Miracle-dz/UniFAR-FTUs huggingface dataset:
Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis. Abiding by the tradition in deep learning, DMs are trained and evaluated on the images with fixed sizes. However, users are demanding for various images with specific sizes and various aspect ratio. This paper focuses on adapting text-to-image diffusion models to handle such variety while maintaining visual fidelity. First we observe that, during the synthesis, lower resolution images suffer from incomplete object portrayal, while higher resolution images exhibit repetitively disordered presentation. Next, we establish a statistical relationship indicating that attention entropy changes with token quantity, suggesting that models aggregate spatial information in proportion to image resolution. The subsequent interpretation on our observations is that objects are incompletely depicted due to limited spatial information for low resolutions, while repetitively disorganized presentation arises from redundant spatial information for high resolutions. From this perspective, we propose a scaling factor to alleviate the change of attention entropy and mitigate the defective pattern observed. Extensive experimental results validate the efficacy of the proposed scaling factor, enabling models to achieve better visual effects, image quality, and text alignment. Notably, these improvements are achieved without additional training or fine-tuning techniques.
New vision of convection induced freckle formation theory in nickel-based superalloys by electron microscopy

Freckles, one of the common defects in blades used in heavy-duty gas turbines, hugely deteriorate the mechanical properties and liability of blades under service conditions. The thermal-solutal convection theory is a widely adopted formation mechanism, but few solid experimental pieces of evidence have been reported. Here, the grain microstructure in freckle chains taken from four different Nickel-based superalloys with either single-crystal or directionally solidified alloy is analyzed for the first time. The relationship between the internal stress and the misorientation throughout the freckle chains is studied by means of state-of-the-art electron microscopy. The results supply new experimental evidence of the thermal-solutal convection theory, which is further supported by the fact that borides at the boundary are randomly orientated to alloys. Therefore, this research enriches the methodology of freckle study, providing new insight into the formation mechanism of casting defects.
The rise of the chaebol: A bibliometric analysis of business groups in South Korea

South Korea has become one of the most important economies in Asia. The largest Korean multinational firms are affiliated with influential family-owned business groups known as the chaebol. Despite the surging academic popularity of the chaebol, there is a considerable knowledge gap in the bibliometric analysis of business groups in Korea. In an attempt to fill this gap, the article aims to provide a systematic review of the chaebol and the role that business groups have played in the economy of Korea. Three distinct bibliometric networks are analyzed, namely the scientific collaboration network, bibliographic coupling network, and keyword co-occurrence network.
Unpacking Privacy Labels: A Measurement and Developer Perspective on Google's Data Safety Section

Google has mandated developers to use Data Safety Sections (DSS) to increase transparency in data collection and sharing practices. In this paper, we present a comprehensive analysis of Google's Data Safety Section (DSS) using both quantitative and qualitative methods. We conduct the first large-scale measurement study of DSS using apps from Android Play store (n=1.1M). We find that there are internal inconsistencies within the reported practices. We also find trends of both over and under-reporting practices in the DSSs. Next, we conduct a longitudinal study of DSS to explore how the reported practices evolve over time, and find that the developers are still adjusting their practices. To contextualize these findings, we conduct a developer study, uncovering the process that app developers undergo when working with DSS. We highlight the challenges faced and strategies employed by developers for DSS submission, and the factors contributing to changes in the DSS. Our research contributes valuable insights into the complexities of implementing and maintaining privacy labels, underlining the need for better resources, tools, and guidelines to aid developers. This understanding is crucial as the accuracy and reliability of privacy labels directly impact their effectiveness.
A CLT for the difference of eigenvalue statistics of sample covariance matrices

In the case where the dimension of the data grows at the same rate as the sample size we prove a central limit theorem for the difference of a linear spectral statistic of the sample covariance and a linear spectral statistic of the matrix that is obtained from the sample covariance matrix by deleting a column and the corresponding row. Unlike previous works, we do neither require that the population covariance matrix is diagonal nor that moments of all order exist. Our proof methodology incorporates subtle enhancements to existing strategies, which meet the challenges introduced by determining the mean and covariance structure for the difference of two such eigenvalue statistics. Moreover, we also establish the asymptotic independence of the difference-type spectral statistic and the usual linear spectral statistic of sample covariance matrices.
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement sub-network (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://rmcong.github. io/proj_PUGAN. html.
Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition

