Miracle-dz/UniFAR-FTUs huggingface dataset:
Deepest far ultraviolet view of a central field in the Coma cluster by AstroSat UVIT

Abstract We present analysis of the far ultraviolet (FUV) emission of sources in the central region of the Coma cluster ( $z=0.023$ ) using the data taken by the UVIT aboard the multi-wavelength satellite mission AstroSat. We find a good correlation between the UVIT FUV flux and the fluxes in both wavebands of the Galex mission, for the common sources. We detect stars and galaxies, amongst which the brightest ( $r \lesssim 17$ mag) galaxies in the field of view are mostly members of the Coma cluster. We also detect three quasars ( $z = 0.38, 0.51, 2.31$ ), one of which is likely the farthest object observed by the UVIT so far. In almost all the optical and UV colour-colour and colour-magnitude planes explored in this work, the Coma galaxies, other galaxies and bright stars could be separately identified, but the fainter stars and quasars often coincide with the faint galaxies. We have also investigated galaxies with unusual FUV morphology which are likely to be galaxies experiencing ram-pressure stripping in the cluster. Amongst others, two confirmed cluster members which were not investigated in the literature earlier, have been found to show unusual FUV emission. All the distorted sources are likely to have fallen into the cluster recently, and hence have not virialised yet. A subset of our data have optical spectroscopic information available from the archives. For these sources ( ${\sim} 10\%$ of the sample), we find that 17 galaxies identify as star-forming, 18 as composite and 13 as host galaxies for active galactic nuclei, respectively on the emission-line diagnostic diagram.
A drop model of integer and fractional quantum Hall effects

We present a drop model for integer and fractional quantum Hall effects (FQHE). We show that the two-dimensional electron gas breaks up into regions with filling factors ν = 1 and ν = 0 in disk geometry, and the formation of drops with a finite number of electrons is possible. Sequences of filling fractions are constructed on the basis of experimental data. For all sequences there are initial FQHE states, which correspond to a drop with five electrons. The remaining FQHE states are composite states of a drop with five electrons and one or more pairs of electrons.
MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction

Relation extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs, needing domain experts. Distant supervision is commonly used to tackle the scarcity of annotated data by automatically pairing knowledge graph relationships with raw texts. Such a pipeline is prone to noise and has added challenges to scale for covering a large number of biomedical concepts. We investigated existing broad-coverage distantly supervised biomedical relation extraction benchmarks and found a significant overlap between training and test relationships ranging from 26% to 86%. Furthermore, we noticed several inconsistencies in the data construction process of these benchmarks, and where there is no train-test leakage, the focus is on interactions between narrower entity types. This work presents a more accurate benchmark MedDistant19 for broad-coverage distantly supervised biomedical relation extraction that addresses these shortcomings and is obtained by aligning the MEDLINE abstracts with the widely used SNOMED Clinical Terms knowledge base. Lacking thorough evaluation with domain-specific language models, we also conduct experiments validating general domain relation extraction findings to biomedical relation extraction.
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis

Federated learning (FL) aims to perform privacy-preserving machine learning on distributed data held by multiple data owners. To this end, FL requires the data owners to perform training locally and share the gradient updates (instead of the private inputs) with the central server, which are then securely aggregated over multiple data owners. Although aggregation by itself does not provably offer privacy protection, prior work showed that it may suffice if the batch size is sufficiently large. In this paper, we propose the Cocktail Party Attack (CPA) that, contrary to prior belief, is able to recover the private inputs from gradients aggregated over a very large batch size. CPA leverages the crucial insight that aggregate gradients from a fully connected layer is a linear combination of its inputs, which leads us to frame gradient inversion as a blind source separation (BSS) problem (informally called the cocktail party problem). We adapt independent component analysis (ICA)--a classic solution to the BSS problem--to recover private inputs for fully-connected and convolutional networks, and show that CPA significantly outperforms prior gradient inversion attacks, scales to ImageNet-sized inputs, and works on large batch sizes of up to 1024.
OmDet: Large‐scale vision‐language multi‐dataset pre‐training with multimodal detection network

