Miracle-dz/UniFAR-FTUs huggingface dataset:
ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation

Multimodal recommendation aims to model user and item representations comprehensively with the involvement of multimedia content for effective recommendations. Existing research has shown that it is beneficial for recommendation performance to combine (user- and item-) ID embeddings with multimodal salient features, indicating the value of IDs. However, there is a lack of a thorough analysis of the ID embeddings in terms of feature semantics in the literature. In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of content and structures. Then, we propose a novel recommendation model by incorporating ID embeddings to enhance the semantic features of both content and structures. Specifically, we put forward a hierarchical attention mechanism to incorporate ID embeddings in modality fusing, coupled with contrastive learning, to enhance content representations. Meanwhile, we propose a lightweight graph convolutional network for each modality to amalgamate neighborhood and ID embeddings for improving structural representations. Finally, the content and structure representations are combined to form the ultimate item embedding for recommendation. Extensive experiments on three real-world datasets (Baby, Sports, and Clothing) demonstrate the superiority of our method over state-of-the-art multimodal recommendation methods and the effectiveness of fine-grained ID embeddings.
Diffusion Shape Prior for Wrinkle-Accurate Cloth Registration

Registering clothes from 4D scans with vertex-accurate correspondence is challenging, yet important for dynamic appearance modeling and physics parameter estimation from real-world data. However, previous methods either rely on texture information, which is not always reliable, or achieve only coarse-level alignment. In this work, we present a novel approach to enabling accurate surface registration of texture-less clothes with large deformation. Our key idea is to effectively leverage a shape prior learned from pre-captured clothing using diffusion models. We also propose a multi-stage guidance scheme based on learned functional maps, which stabilizes registration for large-scale deformation even when they vary significantly from training data. Using high-fidelity real captured clothes, our experiments show that the proposed approach based on diffusion models generalizes better than surface registration with VAE or PCA-based priors, outperforming both optimization-based and learning-based non-rigid registration methods for both interpolation and extrapolation tests.
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy

Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for training. This approach is based on the assumption that the trained UAD model will accurately reconstruct normal patterns but struggles with unseen anomalous patterns. To enhance the UAD performance, reconstruction-by-inpainting based methods have recently been investigated, especially on the masking strategy of suspected defective regions. However, there are still issues to overcome: 1) time-consuming inference due to multiple masking, 2) output inconsistency by random masking strategy, and 3) inaccurate reconstruction of normal patterns when the masked area is large. Motivated by this, we propose a novel reconstruction-by-inpainting method, dubbed Excision And Recovery (EAR), that features single deterministic masking based on the ImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing. Experimental results on the MVTec AD dataset show that deterministic masking by pre-trained attention effectively cuts out suspected defective regions and resolve the aforementioned issues 1 and 2. Also, hint-providing by mosaicing proves to enhance the UAD performance than emptying those regions by binary masking, thereby overcomes issue 3. Our approach achieves a high UAD performance without any change of the neural network structure. Thus, we suggest that EAR be adopted in various manufacturing industries as a practically deployable solution.
Multi-Modal Gaze Following in Conversational Scenarios

Gaze following estimates gaze targets of in-scene person by understanding human behavior and scene information. Existing methods usually analyze scene images for gaze following. However, compared with visual images, audio also provides crucial cues for determining human behavior.This suggests that we can further improve gaze following considering audio cues. In this paper, we explore gaze following tasks in conversational scenarios. We propose a novel multi-modal gaze following framework based on our observation ``audiences tend to focus on the speaker''. We first leverage the correlation between audio and lips, and classify speakers and listeners in a scene. We then use the identity information to enhance scene images and propose a gaze candidate estimation network. The network estimates gaze candidates from enhanced scene images and we use MLP to match subjects with candidates as classification tasks. Existing gaze following datasets focus on visual images while ignore audios.To evaluate our method, we collect a conversational dataset, VideoGazeSpeech (VGS), which is the first gaze following dataset including images and audio. Our method significantly outperforms existing methods in VGS datasets. The visualization result also prove the advantage of audio cues in gaze following tasks. Our work will inspire more researches in multi-modal gaze following estimation.
Layer-wise Auto-Weighting for Non-Stationary Test-Time Adaptation

