Miracle-dz/UniFAR-FTUs huggingface dataset:
Upper ocean temperature and the baroclinic transport stream function relationship in Drake Passage

Repeat hydrographic sections across the Antarctic Circumpolar Current (ACC) in Drake Passage are used to derive an empirical relationship between upper ocean temperature and the baroclinic transport stream function. Cross validation shows this relationship can be used to infer baroclinic transport (above and relative to 2500 m) from expendable bathythermograph (XBT) temperature measurements with an error of a few per cent. Transport errors of less than 2 Sv are obtained if temperature at depths between 600 and 1600 m is used to define the relationship. Temperature at depths above 300 m provides an unreliable index of transport because of variability in temperature-salinity (T-S) properties produced by air-sea interaction. The scatter in the relationship between temperature and stream function from repeat observations along a single line is similar in magnitude to the scatter observed when data from the broader Drake Passage area are considered. In both cases, variability about the mean temperature-stream function relationship reflects advection of water with anomalous T-S properties. The tight relationship between temperature and stream function in Drake Passage and south of Australia suggests baroclinic transports can be inferred from XBT temperatures with high accuracy in the Southern Ocean, providing a cost-effective means of monitoring ACC variability. However, care must be taken at the end points, particularly in the Drake Passage where the strong flow of the Subantarctic Front sometimes lies over the continental slope.
Synthesis of Event-triggered Controllers for SIRS Epidemic Models

In this paper, we investigate the problem of mitigating epidemics by applying an event-triggered control strategy. We consider a susceptible-infected-removed-susceptible (SIRS) model, which builds upon the foundational SIR model by accounting for reinfection cases. The event-triggered control strategy is formulated based on the condition in which the control input (e.g., the level of public measures) is updated only when the fraction of the infected subjects changes over a designed threshold. To synthesize the event-triggered controller, we leverage the notion of a symbolic model, which represents an abstracted expression of the transition system associated with the SIRS model under the event-triggered control strategy. %The symbolic model is constructed based on an approximate alternating simulation relation. Then, by employing safety and reachability games, two event-triggered controllers are synthesized to ensure a desired specification, which is more sophisticated than the one given in previous works. The effectiveness of the proposed approach is illustrated through numerical simulations.
Moral consensus and divergence in partisan language use

Polarization has increased substantially in political discourse, contributing to a widening partisan divide. In this paper, we analyzed large-scale, real-world language use in Reddit communities (294,476,146 comments) and in news outlets (6,749,781 articles) to uncover psychological dimensions along which partisan language is divided. Using word embedding models that captured semantic associations based on co-occurrences of words in vast textual corpora, we identified patterns of affective polarization present in natural political discourse. We then probed the semantic associations of words related to seven political topics (e.g., abortion, immigration) along the dimensions of morality (moral-to-immoral), threat (threatening-to-safe), and valence (pleasant-to-unpleasant). Across both Reddit communities and news outlets, we identified a small but systematic divergence in the moral associations of words between text sources with different partisan leanings. Moral associations of words were highly correlated between conservative and liberal text sources (average $\rho$ = 0.96), but the differences remained reliable to enable us to distinguish text sources along partisan lines with above 85% classification accuracy. These findings underscore that despite a shared moral understanding across the political spectrum, there are consistent differences that shape partisan language and potentially exacerbate political polarization. Our results, drawn from both informal interactions on social media and curated narratives in news outlets, indicate that these trends are widespread. Leveraging advanced computational techniques, this research offers a fresh perspective that complements traditional methods in political attitudes.
Parallel scattering, saturation, and generalized AGK theorem in the EPOS4 framework, with applications for heavy ion collisions at 5.02 ATeV and at 200 AGeV

Ultrarelativistic heavy ion collisions will first realize many nucleon-nucleon scatterings, happening instantaneously and therefore necessarily in parallel, due to the short collision time. An appropriate quantum mechanical tool to treat that problem is S-matrix theory, and it has been known for a long time how to derive a simple geometric probabilistic picture, still widely used, and here the ``AGK theorem'' plays a crucial role. All this is done in a scenario where energy conservation is not taken care of, but this is needed, in particular for Monte Carlo simulations. When introducing energy-momentum sharing properly, the AGK theorem does not apply anymore, nor do simple geometric concepts such as binary scaling. I will discuss this (very serious) problem, and how it can be solved, in the EPOS4 framework. When connecting the multiple Pomeron approach (for parallel scatterings) and pQCD, one is actually forced to implement in a very particular way saturation scales, in order to get an approach free of contradictions. One recovers a ``generalized AGK theorem'' (gAGK), valid at large $p_{t}$ (larger than the relevant saturation scales). I discuss how gAGK is related to factorization (in proton-proton scatterings) and binary scaling (in heavy ion collisions). I will show some applications, using this new approach as an initial condition for hydrodynamical evolutions, for heavy ion collisions at 5.02 ATeV and at 200 AGeV, to get some idea about the energy dependence.
Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance

Multilingual Neural Machine Translation (MNMT) facilitates knowledge sharing but often suffers from poor zero-shot (ZS) translation qualities. While prior work has explored the causes of overall low ZS performance, our work introduces a fresh perspective: the presence of high variations in ZS performance. This suggests that MNMT does not uniformly exhibit poor ZS capability; instead, certain translation directions yield reasonable results. Through systematic experimentation involving 1,560 language directions spanning 40 languages, we identify three key factors contributing to high variations in ZS NMT performance: 1) target side translation capability 2) vocabulary overlap 3) linguistic properties. Our findings highlight that the target side translation quality is the most influential factor, with vocabulary overlap consistently impacting ZS performance. Additionally, linguistic properties, such as language family and writing system, play a role, particularly with smaller models. Furthermore, we suggest that the off-target issue is a symptom of inadequate ZS performance, emphasizing that zero-shot translation challenges extend beyond addressing the off-target problem. We release the data and models serving as a benchmark to study zero-shot for future research at https://github.com/Smu-Tan/ZS-NMT-Variations
ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We?

Large language models (LLMs) like ChatGPT (i.e., gpt-3.5-turbo and gpt-4) exhibited remarkable advancement in a range of software engineering tasks associated with source code such as code review and code generation. In this paper, we undertake a comprehensive study by instructing ChatGPT for four prevalent vulnerability tasks: function and line-level vulnerability prediction, vulnerability classification, severity estimation, and vulnerability repair. We compare ChatGPT with state-of-the-art language models designed for software vulnerability purposes. Through an empirical assessment employing extensive real-world datasets featuring over 190,000 C/C++ functions, we found that ChatGPT achieves limited performance, trailing behind other language models in vulnerability contexts by a significant margin. The experimental outcomes highlight the challenging nature of vulnerability prediction tasks, requiring domain-specific expertise. Despite ChatGPT's substantial model scale, exceeding that of source code-pre-trained language models (e.g., CodeBERT) by a factor of 14,000, the process of fine-tuning remains imperative for ChatGPT to generalize for vulnerability prediction tasks. We publish the studied dataset, experimental prompts for ChatGPT, and experimental results at https://github.com/awsm-research/ChatGPT4Vul.
Efficient calculation of the self magnetic field, self-force, and self-inductance for electromagnetic coils

The design of electromagnetic coils may require evaluation of several quantities that are challenging to compute numerically. These quantities include Lorentz forces, which may be a limiting factor due to stresses; the internal magnetic field, which is relevant for determining stress as well as a superconducting coil's proximity to its quench limit; and the inductance, which determines stored magnetic energy and dynamics. When computing the effect on one coil due to the current in another, these quantities can often be approximated quickly by treating the coils as infinitesimally thin. When computing the effect on a coil due to its own current (e.g., self-force or self-inductance), evaluation is difficult due to the presence of a singularity; coils cannot be treated as infinitesimally thin as each quantity diverges at zero conductor width. Here, we present novel and well-behaved methods for evaluating these quantities using non-singular integral formulae of reduced dimensions. These formulae are determined rigorously by dividing the domain of integration of the magnetic vector potential into two regions, exploiting appropriate approximations in each region, and expanding in high aspect ratio. Our formulae show good agreement to full finite-thickness calculations even at low aspect ratio, both analytically for a torus and numerically for a non-planar coil of a stellarator fusion device, the Helically Symmetric eXperiment (HSX). Because the integrands of these formulae develop fine structure as the minor radius becomes infinitely thin, we also develop a method of evaluating the self-force and self-inductance with even greater efficiency by integrating this sharp feature analytically. We demonstrate with this method that the self-force can be accurately computed for the HSX coil with as few as 12 grid points.
Simulations of cataclysmic outburst floods from Pleistocene Glacial Lake Missoula

