Miracle-dz/UniFAR-FTUs huggingface dataset:
HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction

Temporal link prediction, aiming to predict future edges between paired nodes in a dynamic graph, is of vital importance in diverse applications. However, existing methods are mainly built upon uniform Euclidean space, which has been found to be conflict with the power-law distributions of real-world graphs and unable to represent the hierarchical connections between nodes effectively. With respect to the special data characteristic, hyperbolic geometry offers an ideal alternative due to its exponential expansion property. In this paper, we propose HGWaveNet, a novel hyperbolic graph neural network that fully exploits the fitness between hyperbolic spaces and data distributions for temporal link prediction. Specifically, we design two key modules to learn the spatial topological structures and temporal evolutionary information separately. On the one hand, a hyperbolic diffusion graph convolution (HDGC) module effectively aggregates information from a wider range of neighbors. On the other hand, the internal order of causal correlation between historical states is captured by hyperbolic dilated causal convolution (HDCC) modules. The whole model is built upon the hyperbolic spaces to preserve the hierarchical structural information in the entire data flow. To prove the superiority of HGWaveNet, extensive experiments are conducted on six real-world graph datasets and the results show a relative improvement by up to 6.67% on AUC for temporal link prediction over SOTA methods.
Align-DETR: Improving DETR with Simple IoU-aware BCE loss

DETR has set up a simple end-to-end pipeline for object detection by formulating this task as a set prediction problem, showing promising potential. However, despite the significant progress in improving DETR, this paper identifies a problem of misalignment in the output distribution, which prevents the best-regressed samples from being assigned with high confidence, hindering the model's accuracy. We propose a metric, recall of best-regressed samples, to quantitively evaluate the misalignment problem. Observing its importance, we propose a novel Align-DETR that incorporates a localization precision-aware classification loss in optimization. The proposed loss, IA-BCE, guides the training of DETR to build a strong correlation between classification score and localization precision. We also adopt the mixed-matching strategy, to facilitate DETR-based detectors with faster training convergence while keeping an end-to-end scheme. Moreover, to overcome the dramatic decrease in sample quality induced by the sparsity of queries, we introduce a prime sample weighting mechanism to suppress the interference of unimportant samples. Extensive experiments are conducted with very competitive results reported. In particular, it delivers a 46 (+3.8)% AP on the DAB-DETR baseline with the ResNet-50 backbone and reaches a new SOTA performance of 50.2% AP in the 1x setting on the COCO validation set when employing the strong baseline DINO. Our code is available at https://github.com/FelixCaae/AlignDETR.
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval

Current dense retrievers (DRs) are limited in their ability to effectively process misspelled queries, which constitute a significant portion of query traffic in commercial search engines. The main issue is that the pre-trained language model-based encoders used by DRs are typically trained and fine-tuned using clean, well-curated text data. Misspelled queries are typically not found in the data used for training these models, and thus misspelled queries observed at inference time are out-of-distribution compared to the data used for training and fine-tuning. Previous efforts to address this issue have focused on fine-tuning strategies, but their effectiveness on misspelled queries remains lower than that of pipelines that employ separate state-of-the-art spell-checking components. To address this challenge, we propose ToRoDer (TypOs-aware bottlenecked pre-training for RObust DEnse Retrieval), a novel pre-training strategy for DRs that increases their robustness to misspelled queries while preserving their effectiveness in downstream retrieval tasks. ToRoDer utilizes an encoder-decoder architecture where the encoder takes misspelled text with masked tokens as input and outputs bottlenecked information to the decoder. The decoder then takes as input the bottlenecked embeddings, along with token embeddings of the original text with the misspelled tokens masked out. The pre-training task is to recover the masked tokens for both the encoder and decoder. Our extensive experimental results and detailed ablation studies show that DRs pre-trained with ToRoDer exhibit significantly higher effectiveness on misspelled queries, sensibly closing the gap with pipelines that use a separate, complex spell-checker component, while retaining their effectiveness on correctly spelled queries.
Electrons with a self-field as solutions to nonlnear PDE

The Maxwell-Dirac equations give a model of an electron in an electromagnetic (e-m) eld, in which neither the Dirac or the e-m elds are quantized. The two equations are coupled via the Dirac current which acts as a source in the Maxwell equation, resulting in a nonlinear system of partial di erential equations (PDE's). In this way the selfeld of the electron is included. We review our results to date and give the four real consistency conditions (one of which is conservation of charge) which apply to the components of the wavefunction and its rst derivatives. These must be met by any solutions to the Dirac equation. These conditions prove to be invaluable in the analysis of the nonlinear system, and generalizable to higher dimensional supersymmetric matter. In earlier papers, we have shown analytically that in an isolated stationary system, the surrounding electon eld must be equal and opposite to the central (external) eld. The nonlinearity forces electric neutrality, at least in the static case. We illustrate these properties with a numerical family of orbits which occur in the (static) spherical and cylindrical ODE cases. These solutions are highly localized and die o exponentially with increasing distance from the central charge. 1. The Maxwell-Dirac Equations and QED The coupled Maxwell-Dirac equations can be written as follows: (@ ieA ) + im = 0 where = 0; : : : ; 3 F = A ; A ; @ F = 4 ej
Rapid but narrow – Evolutionary adaptation and transcriptional response of Drosophila melanogaster to toxic mould

