Miracle-dz/UniFAR-FTUs huggingface dataset:
Move up or move over: mapping opportunities for climate adaptation in Pakistan’s Indus plains

The 2022 Indus floods in Pakistan underscore the urgency of adapting to more frequent and severe natural disasters in a warming world. Post-disaster reconstruction offers a chance to built-in adaptation measures, but identifying feasible and cost-effective adaptation options is challenging, especially in data-scarce regions. Here we employ a high-resolution rapid assessment of flood stages combined with demographic data to identify adaptation opportunities and costs for the Indus floodplains. Under a plausible set of assumptions, we find that rebuilding houses in a flood-proof, elevated manner (‘moving up’) or (temporary) relocation (‘moving over’) could have protected 13%, respectively 16% of people affected during the 2022 floods, while the remaining 70% of people were exposed to shallow water levels that could have been addressed with low-cost adaptation. Implementing these measures during ongoing reconstruction could be an effective adaptation to future floods but will come with substantial costs. Rebuilding in a flood-proofed manner (‘moving up’) alone would already increase costs by 26%–63% ($1.5bn–$3.6bn) compared to estimated reconstruction costs without adaptation ($5.8bn). Additional costs would be incurred by relocation and adaptation of other infrastructure. The absence of local flood stage and socio-economic data creates uncertainty and points to future research avenues. Yet, our prototype approach demonstrates the value of rapid assessments for guiding post-disaster adaptation of livelihoods to future floods.
Heterogenous climate impacts on crop yields: evidence from Ukraine

Ukraine ranks among the largest global producers and exporters of a variety of agricultural commodities. Despite its importance, there is limited research on the effect of climate change on the future of Ukraine’s agricultural sector. We investigate how weather affects the yields of Ukraine’s five major crops: winter wheat, spring barley, sunflower, soybean, and corn. Using panel data on village-level surveyed crop yields and fine-scale weather data from 2004–2020, we estimate flexible statistical yield models to characterize the impact of temperature and precipitation changes on crop yields. A simple linear model of the effect of average temperature and precipitation on yields suggests warming is beneficial for all crops in Ukraine. However, a more flexible model of the temperature-yield relationship implies that the yields of two cold-season crops, winter wheat and spring barley, respond negatively to temperatures above 29 °C and 30 °C, respectively. We find no negative effects of high temperatures for the three warm-season crops. We conclude that both average temperature and exposure to high temperatures affect Ukrainian yields.
Dolfin: Diffusion Layout Transformers without Autoencoder

In this paper, we introduce a novel generative model, Diffusion Layout Transformers without Autoencoder (Dolfin), which significantly improves the modeling capability with reduced complexity compared to existing methods. Dolfin employs a Transformer-based diffusion process to model layout generation. In addition to an efficient bi-directional (non-causal joint) sequence representation, we further propose an autoregressive diffusion model (Dolfin-AR) that is especially adept at capturing rich semantic correlations for the neighboring objects, such as alignment, size, and overlap. When evaluated against standard generative layout benchmarks, Dolfin notably improves performance across various metrics (fid, alignment, overlap, MaxIoU and DocSim scores), enhancing transparency and interoperability in the process. Moreover, Dolfin's applications extend beyond layout generation, making it suitable for modeling geometric structures, such as line segments. Our experiments present both qualitative and quantitative results to demonstrate the advantages of Dolfin.
Stencil Matrixization

Current architectures are now equipped with matrix computation units designed to enhance AI and high-performance computing applications. Within these architectures, two fundamental instruction types are matrix multiplication and vector outer product, with the latter being lighter due to its vector inputs. This characteristic not only allows for the development of flexible algorithms beyond dense linear algebra computations but also offers greater potential for implementation optimization. Stencil computations, commonly found in scientific and engineering applications, involve nested loops. This paper introduces a novel stencil algorithm leveraging vector outer products. Unlike previous approaches, this algorithm emerges from the stencil definition in scatter mode and is initially formulated using vector outer product expressions. The implementation integrates a series of optimizations to enhance memory reference patterns, execution pipeline efficiency, and data reuse. These optimizations consider various algorithmic options and data sharing among input vectors. Evaluation conducted on a simulator demonstrates that our proposed design achieves significant speedup compared to vectorized stencil algorithms.
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration

This paper provides a theoretical understanding of Deep Q-Network (DQN) with the $\varepsilon$-greedy exploration in deep reinforcement learning. Despite the tremendous empirical achievement of the DQN, its theoretical characterization remains underexplored. First, the exploration strategy is either impractical or ignored in the existing analysis. Second, in contrast to conventional Q-learning algorithms, the DQN employs the target network and experience replay to acquire an unbiased estimation of the mean-square Bellman error (MSBE) utilized in training the Q-network. However, the existing theoretical analysis of DQNs lacks convergence analysis or bypasses the technical challenges by deploying a significantly overparameterized neural network, which is not computationally efficient. This paper provides the first theoretical convergence and sample complexity analysis of the practical setting of DQNs with $\epsilon$-greedy policy. We prove an iterative procedure with decaying $\epsilon$ converges to the optimal Q-value function geometrically. Moreover, a higher level of $\epsilon$ values enlarges the region of convergence but slows down the convergence, while the opposite holds for a lower level of $\epsilon$ values. Experiments justify our established theoretical insights on DQNs.
Differential nature of inelastic collisions facilitating runaway electron generation in weakly-ionized plasmas

We report extention of the Dreicer generation theory to situation where the small energy exchange no more predominates. In weakly-ionized plamsas, the Dreicer mechanism can be severely underestimated due to the broken assumption of dominant small energy exchange. This Letter numerically demonstrates that the differential nature of inelastic collisions facilitates the Dreicer generation by developing the new Fokker-Planck-Boltzmann operator of electron-hydrogen atom collisions based on experimental data. This work is envisaged to predict runaway electron generations in future fusion reactors.
Multilayer Environment and Toolchain for Holistic NetwOrk Design and Analysis

The recent developments and research in distributed ledger technologies and blockchain have contributed to the increasing adoption of distributed systems. To collect relevant insights into systems' behavior, we observe many evaluation frameworks focusing mainly on the system under test throughput. However, these frameworks often need more comprehensiveness and generality, particularly in adopting a distributed applications' cross-layer approach. This work analyses in detail the requirements for distributed systems assessment. We summarize these findings into a structured methodology and experimentation framework called METHODA. Our approach emphasizes setting up and assessing a broader spectrum of distributed systems and addresses a notable research gap. We showcase the effectiveness of the framework by evaluating four distinct systems and their interaction, leveraging a diverse set of eight carefully selected metrics and 12 essential parameters. Through experimentation and analysis we demonstrate the framework's capabilities to provide valuable insights across various use cases. For instance, we identify that a combination of Trusted Execution Environments with threshold signature scheme FROST introduces minimal overhead on the performance with average latency around \SI{40}{\ms}. We showcase an emulation of realistic systems behavior, e.g., Maximal Extractable Value is possible and could be used to further model such dynamics. The METHODA framework enables a deeper understanding of distributed systems and is a powerful tool for researchers and practitioners navigating the complex landscape of modern computing infrastructures.
DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment

Dialogue assessment plays a critical role in the development of open-domain dialogue systems. Existing work are uncapable of providing an end-to-end and human-epistemic assessment dataset, while they only provide sub-metrics like coherence or the dialogues are conversed between annotators far from real user settings. In this paper, we release a large-scale dialogue quality assessment dataset (DiQAD), for automatically assessing open-domain dialogue quality. Specifically, we (1) establish the assessment criteria based on the dimensions conforming to human judgements on dialogue qualities, and (2) annotate large-scale dialogues that conversed between real users based on these annotation criteria, which contains around 100,000 dialogues. We conduct several experiments and report the performances of the baselines as the benchmark on DiQAD. The dataset is openly accessible at https://github.com/yukunZhao/Dataset_Dialogue_quality_evaluation.
Isotopic Constraints on the Chemical Evolution of Geothermal Fluids, Long Valley, CA

A spatial survey of the chemical and isotopic composition of fluids from the Long Valley hydrothermal system was conducted. Starting at the presumed hydrothermal upwelling zone in the west moat of the caldera, samples were collected from the Casa Diablo geothermal field and a series of monitoring wells defining a nearly linear, ~;;14 km long, west-to-east trend along the proposed fluid flow path (Sorey et al., 1991). Samples were analyzed for the isotopes of water, Sr, Ca, and noble gases, the concentrations of major cations and anions and total CO2. Our data confirm earlier models in which the variations in water isotopes along the flow path reflect mixing of a single hydrothermal fluid with local groundwater. Variations in Sr data are poorly constrained and reflect fluid mixing, multiple fluid-pathways or water-rock exchange along the flow path as suggested by Goff et al. (1991). Correlated variations among total CO2, noble gases and the concentration and isotopic composition of Ca suggest progressive fluid degassing (loss of CO2, noble gases) driving calcite precipitation as the fluid flows west-to-east across the caldera. This is the first evidence that Ca isotopes may trace and provide definitive evidence of calcite precipitation along fluid flow paths inmore » geothermal systems. « less
Task Grouping for Automated Multi-Task Machine Learning via Task Affinity Prediction