The advancement of object detection (OD) in open‐vocabulary and open‐world scenarios is a critical challenge in computer vision. OmDet, a novel language‐aware object detection architecture and an innovative training mechanism that harnesses continual learning and multi‐dataset vision‐language pre‐training is introduced. Leveraging natural language as a universal knowledge representation, OmDet accumulates “visual vocabularies” from diverse datasets, unifying the task as a language‐conditioned detection framework. The multimodal detection network (MDN) overcomes the challenges of multi‐dataset joint training and generalizes to numerous training datasets without manual label taxonomy merging. The authors demonstrate superior performance of OmDet over strong baselines in object detection in the wild, open‐vocabulary detection, and phrase grounding, achieving state‐of‐the‐art results. Ablation studies reveal the impact of scaling the pre‐training visual vocabulary, indicating a promising direction for further expansion to larger datasets. The effectiveness of our deep fusion approach is underscored by its ability to learn jointly from multiple datasets, enhancing performance through knowledge sharing.
Exploiting Expert Knowledge for Assigning Firms to Industries: A Novel Deep Learning Method

Industry assignment, which assigns firms to industries according to a predefined industry classification system (ICS), is fundamental to a large number of critical business practices, ranging from operations and strategic decision-making by firms to economic analyses by government agencies. Three types of expert knowledge are essential to effective industry assignment: definition-based knowledge (i.e., expert definitions of each industry), structure-based knowledge (i.e., structural relationships among industries as specified in an ICS), and assignment-based knowledge (i.e., prior firm-industry assignments performed by domain experts). Existing industry assignment methods utilize only assignment-based knowledge to learn a model that classifies unassigned firms to industries, overlooking definition-based and structure-based knowledge. Moreover, these methods only consider which industry a firm has been assigned to, ignoring the time-specificity of assignment-based knowledge, i.e., when the assignment occurs. To address the limitations of existing methods, we propose a novel deep learning-based method that not only seamlessly integrates the three types of knowledge for industry assignment but also takes the time-specificity of assignment-based knowledge into account. Methodologically, our method features two innovations: dynamic industry representation and hierarchical assignment. The former represents an industry as a sequence of time-specific vectors by integrating the three types of knowledge through our proposed temporal and spatial aggregation mechanisms. The latter takes industry and firm representations as inputs, computes the probability of assigning a firm to different industries, and assigns the firm to the industry with the highest probability. We conduct extensive evaluations with two widely used ICSs and demonstrate the superiority of our method over prevalent existing methods.
Statistical mechanics of dimers on quasiperiodic Ammann-Beenker tilings

We study classical dimers on two-dimensional quasiperiodic Ammann-Beenker (AB) tilings. Despite the lack of periodicity we prove that each infinite tiling admits 'perfect matchings' in which every vertex is touched by one dimer. We introduce an auxiliary 'AB$^*$' tiling obtained from the AB tiling by deleting all 8-fold coordinated vertices. The AB$^*$ tiling is again two-dimensional, infinite, and quasiperiodic. The AB$^*$ tiling has a single connected component, which admits perfect matchings. We find that in all perfect matchings, dimers on the AB$^*$ tiling lie along disjoint one-dimensional loops and ladders, separated by 'membranes', sets of edges where dimers are absent. As a result, the dimer partition function of the AB$^*$ tiling factorizes into the product of dimer partition functions along these structures. We compute the partition function and free energy per edge on the AB$^*$ tiling using an analytic transfer matrix approach. Returning to the AB tiling, we find that membranes in the AB$^*$ tiling become 'pseudomembranes', sets of edges which collectively host at most one dimer. This leads to a remarkable discrete scale-invariance in the matching problem. The structure suggests that the AB tiling should exhibit highly inhomogenous and slowly decaying connected dimer correlations. Using Monte Carlo simulations, we find evidence supporting this supposition in the form of connected dimer correlations consistent with power law behaviour. Within the set of perfect matchings we find quasiperiodic analogues to the staggered and columnar phases observed in periodic systems.
On Detecting Nearby Nanohertz Gravitational Wave Sources via Pulsar Timing Arrays

