Miracle-dz/UniFAR-FTUs huggingface dataset:
AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERT

Keyword extraction has been an important topic for mod- ern natural language processing. With its applications rang-ing from ontology generation, fact veriﬁcation in summarized text, and recommendation systems. While it has had signiﬁcant data-intensive applications, it is often hampered when the data set is small. Downstream training for keyword extractors is a lengthy process and requires a signiﬁcant amount of data. Recently, Few-shot Learning (FSL) and Zero-Shot Learning (ZSL) have been proposed to tackle this problem. Therefore, we propose AdaptKeyBERT, a pipeline for training keyword extractors with LLM bases by incorporating the concept of regularized attention into a pre-training phase for downstream domain adaptation. As we believe our work has implications to be utilized in the pipeline of FSL/ZSL and keyword extraction, we open-source our code as well as pro- vide the ﬁne-tuning library of the same name AdaptKeyBERT at https://github.com/AmanPriyanshu/AdaptKeyBERT.
On maximal 3-edge-connected subgraphs of undirected graphs

We show how to find and efficiently maintain maximal k-edge-connected subgraphs in undirected graphs. In particular, we provide the following results. (1) A general framework for maintaining the maximal k-edge-connected subgraphs upon insertions of edges or vertices, by successively partitioning the graph into its k-edge-connected components. This defines a decomposition tree, which can be maintained by using algorithms for the incremental maintenance of the k-edge-connected components as black boxes at every level of the tree. (2) As an application of this framework, we provide two algorithms for the incremental maintenance of the maximal $3$-edge-connected subgraphs. These algorithms allow for vertex and edge insertions, interspersed with queries asking whether two vertices belong to the same maximal $3$-edge-connected subgraph. The first algorithm has $O(m\alpha(m,n) + n^2\log^2 n)$ total running time and uses $O(n)$ space, where $m$ is the number of edge insertions and queries, and $n$ is the total number of vertices inserted. The second algorithm performs the same operations in faster $O(m\alpha(m,n) + n^2\alpha(n,n))$ time in total, using $O(n^2)$ space. (3) We provide efficient constructions of sparse subgraphs that have the same maximal k-edge-connected subgraphs as the original graph. These are useful in speeding up computations involving the maximal k-edge-connected subgraphs in dense undirected graphs. (4) We give two deterministic algorithms for computing the maximal k-edge-connected subgraphs in undirected graphs, with running times $O(m+k^{O(1)}n\sqrt{n}\mathrm{polylog}(n))$ and $O(m+k^{O(k)}n\sqrt{n}\log{n})$, respectively. (5) A fully dynamic algorithm for maintaining information about the maximal k-edge-connected subgraphs for fixed k. Our update bounds are $O(n\sqrt{n}\log{n})$ worst-case time, and we achieve constant time for maximal k-edge-connected subgraph queries.
Effective band-structure in the insulating phase versus strong dynamical correlations in metallic VO2

Using a general analytical continuation scheme for cluster dynamical mean field calculations, we analyze real-frequency self-energies, momentum-resolved spectral functions, and one-particle excitations of the metallic and insulating phases of VO2. While for the former dynamical correlations and lifetime effects prevent a description in terms of quasi-particles, the excitations of the latter allow for an effective band-structure. We construct an orbital-dependent, but static one-particle potential that reproduces the full many-body spectrum. Yet, the ground state is well beyond a static one-particle description. The emerging picture gives a non-trivial answer to the decade-old question of the nature of the insulator, which we characterize as a ``many-body Peierls'' state.
Bellman-Ford is optimal for shortest hop-bounded paths

This paper is about the problem of finding a shortest $s$-$t$ path using at most $h$ edges in edge-weighted graphs. The Bellman--Ford algorithm solves this problem in $O(hm)$ time, where $m$ is the number of edges. We show that this running time is optimal, up to subpolynomial factors, under popular fine-grained complexity assumptions. More specifically, we show that under the APSP Hypothesis the problem cannot be solved faster already in undirected graphs with non-negative edge weights. This lower bound holds even restricted to graphs of arbitrary density and for arbitrary $h \in O(\sqrt{m})$. Moreover, under a stronger assumption, namely the Min-Plus Convolution Hypothesis, we can eliminate the restriction $h \in O(\sqrt{m})$. In other words, the $O(hm)$ bound is tight for the entire space of parameters $h$, $m$, and $n$, where $n$ is the number of nodes. Our lower bounds can be contrasted with the recent near-linear time algorithm for the negative-weight Single-Source Shortest Paths problem, which is the textbook application of the Bellman--Ford algorithm.
A Shared Cluster-based Stochastic Channel Model for Joint Communication and Sensing Systems