When a number of similar tasks have to be learned simultaneously, multi-task learning (MTL) models can attain significantly higher accuracy than single-task learning (STL) models. However, the advantage of MTL depends on various factors, such as the similarity of the tasks, the sizes of the datasets, and so on; in fact, some tasks might not benefit from MTL and may even incur a loss of accuracy compared to STL. Hence, the question arises: which tasks should be learned together? Domain experts can attempt to group tasks together following intuition, experience, and best practices, but manual grouping can be labor-intensive and far from optimal. In this paper, we propose a novel automated approach for task grouping. First, we study the affinity of tasks for MTL using four benchmark datasets that have been used extensively in the MTL literature, focusing on neural network-based MTL models. We identify inherent task features and STL characteristics that can help us to predict whether a group of tasks should be learned together using MTL or if they should be learned independently using STL. Building on this predictor, we introduce a randomized search algorithm, which employs the predictor to minimize the number of MTL trainings performed during the search for task groups. We demonstrate on the four benchmark datasets that our predictor-driven search approach can find better task groupings than existing baseline approaches.
Electronic structures and transport properties of cove-edged graphene nanoribbons

In this comprehensive study, we undertake a thorough theoretical examination of the electronic subband structures within cove-edged zigzag graphene nanoribbons (CZGNRs) using the tight-binding model. These unique nanostructures arise from the systematic removal of carbon atoms along the zigzag edges of conventional zigzag graphene nanoribbons (ZGNRs). Notably, CZGNRs that exhibit intriguing band gaps can be conceptualized as interconnected graphene quantum dots (GQDs). An essential finding of our investigation is the inverse relationship between the size of GQDs and the band gaps of CZGNRs, a relationship that remains consistent regardless of the number of GQDs present. Additionally, we delve into the examination of electron effective masses in proximity to the edges of the first conduction subband of CZGNRs as GQD sizes expand. We observe a significant increase in electron effective masses as GQDs become larger, which is attributed to the increasing similarity between larger GQDs and ZGNRs. To further understand the practical implications, we explore the transport properties of finite CZGNRs when connected to electrodes through line contacts. The presence of edge defects introduces intriguing asymmetries in the tunneling current, leading to a significant reduction in its magnitude. Notably, we observe that the saturation current magnitude is less influenced by the length of CZGNRs and is instead more sensitive to the choice of materials used for the contacted electrodes. Lastly, we investigate the tunneling currents through GQDs featuring boron nitride textures within the Coulomb blockade region, unveiling an irregular staircase-like pattern in the tunneling current behavior.
Beyond Jacobian-based tasks: Extended set-based tasks for multi-task execution and prioritization

The ability of executing multiple tasks simultaneously is an important feature of redundant robotic systems. As a matter of fact, complex behaviors can often be obtained as a result of the execution of several tasks. Moreover, in safety-critical applications, tasks designed to ensure the safety of the robot and its surroundings have to be executed along with other nominal tasks. In such cases, it is also important to prioritize the former over the latter. In this paper, we formalize the definition of extended set-based tasks, i.e., tasks which can be executed by rendering subsets of the task space asymptotically stable or forward invariant. We propose a mathematical representation of such tasks that allows for the execution of more complex and time-varying prioritized stacks of tasks using kinematic and dynamic robot models alike. We present and analyze an optimization-based framework which is computationally efficient, accounts for input bounds, and allows for the stable execution of time-varying prioritized stacks of extended set-based tasks. The proposed framework is validated using extensive simulations and experiments with robotic manipulators.
Can You Follow Me? Testing Situational Understanding in ChatGPT

Understanding sentence meanings and updating information states appropriately across time -- what we call"situational understanding"(SU) -- is a critical ability for human-like AI agents. SU is essential in particular for chat models, such as ChatGPT, to enable consistent, coherent, and effective dialogue between humans and AI. Previous works have identified certain SU limitations in non-chatbot Large Language models (LLMs), but the extent and causes of these limitations are not well understood, and capabilities of current chat-based models in this domain have not been explored. In this work we tackle these questions, proposing a novel synthetic environment for SU testing which allows us to do controlled and systematic testing of SU in chat-oriented models, through assessment of models' ability to track and enumerate environment states. Our environment also allows for close analysis of dynamics of model performance, to better understand underlying causes for performance patterns. We apply our test to ChatGPT, the state-of-the-art chatbot, and find that despite the fundamental simplicity of the task, the model's performance reflects an inability to retain correct environment states across time. Our follow-up analyses suggest that performance degradation is largely because ChatGPT has non-persistent in-context memory (although it can access the full dialogue history) and it is susceptible to hallucinated updates -- including updates that artificially inflate accuracies. Our findings suggest overall that ChatGPT is not currently equipped for robust tracking of situation states, and that trust in the impressive dialogue performance of ChatGPT comes with risks. We release the codebase for reproducing our test environment, as well as all prompts and API responses from ChatGPT, at https://github.com/yangalan123/SituationalTesting.
Rationale and protocol for a safety, tolerability and feasibility randomized, parallel group, double-blind, placebo-controlled, pilot study of a novel ketone ester targeting frailty via immunometabolic geroscience mechanisms