Given the inevitability of domain shifts during inference in real-world applications, test-time adaptation (TTA) is essential for model adaptation after deployment. However, the real-world scenario of continuously changing target distributions presents challenges including catastrophic forgetting and error accumulation. Existing TTA methods for non-stationary domain shifts, while effective, incur excessive computational load, making them impractical for on-device settings. In this paper, we introduce a layer-wise auto-weighting algorithm for continual and gradual TTA that autonomously identifies layers for preservation or concentrated adaptation. By leveraging the Fisher Information Matrix (FIM), we first design the learning weight to selectively focus on layers associated with log-likelihood changes while preserving unrelated ones. Then, we further propose an exponential min-max scaler to make certain layers nearly frozen while mitigating outliers. This minimizes forgetting and error accumulation, leading to efficient adaptation to non-stationary target distribution. Experiments on CIFAR-10C, CIFAR-100C, and ImageNet-C show our method outperforms conventional continual and gradual TTA approaches while significantly reducing computational load, highlighting the importance of FIM-based learning weight in adapting to continuously or gradually shifting target domains.
Problems on infinite sumset configurations in the integers and beyond

In contrast to finite arithmetic configurations, relatively little is known about which infinite patterns can be found in every set of natural numbers with positive density. Building on recent advances showing infinite sumsets can be found, we explore numerous open problems and obstructions to finding other infinite configurations in every set of natural numbers with positive density.
Minimum norm interpolation by perceptra: Explicit regularization and implicit bias

We investigate how shallow ReLU networks interpolate between known regions. Our analysis shows that empirical risk minimizers converge to a minimum norm interpolant as the number of data points and parameters tends to infinity when a weight decay regularizer is penalized with a coefficient which vanishes at a precise rate as the network width and the number of data points grow. With and without explicit regularization, we numerically study the implicit bias of common optimization algorithms towards known minimum norm interpolants.
Singmaster-type results for Stirling numbers and some related diophantine equations

Motivated by the work of David Singmaster, we study the number of times an integer can appear among the Stirling numbers of both kinds. We provide an upper bound for the occurrences of all the positive integers, and present certain questions for further study. Some numerical results and conjectures concerning the related diohantine equations are collected.
Reframing Audience Expansion through the Lens of Probability Density Estimation

Audience expansion has become an important element of prospective marketing, helping marketers create target audiences based on a mere representative sample of their current customer base. Within the realm of machine learning, a favored algorithm for scaling this sample into a broader audience hinges on a binary classification task, with class probability estimates playing a crucial role. In this paper, we review this technique and introduce a key change in how we choose training examples to ensure the quality of the generated audience. We present a simulation study based on the widely used MNIST dataset, where consistent high precision and recall values demonstrate our approach's ability to identify the most relevant users for an expanded audience. Our results are easily reproducible and a Python implementation is openly available on GitHub: \url{https://github.com/carvalhaes-ai/audience-expansion}
Sea surface temperature from the new Japanese geostationary meteorological Himawari‐8 satellite

Himawari‐8 is a new geostationary meteorological satellite operated by the Japan Meteorological Agency (JMA). The Earth Observation Research Center of the Japan Aerospace Exploration Agency in collaboration with the JMA produces skin sea surface temperatures (SSTs) from Himawari‐8 data. A new quasi‐physical algorithm was used to calculate SSTs. Cloud screening based on the Bayesian inference method was used to detect cloudy pixels. Himawari‐8 SSTs from June to September 2015 were compared with drifting and tropical moored buoy data. This comparison showed a root‐mean‐square difference of ∼0.59 K and a bias of ∼−0.16 K against the buoy data. Positive and variable biases were found in seas along the viewing boundaries.
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks

We consider the problem of learning the exact skeleton of general discrete Bayesian networks from potentially corrupted data. Building on distributionally robust optimization and a regression approach, we propose to optimize the most adverse risk over a family of distributions within bounded Wasserstein distance or KL divergence to the empirical distribution. The worst-case risk accounts for the effect of outliers. The proposed approach applies for general categorical random variables without assuming faithfulness, an ordinal relationship or a specific form of conditional distribution. We present efficient algorithms and show the proposed methods are closely related to the standard regularized regression approach. Under mild assumptions, we derive non-asymptotic guarantees for successful structure learning with logarithmic sample complexities for bounded-degree graphs. Numerical study on synthetic and real datasets validates the effectiveness of our method. Code is available at https://github.com/DanielLeee/drslbn.
One-shot data-driven design of fractional-order PID controller considering closed-loop stability: fictitious reference signal approach

A one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller is proposed. The proposed method tunes the FOPID controller in the model-reference control formulation. A loss function is defined to evaluate the match between a given reference model and the closed-loop response while explicitly considering the closed-loop stability. A loss function value is based on the fictitious reference signal computed using the input/output data. Model matching is achieved via loss function minimization. The proposed method is simple and practical: it needs only one-shot input/output data of a plant (no plant model required), considers the bounded-input bounded-output stability of the closed-loop system, and automatically determines the appropriate parameter value via optimization. Numerical simulations show that the proposed approach facilitates good control performance, and destabilization can be avoided even if perfect model matching is unachievable.
Norm inequalities for weighted Dirichlet spaces with applications to conformal maps