Insects have adapted to a multitude of environmental conditions, including the presence of xenobiotic noxious substances. Environmental microorganisms, particularly rich on ephemeral resources, employ these noxious chemicals in a chemical warfare against predators and competitors, driving co‐evolutionary adaptations. In order to analyse how environmental microbes may be driving such evolutionary adaptations, we experimentally evolved Drosophila melanogaster populations by exposing larvae to the toxin‐producing mould Aspergillus nidulans that infests the flies' breeding substrate. To disentangle the effects of the mycotoxin Sterigmatocystin from other substrate modifications inflicted by the mould, we used the following four selection regimes: (i) control without fungus, (ii) A. nidulans wild type, (iii) a mutant of A. nidulans ΔlaeA with impaired toxin production, (iv) synthetic Sterigmatocystin. Experimental evolution was carried out in five independent D. melanogaster populations each, for a total of 11 generations. We further combined our evolution experiment with transcriptome analysis to identify evolutionary shifts in gene expression due to the selection regimes and mould confrontation. Populations that evolved in presence of the toxin‐producing mould or the pure mycotoxin rapidly adapted to the respective conditions and showed higher viability in subsequent confrontations. Yet, mycotoxin‐selected populations had no advantage in A. nidulans wild type confrontation. Moreover, distinctive changes in gene expression related to the selection‐regime contrast were only associated with the toxin‐producing‐fungus regime and comprised a narrow set of genes. Thus, it needs the specific conditions of the selection agent to enable adaptation to the fungus.
Competitive-exclusion versus competitive-coexistence for systems inthe plane

We investigate global behavior of $x_{n+1} = T(x_{n}),\quad n=0,1,2,. $ (E) where $T:\mathcal{ R}\rightarrow \mathcal{ R}$ is a competitive (monotone with respect to the south-east ordering) map on a set $\mathcal{R}\subset \mathbb{R}^2$ with nonempty interior. We assume the existence of a unique fixed point $\overline{e}$ in the interior of $\mathcal{ R}$. We give very general conditions which are easily verifiable for (E) to exhibit either competitive-exclusion or competitive-coexistence. More specifically, we obtain sufficient conditions for the interior fixed point $\overline{ e}$ to be a global attractor when $\mathcal{ R}$ is a rectangular region. We also show that when $T$ is strongly monotone in $\mathcal{ R}^{\circ}$ (interior of $\mathcal{ R}$), $\mathcal{ R}$ is convex, the unique interior equilibrium $\overline{ e}$ is a saddle, and a technical condition is satisfied, the corresponding global stable and unstable manifolds are the graphs of monotonic functions, and the global stable manifold splits the domain into two connected regions, which under additional conditions on $\mathcal{R}$ and on $T$ are shown to be basins of attraction of fixed points on the boundary of $\mathcal{R}$. Applications of the main results to specific difference equations are given.
Understanding the Influence of Motivation on Requirements Engineering-related Activities

Motivation has been identified as one of the key human aspects for software practitioners involved in Software Engineering (SE) activities to complete their tasks successfully. While prior research on motivation in SE exists, empirical studies on its influence specifically on RE are limited. As requirements engineering (RE)-related activities are a highly human-dependent phase of SE; it is important to identify how motivation influences RE. This study aims to identify the influence of motivation on RE, provide a better understanding of its effects, and offer guidance on how to handle them. By conducting semi-structured interviews with 21 RE-involved software practitioners, we developed a preliminary theoretical framework using socio-technical grounded theory (STGT) to explain the influence of motivation on RE. Our framework attempts to explain the contextual, causal, and intervening conditions that can give rise to or mediate the influence of motivation on RE. Furthermore, we have discovered a set of strategies that can enhance motivating situations or mitigate demotivating ones and the consequences resulting from applying these strategies. Our findings will benefit software practitioners by offering guidance on managing the influence of motivation on RE and assist researchers in refining and exploring our theoretical framework for diverse SE contexts in the future.
Exploring Incompatible Knowledge Transfer in Few-shot Image Generation

