Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing the nature of dark matter via gravitational waves lensed by small dark matter halos

Dark matter (DM) occupies the majority of matter content in the universe and is probably cold (CDM). However, modiﬁcations to the standard CDM model may be required by the small-scale observations, and DM may be self-interacting (SIDM) or warm (WDM). Here we show that the diﬀractive lensing of gravitational waves (GWs) from binary black hole mergers by small halos ( ∼ 10 3 − 10 6 M (cid:12) ; mini-halos) may serve as a clean probe to the nature of DM, free from the contamination of baryonic processes in the DM studies based on dwarf/satellite galaxies. The expected lensed GW signals and event rates resulting from CDM, WDM, and SIDM models are signiﬁcantly diﬀerent from each other, because of the diﬀerences in halo density proﬁles and abundances predicted by these models. We estimate the detection rates of such lensed GW events for a number of current and future GW detectors, such as the Laser Interferometer Gravitational Observatories (LIGO), the Einstein Telescope (ET), the Cosmic Explorer (CE), Gravitational-wave Lunar Observatory for Cosmology (GLOC), the Deci-Hertz Interferometer Gravitational Wave Observatory (DECIGO), and the Big Bang Observer (BBO). We ﬁnd that GLOC may detect one such events per year assuming the CDM model, DECIGO (BBO) may detect more than several (hundreds of) such events per year, by assuming the CDM, WDM (with mass > 30 keV) or SIDM model, suggest-ing that the DM nature may be strongly constrained by DECIGO and BBO via the detection of diﬀractive lensed GW events by mini-halos. Other GW detectors are unlikely to detect a signiﬁcant number of such events within a limited observational time period. However, if the inner slope of the mini-halo density proﬁle is suﬃciently steeper than the Navarro-Frenk-White (NFW) proﬁle, e.g., the pseudo-Jaﬀe proﬁle, one may be able to detect one to more than hundred such GW events by ET and CE.
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label

Due to its powerful feature learning capability and high efficiency, deep hashing has achieved great success in large-scale image retrieval. Meanwhile, extensive works have demonstrated that deep neural networks (DNNs) are susceptible to adversarial examples, and exploring adversarial attack against deep hashing has attracted many research efforts. Nevertheless, backdoor attack, another famous threat to DNNs, has not been studied for deep hashing yet. Although various backdoor attacks have been proposed in the field of image classification, existing approaches failed to realize a truly imperceptive backdoor attack that enjoys invisible triggers and clean label setting simultaneously, and they cannot meet the intrinsic demand of image retrieval backdoor. In this paper, we propose BadHash, the first imperceptible backdoor attack against deep hashing, which can effectively generate invisible and input-specific poisoned images with clean label. We first propose a new conditional generative adversarial network (cGAN) pipeline to effectively generate poisoned samples. For any given benign image, it seeks to generate a natural-looking poisoned counterpart with a unique invisible trigger. In order to improve the attack effectiveness, we introduce a label-based contrastive learning network LabCLN to exploit the semantic characteristics of different labels, which are subsequently used for confusing and misleading the target model to learn the embedded trigger. We finally explore the mechanism of backdoor attacks on image retrieval in the hash space. Extensive experiments on multiple benchmark datasets verify that BadHash can generate imperceptible poisoned samples with strong attack ability and transferability over state-of-the-art deep hashing schemes.
On Cheeger constants of hyperbolic surfaces

It is a well-known result due to Bollobas that the maximal Cheeger constant of large $d$-regular graphs cannot be close to the Cheeger constant of the $d$-regular tree. We prove analogously that the Cheeger constant of closed hyperbolic surfaces of large genus is bounded from above by $2/\pi \approx 0.63. $ which is strictly less than the Cheeger constant of the hyperbolic plane. The proof uses a random construction based on a Poisson--Voronoi tessellation of the surface with a vanishing intensity.
End-to-end Learning for Image-based Detection of Molecular Alterations in Digital Pathology

Current approaches for classification of whole slide images (WSI) in digital pathology predominantly utilize a two-stage learning pipeline. The first stage identifies areas of interest (e.g. tumor tissue), while the second stage processes cropped tiles from these areas in a supervised fashion. During inference, a large number of tiles are combined into a unified prediction for the entire slide. A major drawback of such approaches is the requirement for task-specific auxiliary labels which are not acquired in clinical routine. We propose a novel learning pipeline for WSI classification that is trainable end-to-end and does not require any auxiliary annotations. We apply our approach to predict molecular alterations for a number of different use-cases, including detection of microsatellite instability in colorectal tumors and prediction of specific mutations for colon, lung, and breast cancer cases from The Cancer Genome Atlas. Results reach AUC scores of up to 94% and are shown to be competitive with state of the art two-stage pipelines. We believe our approach can facilitate future research in digital pathology and contribute to solve a large range of problems around the prediction of cancer phenotypes, hopefully enabling personalized therapies for more patients in future.
Transferable Graph Backdoor Attack