Joint communication and sensing (JCAS) has been recognized as a promising technology in the sixth generation (6G) communication. A realistic channel model is a prerequisite for designing JCAS systems. Most existing channel models independently generate the communication and sensing channels under the same framework. However, due to the multiplexing of hardware resources (e.g., antennas) and the same environment, signals enabled for communication and sensing may experience some shared propagation scatterers. This practical sharing feature necessities the joint generation of communication and sensing channels for realistic modeling, where the shared clusters (contributed by the shared scatterers) should be jointly reconstructed for both channels. In this paper, we first conduct communication and sensing channel measurements for an indoor scenario at 28 GHz. The power-angular-delay profiles (PADPs) of multipath components (MPCs) are obtained, and the shared scatterers by communication and sensing channels are intuitively observed. Then, a stochastic JCAS channel model is proposed to capture the sharing feature, where shared and non-shared clusters by the two channels are defined and superimposed. To extract those clusters from measured JCAS channels, a KPowerMeans-based joint clustering algorithm (KPM-JCA) is novelly introduced. Finally, stochastic channel characteristics are analyzed, and the practicality and controllability of the proposed model are validated based on the measurements and empirical simulations. The proposed model can realistically capture the sharing feature of JCAS channels, which is valuable for the design and deployment of JCAS systems.
Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis

We propose a novel ECGAN for the challenging semantic image synthesis task. Although considerable improvement has been achieved, the quality of synthesized images is far from satisfactory due to three largely unresolved challenges. 1) The semantic labels do not provide detailed structural information, making it difficult to synthesize local details and structures. 2) The widely adopted CNN operations such as convolution, down-sampling, and normalization usually cause spatial resolution loss and thus cannot fully preserve the original semantic information, leading to semantically inconsistent results. 3) Existing semantic image synthesis methods focus on modeling local semantic information from a single input semantic layout. However, they ignore global semantic information of multiple input semantic layouts, i.e., semantic cross-relations between pixels across different input layouts. To tackle 1), we propose to use edge as an intermediate representation which is further adopted to guide image generation via a proposed attention guided edge transfer module. Edge information is produced by a convolutional generator and introduces detailed structure information. To tackle 2), we design an effective module to selectively highlight class-dependent feature maps according to the original semantic layout to preserve the semantic information. To tackle 3), inspired by current methods in contrastive learning, we propose a novel contrastive learning method, which aims to enforce pixel embeddings belonging to the same semantic class to generate more similar image content than those from different classes. Doing so can capture more semantic relations by explicitly exploring the structures of labeled pixels from multiple input semantic layouts. Experiments on three challenging datasets show that our ECGAN achieves significantly better results than state-of-the-art methods.
A Survey on Preserving Fairness Guarantees in Changing Environments

Human lives are increasingly being affected by the outcomes of automated decision-making systems and it is essential for the latter to be, not only accurate, but also fair. The literature of algorithmic fairness has grown considerably over the last decade, where most of the approaches are evaluated under the strong assumption that the train and test samples are independently and identically drawn from the same underlying distribution. However, in practice, dissimilarity between the training and deployment environments exists, which compromises the performance of the decision-making algorithm as well as its fairness guarantees in the deployment data. There is an emergent research line that studies how to preserve fairness guarantees when the data generating processes differ between the source (train) and target (test) domains, which is growing remarkably. With this survey, we aim to provide a wide and unifying overview on the topic. For such purpose, we propose a taxonomy of the existing approaches for fair classification under distribution shift, highlight benchmarking alternatives, point out the relation with other similar research fields and eventually, identify future venues of research.
MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning

While neural networks-based photo processing solutions can provide a better image quality compared to the traditional ISP systems, their application to mobile devices is still very limited due to their very high computational complexity. In this paper, we present a novel MicroISP model designed specifically for edge devices, taking into account their computational and memory limitations. The proposed solution is capable of processing up to 32MP photos on recent smartphones using the standard mobile ML libraries and requiring less than 1 second to perform the inference, while for FullHD images it achieves real-time performance. The architecture of the model is flexible, allowing to adjust its complexity to devices of different computational power. To evaluate the performance of the model, we collected a novel Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The experiments demonstrated that, despite its compact size, the MicroISP model is able to provide comparable or better visual results than the traditional mobile ISP systems, while outperforming the previously proposed efficient deep learning based solutions. Finally, this model is also compatible with the latest mobile AI accelerators, achieving good runtime and low power consumption on smartphone NPUs and APUs. The code, dataset and pre-trained models are available on the project website: https://people.ee. ethz.ch/~ihnatova/microisp.html
The CGM2 Survey: Quenching and the Transformation of the Circumgalactic Medium