Few-shot image generation (FSIG) learns to generate diverse and high-fidelity images from a target domain using a few (e.g., 10) reference samples. Existing FSIG methods select, preserve and transfer prior knowledge from a source generator (pretrained on a related domain) to learn the target generator. In this work, we investigate an underexplored issue in FSIG, dubbed as incompatible knowledge transfer, which would significantly degrade the realisticness of synthetic samples. Empirical observations show that the issue stems from the least significant filters from the source generator. To this end, we propose knowledge truncation to mitigate this issue in FSIG, which is a complementary operation to knowledge preservation and is implemented by a lightweight pruning-based method. Extensive experiments show that knowledge truncation is simple and effective, consistently achieving state-of-the-art performance, including challenging setups where the source and target domains are more distant. Project Page: yunqing-me.github. io/RICK.
The diameters and velocities of the droplets ejected after splashing

When a drop impacts a smooth, dry surface at a velocity above the so-called critical speed for drop splashing, the initial liquid volume loses its integrity, fragmenting into tiny droplets that are violently ejected radially outwards. Here, we make use of the model of Riboux & Gordillo (Phys. Rev. Lett., vol. 113, 2014, 024507), together with a one-dimensional approximation describing the flow in the ejected liquid sheet and of balances of mass and momentum at the border of the sheet, to calculate mean sizes and velocities of the ejected drops. The predictions of the model are in good agreement with experiments.
A tutorial on the Bayesian statistical approach to inverse problems

Inverse problems are ubiquitous in science and engineering. Two categories of inverse problems concerning a physical system are (1) estimate parameters in a model of the system from observed input–output pairs and (2) given a model of the system, reconstruct the input to it that caused some observed output. Applied inverse problems are challenging because a solution may (i) not exist, (ii) not be unique, or (iii) be sensitive to measurement noise contaminating the data. Bayesian statistical inversion (BSI) is an approach to tackle ill-posed and/or ill-conditioned inverse problems. Advantageously, BSI provides a “solution” that (i) quantifies uncertainty by assigning a probability to each possible value of the unknown parameter/input and (ii) incorporates prior information and beliefs about the parameter/input. Herein, we provide a tutorial of BSI for inverse problems by way of illustrative examples dealing with heat transfer from ambient air to a cold lime fruit. First, we use BSI to infer a parameter in a dynamic model of the lime temperature from measurements of the lime temperature over time. Second, we use BSI to reconstruct the initial condition of the lime from a measurement of its temperature later in time. We demonstrate the incorporation of prior information, visualize the posterior distributions of the parameter/initial condition, and show posterior samples of lime temperature trajectories from the model. Our Tutorial aims to reach a wide range of scientists and engineers.
PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction

The ability to predict future structure features of environments based on past perception information is extremely needed by autonomous vehicles, which helps to make the following decision-making and path planning more reasonable. Recently, point cloud prediction (PCP) is utilized to predict and describe future environmental structures by the point cloud form. In this letter, we propose a novel efficient Transformer-based network to predict the future LiDAR point clouds exploiting the past point cloud sequences. We also design a semantic auxiliary training strategy to make the predicted LiDAR point cloud sequence semantically similar to the ground truth and thus improves the significance of the deployment for more tasks in real-vehicle applications. Our approach is completely self-supervised, which means it does not require any manual labeling and has a solid generalization ability toward different environments. The experimental results show that our method outperforms the state-of-the-art PCP methods on the prediction results and semantic similarity, and has a good real-time performance.
Resonant decay of global MHD modes at “thick” interfaces

A global surface mode can exist on a surface which is not a true discontinuity, but without sufficient dissipation it is not a normal mode and it decays in time via resonance absorption. The decay rate can be calculated analytically when the surface is thin. The goal of this paper is to numerically estimate the decay rate when the surface is not thin. The author considers a cold plasma in a uniform magnetic field, and he takes the density to vary linearly across the surface. In his linearized calculation, the globalsurface mode is driven in steady state by an antenna located in one of the uniform regions external to the surface. The frequency is a free parameter, and resonance curves are computed numerically without approximations. The widths of the resonance curves are used to estimate the free decay times of undriven surface modes, via the uncertainty principle, equation (1). This procedure agrees with the analytical results for a thin surface. The thin surface results are found to break down when kT {approx} 0.3, where T is the surface thickness and k is the wave number along the surface. When the angle between k and B{sub 0} exceeds about 40 degrees,more » the decay rates show distinct maxima at kT {approx} 0.5-1.0. When applied to the active solar corona, the decay rates are large enough to account for the coronal heating, but it should be kept in mind that the role of nonlinearity in resonance absorption is still undetermined. « less
ImpressionGPT: An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT

The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians, and it is typically written by radiologists based on the 'Findings' section. However, writing numerous impressions can be laborious and error-prone for radiologists. Although recent studies have achieved promising results in automatic impression generation using large-scale medical text data for pre-training and fine-tuning pre-trained language models, such models often require substantial amounts of medical text data and have poor generalization performance. While large language models (LLMs) like ChatGPT have shown strong generalization capabilities and performance, their performance in specific domains, such as radiology, remains under-investigated and potentially limited. To address this limitation, we propose ImpressionGPT, which leverages the in-context learning capability of LLMs by constructing dynamic contexts using domain-specific, individualized data. This dynamic prompt approach enables the model to learn contextual knowledge from semantically similar examples from existing data. Additionally, we design an iterative optimization algorithm that performs automatic evaluation on the generated impression results and composes the corresponding instruction prompts to further optimize the model. The proposed ImpressionGPT model achieves state-of-the-art performance on both MIMIC-CXR and OpenI datasets without requiring additional training data or fine-tuning the LLMs. This work presents a paradigm for localizing LLMs that can be applied in a wide range of similar application scenarios, bridging the gap between general-purpose LLMs and the specific language processing needs of various domains.
An arbitrarily high order and asymptotic preserving kinetic scheme in compressible fluid dynamic

We present a class of arbitrarily high order fully explicit kinetic numerical methods in compressible fluid dynamics, both in time and space, which include the relaxation schemes by Jin and Xin. These methods can use the CFL number larger or equal to unity on regular Cartesian meshes for the multi-dimensional case. These kinetic models depend on a small parameter that can be seen as a “Knudsen” number. The method is asymptotic preserving in this Knudsen number. Also, the computational costs of the method are of the same order of a fully explicit scheme. This work is the extension of Abgrall et al. (2022) [3] to multi-dimensional systems. We have assessed our method on several problems for two-dimensional scalar problems and Euler equations and the scheme has proven to be robust and to achieve the theoretically predicted high order of accuracy on smooth solutions.
Freeze-in baryogenesis and early matter domination

The freeze-in mechanism has been shown to allow the simultaneous generation of cosmic dark matter and a viable matter-antimatter asymmetry in the universe. When the underlying interactions are described by higher-dimensional, non-renormalizable operators, the relevant freeze-in processes take place close to the highest considered cosmic temperatures. In this paper we study how the presence of a fluid that temporarily dominates the energy content of the early universe affects the predictions of this ``Ultraviolet Freeze-In Baryogenesis'' scenario. We find that this additional cosmic component has a significant impact on the predictions of concrete microscopic models, allowing for reheating temperatures which are much lower than those required in the simplest cosmological scenario. Moreover, we show that inflationary observables can constrain the parameter space of such models, once the latter are examined in conjunction with concrete models of inflation.
Prefix monoids of groups and right units of special inverse monoids

Abstract A prefix monoid is a finitely generated submonoid of a finitely presented group generated by the prefixes of its defining relators. Important results of Guba (1997), and of Ivanov, Margolis and Meakin (2001), show how the word problem for certain one-relator monoids, and inverse monoids, can be reduced to solving the membership problem in prefix monoids of certain one-relator groups. Motivated by this, in this paper, we study the class of prefix monoids of finitely presented groups. We obtain a complete description of this class of monoids. All monoids in this family are finitely generated, recursively presented and group-embeddable. Our results show that not every finitely generated recursively presented group-embeddable monoid is a prefix monoid, but for every such monoid, if we take a free product with a suitably chosen free monoid of finite rank, then we do obtain a prefix monoid. Conversely, we prove that every prefix monoid arises in this way. Also, we show that the groups that arise as groups of units of prefix monoids are precisely the finitely generated recursively presented groups, whereas the groups that arise as Schützenberger groups of prefix monoids are exactly the recursively enumerable subgroups of finitely presented groups. We obtain an analogous result classifying the Schützenberger groups of monoids of right units of special inverse monoids. We also give some examples of right cancellative monoids arising as monoids of right units of finitely presented special inverse monoids, and we show that not all right cancellative recursively presented monoids belong to this class.
A general approach to asymptotic elimination of aggregation functions and generalized quantifiers

We consider a logic with truth values in the unit interval and which uses aggregation functions instead of quantifiers, and we describe a general approach to asymptotic elimination of aggregation functions and, indirectly, of asymptotic elimination of Mostowski style generalized quantifiers, since such can be expressed by using aggregation functions. The notion of ``local continuity'' of an aggregation function, which we make precise in two (related) ways, plays a central role in this approach.
Single-photon optical bistability in a small nonlinear cavity

We investigate the bistability in a small Fabry-Perot interferometer (FPI) with the optical wavelength size cavity, the nonlinear Kerr medium and only a few photons, on average, excited by the external quantum field. Analytical expressions for the stationary mean photon number, the bistability domain, the field and the photon number fluctuation spectra are obtained. Multiple stationary states of the FPI cavity field with different spectra are possible at realistic conditions, for example, in the FPI with the photonic crystal cavity and the semiconductor-doped glass nonlinear medium.