Graph Neural Networks (GNNs) have achieved tremendous success in many graph mining tasks benefitting from the message passing strategy that fuses the local structure and node features for better graph representation learning. Despite the success of GNNs, and similar to other types of deep neural networks, GNNs are found to be vulnerable to unnoticeable perturbations on both graph structure and node features. Many adversarial attacks have been proposed to disclose the fragility of GNNs under different perturbation strategies to create adversarial examples. However, vulnerability of GNNs to successful backdoor attacks was only shown recently. In this paper, we disclose the TRAP attack, a Transferable GRAPh backdoor attack. The core attack principle is to poison the training dataset with perturbation-based triggers that can lead to an effective and transferable backdoor attack. The perturbation trigger for a graph is generated by performing the perturbation actions on the graph structure via a gradient based score matrix from a surrogate model. Compared with prior works, TRAP attack is different in several ways: i) it exploits a surrogate Graph Convolutional Network (GCN) model to generate perturbation triggers for a blackbox based backdoor attack; ii) it generates sample-specific perturbation triggers which do not have a fixed pattern; and iii) the attack transfers, for the first time in the context of GNNs, to different GNN models when trained with the forged poisoned training dataset. Through extensive evaluations on four real-world datasets, we demonstrate the effectiveness of the TRAP attack to build transferable backdoors in four different popular GNNs using four real-world datasets.
Neural Parameterization for Dynamic Human Head Editing

Implicit radiance functions emerged as a powerful scene representation for reconstructing and rendering photo-realistic views of a 3D scene. These representations, however, suffer from poor editability. On the other hand, explicit representations such as polygonal meshes allow easy editing but are not as suitable for reconstructing accurate details in dynamic human heads, such as fine facial features, hair, teeth, and eyes. In this work, we present Neural Parameterization (NeP), a hybrid representation that provides the advantages of both implicit and explicit methods. NeP is capable of photo-realistic rendering while allowing fine-grained editing of the scene geometry and appearance. We first disentangle the geometry and appearance by parameterizing the 3D geometry into 2D texture space. We enable geometric editability by introducing an explicit linear deformation blending layer. The deformation is controlled by a set of sparse key points, which can be explicitly and intuitively displaced to edit the geometry. For appearance, we develop a hybrid 2D texture consisting of an explicit texture map for easy editing and implicit view and time-dependent residuals to model temporal and view variations. We compare our method to several reconstruction and editing baselines. The results show that the NeP achieves almost the same level of rendering accuracy while maintaining high editability.
Quick Relaxation in Collective Motion

We establish sufficient conditions for the quick relaxation to kinetic equilibrium in the classic Vicsek-Cucker-Smale model of bird flocking. The convergence time is polynomial in the number of birds as long as the number of flocks remains bounded. This new result relies on two key ingredients: exploiting the convex geometry of embedded averaging systems; and deriving new bounds on the s-energy of disconnected agreement systems. We also apply our techniques to bound the relaxation time of certain pattern-formation robotic systems investigated by Sugihara and Suzuki.
Remarkable relations between the central binomial series, Eulerian polynomials, and poly-Bernoulli numbers

The central binomial series at negative integers are expressed as a linear combination of values of certain two polynomials. We show that one of the polynomials is a special value of the bivariate Eulerian polynomial and the other polynomial is related to the antidiagonal sum of poly- Bernoulli numbers. As an application, we prove Stephan’s observation from 2004.
A New Method to Constrain the Appearance and Disappearance of Observed Jellyfish Galaxy Tails

We present a new approach for observationally constraining where the tails of Jellyfish (JF) galaxies in groups and clusters first appear and how long they remain visible for with respect to the moment of their orbital pericenter. This is accomplished by measuring the distribution of their tail directions, with respect to their host centers, and their distribution in a projected velocity–radius phase-space diagram. We then model these observed distributions using a fast and flexible approach, where JF tails are painted onto dark matter halos, according to a simple parameterized prescription, before a Bayesian analysis is performed to estimate the parameters. We demonstrate the effectiveness of our approach using observational mocks, then apply it to a known observational sample of 106 JF galaxies, with radio-continuum tails located inside 68 hosts such as groups and clusters. We find that, typically, the radio-continuum tails become visible on first infall, when the galaxy reaches roughly three-quarters of r 200, and the tails remain visible for a few hundred Myr after pericenter passage. Lower-mass galaxies in more massive hosts tend to form visible tails further out and their tails disappear more quickly after pericenter. We argue that this indicates that they are more sensitive to ram pressure stripping. With upcoming large-area surveys of JF galaxies in progress, this is a promising new method for constraining the environmental conditions in which visible JF tails exist.
Unsupervised High-Resolution Portrait Gaze Correction and Animation

