Miracle-dz/UniFAR-FTUs huggingface dataset:
Marked Length Spectrum Rigidity for Surface Amalgams

In this paper, we show that simple, thick negatively curved two-dimensional P-manifolds, a large class of surface amalgams, are marked length spectrum rigid. That is, if two piecewise negatively curved Riemannian metrics (satisfying certain smoothness conditions) on a simple, thick two-dimensional P-manifold assign the same lengths to all closed geodesics, then they differ by an isometry up to isotopy. Our main theorem is a natural generalization of Croke and Otal's celebrated results about marked length spectrum rigidity of negatively curved surfaces.
Effects of microbial processes on electrolytic and interfacial electrical properties of unconsolidated sediments

The effect of microbial processes on electrical properties of unconsolidated sediments was investigated in a laboratory experiment consisting of biotic and abiotic sand columns. The biotic column (nutrient, diesel and bacteria) showed (a) temporal increase in the real, imaginary, and surface conductivity, and (b) temporal decrease in the formation factor. The abiotic columns (nutrient; and nutrient and diesel) showed no significant changes. Increase in microbial population numbers, decrease in organic carbon source, nitrate, and sulfate and increase in dissolved inorganic carbon and fluid conductivity were indicative of microbial activity in the biotic column. We also measure relative increase in the interfacial electrical properties that exceed relative increase in the electrolytic conductivity. Thus changes in the real and imaginary conductivity were induced by microbial processes. These results suggest that interpretation of geoelectrical data from near surface environments should consider effects of microbial processes.
Chinese Painting Style Transfer Using Deep Generative Models

Artistic style transfer aims to modify the style of the image while preserving its content. Style transfer using deep learning models has been widely studied since 2015, and most of the applications are focused on specific artists like Van Gogh, Monet, Cezanne. There are few researches and applications on traditional Chinese painting style transfer. In this paper, we will study and leverage different state-of-the-art deep generative models for Chinese painting style transfer and evaluate the performance both qualitatively and quantitatively. In addition, we propose our own algorithm that combines several style transfer models for our task. Specifically, we will transfer two main types of traditional Chinese painting style, known as"Gong-bi"and"Shui-mo"(to modern images like nature objects, portraits and landscapes.
Constraints on charged black holes from merger-ringdown signals in GWTC-3 and prospects for the Einstein Telescope

Whether astrophysical black holes (BHs) can have charge is a question to be addressed by observations. In the era of gravitational wave (GW) astronomy, one can constrain the charge of a merged BH remnant using the merger-ringdown signal of the GW data. Extending earlier studies, we analyze five GW events in GWTC-3, assuming Kerr-Newman BHs. Our results show no strong evidence for a charged BH, and give a limit on the charge-to-mass-ratio $Q<0.37$ at $90\%$ credible level (CL). Due to the charge-spin degeneracy in the waveform and the limited signal-to-noise ratios (SNRs), it is challenging for LIGO/Virgo/KAGRA observations to provide better constraints. We further simulate data for the Einstein Telescope (ET), where SNRs can be as large as $\sim270$ in the ringdown signal. These simulated events allow us to consider the 220, 221, and 330 ringdown modes altogether, which can help break the charge-spin degeneracy. The analysis of a simulated GW150914-like signal shows that ET can improve the constraints on the charge-to-mass-ratio to $Q \lesssim 0.2$ at $90\%$ CL with one ringdown signal.
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems

Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and adopt the corresponding rank metrics (e.g. OPA and NDCG@k) as optimization targets. However, these targets can not adapt to various cascade ranking scenarios with varying data complexities and model capabilities; and the existing metric-driven methods such as the Lambda framework can only optimize a rough upper bound of limited metrics, potentially resulting in sub-optimal and performance misalignment. To address these issues, we propose a novel perspective on optimizing cascade ranking systems by highlighting the adaptability of optimization targets to data complexities and model capabilities. Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively. We also introduce permutation matrix to represent the rank metrics and employ differentiable sorting techniques to relax hard permutation matrix with controllable approximate error bound. This enables us to optimize both the relaxed and full targets directly and more appropriately. We named this method as Adaptive Neural Ranking Framework (abbreviated as ARF). Furthermore, we give a specific practice under ARF. We use the NeuralSort to obtain the relaxed permutation matrix and draw on the variant of the uncertainty weight method in multi-task learning to optimize the proposed losses jointly. Experiments on a total of 4 public and industrial benchmarks show the effectiveness and generalization of our method, and online experiment shows that our method has significant application value.
Unified High-binding Watermark for Unconditional Image Generation Models

