Miracle-dz/UniFAR-FTUs huggingface dataset:
Antarctic Sea Ice Thickness and Snow-to-Ice Conversion from Atmospheric Reanalysis and Passive Microwave Snow Depth

[1] Passive microwave snow depth, ice concentration, and ice motion estimates are combined with snowfall from the European Centre for Medium-Range Weather Forecasting (ECMWF) reanalysis (ERA-40) from 1979–2001 to estimate the prevalence of snow-to-ice conversion (snow-ice formation) on level sea ice in the Antarctic for April–October. Snow ice is ubiquitous in all regions throughout the growth season. Calculated snow-ice thicknesses fall within the range of estimates from ice core analysis for most regions. However, uncertainties in both this analysis and in situ data limit the usefulness of snow depth and snow-ice production to evaluate the accuracy of ERA-40 snowfall. The East Antarctic is an exception, where calculated snow-ice production exceeds observed ice thickness over wide areas, suggesting that ERA-40 precipitation is too high there. Snow-ice thickness variability is strongly controlled not just by snow accumulation rates, but also by ice divergence. Surprisingly, snow-ice production is largely independent of snow depth, indicating that the latter may be a poor indicator of total snow accumulation. Using the presence of snow-ice formation as a proxy indicator for near-zero freeboard, we examine the possibility of estimating level ice thickness from satellite snow depths. A best estimate for the mean level ice thickness in September is 53 cm, comparing well with 51 cm from ship-based observations. The error is estimated to be 10–20 cm, which is similar to the observed interannual and regional variability. Nevertheless, this is comparable to expected errors for ice thickness determined by satellite altimeters. Improvement in satellite snow depth retrievals would benefit both of these methods.
Regularity from $p$-harmonic potentials to $\infty$-harmonic potentials in convex rings

The exploration of shape metamorphism, surface reconstruction, and image interpolation raises fundamental inquiries concerning the $C^1$ and higher-order regularity of $\infty$-harmonic potentials -- a specialized category of $\infty$-harmonic functions. Additionally, it prompts questions regarding their corresponding approximations using $p$-harmonic potentials. It is worth noting that establishing $C^1$ and higher-order regularity for $\infty$-harmonic functions remains a central concern within the realm of $\infty$-Laplace equations and $L^\infty$-variational problems. In this study, we investigate the regularity properties from $p$-harmonic potentials to $\infty$-harmonic potentials within arbitrary convex ring domains $\Omega=\Omega_0\backslash \overline \Omega_1$ in $\mathbb R^n$. Here $\Omega_0$ is a bounded convex domain in $\mathbb R^n$ and $\overline\Omega_1\subset \Omega_0$ is a compact convex set. We prove the interior $C^1$ and some Sobolev regularity for $\infty$-harmonic potentials.
The Connection Between Political Stability and Inflation: Insights from Four South Asian Nations

This study explores the relationship between political stability and inflation in four South Asian countries, employing panel data spanning from 2001 to 2021. To analyze this relationship, the study utilizes the dynamic ordinary least square (DOLS) and fully modified ordinary least square (FMOLS) methods, which account for cross-sectional dependence and slope homogeneity in panel data analysis. The findings consistently reveal that increased political stability is associated with lower inflation, while reduced political stability is linked to higher inflation.
WinSyn: A High Resolution Testbed for Synthetic Data

We present WinSyn, a unique dataset and testbed for creating high-quality synthetic data with procedural modeling techniques. The dataset contains high-resolution photographs of windows, selected from locations around the world, with 89,318 individual window crops showcasing diverse geometric and material characteristics. We evaluate a procedural model by training semantic segmentation networks on both synthetic and real images and then comparing their performances on a shared test set of real images. Specifically, we measure the difference in mean Intersection over Union (mIoU) and determine the effective number of real images to match synthetic data's training performance. We design a baseline procedural model as a benchmark and provide 21,290 synthetically generated images. By tuning the procedural model, key factors are identified which significantly influence the model's fidelity in replicating real-world scenarios. Importantly, we highlight the challenge of procedural modeling using current techniques, especially in their ability to replicate the spatial semantics of real-world scenarios. This insight is critical because of the potential of procedural models to bridge to hidden scene aspects such as depth, reflectivity, material properties, and lighting conditions.
Lake productivity and water chemistry as governors of the uptake of persistent pollutants in fish

