Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Fusion: Scaling Subspace-Driven Approaches

In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the group subspace distribution of the information using the Convolutional Neural Network (CNN) formalism. Upon unfolding the set of subspaces constituting each data modality, and learning their corresponding encoders, an optimized integration of the generated inherent information is carried out to yield a characterization of various classes. Referred to as deep Multimodal Robust Group Subspace Clustering (DRoGSuRe), this approach is compared against the independently developed state-of-the-art approach named Deep Multimodal Subspace Clustering (DMSC). Experiments on different multimodal datasets show that our approach is competitive and more robust in the presence of noise.
Constraining High-redshift Stellar-mass Primordial Black Holes with Next-generation Ground-based Gravitational-wave Detectors

The possible existence of primordial black holes in the stellar-mass window has received considerable attention because their mergers may contribute to current and future gravitational-wave detections. Primordial black hole mergers, together with mergers of black holes originating from Population III stars, are expected to dominate at high redshifts (z ≳ 10). However, the primordial black hole merger rate density is expected to rise monotonically with redshift, while Population III mergers can only occur after the birth of the first stars. Next-generation gravitational-wave detectors such as the Cosmic Explorer (CE) and Einstein Telescope (ET) can access this distinctive feature in the merger rates as functions of redshift, allowing for direct measurement of the abundance of the two populations and hence for robust constraints on the abundance of primordial black holes. We simulate four months’ worth of data observed by a CE-ET detector network and perform hierarchical Bayesian analysis to recover the merger rate densities. We find that if the universe has no primordial black holes with masses of (10M⊙) , the projected upper limit on their abundance f PBH as a fraction of dark matter energy density may be as low as fPBH∼(10−5) , about two orders of magnitude lower than the current upper limits in this mass range. If instead f PBH ≳ 10−4, future gravitational-wave observations would exclude f PBH = 0 at the 95% credible interval.
Confined Penrose process and black-hole bomb

We consider the decay of a particle with some energy E 0 > 0 inside the ergosphere of a black hole. After the ﬁrst decay one of particles with the energy E 1 < 0 falls towards a black hole while the second one with E 2 > E 0 moves in the outward direction. It bounces back from a reﬂecting shell and, afterwards, the process repeats. For radial motion of charged particles in the Reissner-Nordst¨om metric, the result depends strongly on a concrete scenario. In particular, an indeﬁnitely large growth of energy inside a shell is possible that gives rise to a black-hole bomb. We also consider a similar multiple process with neutral particles in the background of a rotating axially symmetric stationary black hole. We demonstrate that, if particle decay occurs in the turning point, a black-hole bomb in this case is impossible at all. For a generic point inside the ergoregion, there is a condition for a black-hole bomb to exist. It relates the ratio of masses before and after decay and the velocity of a fragment in the center of mass frame.
EmpHi: Generating Empathetic Responses with Human-like Intents

In empathetic conversations, humans express their empathy to others with empathetic intents. However, most existing empathetic conversational methods suffer from a lack of empathetic intents, which leads to monotonous empathy. To address the bias of the empathetic intents distribution between empathetic dialogue models and humans, we propose a novel model to generate empathetic responses with human-consistent empathetic intents, EmpHi for short. Precisely, EmpHi learns the distribution of potential empathetic intents with a discrete latent variable, then combines both implicit and explicit intent representation to generate responses with various empathetic intents. Experiments show that EmpHi outperforms state-of-the-art models in terms of empathy, relevance, and diversity on both automatic and human evaluation. Moreover, the case studies demonstrate the high interpretability and outstanding performance of our model.
User Trust on an Explainable AI-based Medical Diagnosis Support System

Recent research has supported that system explainability improves user trust and willingness to use medical AI for diagnostic support. In this paper, we use chest disease diagnosis based on X-Ray images as a case study to investigate user trust and reliance. Building off explainability, we propose a support system where users (radiologists) can view causal explanations for final decisions. After observing these causal explanations, users provided their opinions of the model predictions and could correct explanations if they did not agree. We measured user trust as the agreement between the model's and the radiologist's diagnosis as well as the radiologists' feedback on the model explanations. Additionally, they reported their trust in the system. We tested our model on the CXR-Eye dataset and it achieved an overall accuracy of 74.1%. However, the experts in our user study agreed with the model for only 46.4% of the cases, indicating the necessity of improving the trust. The self-reported trust score was 3.2 on a scale of 1.0 to 5.0, showing that the users tended to trust the model but the trust still needs to be enhanced.
A linear program for testing nonclassicality and an open-source implementation