This paper proposes a gaze correction and animation method for high-resolution, unconstrained portrait images, which can be trained without the gaze angle and the head pose annotations. Common gaze-correction methods usually require annotating training data with precise gaze, and head pose information. Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels. To address this issue, we first create two new portrait datasets: CelebGaze ( $256 \times 256$ ) and high-resolution CelebHQGaze ( $512 \times 512$ ). Second, we formulate the gaze correction task as an image inpainting problem, addressed using a Gaze Correction Module (GCM) and a Gaze Animation Module (GAM). Moreover, we propose an unsupervised training strategy, i.e., Synthesis-As-Training, to learn the correlation between the eye region features and the gaze angle. As a result, we can use the learned latent space for gaze animation with semantic interpolation in this space. Moreover, to alleviate both the memory and the computational costs in the training and the inference stage, we propose a Coarse-to-Fine Module (CFM) integrated with GCM and GAM. Extensive experiments validate the effectiveness of our method for both the gaze correction and the gaze animation tasks in both low and high-resolution face datasets in the wild and demonstrate the superiority of our method with respect to the state of the art.
Searching for stellar flares from low mass stars using ASKAP and TESS

Solar radio emission at low frequencies (<1 GHz) can provide valuable information on processes driving flares and coronal mass ejections (CMEs). Radio emission has been detected from active M dwarf stars, suggestive of much higher levels of activity than previously thought. Observations of active M dwarfs at low frequencies can provide information on the emission mechanism for high energy flares and possible stellar CMEs. Here, we conducted two observations with the Australian Square Kilometre Pathfinder Telescope (ASKAP) totalling 26 hours and scheduled to overlap with the Transiting Exoplanet Survey Satellite (TESS) Sector 36 field, utilising the wide fields of view of both telescopes to search for multiple M dwarfs. We detected variable radio emission in Stokes I centered at 888 MHz from four known active M dwarfs. Two of these sources were also detected with Stokes V circular polarisation. When examining the detected radio emission characteristics, we were not able distinguish between the models for either electron cyclotron maser or gyrosynchrotron emission. These detections add to the growing number of M dwarfs observed with variable low frequency emission.
On the p-adic weight-monodromy conjecture for complete intersetions in toric varieties

A BSTRACT . We give a proof of the p -adic weight monodromy conjecture for scheme-theoretic complete intersections in projective smooth toric varieties. The strategy is based on Scholze’s proof in the ℓ -adic setting, which we adapt using homotopical results developed in the context of rigid analytic motives
Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling

Ultrasound (US) is widely used for its advantages of real-time imaging, radiation-free and portability. In clinical practice, analysis and diagnosis often rely on US sequences rather than a single image to obtain dynamic anatomical information. This is challenging for novices to learn because practicing with adequate videos from patients is clinically unpractical. In this paper, we propose a novel framework to synthesize high-ﬁdelity US videos. Speciﬁcally, the synthesis videos are generated by animating source content images based on the motion of given driving videos. Our highlights are three-fold. First, leveraging the advantages of self- and fully-supervised learning, our proposed system is trained in weakly-supervised manner for keypoint detection. These keypoints then provide vital information for handling complex high dynamic motions in US videos. Second, we decouple content and texture learning using the dual decoders to eﬀectively reduce the model learning diﬃculty. Last, we adopt the adversarial training strategy with GAN losses for further improving the sharpness of the generated videos, narrowing the gap between real and synthesis videos. We validate our method on a large in-house pelvic dataset with high dynamic motion. Extensive evaluation metrics and user study prove the eﬀectiveness of our proposed method.
Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer

Across a multitude of work environments, expert knowledge is imperative for humans to conduct tasks with high performance and ensure business success. These humans possess task-specific expert knowledge (TSEK) and hence, represent subject matter experts (SMEs). However, not only demographic changes but also personnel downsizing strategies lead and will continue to lead to departures of SMEs within organizations, which constitutes the challenge of how to retain that expert knowledge and train novices to keep the competitive advantage elicited by that expert knowledge. SMEs training novices is time- and cost-intensive, which intensifies the need for alternatives. Human-AI collaboration (HAIC) poses a way out of this dilemma, facilitating alternatives to preserve expert knowledge and teach it to novices for tasks conducted by SMEs beforehand. In this workshop paper, we (1) propose a framework on how HAIC can be utilized to train novices on particular tasks, (2) illustrate the role of explicit and tacit knowledge in this training process via HAIC, and (3) outline a preliminary experiment design to assess the ability of AI systems in HAIC to act as a trainer to transfer TSEK to novices who do not possess prior TSEK.
Learning Job Titles Similarity from Noisy Skill Labels