Factors influencing uptake and levels of persistent pollutants (PCBs, p,p'-DDE) were investigated in 341 northern pike from 61 lakes in southern Scandinavia. Although the inflow of pollutants from the atmosphere was similar throughout the area studied, large differences in levels of PCBs and DDE in predatory fish were found between lakes. The factors primarily responsible for this variation appeared to be lake trophic status and content of humic substances. Levels of persistent pollutants in the fish decreased as productivity increased. Contents of total phosphorus and chlorophyll alpha and lake water transparency (mainly influenced by phytoplankton biomass) were used as productivity indicators. The reasons for the lower levels of pollutants in fish in lakes of increasing productivity were higher growth rate of pike and higher turnover time and sedimentation of particles (phytoplankton) to which the pollutants are adsorbed. Levels of persistent pollutants decreased as the amount of humic substances (water color) increased, apparently because humus adsorbs persistent pollutants, rendering them less available for uptake in fish. The results show that it should be possible to predict levels of persistent pollutants in fish, based on the productivity and chemical properties of the lake. (Less)
Reconstruction of extended regions in EIT with a generalized Robin transmission condition

We consider an inverse shape problem coming from electrical impedance tomography with a generalized Robin transmission condition. We will derive an algorithm in order to detect whether two materials that should be in contact are separated or delaminated. More precisely, we assume that the undamaged material or background state is known and shares an interface or boundary with the damaged subregion. The Robin transmission condition on this boundary asymptotically models delamination. We assume that the DtN operator is given from measuring the current on the surface of the material from an imposed voltage. We show that this mapping uniquely recovers the boundary parameters. Furthermore, using this electrostatic Cauchy data as physical measurements, we can determine if all of the coefficients from the Robin transmission condition are real-valued or complex-valued. We study these two cases separately and show that the regularized factorization method can be used to detect whether delamination has occurred and recover the damaged subregion. Numerical examples will be presented for both cases in two dimensions in the unit circle.
Who Said That? Benchmarking Social Media AI Detection

AI-generated text has proliferated across various online platforms, offering both transformative prospects and posing significant risks related to misinformation and manipulation. Addressing these challenges, this paper introduces SAID (Social media AI Detection), a novel benchmark developed to assess AI-text detection models' capabilities in real social media platforms. It incorporates real AI-generate text from popular social media platforms like Zhihu and Quora. Unlike existing benchmarks, SAID deals with content that reflects the sophisticated strategies employed by real AI users on the Internet which may evade detection or gain visibility, providing a more realistic and challenging evaluation landscape. A notable finding of our study, based on the Zhihu dataset, reveals that annotators can distinguish between AI-generated and human-generated texts with an average accuracy rate of 96.5%. This finding necessitates a re-evaluation of human capability in recognizing AI-generated text in today's widely AI-influenced environment. Furthermore, we present a new user-oriented AI-text detection challenge focusing on the practicality and effectiveness of identifying AI-generated text based on user information and multiple responses. The experimental results demonstrate that conducting detection tasks on actual social media platforms proves to be more challenging compared to traditional simulated AI-text detection, resulting in a decreased accuracy. On the other hand, user-oriented AI-generated text detection significantly improve the accuracy of detection.
Auroral N2 emissions and the effect of collisional processes on N2 triplet state vibrational populations

Previous model results have shown that the N2 triplet vibrational level populations in the aurora are strongly affected by cascade and quenching by atomic and molecular oxygen. As the aurora penetrates to lower altitudes (less than 100 km) the role of quenching by atomic oxygen becomes less important and processes involving N2 collisions begin to play a more prominent part. We are developing a model which will yield steady state vibrational level populations for both the singlet and triplet valence states of N2. The model currently provides results for the seven low-lying N2 triplet states (A 3Σu+, B 3IIg, W3Δu, B′ 3Σu−, C3IIu, D3Σu+, and E3Σg+). These states are responsible for auroral emissions from the UV (Vegard-Kaplan (VK), second positive (2PG)) through the visible to the infrared (first positive (1PG), infrared afterglow (IRA), Wu-Benesch (WB)). We have included two additional collisional processes in the current model which were not treated previously. These are the intersystem collisional transfer of excitation (ICT) between the B state and the A, W, and B′ states and vibrational redistribution within the A state vibrational manifold, both due to collisions with ground state N2. The present work compares our current model results with those of a previous model, as well as ground, airborne, and rocket observations. The comparison between N2(A) (VK) and N2(B) (1PG) vibrational level populations predicted by our model and a number of auroral observations indicate that the current model achieves a significant improvement in the fit between calculation and observation. In addition, the current model predicts a shift in the band intensity distribution of the 1PG Δν = 3 sequence from the infrared into the visible red at the lower altitudes (less than 90 km) as well as an overall enhancement in the entire 1PG system. Consequently, this provides a possible explanation of a dominate feature of type b aurora, the auroral red lower border.
Diurnal and seasonal variability of surface ozone and NOx at a tropical coastal site: Association with mesoscale and synoptic meteorological conditions