Masked autoencoder (MAE) has attracted unprecedented attention and achieves remarkable performance in many vision tasks. It reconstructs random masked image patches (known as proxy task) during pretraining and learns meaningful semantic representations that can be transferred to downstream tasks. However, MAE has not been thoroughly explored in ultrasound imaging. In this work, we investigate the potential of MAE for ultrasound image recognition. Motivated by the unique property of ultrasound imaging in high noise-to-signal ratio, we propose a novel deblurring MAE approach that incorporates deblurring into the proxy task during pretraining. The addition of deblurring facilitates the pretraining to better recover the subtle details presented in the ultrasound images, thus improving the performance of the downstream classification task. Our experimental results demonstrate the effectiveness of our deblurring MAE, achieving state-of-the-art performance in ultrasound image classification. Overall, our work highlights the potential of MAE for ultrasound image recognition and presents a novel approach that incorporates deblurring to further improve its effectiveness.
RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation

In this paper we propose RecFusion, which comprise a set of diffusion models for recommendation. Unlike image data which contain spatial correlations, a user-item interaction matrix, commonly utilized in recommendation, lacks spatial relationships between users and items. We formulate diffusion on a 1D vector and propose binomial diffusion, which explicitly models binary user-item interactions with a Bernoulli process. We show that RecFusion approaches the performance of complex VAE baselines on the core recommendation setting (top-n recommendation for binary non-sequential feedback) and the most common datasets (MovieLens and Netflix). Our proposed diffusion models that are specialized for 1D and/or binary setups have implications beyond recommendation systems, such as in the medical domain with MRI and CT scans.
Modernising the Design and Analysis of Prevalence Surveys for Neglected Tropical Diseases

Current WHO guidelines set prevalence thresholds below which a Neglected Tropical Disease can be considered to have been eliminated as a public health problem, and specify how surveys to assess whether elimination has been achieved should be designed and analysed, based on classical survey sampling methods. In this paper we describe an alternative approach based on geospatial statistical modelling. We first show the gains in efficiency that can be obtained by exploiting any spatial correlation in the underlying prevalence surface. We then suggest that the current guidelines implicit use of a significance testing argument is not appropriate; instead, we argue for a predictive inferential framework, leading to design criteria based on controlling the rates at which areas whose true prevalence lies above and below the elimination threshold are incorrectly classified. We describe how this approach naturally accommodates context-specific information in the form of georeferenced covariates that have been shown to be predictive of disease prevalence. Finally, we give a progress report of an ongoing collaboration with the Guyana Ministry of Health Neglected Tropical Disease program on the design of an IDA (Ivermectin, Diethylcarbamazine and Albendazole) Impact Survey (IIS) of lymphatic filariasis to be conducted in Guyana in early 2023
A9 Intersection Dataset: All You Need for Urban 3D Camera-LiDAR Roadside Perception

Intelligent Transportation Systems (ITS) allow a drastic expansion of the visibility range and decrease occlusions for autonomous driving. To obtain accurate detections, detailed labeled sensor data for training is required. Unfortunately, high-quality 3D labels of LiDAR point clouds from the infrastructure perspective of an intersection are still rare. Therefore, we provide the A9 Intersection Dataset, which consists of labeled LiDAR point clouds and synchronized camera images. Here, we recorded the sensor output from two roadside cameras and LiDARs mounted on intersection gantry bridges. The point clouds were labeled in 3D by experienced annotators. Furthermore, we provide calibration data between all sensors, which allow the projection of the 3D labels into the camera images and an accurate data fusion. Our dataset consists of 4.8k images and point clouds with more than 57.4k manually labeled 3D boxes. With ten object classes, it has a high diversity of road users in complex driving maneuvers, such as left and right turns, overtaking, and U-turns. In experiments, we provided multiple baselines for the perception tasks. Overall, our dataset is a valuable contribution to the scientific community to perform complex 3D camera-LiDAR roadside perception tasks. Find data, code, and more information at https://a9-dataset.com.
Cosmic ray heliospheric transport study with neutron monitor data