Measuring semantic similarity between job titles is an essential functionality for automatic job recommendations. This task is usually approached using supervised learning techniques, which requires training data in the form of equivalent job title pairs. In this paper, we instead propose an unsupervised representation learning method for training a job title similarity model using noisy skill labels. We show that it is highly effective for tasks such as text ranking and job normalization.
Solving the Multimessenger Puzzle of the AGN-starburst Composite Galaxy NGC 1068

Multiwavelength observations indicate that some starburst galaxies show a dominant nonthermal contribution from their central region. These active galactic nuclei (AGN)-starburst composites are of special interest, as both phenomena on their own are potential sources of highly energetic cosmic rays and associated γ-ray and neutrino emission. In this work, a homogeneous, steady-state two-zone multimessenger model of the nonthermal emission from the AGN corona as well as the circumnuclear starburst region is developed and subsequently applied to the case of NGC 1068, which has recently shown some first indications of high-energy neutrino emission. Here, we show that the entire spectrum of multimessenger data—from radio to γ-rays including the neutrino constraint—can be described very well if both, starburst and AGN corona, are taken into account. Using only a single emission region is not sufficient.
Integrative learning of structured high‐dimensional data from multiple datasets

Integrative learning of multiple datasets has the potential to mitigate the challenge of small n$$ n $$ and large p$$ p $$ that is often encountered in analysis of big biomedical data such as genomics data. Detection of weak yet important signals can be enhanced by jointly selecting features for all datasets. However, the set of important features may not always be the same across all datasets. Although some existing integrative learning methods allow heterogeneous sparsity structure where a subset of datasets can have zero coefficients for some selected features, they tend to yield reduced efficiency, reinstating the problem of losing weak important signals. We propose a new integrative learning approach which can not only aggregate important signals well in homogeneous sparsity structure, but also substantially alleviate the problem of losing weak important signals in heterogeneous sparsity structure. Our approach exploits a priori known graphical structure of features and encourages joint selection of features that are connected in the graph. Integrating such prior information over multiple datasets enhances the power, while also accounting for the heterogeneity across datasets. Theoretical properties of the proposed method are investigated. We also demonstrate the limitations of existing approaches and the superiority of our method using a simulation study and analysis of gene expression data from ADNI.
(Un)likelihood Training for Interpretable Embedding

Cross-modal representation learning has become a new normal for bridging the semantic gap between text and visual data. Learning modality agnostic representations in a continuous latent space, however, is often treated as a black-box data-driven training process. It is well-known that the effectiveness of representation learning depends heavily on the quality and scale of training data. For video representation learning, having a complete set of labels that annotate the full spectrum of video content for training is highly difficult, if not impossible. These issues, black-box training and dataset bias, make representation learning practically challenging to be deployed for video understanding due to unexplainable and unpredictable results. In this paper, we propose two novel training objectives, likelihood and unlikelihood functions, to unroll the semantics behind embeddings while addressing the label sparsity problem in training. The likelihood training aims to interpret semantics of embeddings beyond training labels, while the unlikelihood training leverages prior knowledge for regularization to ensure semantically coherent interpretation. With both training objectives, a new encoder-decoder network, which learns interpretable cross-modal representation, is proposed for ad-hoc video search. Extensive experiments on TRECVid and MSR-VTT datasets show that the proposed network outperforms several state-of-the-art retrieval models with a statistically significant performance margin.
When Does Differentially Private Learning Not Suffer in High Dimensions?

Large pretrained models can be privately fine-tuned to achieve performance approaching that of non-private models. A common theme in these results is the surprising observation that high-dimensional models can achieve favorable privacy-utility trade-offs. This seemingly contradicts known results on the model-size dependence of differentially private convex learning and raises the following research question: When does the performance of differentially private learning not degrade with increasing model size? We identify that the magnitudes of gradients projected onto subspaces is a key factor that determines performance. To precisely characterize this for private convex learning, we introduce a condition on the objective that we term \emph{restricted Lipschitz continuity} and derive improved bounds for the excess empirical and population risks that are dimension-independent under additional conditions. We empirically show that in private fine-tuning of large language models, gradients obtained during fine-tuning are mostly controlled by a few principal components. This behavior is similar to conditions under which we obtain dimension-independent bounds in convex settings. Our theoretical and empirical results together provide a possible explanation for recent successes in large-scale private fine-tuning. Code to reproduce our results can be found at \url{https://github.com/lxuechen/private-transformers/tree/main/examples/classification/spectral_analysis}.