Miracle-dz/UniFAR-FTUs huggingface dataset:
VFed-SSD: Towards Practical Vertical Federated Advertising

As an emerging secure learning paradigm in lever-aging cross-agency private data, vertical federatedlearning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher. However, there are two key challenges in applying it to advertising systems: a) the limited scale of labeled overlapping samples, and b) the high cost of real-time cross-agency serving. In this paper, we propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. We identify that: i)there are massive unlabeled overlapped data available in advertising systems, and ii) we can keep a balance between model performance and inference cost by decomposing the federated model. Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data and propose the Split Knowledge Distillation (SplitKD) schema to avoid cross-agency serving. Empirical studies on three industrial datasets exhibit the effectiveness of ourmethods, with the median AUC over all datasets improved by 0.86% and 2.6% in the local andthe federated deployment mode respectively. Overall, our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
Image Harmonization with Diffusion Model

Image composition in image editing involves merging a foreground image with a background image to create a composite. Inconsistent lighting conditions between the foreground and background often result in unrealistic composites. Image harmonization addresses this challenge by adjusting illumination and color to achieve visually appealing and consistent outputs. In this paper, we present a novel approach for image harmonization by leveraging diffusion models. We conduct a comparative analysis of two conditional diffusion models, namely Classifier-Guidance and Classifier-Free. Our focus is on addressing the challenge of adjusting illumination and color in foreground images to create visually appealing outputs that seamlessly blend with the background. Through this research, we establish a solid groundwork for future investigations in the realm of diffusion model-based image harmonization.
The inverse sturn−Liouville problem III

On considere la theorie spectrale inverse du probleme de Sturm-Liouville-y''+q(x)y+λy, 0≤x≤1 avec les conditions aux limites y(0)=0, by(1)+y'(1)=0, q∈L R 2 (0,1) et b∈R
Multi-Scale McKean-Vlasov SDEs: Moderate Deviation Principle in Different Regimes

The main aim of this paper is to study the moderate deviation principle for McKean-Vlasov stochastic differential equations with multiple scales. Specifically, we are interested in the asymptotic estimates of the deviation processes $\frac{X^{\delta}-\bar{X}}{\lambda(\delta)}$ as $\delta\to 0$ in different regimes (i.e. $\varepsilon=o(\delta)$ and $\varepsilon=O(\delta)$), where $\delta$ stands for the intensity of the noise and $\varepsilon:=\varepsilon(\delta)$ stands for the time scale separation. The rate functions in two regimes are different, in particular, we show that it is strongly affected by the noise of the fast component in latter regime, which is essentially different from the former one and the case of large deviations (cf. \cite{HLLS}). As a by-product, the explicit representation formulas of the rate functions in all of regimes are also given. The main techniques are based on the weak convergence approach and the functional occupation measure approach.
Provably Powerful Graph Neural Networks for Directed Multigraphs

This paper analyses a set of simple adaptations that transform standard message-passing Graph Neural Networks (GNN) into provably powerful directed multigraph neural networks. The adaptations include multigraph port numbering, ego IDs, and reverse message passing. We prove that the combination of these theoretically enables the detection of any directed subgraph pattern. To validate the effectiveness of our proposed adaptations in practice, we conduct experiments on synthetic subgraph detection tasks, which demonstrate outstanding performance with almost perfect results. Moreover, we apply our proposed adaptations to two financial crime analysis tasks. We observe dramatic improvements in detecting money laundering transactions, improving the minority-class F1 score of a standard message-passing GNN by up to 30%, and closely matching or outperforming tree-based and GNN baselines. Similarly impressive results are observed on a real-world phishing detection dataset, boosting three standard GNNs’ F1 scores by around 15% and outperforming all baselines. An extended version with appendices can be found on arXiv: https://arxiv.org/abs/2306.11586.
Modified Scattering of Solutions to the Relativistic Vlasov-Maxwell System Inside the Light Cone

We consider the relativistic Vlasov-Maxwell system in three dimensions and study the limiting asymptotic behavior as $t \to \infty$ of solutions launched by small, compactly supported initial data. In particular, we prove that such solutions scatter to a modification of the free-streaming asymptotic profile. More specifically, we show that the spatial average of the particle distribution function converges to a smooth, compactly-supported limit and establish the precise, self-similar asymptotic behavior of the electric and magnetic fields, as well as, the macroscopic densities and their derivatives in terms of this limiting function. Upon constructing the limiting fields, a modified $L^\infty$ scattering result for the particle distribution function along the associated trajectories of free transport corrected by the limiting Lorentz force is then obtained. When the limiting charge density does not vanish, our estimates are sharp up to a logarithmic correction. However, when this quantity is identically zero in the limit, the limiting current density and fields may also vanish, which gives rise to decay rates that are faster than those attributed to the dispersive mechanisms in the system.
Numerical modeling of time dependent Diffusive Shock Acceleration