[1] Simultaneous measurements of near-surface ozone, NOx (NO + NO2), and meteorological parameters were carried out at the tropical coastal location of Trivandrum (8.55°N, 77°E) in India from November 2007 to May 2009. The data have been used to investigate the diurnal and seasonal patterns of ozone and its precursor, NOx, and also the interdependence of these two chemical species. The diurnal pattern is found to be closely associated with the mesoscale circulation (sea breeze and land breeze) and the availability of NOx. The daytime peak in ozone extends until the onset of land breeze, which brings in NOx for titration of ozone. Near-surface ozone concentration reaches peak values during the postmonsoon or winter months and shows minima during the summer or monsoon season. The high ozone concentration during winter is due to the presence of northeasterly winds that transport precursor gases to the site. The daytime concentration of ozone is found to be directly linked to the nighttime level of NOx. The present analysis reveals that one molecule of NOx or NO2 is responsible for the formation of about seven to nine molecules of ozone. A study of satellite-derived tropospheric ozone and total ozone has shown that tropospheric ozone contributes 8%–15% of total ozone over this site and near-surface ozone contributes 34%–83% of tropospheric ozone. The seasonal pattern of tropospheric column ozone is similar to that of tropospheric NO2.
Leader-Follower Neural Networks with Local Error Signals Inspired by Complex Collectives

The collective behavior of a network with heterogeneous, resource-limited information processing units (e.g., group of fish, flock of birds, or network of neurons) demonstrates high self-organization and complexity. These emergent properties arise from simple interaction rules where certain individuals can exhibit leadership-like behavior and influence the collective activity of the group. Motivated by the intricacy of these collectives, we propose a neural network (NN) architecture inspired by the rules observed in nature's collective ensembles. This NN structure contains workers that encompass one or more information processing units (e.g., neurons, filters, layers, or blocks of layers). Workers are either leaders or followers, and we train a leader-follower neural network (LFNN) by leveraging local error signals and optionally incorporating backpropagation (BP) and global loss. We investigate worker behavior and evaluate LFNNs through extensive experimentation. Our LFNNs trained with local error signals achieve significantly lower error rates than previous BP-free algorithms on MNIST and CIFAR-10 and even surpass BP-enabled baselines. In the case of ImageNet, our LFNN-l demonstrates superior scalability and outperforms previous BP-free algorithms by a significant margin.
Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach

In many cases, a machine learning model must learn to correctly predict a few data points with particular values of interest in a broader range of data where many target values are zero. Zero-inflated data can be found in diverse scenarios, such as lumpy and intermittent demands, power consumption for home appliances being turned on and off, impurities measurement in distillation processes, and even airport shuttle demand prediction. The presence of zeroes affects the models' learning and may result in poor performance. Furthermore, zeroes also distort the metrics used to compute the model's prediction quality. This paper showcases two real-world use cases (home appliances classification and airport shuttle demand prediction) where a hierarchical model applied in the context of zero-inflated data leads to excellent results. In particular, for home appliances classification, the weighted average of Precision, Recall, F1, and AUC ROC was increased by 27%, 34%, 49%, and 27%, respectively. Furthermore, it is estimated that the proposed approach is also four times more energy efficient than the SOTA approach against which it was compared to. Two-fold models performed best in all cases when predicting airport shuttle demand, and the difference against other models has been proven to be statistically significant.
Chrono: A Peer-to-Peer Network with Verifiable Causality

Logical clocks are a fundamental tool to establish causal ordering of events in a distributed system. They have been used as the building block in weakly consistent storage systems, causally ordered broadcast, distributed snapshots, deadlock detection, and distributed system debugging. However, prior logical clock constructs fail to work in a permissionless setting with Byzantine participants. In this work, we introduce Chrono, a novel logical clock system that targets an open and decentralized network. Chrono introduces a new logical clock construct, the Decaying Onion Bloom Clock (DOBC), that scales independently to the size of the network. To tolerate Byzantine behaviors, Chrono leverages non-uniform incrementally verifiable computation (IVC) to efficiently prove and verify the construction of DOBC clocks. We have applied Chrono to build two decentralized applications, a weakly consistent key-value store and an anti-censorship social network, demonstrating the power of scalable, verifiable causality in a decentralized network.
Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions

Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability and an economic model. In serverless computing, users pay only for the time they actually spend using the resources. Although zero scaling optimises cost and resource utilisation, it is the fundamental reason for the serverless cold start problem. Various academic and corporate sector studies are being conducted to tackle the cold start problem, which has large research challenges. To study the"cold start"problem in serverless computing, this article provides a comprehensive literature overview of recent research. In addition, we present a detailed taxonomy of several approaches to addressing the issue of cold start latency in serverless computing. Several academic and industrial organisations have proposed methods for cutting down the cold start time and cold start frequency, and this taxonomy is being used to explore these methods. There are several categories in which a current study on cold start latency is organised: caching and application-level optimization-based solutions, as well as AI/ML-based solutions. We have analysed the current methods and grouped them into categories based on their commonalities and features. Finally, we conclude with a review of current challenges and possible future research directions.
On Using GUI Interaction Data to Improve Text Retrieval-based Bug Localization