Determining transport coefficients for galactic cosmic ray (GCR) propagation in the turbulent interplanetary magnetic field (IMF) poses a fundamental challenge in modeling cosmic ray modulation processes. GCR scattering in the solar wind involves wave‐particle interaction, the waves being Alfven waves which propagate along the ambient field (B). Empirical values at 1 AU are determined for the components of the diffusion tensor for GCR propagation in the heliosphere using neutron monitor (NM) data. At high rigidities, particle density gradients and mean free paths at 1 AU in B can only be computed from the solar diurnal anisotropy (SDA) represented by a vector A (components Ar, Aϕ, and Aθ) in a heliospherical polar coordinate system. Long‐term changes in SDA components of NMs (with long track record and the median rigidity of response Rm ~ 20 GV) are used to compute yearly values of the transport coefficients for 1963–2013. We confirm the previously reported result that the product of the parallel (to B) mean free path (λ||) and radial density gradient (Gr) computed from NM data exhibits a weak Schwabe cycle (11y) but strong Hale magnetic cycle (22y) dependence. Its value is most depressed in solar activity minima for positive (p) polarity intervals (solar magnetic field in the Northern Hemisphere points outward from the Sun) when GCRs drift from the polar regions toward the helioequatorial plane and out along the heliospheric current sheet (HCS), setting up a symmetric gradient Gθs pointing away from HCS. Gr drives all SDA components and λ|| Gr contributes to the diffusive component (Ad) of the ecliptic plane anisotropy (A). GCR transport is commonly discussed in terms of an isotropic hard sphere scattering (also known as billiard‐ball scattering) in the solar wind plasma. We use it with a flat HCS model and the Ahluwalia‐Dorman master equations to compute the coefficients α (=λ⊥/λ∥) and ωτ (a measure of turbulence in the solar wind) and transport parameters λ||, λ⊥, Gr, Gθs, and an asymmetric gradient Gθa normal to the ecliptic plane. We study their dependence on rigidity (R), p/n intervals, sunspot numbers (SSNs), and solar wind parameters at 1 AU. λ|| exhibits a strong 22y dependence but Gr does not, explaining solar polarity dependence of λ|| Gr. The computed Gr values are an order of magnitude greater than those reported by our colleagues making an ad hoc assumption that α is low (0.01). At high rigidities, the drift contribution at 1 AU is small and unsteady. A new methodology is outlined to compute yearly GCR north‐south anisotropy (Aθ) from the data for a single detector sorted for p/n intervals. We show that Gθa is the main contributor to Aθ in the steady state, and Gθa is shown not correlated with the north‐south excess SSNs.
The Effect of Salinity on Ocean Circulation and Ice–Ocean Interaction on Enceladus

Observational data suggest that the ice shell on Enceladus is thicker at the equator than at the pole, indicating an equator-to-pole ice flow. If the ice shell is in an equilibrium state, the mass transport of the ice flow must be balanced by the freezing and melting of the ice shell, which in turn is modulated by the ocean heat transport. Here we use a numerical ocean model to study the ice–ocean interaction and ocean circulation on Enceladus with different salinities. We find that salinity fundamentally determines the ocean stratification. A stratified layer forms in the low-salinity ocean, affecting the ocean circulation and heat transport. However, in the absence of tidal heating in the ice shell, the ocean heat transport is found to always be toward lower latitudes, resulting in freezing at the poles, which cannot maintain the ice shell geometry against the equator-to-pole ice flow. The simulation results suggest that either the ice shell on Enceladus is not in an equilibrium state or tidal dissipation in the ice shell is important in maintaining the ice shell geometry. The simulations also suggest that a positive feedback between cross-equatorial ocean heat transport and ice melting results in spontaneous symmetry breaking between the two hemispheres. This feedback may play a role in the observed interhemispheric asymmetry in the ice shell.
Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder

Masked autoencoder (MAE) is a promising self-supervised pre-training technique that can improve the representation learning of a neural network without human intervention. However, applying MAE directly to volumetric medical images poses two challenges: (i) a lack of global information that is crucial for understanding the clinical context of the holistic data, (ii) no guarantee of stabilizing the representations learned from randomly masked inputs. To address these limitations, we propose the \textbf{G}lobal-\textbf{L}ocal \textbf{M}asked \textbf{A}uto\textbf{E}ncoder (GL-MAE), a simple yet effective self-supervised pre-training strategy. In addition to reconstructing masked local views, as in previous methods, GL-MAE incorporates global context learning by reconstructing masked global views. Furthermore, a complete global view is integrated as an anchor to guide the reconstruction and stabilize the learning process through global-to-global consistency learning and global-to-local consistency learning. Finetuning results on multiple datasets demonstrate the superiority of our method over other state-of-the-art self-supervised algorithms, highlighting its effectiveness on versatile volumetric medical image segmentation tasks, even when annotations are scarce. Our codes and models will be released upon acceptance.
Does mBERT understand Romansh? Evaluating word embeddings using word alignment.

