: 5,309 renal transplant recipients were identified, of whom 553 (10.4%) were diagnosed with post-transplant fractures. Independent predictors of post-transplant fractures included an age at transplant ≥65 years (p < 0.001), female sex (p < 0.001), fractures within 3 years prior to transplantation (p < 0.001), and diabetes mellitus (p < 0.001). In addition, daily prednisolone doses >2.9–5.3 mg/day (p < 0.001), >5.3–8.7 mg/day (p < 0.001), and >8.7 mg/day (p < 0.001) were also independent predictors of post-transplant fractures. Conversely, the use of peritoneal dialysis before renal transplantation (p = 0.021), hypertension (p = 0.005), and the use of tacrolimus (p < 0.001), azathioprine (p = 0.006), mycophenolate mofetil/mycophenolic acid (p = 0.002), mTOR inhibitors (p = 0.004), and calcium supplements (p = 0.009) were inversely correlated with post-transplant fractures. Conclusion: We recommend minimizing daily glucocorticoids as early and as far as possible in conjunction with immunosuppressive regimens such as tacrolimus, azathioprine, mycophenolate mofetil/mycophenolic acid, mTOR inhibitors, and calcium supplements, especially in older female recipients and in recipients with diabetes and a history of prior fractures.
Towards Automatic Design of Factorio Blueprints

Factorio is a 2D construction and management simulation video game about building automated factories to produce items of increasing complexity. A core feature of the game is its blueprint system, which allows players to easily save and replicate parts of their designs. Blueprints can reproduce any layout of objects in the game, but are typically used to encapsulate a complex behaviour, such as the production of a non-basic object. Once created, these blueprints are then used as basic building blocks, allowing the player to create a layer of abstraction. The usage of blueprints not only eases the expansion of the factory but also allows the sharing of designs with the game's community. The layout in a blueprint can be optimised using various criteria, such as the total space used or the final production throughput. The design of an optimal blueprint is a hard combinatorial problem, interleaving elements of many well-studied problems such as bin-packing, routing or network design. This work presents a new challenging problem and explores the feasibility of a constraint model to optimise Factorio blueprints, balancing correctness, optimality, and performance.
Decision-Oriented Learning Using Differentiable Submodular Maximization for Multi-Robot Coordination

We present a differentiable, decision-oriented learning framework for cost prediction in a class of multi-robot decision-making problems, in which the robots need to trade off the task performance with the costs of taking actions when they select actions to take. Specifically, we consider the cases where the task performance is measured by a known monotone submodular function (e.g., coverage, mutual information), and the cost of actions depends on the context (e.g., wind and terrain conditions). We need to learn a function that maps the context to the costs. Classically, we treat such a learning problem and the downstream decision-making problem as two decoupled problems, i.e., we first learn to predict the cost function without considering the downstream decision-making problem, and then use the learned function for predicting the cost and using it in the decision-making problem. However, the loss function used in learning a prediction function may not be aligned with the downstream decision-making. We propose a decision-oriented learning framework that incorporates the downstream task performance in the prediction phase via a differentiable optimization layer. The main computational challenge in such a framework is to make the combinatorial optimization, i.e., non-monotone submodular maximization, differentiable. This function is not naturally differentiable. We propose the Differentiable Cost Scaled Greedy algorithm (D-CSG), which is a continuous and differentiable relaxation of CSG. We demonstrate the efficacy of the proposed framework through numerical simulations. The results show that the proposed framework can result in better performance than the traditional two-stage approach.
Adapting LLM Agents Through Communication

Recent advancements in large language models (LLMs) have shown potential for human-like agents. To help these agents adapt to new tasks without extensive human supervision, we propose the Learning through Communication (LTC) paradigm, a novel training approach enabling LLM agents to improve continuously through interactions with their environments and other agents. Recent advancements in large language models (LLMs) have shown potential for human-like agents. To help these agents adapt to new tasks without extensive human supervision, we propose the Learning through Communication (LTC) paradigm, a novel training approach enabling LLM agents to improve continuously through interactions with their environments and other agents. Through iterative exploration and PPO training, LTC empowers the agent to assimilate short-term experiences into long-term memory. To optimize agent interactions for task-specific learning, we introduce three structured communication patterns: Monologue, Dialogue, and Analogue-tailored for common tasks such as decision-making, knowledge-intensive reasoning, and numerical reasoning. We evaluated LTC on three datasets: ALFWorld (decision-making), HotpotQA (knowledge-intensive reasoning), and GSM8k (numerical reasoning). On ALFWorld, it exceeds the instruction tuning baseline by 12% in success rate. On HotpotQA, LTC surpasses the instruction-tuned LLaMA-7B agent by 5.1% in EM score, and it outperforms the instruction-tuned 9x larger PaLM-62B agent by 0.6%. On GSM8k, LTC outperforms the CoT-Tuning baseline by 3.6% in accuracy. The results showcase the versatility and efficiency of the LTC approach across diverse domains. We will open-source our code to promote further development of the community.
CALIOP and AERONET aerosol optical depth comparisons: One size fits none