This study addresses how the incidence rate of strong O vi absorbers in a galaxy’s circumgalactic medium (CGM) depends on galaxy mass and, independently, on the amount of star formation in the galaxy. We use Hubble Space Telescope/Cosmic Origins Spectrograph absorption spectroscopy of quasars to measure O vi absorption within 400 projected kpc and 300 km s−1 of 52 galaxies with M * ∼ 3 × 1010 M ⊙. The galaxies have redshifts 0.12 < z < 0.6, stellar masses 1010.1 M ⊙ < M * < 1010.9 M ⊙, and spectroscopic classifications as star-forming or passive. We compare the incidence rates of high column density O vi absorption (N O VI ≥ 1014.3 cm−2) near star-forming and passive galaxies in two narrow ranges of stellar mass and, separately, in a matched range of halo mass. In all three mass ranges, the O vi covering fraction within 150 kpc is higher around star-forming galaxies than around passive galaxies with greater than 3σ-equivalent statistical significance. On average, the CGM of star-forming galaxies with M * ∼ 3 × 1010 M ⊙ contains more O vi than the CGM of passive galaxies with the same mass. This difference is evidence for a CGM transformation that happens together with galaxy quenching and is not driven primarily by halo mass.
The Implicit Delta Method

Epistemic uncertainty quantification is a crucial part of drawing credible conclusions from predictive models, whether concerned about the prediction at a given point or any downstream evaluation that uses the model as input. When the predictive model is simple and its evaluation differentiable, this task is solved by the delta method, where we propagate the asymptotically-normal uncertainty in the predictive model through the evaluation to compute standard errors and Wald confidence intervals. However, this becomes difficult when the model and/or evaluation becomes more complex. Remedies include the bootstrap, but it can be computationally infeasible when training the model even once is costly. In this paper, we propose an alternative, the implicit delta method, which works by infinitesimally regularizing the training loss of the predictive model to automatically assess downstream uncertainty. We show that the change in the evaluation due to regularization is consistent for the asymptotic variance of the evaluation estimator, even when the infinitesimal change is approximated by a finite difference. This provides both a reliable quantification of uncertainty in terms of standard errors as well as permits the construction of calibrated confidence intervals. We discuss connections to other approaches to uncertainty quantification, both Bayesian and frequentist, and demonstrate our approach empirically.
Parallel and I/O-Efficient Algorithms for Non-Linear Preferential Attachment

Preferential attachment lies at the heart of many network models aiming to replicate features of real world networks. To simulate the attachment process, conduct statistical tests, or obtain input data for benchmarks, efficient algorithms are required that are capable of generating large graphs according to these models. Existing graph generators are optimized for the most simple model, where new nodes that arrive in the network are connected to earlier nodes with a probability $P(h) \propto d$ that depends linearly on the degree $d$ of the earlier node $h$. Yet, some networks are better explained by a more general attachment probability $P(h) \propto f(d)$ for some function $f \colon \mathbb N~\to~\mathbb R$. Here, the polynomial case $f(d) = d^\alpha$ where $\alpha \in \mathbb R_{>0}$ is of particular interest. In this paper, we present efficient algorithms that generate graphs according to the more general models. We first design a simple yet optimal sequential algorithm for the polynomial model. We then parallelize the algorithm by identifying batches of independent samples and obtain a near-optimal speedup when adding many nodes. In addition, we present an I/O-efficient algorithm that can even be used for the fully general model. To showcase the efficiency and scalability of our algorithms, we conduct an experimental study and compare their performance to existing solutions.
Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation

Incremental or continual learning has been extensively studied for image classification tasks to alleviate catastrophic forgetting, a phenomenon that earlier learned knowledge is forgotten when learning new concepts. For class incremental semantic segmentation, such a phenomenon often becomes much worse due to the background shift, i.e., some concepts learned at previous stages are assigned to the background class at the current training stage, therefore, significantly reducing the performance of these old concepts. To address this issue, we propose a simple yet effective method in this paper, named Mining unseen Classes via Regional Objectness for Segmentation (MicroSeg). Our MicroSeg is based on the assumption that background regions with strong objectness possibly belong to those concepts in the historical or future stages. Therefore, to avoid forgetting old knowledge at the current training stage, our MicroSeg first splits the given image into hundreds of segment proposals with a proposal generator. Those segment proposals with strong objectness from the background are then clustered and assigned newly-defined labels during the optimization. In this way, the distribution characterizes of old concepts in the feature space could be better perceived, relieving the catastrophic forgetting caused by the background shift accordingly. Extensive experiments on Pascal VOC and ADE20K datasets show competitive results with state-of-the-art, well validating the effectiveness of the proposed MicroSeg.
Magnetic and transport signatures of Rashba spin-orbit coupling on the ferromagnetic Kondo lattice model in two dimensions