Massive binary black holes (MBBHs) in nearby galactic centers, if any, may be nanohertz gravitational wave (GW) sources for pulsar timing arrays (PTAs) to detect. Normally the objective GWs for PTA experiments are approximated as plane waves because their sources are presumably located far away. For nearby GW sources, however, this approximation may be inaccurate due to the curved GW wave front and the GW strength changes along the paths of PTA pulsar pulses. In this paper, we analyze the near-field effect in the PTA detection of nearby sources and find it is important if the source distance is less than a few tens of megaparsecs, and ignoring this effect may lead to a significant signal-to-noise underestimation especially when the source distance is comparable to the pulsar distances. As examples, we assume a nanohertz MBBH source located at either the Galactic Center (GC) or the Large Magellanic Cloud (LMC) according to the observational constraints/hints on the MBBH parameter space, and estimate its detectability by current/future PTAs. We find that the GC MBBH may be detectable by the Square Kilometer Array PTA. Detecting the LMC MBBH is challenging; however, if a number (N ≳ 10) of stable millisecond pulsars can be found in the LMC center, the MBBH may be detectable via a PTA formed by these pulsars. We further illustrate the near-field effects on the PTA detection of an isotropic GW background contributed mainly by nearby GW sources, and the resulting angular correlation is similar to the Hellings–Downs curve.
FlipDyn: A game of resource takeovers in dynamical systems

We introduce a game in which two players with opposing objectives seek to repeatedly takeover a common resource. The resource is modeled as a discrete time dynamical system over which a player can gain control after spending a state-dependent amount of energy at each time step. We use a FlipIT-inspired deterministic model that governs which player is in control at every time step. A player’s policy is the probability with which it should spend energy to gain control of the resource at a given time step. Our main results are three-fold. First, we present analytic expressions for the cost-to-go as a function of the hybrid state of the system, i.e., the physical state of the dynamical system and the binary FlipDyn state for any general system with arbitrary costs. These expressions are exact when the physical state is also discrete and has finite cardinality. Second, for a continuous physical state with linear dynamics and quadratic costs, we derive expressions for Nash equilibrium (NE). For scalar physical states, we show that the NE depends only on the parameters of the value function and costs, and is independent of the state. Third, we derive an approximate value function for higher dimensional linear systems with quadratic costs. Finally, we illustrate our results through a numerical study on the problem of controlling a linear system in a given environment in the presence of an adversary.
The interplay between bulk flow and boundary conditions on the distribution of microswimmers in channel flow

Abstract While previous experimental and numerical studies of dilute microswimmer suspensions have focused on the behaviours of swimmers in the bulk flow and near boundaries, models typically do not account for the interplay between bulk flow and the choice of boundary conditions imposed in continuum models. In our work, we highlight the effect of boundary conditions on the bulk flow distributions, such as through the development of boundary layers or secondary peaks of cell accumulation in bulk-flow swimmer dynamics. For the case of a dilute swimmer suspension in Poiseuille flow, we compare the distribution (in physical and orientation space) obtained from individual-based stochastic models with those from continuum models, and identify under what conditions it is mathematically sensible to use specific continuum boundary conditions to capture different physical scenarios (i.e. specular reflection, uniform random reflection and absorbing boundaries). We identify that the spread of preferred cell orientations is dependent on the interplay between rotation driven by the shear flow (Jeffery orbits) and rotational diffusion. We find that in the absence of hydrodynamic wall interactions, swimmers preferentially approach the walls perpendicular to the surface in the presence of high rotational diffusion, and that the preferential approach of swimmers to the walls is shape-dependent at low rotational diffusion (when suspensions tend towards a fully deterministic case). In the latter case, the preferred orientations are nearly parallel to the surface for elongated swimmers and nearly perpendicular to the surface for near-spherical swimmers. Furthermore, we highlight the effects of swimmer geometries and shear throughout the bulk-flow on swimmer trajectories and show how the full history of bulk-flow dynamics affects the orientation distributions of microswimmer wall incidence.
Patching Weak Convolutional Neural Network Models through Modularization and Composition