We compare the aerosol optical depths (AOD) retrieved from backscatter measurements of the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) satellite with coincident Aerosol Robotic Network (AERONET) measurements. Overpass coincidence criteria of ±2 h and within a 40 km radius are satisfied at least once at 149 globally distributed AERONET sites from 2006 to 2010. Most data pairs (>80%) use AERONET measurements acquired ±30 min of the overpass. We examine the differences in AOD estimates between CALIOP and AERONET for various aerosol, environmental, and geographic conditions. Results show CALIOP AOD are lower than AERONET AOD especially at low optical depths as measured by AERONET (500 nm AOD < 0.1). Furthermore, the median relative AOD difference between the two measurements is 25% of the AERONET AOD for AOD > 0.1. Differences in AOD between CALIOP and AERONET are possibly due to cloud contamination, scene inhomogeneity, instrument view angle differences, CALIOP retrieval errors, and detection limits. Comparison of daytime to nighttime number of 5 km × 60 m (60 m in the vertical) features detected by CALIOP show that there are 20% more aerosol features at night. We find that CALIPSO and AERONET do not agree on the cloudiness of scenes. Of the scenes that meet the above coincidence criteria, CALIPSO finds clouds in more than 45% of the coincident atmospheric columns AERONET classifies as clear.
Selective Feature Adapter for Dense Vision Transformers

Fine-tuning pre-trained transformer models, e.g., Swin Transformer, are successful in numerous downstream for dense prediction vision tasks. However, one major issue is the cost/storage of their huge amount of parameters, which becomes increasingly challenging to handle with the growing amount of vision tasks. In this paper, we propose an effective approach to alleviate the issue, namely selective feature adapter (SFA). It achieves state-of-the-art (SoTA) performance under any given budget of trainable parameters, and demonstrates comparable or better performance than fully fine-tuned models across various dense tasks. Specifically, SFA consists of external adapters and internal adapters which are sequentially operated over a transformer model. For external adapters, we properly select the places and amount of additional multilayer perception (MLP). For internal adapters, we transform a few task-important parameters inside the transformer, which are automatically discovered through a simple yet effective lottery ticket algorithm. Our experiments show that the dual adapter module, a.k. a SFA, is essential to achieve the best trade-off on dense vision tasks, such as segmentation, detection and depth-estimation, outperforming other adapters with a single module.
On the number and geometric location of critical points of solutions to a semilinear elliptic equation in annular domains

In this paper, one of our aims is to investigate the instability of the distribution of the critical point set $\mathcal{C}(u)$ of a solution $u$ to a semilinear equation with Dirichlet boundary condition in the planar annular domains. Precisely, we prove that $\mathcal{C}(u)$ in an eccentric circle annular domain, or a petal-like domain, or an annular domain where the interior and exterior boundaries are equally scaled ellipses contains only finitely many points rather than a Jordan curve. This result indicates that the critical point set $\mathcal{C}(u)$ is unstable when any boundary of planar concentric circle annular domain $\Omega$ has some small deformation or minor perturbation. Based on studying the distribution of the nodal sets $u^{-1}_\theta(0)(u_\theta=\nabla u\cdot \theta)$ and $u^{-1}(0)$, we further obtain that $u$ has at least two critical points on each symmetric axis of the petal-like domain. In particular, we prove that the solution $u$ on each symmetric axis has exactly two critical points under some conditions. Moreover, we describe the geometric location of critical points of the solution $u$ by the moving plane method.
Editing Personality for LLMs