We test similarity-based word alignment models (SimAlign and awesome-align) in combination with word embeddings from mBERT and XLM-R on parallel sentences in German and Romansh. Since Romansh is an unseen language, we are dealing with a zero-shot setting. Using embeddings from mBERT, both models reach an alignment error rate of 0.22, which outperforms fast_align, a statistical model, and is on par with similarity-based word alignment for seen languages. We interpret these results as evidence that mBERT contains information that can be meaningful and applicable to Romansh. To evaluate performance, we also present a new trilingual corpus, which we call the DERMIT (DE-RM-IT) corpus, containing press releases made by the Canton of Grisons in German, Romansh and Italian in the past 25 years. The corpus contains 4 547 parallel documents and approximately 100 000 sentence pairs in each language combination. We additionally present a gold standard for German-Romansh word alignment. The data is available at https://github.com/eyldlv/DERMIT-Corpus.
Gravitational radiation with $\Lambda>0$

We study gravitational radiation for a positive value of the cosmological constant $\Lambda$. We rely on two battle-tested procedures: (i) We start from the same null coordinate system used by Bondi and Sachs for $\Lambda = 0$, but, introduce boundary conditions adapted to allow radiation when $\Lambda>0$. (ii) We determine the asymptotic symmetries by studying, \`a la Regge-Teitelboim, the surface integrals generated in the action by these boundary conditions. A crucial difference with the $\Lambda=0$ case is that the wave field does not vanish at large distances, but is of the same order as de Sitter space. This novel property causes no difficulty; on the contrary, it makes quantities finite at every step, without any regularization. A direct consequence is that the asymptotic symmetry algebra consists only of time translations and space rotations. Thus, it is not only finite-dimensional, but smaller than de Sitter algebra. We exhibit formulas for the energy and angular momentum and their fluxes. In the limit of $\Lambda$ tending to zero, these formulas go over continuously into those of Bondi, but the symmetry jumps to that of Bondi, Metzner and Sachs. The expressions are applied to exact solutions, with and without radiation present, and also to the linearized theory.
Revisiting Stereo Triangulation in UAV Distance Estimation

Distance estimation plays an important role for path planning and collision avoidance of swarm UAVs. However, the lack of annotated data seriously hinders the related studies. In this work, we build and present a UAVDE dataset for UAV distance estimation, in which distance between two UAVs is obtained by UWB sensors. During experiments, we surprisingly observe that the stereo triangulation cannot stand for UAV scenes. The core reason is the position deviation issue due to long shooting distance and camera vibration, which is common in UAV scenes. To tackle this issue, we propose a novel position correction module, which can directly predict the offset between the observed positions and the actual ones and then perform compensation in stereo triangulation calculation. Besides, to further boost performance on hard samples, we propose a dynamic iterative correction mechanism, which is composed of multiple stacked PCMs and a gating mechanism to adaptively determine whether further correction is required according to the difficulty of data samples. We conduct extensive experiments on UAVDE, and our method can achieve a significant performance improvement over a strong baseline (by reducing the relative difference from 49.4% to 9.8%), which demonstrates its effectiveness and superiority. The code and dataset are available at https://github.com/duanyuan13/PCM.
How are the people in the photos judged? Analysis of brain activity when assessing levels of trust and attractiveness

Trust is the foundation of every area of life. Without it, it is difficult to build lasting relationships. Unfortunately, in recent years, trust has been severely damaged by the spread of fake news and disinformation, which has become a serious social problem. In addition to trust, the factor influencing interpersonal relationships is perceived attractiveness, which is currently created to a large extent by digital media. Understanding the principles of judging others can be helpful in fighting prejudice and rebuilding trust in society. One way to learn about people's choices is to record their brain activity as they make choices. The article presents an experiment in which the faces of different people were presented, and the participants' task was to assess how much they can trust a given person and how attractive they are. During the study, the EEG signal was recorded, which was used to build models of logistic regression classifiers. In addition, the most active areas of the brain that participate in the assessment of trust and attractiveness of the face were indicated.