Despite great success in many applications, deep neural networks are not always robust in practice. For instance, a convolutional neuron network (CNN) model for classification tasks often performs unsatisfactorily in classifying some particular classes of objects. In this work, we are concerned with patching the weak part of a CNN model instead of improving it through the costly retraining of the entire model. Inspired by the fundamental concepts of modularization and composition in software engineering, we propose a compressed modularization approach, CNNSplitter, which decomposes a strong CNN model for N-class classification into N smaller CNN modules. Each module is a sub-model containing a part of the convolution kernels of the strong model. To patch a weak CNN model that performs unsatisfactorily on a target class (TC), we compose the weak CNN model with the corresponding module obtained from a strong CNN model. The ability of the weak CNN model to recognize the TC can thus be improved through patching. Moreover, the ability to recognize non-TCs is also improved, as the samples misclassified as TC could be classified as non-TCs correctly. Experimental results with two representative CNNs on three widely-used datasets show that the averaged improvement on the TC in terms of precision and recall are 12.54% and 2.14%, respectively. Moreover, patching improves the accuracy of non-TCs by 1.18%. The results demonstrate that CNNSplitter can patch a weak CNN model through modularization and composition, thus providing a new solution for developing robust CNN models.
Simulations of PBH formation at the QCD epoch and comparison with the GWTC-3 catalog

The probability of primordial black hole (PBH) formation is known to be boosted during the Quantum Chromodynamics (QCD) crossover due to a slight reduction of the equation of state. This induces a high peak and other features in the PBH mass distribution. But the impact of this variation during the process of PBH formation has so far not been considered in numerical simulations. In this work we simulate the formation of PBHs by taking into account the varying equation of state at the QCD epoch, compute the over-density threshold using different curvature profiles and find that the resulting PBH mass distributions are significantly impacted. The expected merger rate distributions of early and late PBH binaries is comparable to the ones inferred from the GWTC-3 catalog for dark matter fractions in PBHs within 0.1 < f PBH < 1. The distribution of gravitational-wave events estimated from the volume sensitivity could explain mergers around 30–50 M ⊙, with asymmetric masses like GW190814, or in the pair-instability mass gap like GW190521. However, none of the considered cases leads to a multi-modal distribution with a secondary peak around 8–15 M ⊙, as suggested by the GWTC-3 catalog, possibly pointing to a mixed population of astrophysical and primordial black holes.
Amorphous ice in comets: evidence and consequences

Ice naturally forms in the disordered or ``amorphous'' state when accreted from vapor at temperatures and pressures found in the interstellar medium and in the frigid, low density outer regions of the Sun's protoplanetary disk. It is therefore the expected form of ice in comets and other primitive bodies that have escaped substantial heating since formation. Despite expectations, however, the observational evidence for amorphous ice in comets remains largely indirect. This is both because the spectral features of amorphous ice are subtle and because the solar system objects for which we possess high quality data are mostly too close to the Sun and too hot for amorphous ice to survive near the surface, where it can be detected. This chapter reviews the properties of amorphous ice, the evidence for its existence and its consequences for the behavior of comets.
CU-Net: Efficient Point Cloud Color Upsampling Network