This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs). This task seeks to adjust the models' responses to opinion-related questions on specified topics since an individual's personality often manifests in the form of their expressed opinions, thereby showcasing different personality traits. Specifically, we construct a new benchmark dataset PersonalityEdit to address this task. Drawing on the theory in Social Psychology, we isolate three representative traits, namely Neuroticism, Extraversion, and Agreeableness, as the foundation for our benchmark. We then gather data using GPT-4, generating responses that not only align with a specified topic but also embody the targeted personality trait. We conduct comprehensive experiments involving various baselines and discuss the representation of personality behavior in LLMs. Our intriguing findings uncover potential challenges of the proposed task, illustrating several remaining issues. We anticipate that our work can provide the NLP community with insights. Code and datasets will be released at https://github.com/zjunlp/EasyEdit.
Language Models Represent Space and Time

The capabilities of large language models (LLMs) have sparked debate over whether such systems just learn an enormous collection of superficial statistics or a set of more coherent and grounded representations that reflect the real world. We find evidence for the latter by analyzing the learned representations of three spatial datasets (world, US, NYC places) and three temporal datasets (historical figures, artworks, news headlines) in the Llama-2 family of models. We discover that LLMs learn linear representations of space and time across multiple scales. These representations are robust to prompting variations and unified across different entity types (e.g. cities and landmarks). In addition, we identify individual"space neurons"and"time neurons"that reliably encode spatial and temporal coordinates. While further investigation is needed, our results suggest modern LLMs learn rich spatiotemporal representations of the real world and possess basic ingredients of a world model.
Combined Use of Remote Sensing and Continuous Monitoring to Analyse the Variability of Suspended-Sediment Concentrations in San Francisco Bay, California

Abstract Analysis of suspended-sediment concentration data in San Francisco Bay is complicated by spatial and temporal variability. In situ optical backscatterance sensors provide continuous suspended-sediment concentration data, but inaccessibility, vandalism, and cost limit the number of potential monitoring stations. Satellite imagery reveals the spatial distribution of surficial-suspended sediment concentrations in the Bay; however, temporal resolution is poor. Analysis of the in situ sensor data in conjunction with the satellite reflectance data shows the effects of physical processes on both the spatial and temporal distribution of suspended sediment in San Francisco Bay. Plumes can be created by large freshwater flows. Zones of high suspended-sediment concentrations in shallow subembayments are associated with wind-wave resuspension and the spring-neap cycle. Filaments of clear and turbid water are caused by different transport processes in deep channels, as opposed to adjacent shallow water. Crown
AVO attribute inversion for finely layered reservoirs

Assuming that a turbidite reservoir can be approximated by a stack of thin shale-sand layers, we use standard amplitude variaiton with offset (AVO) attributes to estimate net-to-gross (N/G) and oil saturation. Necessary input is Gassmann rock-physics properties for sand and shale, as well as the fluid properties for hydrocarbons. Required seismic input is AVO intercept and gradient. The method is based upon thin-layer reflectivity modeling. It is shown that random variability in thickness and seismic properties of the thin sand and shale layers does not change significantly the AVO attributes at the top and base of the turbidite-reservoir sequence. The method is tested on seismic data from offshore Brazil. The results show reasonable agreement between estimated and observed N/G and oil saturation. The methodology can be developed further for estimating changes in pay thickness from time-lapse seismic data.
Gyre Turbulence

Investigation of a two-dimensional model for large-scale oceanic gyres reveals the existence of an asymptotic turbulent regime in which the energy dissipation becomes independent of the fluid viscosity. The role of the no-slip boundary conditions is critical: the inverse cascading behaviour of two-dimensional turbulence described previously for free-slip conditions is halted by vortex fragmentation through collisions with lateral walls. This leads to a distribution of dissipation events resembling a lognormal function, but a departure from this lognormal pattern appears in the distribution's tail, corresponding to intense vortices that dominate the overall dissipation. While the instantaneous vorticity field is dominated by a vortex gas, the time-averaged flow remains close to an ocean gyre predicted by laminar theory. This regime shows an unexpected role of two-dimensional turbulence in the organisation of oceanic flow.
Diagnosing Viral Infections Through T-Cell Receptor Sequencing of Activated CD8+ T Cells