Motivated by emergent phenomena at oxide surfaces and interfaces, particularly those involving transition metal oxides with perovskite crystal structure such as LaTiO3/SrTiO3, we examine the ferromagnetic Kondo lattice model (FKLM) in the presence of a Rashba spin-orbit coupling (RSOC). Using numerical techniques, under the assumption that the electrons on localized orbitals may be treated as classical continuum spins, we compute various charge, spin and transport properties on square clusters at zero temperature. We find that the main effect of the RSOC is the destruction of the ferromagnetic state present in the FKLM at low electron fillings, with the consequent suppression of conductivity. In addition, near half-filling the RSOC leads to a departure of the antiferromagnetic state of the FKLM with a consequent reduction to the intrinsic tendency to electronic phase separation. The interplay between phase separation on one side, and magnetic and transport properties on the other, is carefully analyzed as a function of the RSOC/hopping ratio.
The role of emerging bipoles in the formation of a sunspot penumbra

The generation of magnetic flux in the solar interior and its transport from the convection zone into the photosphere, the chromosphere, and the corona will be in the focus of solar physics research for the next decades. With 4 m class telescopes, one plans to measure essential processes of radiative magneto-hydrodynamics that are needed to understand the nature of solar magnetic fields. One key-ingredient to understand the behavior of solar magnetic field is the process of flux emergence into the solar photosphere, and how the magnetic flux reorganizes to form the magnetic phenomena of active regions like sunspots and pores. Here, we present a spectropolarimetric and imaging data set from a region of emerging magnetic flux, in which a proto-spot without penumbra forms a penumbra. During the formation of the penumbra the area and the magnetic flux of the spot increases. First results of our data analysis demonstrate that the additional magnetic flux, which contributes to the increasing area of the penumbra, is supplied by the region of emerging magnetic flux. We observe emerging bipoles that are aligned such that the spot polarity is closer to the spot. As an emerging bipole separates, the pole of the spot polarity migrates towards the spot, and finally merges with it. We speculate that this is a fundamental process, which makes the sunspot accumulate magnetic flux. As more and more flux is accumulated a penumbra forms and transforms the proto-spot into a full-fledged sunspot (© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Secure Robotics: A Definition and a Brief Review from a Cybersecurity Control and Implementation Methodology Perspective

Secure robotics is a multi-disciplinary endeavour for improving the cybersecurity posture of robotic and embodied Artificial Intelligence systems. The article surveys emerging concepts and ideas encapsulating the notion of secure robotics and identifies five Secure Robotics Cybersecurity Control Implementation Layers as a crucial starting point for consideration by practitioners. It also recognises the need for further studies on the relationship between Human-robot trust and the implementation of established and novel cybersecurity controls.
NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets

We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requires no additional data or retraining. Instead, it uses the confidence score predicted by the backbone SOT network to automatically derive neighbor information and then uses this information to improve the tracking results. When tracking an occluded target, its appearance features are untrustworthy. However, a general siamese network often cannot tell whether the tracked object is occluded by reading the confidence score alone, because it could be misled by neighbors with high confidence scores. Our proposed NeighborTrack takes advantage of unoccluded neighbors' information to reconfirm the tracking target and reduces false tracking when the target is occluded. It not only reduces the impact caused by occlusion, but also fixes tracking problems caused by object appearance changes. NeighborTrack is agnostic to SOT networks and post-processing methods. For the VOT challenge dataset commonly used in short-term object tracking, we improve three famous SOT networks, Ocean, TransT, and OSTrack, by an average of ${1.92\%}$ EAO and ${2.11\%}$ robustness. For the mid- and long-term tracking experiments based on OSTrack, we achieve state-of-the-art ${72.25\%}$ AUC on LaSOT and ${75.7\%}$ AO on GOT-10K. Code duplication can be found in https://github.com/franktpmvu/NeighborTrack.
Differentially Private Vertical Federated Learning

A successful machine learning (ML) algorithm often relies on a large amount of high-quality data to train well-performed models. Supervised learning approaches, such as deep learning techniques, generate high-quality ML functions for real-life applications, however with large costs and human efforts to label training data. Recent advancements in federated learning (FL) allow multiple data owners or organisations to collaboratively train a machine learning model without sharing raw data. In this light, vertical FL allows organisations to build a global model when the participating organisations have vertically partitioned data. Further, in the vertical FL setting the participating organisation generally requires fewer resources compared to sharing data directly, enabling lightweight and scalable distributed training solutions. However, privacy protection in vertical FL is challenging due to the communication of intermediate outputs and the gradients of model update. This invites adversary entities to infer other organisations underlying data. Thus, in this paper, we aim to explore how to protect the privacy of individual organisation data in a differential privacy (DP) setting. We run experiments with different real-world datasets and DP budgets. Our experimental results show that a trade-off point needs to be found to achieve a balance between the vertical FL performance and privacy protection in terms of the amount of perturbation noise.