Point cloud upsampling is necessary for Augmented Reality, Virtual Reality, and telepresence scenarios. Although the geometry upsampling is well studied to den-sify point cloud coordinates, the upsampling of colors has been largely overlooked. In this paper, we propose CU-Net, the ﬁrst deep-learning point cloud color upsampling model. Leveraging a feature extractor based on sparse convolution and a color prediction module based on neural implicit function, CU-Net achieves linear time and space complexity. Therefore, CU-Net is theoretically guaranteed to be more efﬁcient than most existing methods with quadratic complexity. Experimental results demonstrate that CU-Net can colorize a photo-realistic point cloud with nearly a million points in real time, while having better visual quality than baselines. Besides, CU-Net can adapt to an arbitrary upsampling ratio and unseen objects. Our source code will be released to the public soon.
Entity Tagging: Extracting Entities in Text Without Mention Supervision

Detection and disambiguation of all entities in text is a crucial task for a wide range of applications. The typical formulation of the problem involves two stages: detect mention boundaries and link all mentions to a knowledge base. For a long time, mention detection has been considered as a necessary step for extracting all entities in a piece of text, even if the information about mention spans is ignored by some downstream applications that merely focus on the set of extracted entities. In this paper we show that, in such cases, detection of mention boundaries does not bring any considerable performance gain in extracting entities, and therefore can be skipped. To conduct our analysis, we propose an"Entity Tagging"formulation of the problem, where models are evaluated purely on the set of extracted entities without considering mentions. We compare a state-of-the-art mention-aware entity linking solution against GET, a mention-agnostic sequence-to-sequence model that simply outputs a list of disambiguated entities given an input context. We find that these models achieve comparable performance when trained both on a fully and partially annotated dataset across multiple benchmarks, demonstrating that GET can extract disambiguated entities with strong performance without explicit mention boundaries supervision.
A lightweight Transformer-based model for fish landmark detection

—Transformer-based models, such as the Vision Transformer (ViT), can outperform Convolutional Neural Networks (CNNs) in some vision tasks when there is sufﬁcient training data. However, CNNs have a strong and useful inductive bias for vision tasks (i.e. translation equivariance and locality). In this work, we developed a novel model architecture that we call a Mobile ﬁsh landmark detection network (MFLD-net). We have made this model using convolution operations based on ViT (i.e. Patch embeddings, Multi-Layer Perceptrons). MFLD- net can achieve competitive or better results in low data regimes while being lightweight and therefore suitable for embedded and mobile devices. Furthermore, we show that MFLD-net can achieve keypoint (landmark) estimation accuracies on-par or even better than some of the state-of-the-art CNNs on a ﬁsh image dataset. Additionally, unlike ViT, MFLD-net does not need a pre-trained model and can generalise well when trained on a small dataset. We provide quantitative and qualitative results that demonstrate the model’s generalisation capabilities. This work will provide a foundation for future efforts in developing mobile, but efﬁcient ﬁsh monitoring systems and devices.
Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy

—We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task. Our bounds utilize a quantity called the majority predictor accuracy, which can be computed efﬁciently from data. We show that our theory is useful in practice since it implies that the majority predictor accuracy can be used as a transferability measure, a fact that is also validated by our experiments.
No existence of linear algorithm for Fourier phase retrieval

F ourier phase retrieval, which seeks to reconstruct a signal from its Fourier magnitude, is of fundamental importance in ﬁelds of engineering and science. In this paper, we give a theoretical understanding of algorithms for Fourier phase retrieval. Particularly, we show if there exists an algorithm which could reconstruct an arbitrary signal x ∈ C N in Poly ( N ) log(1 /ǫ ) time to reach ǫ -precision from its magnitude of discrete Fourier transform and its initial value x (0) , then P = NP . This demystiﬁes the phenomenon that, although almost all signals are determined uniquely by their Fourier magnitude with a prior conditions, there is no algorithm with theoretical guarantees being proposed over the past few decades. Our proofs employ the result in computational complexity theory that Product Partition problem is NP-complete in the strong sense.