Abstract T-cell–based diagnostic tools identify pathogen exposure but lack differentiation between recent and historical exposures in acute infectious diseases. Here, T-cell receptor (TCR) RNA sequencing was performed on HLA-DR+/CD38+CD8+ T-cell subsets of hospitalized coronavirus disease 2019 (COVID-19) patients (n = 30) and healthy controls (n = 30; 10 of whom had previously been exposed to severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]). CDR3α and CDR3β TCR regions were clustered separately before epitope specificity annotation using a database of SARS-CoV-2–associated CDR3α and CDR3β sequences corresponding to >1000 SARS-CoV-2 epitopes. The depth of the SARS-CoV-2–associated CDR3α/β sequences differentiated COVID-19 patients from the healthy controls with a receiver operating characteristic area under the curve of 0.84 ± 0.10. Hence, annotating TCR sequences of activated CD8+ T cells can be used to diagnose an acute viral infection and discriminate it from historical exposure. In essence, this work presents a new paradigm for applying the T-cell repertoire to accomplish TCR-based diagnostics.
Carbon inputs by wheat and vetch roots to an irrigated Vertosol

Research on the amounts of carbon that can be added to Vertosols of New South Wales and Queensland by crop roots in irrigated cotton farming systems is sparse. The objective of this study was to determine the amounts of carbon added to soil by roots of wheat (Triticum aestivum L.) and purple vetch (Vicia benghalensis L.) sown in rotation with irrigated cotton (Gossypium hirsutum L.). Measurements were made from 2008 to 2010 in an ongoing experiment near Narrabri, northern NSW, using a combination of soil cores and minirhizotron observations. The experimental treatments were: cotton monoculture; cotton–vetch (CV); cotton–wheat, in which wheat stubble was incorporated into the beds with a disc-hiller (CW); and cotton–wheat–vetch, in which wheat stubble was retained as in-situ mulch (CWV). Vetch was killed by a combination of mowing and contact herbicides, and the residues were retained as in situ mulch. Root length per unit area of vetch in CWV and wheat in both CW and CWV was comparable, although wheat had a higher concentration of roots in surface 0.10 m. Root growth of the CV treatment was sparse. Root carbon available for addition to soil was greater with vetch than with wheat and was in the order: vetch in CWV (5.1 t C/ha.year) > vetch in CV (1.9 t C/ha.year) > wheat in CW (1.6 t C/ha.year) = wheat in CWV (1.7 t C/ha.year). Intra-seasonal root mortality accounted for 12% of total root carbon in vetch and 36% in wheat. The remaining fraction consisted of carbon in the root mass at the end of the growing season. Carbon sequestered by root inputs of the rotation crops was estimated to be ~0.34 t C/ha.year for the vetch and wheat crops in the CWV rotation, 0.10 t C/ha.year for vetch in CV, and 0.08 t C/ha.year for wheat in CW. Rotation CWV was, therefore, the most effective in sequestering carbon from roots.
Evaluating the Reliability of Fluoride Dating at Two Prehistoric Mound Sites in Mississippi

Abstract Ion-selective electrode fluoride dating is used to address chronological problems at two mound sites located in Mississippi. At the Mississippian period (A.D. 900–1520) Lyon's Bluff site, 220K520, the fluoride content of deer and human bone is compared with radiocarbon dates and stratigraphic levels to evaluate the reliability of the fluoride dating method for understanding relative burial chronology. At a second site, Pocahontas Mound A (22HI500), fluoride content of deer bone is used to corroborate radiocarbon dates and to establish the proper chronology of levels associated with two distinct occupations (Archaic and Coles Creek/Plaquemine) identified in a large midden area. Fluoride analysis also is used to date a second midden area at this site. For Lyon's Bluff; fluoride dates did not correspond with accelerator mass spectrometry (AMS)-dated burials and midden strata. In contrast, fluoride dating proved very useful in determining chronological placement of midden levels and dating other areas at Pocahontas Mound A. Sample age, site location, soil, and bone preservation are considered as possible causes for the negative results at Lyon's Bluff.
HoloNets: Spectral Convolutions do extend to Directed Graphs

Within the graph learning community, conventional wisdom dictates that spectral convolutional networks may only be deployed on undirected graphs: Only there could the existence of a well-defined graph Fourier transform be guaranteed, so that information may be translated between spatial- and spectral domains. Here we show this traditional reliance on the graph Fourier transform to be superfluous and -- making use of certain advanced tools from complex analysis and spectral theory -- extend spectral convolutions to directed graphs. We provide a frequency-response interpretation of newly developed filters, investigate the influence of the basis used to express filters and discuss the interplay with characteristic operators on which networks are based. In order to thoroughly test the developed theory, we conduct experiments in real world settings, showcasing that directed spectral convolutional networks provide new state of the art results for heterophilic node classification on many datasets and -- as opposed to baselines -- may be rendered stable to resolution-scale varying topological perturbations.