Background: Frailty is a geriatric syndrome characterized by chronic inflammation and metabolic insufficiency that creates vulnerability to poor outcomes with aging. We hypothesize that geroscience interventions, which target mechanisms of aging, could ameliorate frailty. Metabolites such as ketone bodies are candidate geroscience interventions, having pleiotropic effects on inflammo-metabolic aging mechanisms. Ketone esters (KEs) induce ketosis without dietary changes, but KEs have not been studied in an older adult population. Our long-term goal is to examine if KEs modulate geroscience mechanisms and clinical outcomes relevant to frailty in older adults. Objectives: The primary objective of this randomized, placebo-controlled, double-blinded, parallel-group, pilot trial is to determine tolerability of 12-weeks of KE ingestion in a generalizable population of older adults ([≥] 65 years). Secondary outcomes include safety and acute blood ketone kinetics. Exploratory outcomes include physical function, cognitive function, quality of life, aging biomarkers and inflammatory measures. Methods: Community-dwelling adults who are independent in activities of daily living, with no unstable acute medical conditions (n=30) will be recruited. The study intervention is a KE or a taste, appearance, and calorie matched placebo beverage. Initially, acute 4-hour ketone kinetics after 12.5g or 25g of KE consumption will be assessed. After collection of baseline safety, functional, and biological measurements, subjects will randomly be allocated to consume KE 25g or placebo once daily for 12-weeks. Questionnaires will assess tolerability daily for 2-weeks, and then via phone interview at bi-monthly intervals. Safety assessments will be repeated at week 4. All measures will be repeated at week 12. Conclusion: This study will evaluate feasibility, tolerability, and safety of KE consumption in older adults and provide exploratory data across a range of geroscience-related endpoints. This data will inform design of larger trials to rigorously test KE effects on geroscience mechanisms and clinical outcomes relevant to frailty.
Nucleus-independent transgenerational small RNA inheritance in Caenorhabditis elegans

In Caenorhabditis elegans worms, epigenetic information transmits transgenerationally. Still, it is unknown whether the effects transfer to the next generation inside or outside of the nucleus. Here, we use the tractability of gene-specific double-stranded RNA–induced silencing to demonstrate that RNA interference can be inherited independently of any nuclear factors via mothers that are genetically engineered to transmit only their ooplasm but not the oocytes’ nuclei to the next generation. We characterize the mechanisms and, using RNA sequencing, chimeric worms, and sequence polymorphism between different isolates, identify endogenous small RNAs which, similarly to exogenous siRNAs, are inherited in a nucleus-independent manner. From a historical perspective, these results might be regarded as partial vindication of discredited cytoplasmic inheritance theories from the 19th century, such as Darwin’s “pangenesis” theory.
A machine learning approach to graduate admissions and the role of letters of recommendation

The graduate admissions process is time-consuming, subjective, and complicated by the need to combine information from diverse data sources. Letters of recommendation (LORs) are particularly difficult to evaluate and it is unclear how much impact they have on admissions decisions. This study addresses these concerns by building machine learning models to predict admissions decisions for two STEM graduate programs, with a focus on examining the contribution of LORs in the decision-making process. We train our predictive models leveraging information extracted from structured application forms (e.g., undergraduate GPA, standardized test scores, etc.), applicants’ resumes, and LORs. A particular challenge in our study is the different modalities of application data (i.e., text vs. structured forms). To address this issue, we converted the textual LORs into features using a commercial natural language processing product and a manual rating process that we developed. By analyzing the predictive performance of the models using different subsets of features, we show that LORs alone provide only modest, but useful, predictive signals to admission decisions; the best model for predicting admissions decisions utilized both LOR and non-LOR data and achieved 89% accuracy. Our experiments demonstrate promising results in the utility of automated systems for assisting with graduate admission decisions. The findings confirm the value of LORs and the effectiveness of our feature engineering methods from LOR text. This study also assesses the significance of individual features using the SHAP method, thereby providing insight into key factors affecting graduate admission decisions.