Deep learning techniques have implemented many unconditional image generation (UIG) models, such as GAN, Diffusion model, etc. The extremely realistic images (also known as AI-Generated Content, AIGC for short) produced by these models bring urgent needs for intellectual property protection such as data traceability and copyright certification. An attacker can steal the output images of the target model and use them as part of the training data to train a private surrogate UIG model. The implementation mechanisms of UIG models are diverse and complex, and there is no unified and effective protection and verification method at present. To address these issues, we propose a two-stage unified watermark verification mechanism with high-binding effects for such models. In the first stage, we use an encoder to invisibly write the watermark image into the output images of the original AIGC tool, and reversely extract the watermark image through the corresponding decoder. In the second stage, we design the decoder fine-tuning process, and the fine-tuned decoder can make correct judgments on whether the suspicious model steals the original AIGC tool data. Experiments demonstrate our method can complete the verification work with almost zero false positive rate under the condition of only using the model output images. Moreover, the proposed method can achieve data steal verification across different types of UIG models, which further increases the practicality of the method.
Federated Reinforcement Learning for Resource Allocation in V2X Networks

Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or machine learning (e.g., reinforcement learning). In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL). On one hand, the usage of RL overcomes many challenges from the model-based optimization schemes. On the other hand, federated learning (FL) enables agents to deal with a number of practical issues, such as privacy, communication overhead, and exploration efficiency. The framework of FRL is then implemented by the inexact alternative direction method of multipliers (ADMM), where subproblems are solved approximately using policy gradients and accelerated by an adaptive step size calculated from their second moments. The developed algorithm, PASM, is proven to be convergent under mild conditions and has a nice numerical performance compared with some baseline methods for solving the resource allocation problem in a V2X network.
Hypergraph Tur\'an problems in $\ell_2$-norm, the Kleitman-West problem and Erd\H{o}s-Ko-Rado

For a $k$-uniform hypergraph $\mathcal{H}$, the \emph{codegree squared sum} $\text{co}_2(\mathcal{H})$ is the square of the $\ell_2$-norm of the codegree vector of $\mathcal{H}$, and for a family $\mathscr{F}$ of $k$-uniform hypergraphs, the codegree squared extremal number $\text{exco}_2(n, \mathscr{F})$ is the maximum codegree squared sum of a hypergraph on $n$ vertices which does not contain any hypergraph in $\mathscr{F}$. Balogh, Clemen and Lidick\'y recently introduced the codegree squared extremal number and determined it for a number of $3$-uniform hypergraphs, including the complete graphs $K_4^3$ and $K_5^3$. In this paper, we show that Tur\'an problems in the $\ell_2$-norm are closely connected with a forbidden subgraph version of the Kleitman-West problem, the edge isoperimetric problem in the Johnson graph. We prove a version of the classical Erd\H{o}s-Ko-Rado theorem for the codegree squared extremal number: if $\mathcal{F} \subset \binom{[n]}{k}$ is intersecting and $n\ge 2k$, then \[\text{co}_2(\mathcal{F}) \le \binom{n-1}{k-1}(1+(n-k+1)(k-1)),\] with equality only for the star for $n>2k$. Our main tool is an inequality of Bey, which also gives a general upper bound on $\text{exco}_2(n, \mathscr{F})$. We also prove versions of the Erd\H{o}s Matching Conjecture and the $t$-intersecting Erd\H{o}s-Ko-Rado theorem for the codegree squared extremal number for large $n$, determine the exact codegree squared extremal number of minimal and linear $3$-paths and $3$-cycles, and determine asymptotically the codegree squared extremal number of minimal and linear $s$-paths and $s$-cycles for $s\ge 4$.
Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization

Dialogue summarization involves a wide range of scenarios and domains. However, existing methods generally only apply to specific scenarios or domains. In this study, we propose a new pre-trained model specifically designed for multi-scenario multi-domain dialogue summarization. It adopts a multi-stage pre-training strategy to reduce the gap between the pre-training objective and fine-tuning objective. Specifically, we first conduct domain-aware pre-training using large-scale multi-scenario multi-domain dialogue data to enhance the adaptability of our pre-trained model. Then, we conduct task-oriented pre-training using large-scale multi-scenario multi-domain"dialogue-summary"parallel data annotated by ChatGPT to enhance the dialogue summarization ability of our pre-trained model. Experimental results on three dialogue summarization datasets from different scenarios and domains indicate that our pre-trained model significantly outperforms previous state-of-the-art models in full fine-tuning, zero-shot, and few-shot settings.
Proposições para análise da diferenciação espacial nos estudos sobre rede urbana: as noções de convergência e divergência