Using a flow domain that we constructed from 30 m digital-elevation model data of western United States and Canada and a two-dimensional numerical model for shallow-water flow over rugged terrain, we simulated outburst floods from Pleistocene Glacial Lake Missoula. We modeled a large, but not the largest, flood, using initial lake elevation at 1250 m instead of 1285 m. Rupture of the ice dam, centered on modern Lake Pend Oreille, catastrophically floods eastern Washington and rapidly fills the broad Pasco, Yakima, and Umatilla Basins. Maximum flood stage is reached in Pasco and Yakima Basins 38 h after the dam break, whereas maximum flood stage in Umatilla Basin occurs 17 h later. Drainage of these basins through narrow Columbia gorge takes an additional 445 h. For this modeled flood, peak discharges in eastern Washington range from 10 to 20 × 10 6 m 3 /s. However, constrictions in Columbia gorge limit peak discharges to 6 m 3 /s and greatly extend the duration of flooding. We compare these model results with field observations of scabland distribution and high-water indicators. Our model predictions of the locations of maximum scour (product of bed shear stress and average flow velocity) match the distribution of existing scablands. We compare model peak stages to high-water indicators from the Rathdrum-Spokane valley, Walulla Gap, and along Columbia gorge. Though peak stages from this less-than-maximal flood model attain or exceed peak-stage indicators along Rathdrum-Spokane valley and along Columbia gorge, simulated peak stages near Walulla Gap are 10–40 m below observed peak-stage indicators. Despite this discrepancy, our match to field observations in most of the region indicates that additional sources of water other than Glacial Lake Missoula are not required to explain the Missoula floods.
The seasonal cycle of N2O

We have carried out an empirical study of the seasonal cycle of nitrous oxide (N2O) using the data archived by the National Oceanic and Atmospheric Administration – Climate Monitoring and Diagnostics Laboratory (NOAA‐CMDL) Global Cooperative Air Sampling Network from 1977 to 2000. In order to isolate the seasonal cycle, we first detrended the data using least square polynomial fits. The remaining variability was averaged to extract the seasonal cycle, which has an amplitude of about 0.8 ppbv. The statistical significance of the seasonal signal was established using the multitaper method and Welch's method for power spectrum analysis.
Mata, a Fast and Simple Finite Automata Library (Technical Report)

Mata is a well-engineered automata library written in C++ that offers a unique combination of speed and simplicity. It is meant to serve in applications such as string constraint solving and reasoning about regular expressions, and as a~reference implementation of automata algorithms. Besides basic algorithms for (non)deterministic automata, it implements a fast simulation reduction and antichain-based language inclusion checking. The simplicity allows a straightforward access to the low-level structures, making it relatively easy to extend and modify. Besides the C++ API, the library also implements a Python binding. The library comes with a large benchmark of automata problems collected from relevant applications such as string constraint solving, regular model checking, and reasoning about regular expressions. We show that Mata is on this benchmark significantly faster than all libraries from a wide range of automata libraries we collected. Its usefulness in string constraint solving is demonstrated by the string solver Z3-Noodler, which is based on Mata and outperforms the state of the art in string constraint solving on many standard benchmarks.
Bayesian Filtering for Homography Estimation

This letter considers homography estimation in a Bayesian filtering framework using rate gyro and camera measurements. The use of rate gyro measurements facilitates a more reliable estimate of homography in the presence of occlusions, while a Bayesian filtering approach generates both a homography estimate along with an uncertainty. Uncertainty information opens the door to adaptive filtering approaches, post-processing procedures, and safety protocols. In particular, herein an iterative extended Kalman filter and an interacting multiple model (IMM) filter are tested using both simulated and experimental datasets. The IMM is shown to have good consistency properties and better overall performance when compared to the state-of-the-art homography nonlinear deterministic observer in both simulations and experiments.
A nonautonomous $C^r-$topological equivalence involving contractions and unbounded nonlinearities

We study the smoothness of the topological equivalence between a linear equation and its nonlinear perturbation, which is regarded as unbounded. To the best of our knowledge, it has not previously been considered such study in the literature. Therefore, the main result of this work copes with this lack, that is, it is shown, on the positive half line, that such topological equivalence is of class $C^r (r \geq 1)$ when the linear part is a uniform contraction and the nonlinearities considered are unbounded.
Excitonic effects in nonlinear optical responses: Exciton-state formalism and first-principles calculations

Nonlinear optical (NLO) responses have garnered tremendous interest for decades due to their fundamental and technological interests. The theory and calculations of NLO responses including electron-hole interactions, which is especially crucial for reduced-dimensional materials, however, remain underdeveloped. Here, we develop an ab initio approach to calculate second-order nonlinear responses (such as second harmonic generation (SHG) and shift current) with excitonic effects in an exciton-state basis, going beyond the independent-particle approximation. We compute SHG in monolayer h-BN and MoS2 employing exciton states from GW-Bethe-Salpeter equation (GW-BSE) calculations and show both materials exhibit huge excitonic enhancement. The physical origin of the enhancement is directly understood through the coupling amplitudes among exciton states, assisted with diagrammatic representations. Our method provides an accurate and ab initio description of second-order NLO responses, capturing self-energy and electron-hole interaction effects.
DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit

Multi-component compounding is a prevalent phenomenon in Sanskrit, and understanding the implicit structure of a compound's components is crucial for deciphering its meaning. Earlier approaches in Sanskrit have focused on binary compounds and neglected the multi-component compound setting. This work introduces the novel task of nested compound type identification (NeCTI), which aims to identify nested spans of a multi-component compound and decode the implicit semantic relations between them. To the best of our knowledge, this is the first attempt in the field of lexical semantics to propose this task. We present 2 newly annotated datasets including an out-of-domain dataset for this task. We also benchmark these datasets by exploring the efficacy of the standard problem formulations such as nested named entity recognition, constituency parsing and seq2seq, etc. We present a novel framework named DepNeCTI: Dependency-based Nested Compound Type Identifier that surpasses the performance of the best baseline with an average absolute improvement of 13.1 points F1-score in terms of Labeled Span Score (LSS) and a 5-fold enhancement in inference efficiency. In line with the previous findings in the binary Sanskrit compound identification task, context provides benefits for the NeCTI task. The codebase and datasets are publicly available at: https://github.com/yaswanth-iitkgp/DepNeCTI
Pointwise modulus of continuity of the Lyapunov exponent and integrated density of states for analytic multi-frequency quasi-periodic M(2,C) cocycles

It is known that the Lyapunov exponent for multifrequency analytic cocycles is weak-Hölder continuous in cocycle for certain Diophantine frequencies, and that this implies certain regularity of the integrated density of states in energy for Jacobi operators. In this paper, we establish the pointwise modulus of continuity in both cocycle and frequency and obtain analogous regularity of the integrated density of states in energy, potential, and frequency.
Expression Domain Translation Network for Cross-domain Head Reenactment

Despite the remarkable advancements in head reenactment, the existing methods face challenges in cross-domain head reenactment, which aims to transfer human motions to domains outside the human, including cartoon characters. It is still difficult to extract motion from out-of-domain images due to the distinct appearances, such as large eyes. Recently, previous work introduced a large-scale anime dataset called AnimeCeleb and a cross-domain head reenactment model, including an optimization-based mapping function to translate the human domain's expressions to the anime domain. However, we found that the mapping function, which relies on a subset of expressions, imposes limitations on the mapping of various expressions. To solve this challenge, we introduce a novel expression domain translation network that transforms human expressions into anime expressions. Specifically, to maintain the geometric consistency of expressions between the input and output of the expression domain translation network, we employ a 3D geometric-aware loss function that reduces the distances between the vertices in the 3D mesh of the human and anime. By doing so, it forces high-fidelity and one-to-one mapping with respect to two cross-expression domains. Our method outperforms existing methods in both qualitative and quantitative analysis, marking a significant advancement in the field of cross-domain head reenactment.
Mirage: Model-Agnostic Graph Distillation for Graph Classification

GNNs, like other deep learning models, are data and computation hungry. There is a pressing need to scale training of GNNs on large datasets to enable their usage on low-resource environments. Graph distillation is an effort in that direction with the aim to construct a smaller synthetic training set from the original training data without significantly compromising model performance. While initial efforts are promising, this work is motivated by two key observations: (1) Existing graph distillation algorithms themselves rely on training with the full dataset, which undermines the very premise of graph distillation. (2) The distillation process is specific to the target GNN architecture and hyper-parameters and thus not robust to changes in the modeling pipeline. We circumvent these limitations by designing a distillation algorithm called Mirage for graph classification. Mirage is built on the insight that a message-passing GNN decomposes the input graph into a multiset of computation trees. Furthermore, the frequency distribution of computation trees is often skewed in nature, enabling us to condense this data into a concise distilled summary. By compressing the computation data itself, as opposed to emulating gradient flows on the original training set-a prevalent approach to date-Mirage transforms into an unsupervised and architecture-agnostic distillation algorithm. Extensive benchmarking on real-world datasets underscores Mirage's superiority, showcasing enhanced generalization accuracy, data compression, and distillation efficiency when compared to state-of-the-art baselines.
Towards More Accurate Diffusion Model Acceleration with A Timestep Aligner

A diffusion model, which is formulated to produce an image using thousands of denoising steps, usually suffers from a slow inference speed. Existing acceleration algorithms simplify the sampling by skipping most steps yet exhibit considerable performance degradation. By viewing the generation of diffusion models as a discretized integrating process, we argue that the quality drop is partly caused by applying an inaccurate integral direction to a timestep interval. To rectify this issue, we propose a timestep aligner that helps find a more accurate integral direction for a particular interval at the minimum cost. Specifically, at each denoising step, we replace the original parameterization by conditioning the network on a new timestep, which is obtained by aligning the sampling distribution to the real distribution. Extensive experiments show that our plug-in design can be trained efficiently and boost the inference performance of various state-of-the-art acceleration methods, especially when there are few denoising steps. For example, when using 10 denoising steps on the popular LSUN Bedroom dataset, we improve the FID of DDIM from 9.65 to 6.07, simply by adopting our method for a more appropriate set of timesteps. Code will be made publicly available.