A well motivated method for demonstrating that an experiment resists any classical explanation is to show that its statistics violate generalized noncontextuality. We here formulate this problem as a linear program and provide an open-source implementation of it which tests whether or not any given prepare-measure experiment is classically-explainable in this sense. The input to the program is simply an arbitrary set of quantum states and an arbitrary set of quantum effects; the program then determines if the Born rule statistics generated by all pairs of these can be explained by a classical (noncontextual) model. If a classical model exists, it provides an explicit model. If it does not, then it computes the minimal amount of noise that must be added such that a model does exist, and then provides this model. We generalize all these results to arbitrary generalized probabilistic theories (and accessible fragments thereof) as well; indeed, our linear program is a test of simplex-embeddability.
Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items

Interactive 3D simulations have enabled break-throughs in robotics and computer vision, but simulating the broad diversity of environments needed for deep learning requires large corpora of photo-realistic 3D object models. To address this need, we present Google Scanned Objects, an open-source collection of over one thousand 3D-scanned household items released under a Creative Commons license; these models are preprocessed for use in Ignition Gazebo and the Bullet simulation platforms, but are easily adaptable to other simulators. We describe our object scanning and curation pipeline, then provide statistics about the contents of the dataset and its usage. We hope that the diversity, quality, and flexibility of Google Scanned Objects will lead to advances in interactive simulation, synthetic perception, and robotic learning.
Unified GCNs: Towards Connecting GCNs with CNNs

Graph Convolutional Networks (GCNs) have been widely demonstrated their powerful ability in graph data representation and learning. Existing graph convolution layers are mainly designed based on graph signal processing and transform aspect which usually suffer from some limitations, such as over-smoothing, over-squashing and non-robustness, etc. As we all know that Convolution Neural Networks (CNNs) have received great success in many computer vision and machine learning. One main aspect is that CNNs leverage many learnable convolution filters (kernels) to obtain rich feature descriptors and thus can have high capacity to encode complex patterns in visual data analysis. Also, CNNs are flexible in designing their network architecture, such as MobileNet, ResNet, Xception, etc. Therefore, it is natural to arise a question: can we design graph convolutional layer as flexibly as that in CNNs? Innovatively, in this paper, we consider connecting GCNs with CNNs deeply from a general perspective of depthwise separable convolution operation. Specifically, we show that GCN and GAT indeed perform some specific depthwise separable convolution operations. This novel interpretation enables us to better understand the connections between GCNs (GCN, GAT) and CNNs and further inspires us to design more Unified GCNs (UGCNs). As two showcases, we implement two UGCNs, i.e., Separable UGCN (S-UGCN) and General UGCN (G-UGCN) for graph data representation and learning. Promising experiments on several graph representation benchmarks demonstrate the effectiveness and advantages of the proposed UGCNs.
K-matrix Analysis of $e^+e^-$ Annihilation in the Bottomonium Region

We perform the first global and unitary analysis of $e^+e^-\to b\bar{b}$ cross sections. We analyze exclusive cross sections in the $B\bar{B}$, $B^* \bar{B}(+c.c.)$, $B^*\bar{B}^*$, $B_s^*\bar{B}_s^*$, $\Upsilon(nS)\pi^+\pi^-$ and $h_b(nP)\pi^+\pi^-$ channels as well as the total inclusive cross section for $b\bar{b}$ production. Pole positions and residues are determined for four vector states, which we associate with the $\Upsilon(4S)$, $\Upsilon(10750)$, $\Upsilon(5S)$ (or $\Upsilon(10860)$), and $\Upsilon(6S)$ (or $\Upsilon(11020)$). We find strong evidence for the new $\Upsilon(10750)$ recently claimed by Belle, although with parameters not well constrained by the data. Results presented here cast doubt on the validity of branching ratios reported earlier using Breit-Wigner parameterizations or ratios of cross sections. We also compare our results with a selection of theoretical calculations for the vector bottomonium spectrum.
Spin Waves and Magnetic Exchange Hamiltonian in CrSBr

CrSBr is an air‐stable two‐dimensional (2D) van der Waals semiconducting magnet with great technological promise, but its atomic‐scale magnetic interactions—crucial information for high‐frequency switching—are poorly understood. An experimental study is presented to determine the CrSBr magnetic exchange Hamiltonian and bulk magnon spectrum. The A‐type antiferromagnetic order using single crystal neutron diffraction is confirmed here. The magnon dispersions are also measured using inelastic neutron scattering and rigorously fit the excitation modes to a spin wave model. The magnon spectrum is well described by an intra‐plane ferromagnetic Heisenberg exchange model with seven nearest in‐plane exchanges. This fitted exchange Hamiltonian enables theoretical predictions of CrSBr behavior: as one example, the fitted Hamiltonian is used to predict the presence of chiral magnon edge modes with a spin‐orbit enhanced CrSBr heterostructure.
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation

Although no specific domain knowledge is considered in the design, plain vision transformers have shown excellent performance in visual recognition tasks. However, little effort has been made to reveal the potential of such simple structures for pose estimation tasks. In this paper, we show the surprisingly good capabilities of plain vision transformers for pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model called ViTPose. Specifically, ViTPose employs plain and non-hierarchical vision transformers as backbones to extract features for a given person instance and a lightweight decoder for pose estimation. It can be scaled up from 100M to 1B parameters by taking the advantages of the scalable model capacity and high parallelism of transformers, setting a new Pareto front between throughput and performance. Besides, ViTPose is very flexible regarding the attention type, input resolution, pre-training and finetuning strategy, as well as dealing with multiple pose tasks. We also empirically demonstrate that the knowledge of large ViTPose models can be easily transferred to small ones via a simple knowledge token. Experimental results show that our basic ViTPose model outperforms representative methods on the challenging MS COCO Keypoint Detection benchmark, while the largest model sets a new state-of-the-art. The code and models are available at https://github.com/ViTAE-Transformer/ViTPose.
Ab-initio QCD Calculations Impact the Inference of the Neutron-star-matter Equation of State

We demonstrate that ab-initio calculations in QCD at high densities offer significant and nontrivial information about the equation of state of matter in the cores of neutron stars, going beyond that which is obtainable from current astrophysical observations. We do so by extrapolating the equation of state to neutron-star densities using a Gaussian process and conditioning it sequentially with astrophysical observations and QCD input. Using our recent work, imposing the latter does not require an extrapolation to asymptotically high density. We find the QCD input to be complementary to the astrophysical observations, offering strong additional constraints at the highest densities reached in the cores of neutron stars; with the QCD input, the equation of state is no longer prior dominated at any density. The QCD input reduces the pressure and speed of sound at high densities, and it predicts that binary collisions of equal-mass neutron stars will produce a black hole with greater than 95% (68%) credence for masses M ≥ 1.38M ⊙ (M ≥ 1.25M ⊙). We provide a Python implementation of the QCD likelihood function so that it can be conveniently used within other inference setups.
INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering

Collaborative filtering is one of the most common scenarios and popular research topics in recommender systems. Among existing methods, latent factor models, i.e., learning a specific embedding for each user/item by reconstructing the observed interaction matrix, have shown excellent performances. However, such user-specific and item-specific embeddings are intrinsically transductive, making it difficult for them to deal with new users and new items unseen during training. Besides, the number of model parameters heavily depends on the number of all users and items, restricting their scalability to real-world applications. To solve the above challenges, in this paper, we propose a novel model-agnostic and scalable Inductive Embedding Module for collaborative filtering, namely INMO. INMO generates the inductive embeddings for users (items) by characterizing their interactions with some template items (template users), instead of employing an embedding lookup table. Under the theoretical analysis, we further propose an effective indicator for the selection of template users and template items. Our proposed INMO can be attached to existing latent factor models as a pre-module, inheriting the expressiveness of backbone models, while bringing the inductive ability and reducing model parameters. We validate the generality of INMO by attaching it to Matrix Factorization (MF) and LightGCN, which are two representative latent factor models for collaborative filtering. Extensive experiments on three public benchmarks demonstrate the effectiveness and efficiency of INMO in both transductive and inductive recommendation scenarios.
Exact DG-categories and fully faithful triangulated inclusion functors

We construct an"almost involution"assigning a new DG-category to a given one, and use this construction in order to recover, say, the abelian category of graded modules over the graded ring $R^*$ from the DG-category of DG-modules over a DG-ring $(R^*,d)$. This provides an appropriate technical background for the definition and discussion of abelian and exact DG-categories. In the setting of exact DG-categories, derived categories of the second kind are defined in the maximal natural generality. We develop the related abstract category-theoretic language and use it in order to formulate and prove several full-and-faithfulness theorems for triangulated functors induced by the inclusions of fully exact DG-subcategories. Such functors are fully faithful for derived categories of the second kind more often than for the conventional derived categories. Examples and applications range from the categories of complexes in abelian/exact categories to matrix factorization categories, and from curved DG-modules over curved DG-rings to quasi-coherent CDG-modules over quasi-coherent CDG-quasi-algebras over schemes.
Production Process as a Site of Critique. Ethnographic Research into the Mediated Interactions during (Documentary) Film Productions.