Os estudos urbanos vem de longa data apontando para os processos de diferenciacao entre os centros e entre suas respectivas redes urbanas, definidos pela complexidade genetica e pela complexidade das estruturas dimensionais, funcionais e espaciais. Tais processos, contudo, apresentam especificidades, dentre as quais se destaca a re-diferenciacao entre centros que estao no comando de dado segmento de rede. Na realidade, trata-se da ascensao de um novo centro, sob bases novas e mais amplas, em detrimento de um antigo, indicando mudancas com descontinuidade, caracterizadas, sobretudo, por processos de convergencia e divergencia, quer dizer, pela contradicao entre acoes igualizadoras e acoes diferenciadoras, que resultam em reestruturacao da rede urbana.
Swap and Predict - Predicting the Semantic Changes in Words across Corpora by Context Swapping

Meanings of words change over time and across domains. Detecting the semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions. We consider the problem of predicting whether a given target word, $w$, changes its meaning between two different text corpora, $\mathcal{C}_1$ and $\mathcal{C}_2$. For this purpose, we propose $\textit{Swapping-based Semantic Change Detection}$ (SSCD), an unsupervised method that randomly swaps contexts between $\mathcal{C}_1$ and $\mathcal{C}_2$ where $w$ occurs. We then look at the distribution of contextualised word embeddings of $w$, obtained from a pretrained masked language model (MLM), representing the meaning of $w$ in its occurrence contexts in $\mathcal{C}_1$ and $\mathcal{C}_2$. Intuitively, if the meaning of $w$ does not change between $\mathcal{C}_1$ and $\mathcal{C}_2$, we would expect the distributions of contextualised word embeddings of $w$ to remain the same before and after this random swapping process. Despite its simplicity, we demonstrate that even by using pretrained MLMs without any fine-tuning, our proposed context swapping method accurately predicts the semantic changes of words in four languages (English, German, Swedish, and Latin) and across different time spans (over 50 years and about five years). Moreover, our method achieves significant performance improvements compared to strong baselines for the English semantic change prediction task. Source code is available at https://github.com/a1da4/svp-swap .
The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration

Abstract To investigate the nature of the peak near 700 nm on reflectance spectrum of water, simultaneous measurements of the reflectance spectra in the region of 400 to 750 nm and relevant water quality constituent concentrations (chlorophyll, dissolved organic matter and suspended matter) were carried out. The data set cover various trophic states of water bodies, all seasons and different climatic regimes. A shift of the peak position from about 680 nm up to 7l5 nm and an increase of the peak magnitude when chlorophyll concentration increased were observed. The magnitude and the peak position could be used as precise indicators and predictors for the phytoplankton concentration. It contributed significantly to the increase in the accuracy of the derivation of chlorophyll values from multispectral data.
On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data

Recent deep learning-based methods outperform traditional learning methods on remote sensing (RS) semantic segmentation/classification tasks. However, they require large training datasets and are generally known for lack of transferability due to the highly disparate RS image content across different geographical regions. Yet, there is no comprehensive analysis of their transferability, i.e., to which extent a model trained on a source domain can be readily applicable to a target domain. Therefore, in this paper, we aim to investigate the raw transferability of traditional and deep learning (DL) models, as well as the effectiveness of domain adaptation (DA) approaches in enhancing the transferability of the DL models (adapted transferability). By utilizing four highly diverse RS datasets, we train six models with and without three DA approaches to analyze their transferability between these datasets quantitatively. Furthermore, we developed a straightforward method to quantify the transferability of a model using the spectral indices as a medium and have demonstrated its effectiveness in evaluating the model transferability at the target domain when the labels are unavailable. Our experiments yield several generally important yet not well-reported observations regarding the raw and adapted transferability. Moreover, our proposed label-free transferability assessment method is validated to be better than posterior model confidence. The findings can guide the future development of generalized RS learning models. The trained models are released under this link: https://github.com/GDAOSU/Transferability-Remote-Sensing
Imaging slow failure in triaxially deformed Etna basalt using 3D acoustic‐emission location and X‐ray computed tomography

We have deformed basalt from Mount Etna (Italy) in triaxial compression tests under an effective confining pressure representative of conditions under a volcanic edifice (40 MPa), and at a constant strain rate of 5 × 10−6 s−1. Despite containing a high level of pre‐existing microcrack damage, Etna basalt retains a high strength of 475 MPa. We have monitored the complete deformation cycle through contemporaneous measurements of axial strain, pore volume change, compressional wave velocity change and acoustic emission (AE) output. We have been able to follow the complete evolution of the throughgoing shear fault without recourse to any artificial means of slowing the deformation. Locations of AE events over time yields an estimate of the fault propagation velocity of between 2 and 4 mm.s−1. We also find excellent agreement between AE locations and post‐test images from X‐ray microtomography scanning that delineates deformation zone architecture.
ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models