Motivated by cosmic ray (CR) re-acceleration at a potential Galactic Wind Termination Shock (GWTS), we present a numerical model for time-dependent Diffusive Shock Acceleration (DSA). We use the stochastic differential equation solver (DiffusionSDE) of the cosmic ray propagation framework CRPropa3.2 with two modifications: An importance sampling module is introduced to improve statistics at high energies in order to keep the simulation time short. An adaptive time step is implemented in the DiffusionSDE module. This ensures to efficiently meet constraints on the time and diffusion step, which is crucial to obtain the correct shock spectra. The time evolution of the spectrum at a one-dimensional planar shock is verified against the solution obtained by the grid-based solver VLUGR3 for both energy-independent and energy-dependent diffusion. We show that the injection of pre-accelerated particles can lead to a broken power law spectrum in momentum if the incoming spectrum of CRs is harder than the re-accelerated spectrum. If the injected spectrum is steeper, the shock spectrum dominates at all energies. We finally apply the developed model to the GWTS by considering a spherically symmetric shock, a spiral Galactic magnetic field, and anisotropic diffusion. The time-dependent spectrum at the shock is modeled as a basis for further studies.
Spin Transport and Magnetic Proximity Effect in CoFeB/Normal Metal/Pt Trilayers

We present a study of the damping and spin pumping properties of CoFeB/X/Pt systems with <inline-formula><tex-math notation="LaTeX">$\rm X=Al,Cr$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$\rm Ta$</tex-math></inline-formula>. We show that the total damping of the CoFeB/Pt systems is strongly reduced when an interlayer is introduced independently of the material. Using a model that considers spin relaxation, we identify the origin of this contribution in the magnetically polarized Pt formed by the magnetic proximity effect (MPE), which is suppressed by the introduction of the interlayer. The induced ferromagnetic order in the Pt layer is confirmed by element-sensitive transverse magneto-optical Kerr spectroscopy at the M<inline-formula><tex-math notation="LaTeX">$_{2,3}$</tex-math></inline-formula> and N<inline-formula><tex-math notation="LaTeX">$_{7}$</tex-math></inline-formula> absorption edges. We discuss the impact of the MPE on parameter extraction in the spin transport model.
A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News

Online news platforms commonly employ personalized news recommendation methods to assist users in discovering interesting articles, and many previous works have utilized language model techniques to capture user interests and understand news content. With the emergence of large language models like GPT-3 and T-5, a new recommendation paradigm has emerged, leveraging pre-trained language models for making recommendations. ChatGPT, with its user-friendly interface and growing popularity, has become a prominent choice for text-based tasks. Considering the growing reliance on ChatGPT for language tasks, the importance of news recommendation in addressing social issues, and the trend of using language models in recommendations, this study conducts an initial investigation of ChatGPT's performance in news recommendations, focusing on three perspectives: personalized news recommendation, news provider fairness, and fake news detection. ChatGPT has the limitation that its output is sensitive to the input phrasing. We therefore aim to explore the constraints present in the generated responses of ChatGPT for each perspective. Additionally, we investigate whether specific prompt formats can alleviate these constraints or if these limitations require further attention from researchers in the future. We also surpass fixed evaluations by developing a webpage to monitor ChatGPT's performance on weekly basis on the tasks and prompts we investigated. Our aim is to contribute to and encourage more researchers to engage in the study of enhancing news recommendation performance through the utilization of large language models such as ChatGPT.
Partial data inverse problem for hyperbolic equation with time-dependent damping coefficient and potential

We study an inverse problem of determining a time-dependent damping coefficient and potential appearing in the wave equation in a compact Riemannian manifold of dimension three or higher. More specifically, we are concerned with the case of conformally transversally anisotropic manifolds, or in other words, compact Riemannian manifolds with boundary conformally embedded in a product of the Euclidean line and a transversal manifold. With an additional assumption of the attenuated geodesic ray transform being injective on the transversal manifold, we prove that the knowledge of a certain partial Cauchy data set determines time-dependent damping coefficient and potential uniquely.
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing

General-purpose foundation models have led to recent breakthroughs in artificial intelligence. In remote sensing, self-supervised learning (SSL) and Masked Image Modeling (MIM) have been adopted to build foundation models. However, these models primarily learn low-level features and require annotated data for fine-tuning. Moreover, they are inapplicable for retrieval and zero-shot applications due to the lack of language understanding. To address these limitations, we propose RemoteCLIP, the first vision-language foundation model for remote sensing that aims to learn robust visual features with rich semantics and aligned text embeddings for seamless downstream application. To address the scarcity of pre-training data, we leverage data scaling which converts heterogeneous annotations into a unified image-caption data format based on Box-to-Caption (B2C) and Mask-to-Box (M2B) conversion. By further incorporating UAV imagery, we produce a 12 $\times$ larger pretraining dataset than the combination of all available datasets. RemoteCLIP can be applied to a variety of downstream tasks, including zero-shot image classification, linear probing, $\textit{k}$-NN classification, few-shot classification, image-text retrieval, and object counting in remote sensing images. Evaluation on 16 datasets, including a newly introduced RemoteCount benchmark to test the object counting ability, shows that RemoteCLIP consistently outperforms baseline foundation models across different model scales. Impressively, RemoteCLIP beats the state-of-the-art method by 9.14% mean recall on the RSITMD dataset and 8.92% on the RSICD dataset. For zero-shot classification, our RemoteCLIP outperforms the CLIP baseline by up to 6.39% average accuracy on 12 downstream datasets. Project website: https://github.com/ChenDelong1999/RemoteCLIP
Variational Disentangled Graph Auto-Encoders for Link Prediction

With the explosion of graph-structured data, link prediction has emerged as an increasingly important task. Embedding methods for link prediction utilize neural networks to generate node embeddings, which are subsequently employed to predict links between nodes. However, the existing embedding methods typically take a holistic strategy to learn node embeddings and ignore the entanglement of latent factors. As a result, entangled embeddings fail to effectively capture the underlying information and are vulnerable to irrelevant information, leading to unconvincing and uninterpretable link prediction results. To address these challenges, this paper proposes a novel framework with two variants, the disentangled graph auto-encoder (DGAE) and the variational disentangled graph auto-encoder (VDGAE). Our work provides a pioneering effort to apply the disentanglement strategy to link prediction. The proposed framework infers the latent factors that cause edges in the graph and disentangles the representation into multiple channels corresponding to unique latent factors, which contributes to improving the performance of link prediction. To further encourage the embeddings to capture mutually exclusive latent factors, we introduce mutual information regularization to enhance the independence among different channels. Extensive experiments on various real-world benchmarks demonstrate that our proposed methods achieve state-of-the-art results compared to a variety of strong baselines on link prediction tasks. Qualitative analysis on the synthetic dataset also illustrates that the proposed methods can capture distinct latent factors that cause links, providing empirical evidence that our models are able to explain the results of link prediction to some extent. All code will be made publicly available upon publication of the paper.
Understanding Contrastive Learning Through the Lens of Margins

Contrastive learning, along with its variations, has been a highly effective self-supervised learning method across diverse domains. Contrastive learning measures the distance between representations using cosine similarity and uses cross-entropy for representation learning. Within the same framework of cosine-similarity-based representation learning, margins have played a significant role in enhancing face and speaker recognition tasks. Interestingly, despite the shared reliance on the same similarity metrics and objective functions, contrastive learning has not actively adopted margins. Furthermore, decision-boundary-based explanations are the only ones that have been used to explain the effect of margins in contrastive learning. In this work, we propose a new perspective to understand the role of margins based on gradient analysis. Based on the new perspective, we analyze how margins affect gradients of contrastive learning and separate the effect into more elemental levels. We separately analyze each and provide possible directions for improving contrastive learning. Our experimental results demonstrate that emphasizing positive samples and scaling gradients depending on positive sample angles and logits are the keys to improving the generalization performance of contrastive learning in both seen and unseen datasets, and other factors can only marginally improve performance.
A Video Game, a Chinese Otaku, and Her Deep Learning of a Language

It is a buyer’s market for employers in today’s global village, where having another language under your belt could make the difference at an interview between employment or the dole queue (unemployment line). Learning additional languages rapidly has been the goal of immersion schools, and their approaches are effective in many respects because they make use of situated learning experiences in communities of practice. Such experiences present their own challenges however, as living in the country of the chosen language for a considerable period of time may not be possible. Migrant workers too may be shunned by native speakers, particularly if they have little or no knowledge of the native language, reducing learning opportunities to engage in discourse. Video games may be one way to address these challenges. In order to do this, however, more must be understood about the ways in which games support these theories, the way individuals learning a second language interact with them, and what researchers and developers of serious games must know to support this use of games. This chapter will outline the relevant theories for second language learning, describe how they operate in games, and present guidelines for research and development of serious games and second language acquisition.
Metric $f(R)$ gravity with dynamical dark energy as a paradigm for the Hubble Tension