n this research I deliberately shift the attention from analyses focusing exclusively on the end result of (Documentary) Film Productions that might be a film, a documentary, a performance, towards an examination of the context of interaction in which this result is submerged. I investigate the mediated interactions between the 'author', the 'viewer' and the 'other' in their plural and variable agencies during the various phases of the production process and its parameters, codes and values. This study therefore explores the relations between the production of knowledge, the different contexts in which these processes occur and the position the researcher has in these processes. As such, I aim to add an investigative tool to the examination of the rich potentiality of visuals in the construction of the self and the formation of sodalities through those media. The fieldwork involved three cases in which I follow Fabian's reorientation as a movement from an interpretive to a performative anthropology. The first is an exploration of a community film project in Brussels directed by artist Els Dietvorst in collaboration with a hybrid tribe she named 'The Swallows' (2004); the other case is situated in California, where I participated in the shoot of Night Passage (2004) by Trinh Minh-ha and Jean-Paul Bourdier; the last case is a personalized recollection of the production process of the film Tu ne verras pas Verapaz (2002) directed by Didier Volckaert and myself. The comparison between these cases demonstrates the different 'cultural intuitions' of each project in relation to the interpretation of form and content. These notions shape the way in which the parameters of cinema are understood or used within the interactions between the main agents.
Goleman-Boyatzis Model of Emotional Intelligence for Dealing with Problems in Project Management

As projects grow in size and complexity the sizes of teams needed to manage them also increases. This places greater emphasis on the need for the project manager to develop people management skills, commonly called soft skills, of which emotional intelligence (EI) has been recognised as an important component. The objective of this research was to investigate the relevance of the Goleman-Boyatzis model of EI in dealing with the problems in large projects identified via a literature review. To achieve this end, a Delphi study using project managers who had been involved in the management of projects in excess of $500 million was used. The responses from the Delphi panel were analysed and the results showed that the competencies contained in the Goleman-Boyatzis model had a relevance of 95% or greater to the problems presented to the panel. A ranking of the various competencies contained within the model was also developed, some competencies being found to be more important than others. By confirming the importance of emotional intelligence, as described by the model, this research adds to the understanding of the necessary skills needed by a project manager to successfully manage large projects.
Prevention of terrorism, extremism and radicalisation in Sweden: a sociological institutional perspective on development and change

ABSTRACT National approaches to prevent terrorism, extremism, and radicalisation have changed considerably over the last decades. Previous studies mapping these changes have primarily relied on empirical analyses of formal policy and political processes. This case-study of Sweden takes an alternative route, and analyses a dataset of 1405 Swedish newspaper articles (1985–2019) using a new institutional theory and social movement theory framework. Therethrough, the paper is able to provide new insights into the emergence and development of an institutional issue field concerned with the prevention of terrorism, extremism, and radicalisation. More specifically, the paper highlights the unstable, fragmented, dynamic and contested character of the field’s development. Frames containing the problems and solutions considered most important during each of the field’s five stages are identified, and the subsequent institutional and organisational consequences are discussed. The paper also considers how terror attacks and other extremism-related events impact the institutionalisation and alternation of dominant frames, and identifies the translation and development of an inclusive vocabulary as pivotal to mobilising a broad and diverse set of actors to co-produce preventive efforts.
Audiometric notch as a sign of noise induced hearing loss (NIHL) among the rice and market flour mill workers in Tamil Nadu, South India

Abstract Objective This study aims to determine the prevalence of NIHL among rice and flour mill workers exposed to high decibel noise levels at the workplace. Methods One-hundred and seven workers exposed to noise from rice and flour mill machinery and 82 age-matched controls not exposed to noise at the workplace were recruited for the study from the south Indian state of Tamil Nadu. All the mill workers and the controls were evaluated for their hearing thresholds by pure-tone audiometry. Results The characteristic bilateral notch in 4000 Hz was detected in 25% of the mill workers, while the prevalence of the unilateral or bilateral notch was observed in 51%. The high-frequency slope (HFS) involving HL at 4000 and 8000 Hz was observed in 23.36 and 29.92% among the left and right ears of the mill workers, respectively. Years of experience and exposure to noise were strongly associated with a bilateral notch in 4000 Hz (the odds of having the notch increased with increased years of noise exposure). Self-reported HL under-represented the rates of actual HL, indicating perception of the HL was absent till audiometric evaluation revealed the loss. Conclusion Our study demonstrates exposure to high noise levels in this unorganized sector of rice and flour mill workers. Awareness education of NIHL, early detection, and the use of personal hearing protection devices can prevent HL from extending to speech frequencies.