One of the most important tasks related to managing bug reports is localizing the fault so that a fix can be applied. As such, prior work has aimed to automate this task of bug localization by formulating it as an information retrieval problem, where potentially buggy files are retrieved and ranked according to their textual similarity with a given bug report. However, there is often a notable semantic gap between the information contained in bug reports and identifiers or natural language contained within source code files. For user-facing software, there is currently a key source of information that could aid in bug localization, but has not been thoroughly investigated - information from the GUI. We investigate the hypothesis that, for end user-facing applications, connecting information in a bug report with information from the GUI, and using this to aid in retrieving potentially buggy files, can improve upon existing techniques for bug localization. To examine this phenomenon, we conduct a comprehensive empirical study that augments four baseline techniques for bug localization with GUI interaction information from a reproduction scenario to (i) filter out potentially irrelevant files, (ii) boost potentially relevant files, and (iii) reformulate text-retrieval queries. To carry out our study, we source the current largest dataset of fully-localized and reproducible real bugs for Android apps, with corresponding bug reports, consisting of 80 bug reports from 39 popular open-source apps. Our results illustrate that augmenting traditional techniques with GUI information leads to a marked increase in effectiveness across multiple metrics, including a relative increase in Hits@10 of 13-18%. Additionally, through further analysis, we find that our studied augmentations largely complement existing techniques.
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?

Transformers pretrained on diverse tasks exhibit remarkable in-context learning (ICL) capabilities, enabling them to solve unseen tasks solely based on input contexts without adjusting model parameters. In this paper, we study ICL in one of its simplest setups: pretraining a linearly parameterized single-layer linear attention model for linear regression with a Gaussian prior. We establish a statistical task complexity bound for the attention model pretraining, showing that effective pretraining only requires a small number of independent tasks. Furthermore, we prove that the pretrained model closely matches the Bayes optimal algorithm, i.e., optimally tuned ridge regression, by achieving nearly Bayes optimal risk on unseen tasks under a fixed context length. These theoretical findings complement prior experimental research and shed light on the statistical foundations of ICL.
Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemic

The proliferation of online misinformation has emerged as one of the biggest threats to society. Considerable efforts have focused on building misinformation detection models, still the perils of misinformation remain abound. Mitigating online misinformation and its ramifications requires a holistic approach that encompasses not only an understanding of its intricate landscape in relation to the complex issue and topic-rich information ecosystem online, but also the psychological drivers of individuals behind it. Adopting a time series analytic technique and robust causal inference-based design, we conduct a large-scale observational study analyzing over 32 million COVID-19 tweets and 16 million historical timeline tweets. We focus on understanding the behavior and psychology of users disseminating misinformation during COVID-19 and its relationship with the historical inclinations towards sharing misinformation on Non-COVID topics before the pandemic. Our analysis underscores the intricacies inherent to cross-topic misinformation, and highlights that users' historical inclination toward sharing misinformation is positively associated with their present behavior pertaining to misinformation sharing on emergent topics and beyond. This work may serve as a valuable foundation for designing user-centric inoculation strategies and ecologically-grounded agile interventions for effectively tackling online misinformation.
Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification

Vision-Language Models (VLMs) such as CLIP are trained on large amounts of image-text pairs, resulting in remarkable generalization across several data distributions. However, in several cases, their expensive training and data collection/curation costs do not justify the end application. This motivates a vendor-client paradigm, where a vendor trains a large-scale VLM and grants only input-output access to clients on a pay-per-query basis in a black-box setting. The client aims to minimize inference cost by distilling the VLM to a student model using the limited available task-specific data, and further deploying this student model in the downstream application. While naive distillation largely improves the In-Domain (ID) accuracy of the student, it fails to transfer the superior out-of-distribution (OOD) generalization of the VLM teacher using the limited available labeled images. To mitigate this, we propose Vision-Language to Vision - Align, Distill, Predict (VL2V-ADiP), which first aligns the vision and language modalities of the teacher model with the vision modality of a pre-trained student model, and further distills the aligned VLM representations to the student. This maximally retains the pre-trained features of the student, while also incorporating the rich representations of the VLM image encoder and the superior generalization of the text embeddings. The proposed approach achieves state-of-the-art results on the standard Domain Generalization benchmarks in a black-box teacher setting as well as a white-box setting where the weights of the VLM are accessible.