We introduce a theoretical framework to interpret the Hubble tension, based on the combination of a metric $f(R)$ gravity with a dynamical dark energy contribution. The modified gravity provides the non-minimally coupled scalar field responsible for the proper scaling of the Hubble constant, in order to accommodate for the local SNIa pantheon+ data and Planck measurements. The dynamical dark energy source, which exhibits a phantom divide line separating the low red-shift quintessence regime ($-1<w<-1/3$) from the phantom contribution ($w<-1$) in early Universe, guarantees the absence of tachyonic instabilities at low red-shift. The resulting $H_0(z)$ profile rapidly approaches the Planck value, with a plateau behaviour for $z\gtrsim 5$. In this scenario, Hubble tension emerges as a low red-shift effect, which can be in principle tested by comparing SNIa predictions with far sources, like QUASARS and Gamma Ray Bursts.
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation

In prediction of forest parameters with data from remote sensing (RS), regression models have traditionally been trained on a small sample of ground reference data. This paper proposes to impute this sample of true prediction targets with data from an existing RS-based prediction map that we consider as pseudo-targets. This substantially increases the amount of target training data and leverages the use of deep learning (DL) for semi-supervised regression modelling. We use prediction maps constructed from airborne laser scanning (ALS) data to provide accurate pseudo-targets and free data from Sentinel-1's C-band synthetic aperture radar (SAR) as regressors. A modified U-Net architecture is adapted with a selection of different training objectives. We demonstrate that when a judicious combination of loss functions is used, the semi-supervised imputation strategy produces results that surpass traditional ALS-based regression models, even though \sen data are considered as inferior for forest monitoring. These results are consistent for experiments on above-ground biomass prediction in Tanzania and stem volume prediction in Norway, representing a diversity in parameters and forest types that emphasises the robustness of the approach.
Chiral spin liquids with projected Gaussian fermionic entangled pair states

We study the parton construction of chiral spin liquids (CSLs) using projected Gaussian fermionic entangled pair states (GfPEPSs). First, we show that GfPEPSs can represent generic spinless Chern insulators faithfully with finite bond dimensions. Then, by applying the Gutzwiller projection to a bi-layer GfPEPSs, spin-1/2 Abelian and non-Abelian CSLs are obtained for Chern number $C=1$ and $C=2$, respectively. As a consequence of the topological obstruction for GfPEPSs, very weak Gossamer tails are observed in the correlation functions of the fermionic projected entangled pair state (PEPS) ansatze, suggesting that the no-go theorem for chiral PEPS is universal but does not bring any practical limitation. Remarkably, without fine tuning, all topological sectors can be constructed showing the expected number of chiral branches in the respective entanglement spectra, providing a sharp improvement with respect to the known bosonic PEPS approach.
Breaking On-device Training Memory Wall: A Systematic Survey

On-device training has become an increasingly popular approach to machine learning, enabling models to be trained directly on mobile and edge devices. However, a major challenge in this area is the limited memory available on these devices, which can severely restrict the size and complexity of the models that can be trained. In this systematic survey, we aim to explore the current state-of-the-art techniques for breaking on-device training memory walls, focusing on methods that can enable larger and more complex models to be trained on resource-constrained devices. Specifically, we first analyze the key factors that contribute to the phenomenon of memory walls encountered during on-device training. Then, we present a comprehensive literature review of on-device training, which addresses the issue of memory limitations. Finally, we summarize on-device training and highlight the open problems for future research. By providing a comprehensive overview of these techniques and their effectiveness in breaking memory walls, we hope to help researchers and practitioners in this field navigate the rapidly evolving landscape of on-device training.
RepoFusion: Training Code Models to Understand Your Repository

Despite the huge success of Large Language Models (LLMs) in coding assistants like GitHub Copilot, these models struggle to understand the context present in the repository (e.g., imports, parent classes, files with similar names, etc.), thereby producing inaccurate code completions. This effect is more pronounced when using these assistants for repositories that the model has not seen during training, such as proprietary software or work-in-progress code projects. Recent work has shown the promise of using context from the repository during inference. In this work, we extend this idea and propose RepoFusion, a framework to train models to incorporate relevant repository context. Experiments on single-line code completion show that our models trained with repository context significantly outperform much larger code models as CodeGen-16B-multi ($\sim73\times$ larger) and closely match the performance of the $\sim 70\times$ larger StarCoderBase model that was trained with the Fill-in-the-Middle objective. We find these results to be a novel and compelling demonstration of the gains that training with repository context can bring. We carry out extensive ablation studies to investigate the impact of design choices such as context type, number of contexts, context length, and initialization within our framework. Lastly, we release Stack-Repo, a dataset of 200 Java repositories with permissive licenses and near-deduplicated files that are augmented with three types of repository contexts. Additionally, we are making available the code and trained checkpoints for our work. Our released resources can be found at \url{https://huggingface.co/RepoFusion}.