A variety of norm inequalities related to Bergman and Dirichlet spaces induced by radial weights are considered. Some of the results obtained can be considered as generalizations of certain known special cases while most of the estimates discovered are completely new. The second objective of the paper is to apply the obtained norm inequalities to relate the growth of the maximum modulus of a conformal map $f$, measured in terms of a weighted integrability condition, to a geometric quantity involving the area of image under $f$ of a disc centered at the origin. Our findings in this direction yield new geometric characterizations of conformal maps in certain weighted Dirichlet and Besov spaces.
Neutron Skins: Weak Elastic Scattering and Neutron Stars

The recently completed PREX-2 campaign - which measured the weak form factor of lead at an optimal momentum transfer - has confirmed that the neutron skin of lead is relatively large and has provided a precise determination of the interior baryon density of a heavy nucleus. In turn, the measured form factor can be related to various nuclear and neutron-star properties. Astrophysical observations by the NICER mission have benefited from improvements in flux, energy resolution, and notably, timing resolution. NICER has the capability to measure pulse profile data, which enables simultaneous mass-radius determinations. During the next decade, measurements in astrophysics, gravitational wave astronomy, and nuclear physics are expected to provide a wealth of more precise data. In this review we provide an overview of the current state of neutron skin measurements and offer insights into the prospects for the future.
LAND USE AND LAND COVER CHANGE, DRIVERS AND ITS IMPACT: A COMPARATIVE STUDY FROM KUHAR MICHAEL AND LENCHE DIMA OF BLUE NILE AND AWASH BASINS OF ETHIOPIA

Land use and land cover change is driven by human actions and also drives changes that limit availability of products and services for human and livestock, and it can undermine environmental health as well. Therefore, this study was aimed at understanding land use and land cover change in Lenche Dima and Kuhar Michael of Amhara region, Ethiopia. Time-series satellite images that included Landsat MSS, TM, ETM+ and ASTER, which covered the time frame between 1972/3 to 2005, were used. Socio-economic Survey and review of documents was carried out to understand historical trends, collect ground truth and other secondary information required. Analysis of data and other data was accomplished through integrated use of ERDAS imagine (version 9.1), ENVI (version 4.3) and ArcGIS (version 9.2) software packages along with Microsoft office analytical tools. Remote sensing analysis revealed landscape level change of cultivated land to have a net increase in Kuhar Michael, while a decline is found for Lenche Dima. However, socio-economic surveys showed that household level cultivated land has decreased from 1.2ha to 1ha and from 2.2ha to 1.8ha in Kuhar Michael and Lenche Dima respectively, over the last 30years. Major contributing factors included population increase, occurrence of drought, land redistribution, and land degradation. Similarly, average land holding per household has decreased from 1.6ha to 1.5ha and from 2.9ha to 2.2ha in Kuhar Michael and Lenche Dima, respectively. This has jeopardized the capacity of individuals to provide land for their siblings further leading to landlessness, which is becoming a common phenomenon among rural youths.
Intraplate seismicity in northern Central Europe is induced by the last glaciation

There is growing evidence that climate-induced melting of large ice sheets has been able to trigger fault reactivation and earthquakes around the migrating ice limit. Even today, the stress due to glacial isostatic adjustment can continue to induce seismicity within the once-glaciated region. Northern Central Europe lies outside the former ice margin and is regarded as a low-seismicity area. However, several historic earthquakes with intensities of up to VII occurred in this region during the past 1200 years. Here we show with numerical simulations that the seismicity can potentially be explained by the decay of the Scandinavian ice sheet after the Weichselian glaciation. Combination of historic earthquake epicenters with fault maps relates historic seismicity to major reverse faults of Late Cretaceous age. Mesozoic normal faults remained inactive in historic times. We suggest that many faults in northern Central Europe are active during postglacial times. This is a novelty that sheds new light on the distribution of postglacial faulting and seismicity. In addition, we present the first consistent model that can explain both the occurrence of deglaciation seismicity and the historic earthquakes in northern Central Europe.
Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models