As large language models are integrated into society, robustness toward a suite of prompts is increasingly important to maintain reliability in a high-variance environment.Robustness evaluations must comprehensively encapsulate the various settings in which a user may invoke an intelligent system. This paper proposes ASSERT, Automated Safety Scenario Red Teaming, consisting of three methods -- semantically aligned augmentation, target bootstrapping, and adversarial knowledge injection. For robust safety evaluation, we apply these methods in the critical domain of AI safety to algorithmically generate a test suite of prompts covering diverse robustness settings -- semantic equivalence, related scenarios, and adversarial. We partition our prompts into four safety domains for a fine-grained analysis of how the domain affects model performance. Despite dedicated safeguards in existing state-of-the-art models, we find statistically significant performance differences of up to 11% in absolute classification accuracy among semantically related scenarios and error rates of up to 19% absolute error in zero-shot adversarial settings, raising concerns for users' physical safety.
A hybrid model for forecasting daily rainfall.

In a hydrologic basin where precipitation rates have strong seasonal characteristics, simple seasonal forecasts of rainfall along with regression analysis on a few related meteorological observations can be used to obtain an estimate of the anticipated rainfall rate one day in advance. In this paper a model for forecasting daily rainfall with one day lead time is presented. The model uses smoothed normal daily rainfall rates as seasonal forecasts and a linear regression model on deviations of atmospheric pressure, temperature, and humidity from their seasonal mean values for estimating departures from seasonal rainfall forecasts. The model has been applied to and tested with daily rainfall data of Dhaka (Dacca) city in Bangladesh.
A Systematic Evaluation of Large Language Models on Out-of-Distribution Logical Reasoning Tasks

Large language models (LLMs), such as GPT-3.5 and GPT-4, have greatly advanced the performance of artificial systems on various natural language processing tasks to human-like levels. However, their generalisation and robustness to perform logical reasoning remain under-evaluated. To probe this ability, we propose three new logical reasoning datasets named"ReClor-plus","LogiQA-plus"and"LogiQAv2-plus", each featuring three subsets: the first with randomly shuffled options, the second with the correct choices replaced by"none of the other options are correct", and a combination of the previous two subsets. We carry out experiments on these datasets with both discriminative and generative LLMs and show that these simple tricks greatly hinder the performance of the language models. Despite their superior performance on the original publicly available datasets, we find that all models struggle to answer our newly constructed datasets. We show that introducing task variations by perturbing a sizable training set can markedly improve the model's generalisation and robustness in logical reasoning tasks. Moreover, applying logic-driven data augmentation for fine-tuning, combined with prompting can enhance the generalisation performance of both discriminative large language models and generative large language models. These results offer insights into assessing and improving the generalisation and robustness of large language models for logical reasoning tasks. We make our source code and data publicly available \url{https://github.com/Strong-AI-Lab/Logical-and-abstract-reasoning}.
Timing and localization of reconnection signatures – Is there a substorm model problem?

Observations place ionospheric substorm onset signatures on field lines that map to the near‐Earth plasma sheet, and field‐aligned currents (FACs) are observed before dipolarization, equatorward of open field lines. The near‐Earth neutral line (NENL) model, however, appears to suggest perturbations to originate from the x‐line on newly reconnected field lines, which map to much higher latitudes. Its order of dipolarization, FACs, and subsequent ionospheric signatures contradicts the observations. Using large‐scale kinetic simulations, we demonstrate that these “flaws” of the NENL model are misconceptions and that the timing and location of signatures in the model are indeed consistent with observations. In a thinned current sheet, energetic ions are virtually unmagnetized, get further energized when passing through the neutral sheet, and travel earthward quickly and close to the neutral sheet, thus providing a pathway for early substorm signatures at relatively low latitudes.
MIR2: Towards Provably Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization

Robust multi-agent reinforcement learning (MARL) necessitates resilience to uncertain or worst-case actions by unknown allies. Existing max-min optimization techniques in robust MARL seek to enhance resilience by training agents against worst-case adversaries, but this becomes intractable as the number of agents grows, leading to exponentially increasing worst-case scenarios. Attempts to simplify this complexity often yield overly pessimistic policies, inadequate robustness across scenarios and high computational demands. Unlike these approaches, humans naturally learn adaptive and resilient behaviors without the necessity of preparing for every conceivable worst-case scenario. Motivated by this, we propose MIR2, which trains policy in routine scenarios and minimize Mutual Information as Robust Regularization. Theoretically, we frame robustness as an inference problem and prove that minimizing mutual information between histories and actions implicitly maximizes a lower bound on robustness under certain assumptions. Further analysis reveals that our proposed approach prevents agents from overreacting to others through an information bottleneck and aligns the policy with a robust action prior. Empirically, our MIR2 displays even greater resilience against worst-case adversaries than max-min optimization in StarCraft II, Multi-agent Mujoco and rendezvous. Our superiority is consistent when deployed in challenging real-world robot swarm control scenario. See code and demo videos in Supplementary Materials.