We propose the Data Contamination Quiz (DCQ), a simple and effective approach to detect data contamination in large language models (LLMs) and estimate the amount of it. Specifically, we frame data contamination detection as a series of multiple-choice questions and devise a quiz format wherein three perturbed versions of each dataset instance are created. These changes only include word-level perturbations. The generated perturbed versions, along with the original instance, form the options in the DCQ, with an extra option accommodating the possibility that none of the provided choices is correct. Given that the only distinguishing signal among the choices is the exact wording relative to the original instance, an LLM, when tasked with identifying the original instance from the choices, gravitates towards the original one if it has been exposed to it in its pre-training phase--a trait intrinsic to LLMs. Tested over several datasets with GPT-4/3.5, our findings--while fully lacking access to LLMs' pre-training data and internal parameters--suggest that DCQ uncovers greater contamination levels compared to existing detection methods and proficiently bypasses more safety filters, especially those set to avoid generating copyrighted contents.
PERSISTENT SOIL SEED BANK OF EUPATORIUM ADENOPHORUM

Plants with persistent soil seed banks have advantages in stress and harsh environments. Soil samples were collected from 19 different soil sites with different vegetation at 5 locations across Yunnan, Southern China, at different times from July to April after the germination season of Eupatorium adenophorum seeds. Germination research results showed that E. adenophorum had a persistent soil seed bank. Its seeds distributed widely across soils in Yunnan and seeds were found at all 19 sample sites. Seed density in the 0-10 cm soil layer varied between 47-13 806 ind. ·m -2 with an average of 2 202 ind. ·m -2 . The density did not show a direct relationship with the mother plant of E. adenophorum, but did show strong relationships with standing vegetation types. The density increased from 47 ind. ·m -2 in newly deposited material of a slide to 801 ind. ·m -2 under grass cover, to 2 349 ind. ·m -2 under shrub cover, and the highest density of 3 255 ind. ·m -2 under forest cover. Frequency of seeds identified from soil samples across all 19 soil types varied between 60%-100%. Vertically, seed density in the 0-2 cm soil layer was the highest, followed by the 2-5 cm layer, and the 5-10 cm depth had the lowest seed density. E. adenophorum seeds accounted for 56.1%, 25.2% and 18.6% of the total seeds in the three soil layers, respectively. Although the total percentage of seeds in the 5-10 cm layer was low, the total density was high (270 seeds·m -2 ) and, although not in the position of germination, had the potential for germination and being environmentally harmful. The wide distribution and high density of E. adenophorum in the soil seed bank have significant implications for any prevention and control measures, and we suggest that a long_term, large_scale approach should be taken to deal with the spread of E. adenophorum.
Imprints of Sagittarius accretion event: Young O-rich stars and discontinuous chemical evolution in Milky Way disc

The Milky Way has undergone significant transformations in its early history, characterised by violent mergers and the accretion of satellite galaxies. Among these events, the infall of the satellite galaxy Gaia-Enceladus/Sausage is recognised as the last major merger event, fundamentally altering the evolution of the Milky Way and shaping its chemo-dynamical structure. However, recent observational evidence suggests that the Milky Way remains undergone notable events of star formation in the past 4 Gyr, which is thought to be triggered by the perturbations from Sagittarius dwarf galaxy (Sgr). Here we report chemical signatures of the Sgr accretion event in the past 4 Gyr, using the [Fe/H] and [O/Fe] ratios in the thin disc, which is reported for the first time. It reveals that the previously discovered V-shape structure of age-[Fe/H] relation varies across different Galactic locations and has rich substructures. Interestingly, we discover a discontinuous structure at z$_{\rm max}$ $<$ 0.3 kpc, interrupted by a recent burst of star formation from 4 Gyr to 2 Gyr ago. In this episode, we find a significant rise in oxygen abundance leading to a distinct [O/Fe] gradient, contributing to the formation of young O-rich stars. Combined with the simulated star formation history and chemical abundance of Sgr, we suggest that the Sgr is an important actor in the discontinuous chemical evolution of the Milky Way disc.
ASSIST: Interactive Scene Nodes for Scalable and Realistic Indoor Simulation

We present ASSIST, an object-wise neural radiance field as a panoptic representation for compositional and realistic simulation. Central to our approach is a novel scene node data structure that stores the information of each object in a unified fashion, allowing online interaction in both intra- and cross-scene settings. By incorporating a differentiable neural network along with the associated bounding box and semantic features, the proposed structure guarantees user-friendly interaction on independent objects to scale up novel view simulation. Objects in the scene can be queried, added, duplicated, deleted, transformed, or swapped simply through mouse/keyboard controls or language instructions. Experiments demonstrate the efficacy of the proposed method, where scaled realistic simulation can be achieved through interactive editing and compositional rendering, with color images, depth images, and panoptic segmentation masks generated in a 3D consistent manner.