Miracle-dz/UniFAR-FTUs huggingface dataset:
Content-based Image Retrieval using Capsule Networks

Content-based image retrieval (CBIR) uses visual information in images to identify images relevant to a query image. Feature extraction is an essential step for CBIR. Learning-based feature extraction methods are the current state of the art. Nonetheless, these methods are not robust to 3D transformations if these transformations are not extensively represented in the dataset. Capsule networks are a novel neural network architecture that has achieved state of the art results in image recognition tasks. One particular promising characteristic of capsule network models is that they are rotation, translation, and viewpoint equivariant which make them robust to 3D transformations without the need of data augmentation. In this project we study and implement two capsule network architectures and their respective routing algorithms and apply them to the CBIR task on the Google Landmark dataset.
Holographic Learning – the use of augmented reality technology in chemistry teaching to develop students’ spatial ability

In chemistry teaching, three-dimensional molecules are traditionally visualised and drawn in 2D on paper or on a whiteboard/screen. However, the conceptual transition from 2D to 3D (i.e. spatial ability) is important for the understanding of chemistry. This is something with which many university students struggle, and they need to practice this through the visualisation of representations of atoms, molecules and reaction mechanisms. This paper reports on a study exploring how university students perceive the use of augmented reality (AR) technology in chemistry teaching. More specifically, the aim was to explore and understand what opportunities and challenges the students perceive when using AR technology for the purpose of enhancing their transition from a 2D representation of a molecule to a 3D structure visualised using AR glasses. The study was conducted during spring 2020. In this project, two groups of students of organic chemistry, in total 19 students, were given the opportunity to ‘see’ a holographic 3D structure of a molecule using AR glasses (e.g. Microsoft HoloLens 1). The empirical material is based on field test discussions, observation notes and 19 surveys that the students completed anonymously afterwards. The students described an immersive experience and the holographic 3D molecule was perceived as appearing like a very real object in the room. The student described the specific added value as being a support to visualising in 3D. They also stated that the amount of information on the 3D object was larger compared to a 2D representation. The challenges primarily concerned the rather narrow field of view of the AR glasses, and that training was required in order to use the device properly.
Massive Black Hole Binaries from the TNG50-3 Simulation. I. Coalescence and LISA Detection Rates

We evaluate the cosmological coalescence and detection rates for massive black hole (MBH) binaries targeted by the gravitational wave observatory Laser Interferometer Space Antenna (LISA). Our calculation starts with a population of gravitationally unbound MBH pairs, drawn from the TNG50-3 cosmological simulation, and follows their orbital evolution from kiloparsec scales all the way to coalescence using a semi-analytic model developed in our previous work. We find that for the majority of MBH pairs that coalesce within a Hubble time dynamical friction is the most important mechanism that determines their coalescence rate. Our model predicts an MBH coalescence rate ≲0.45 yr−1 and a LISA detection rate ≲0.34 yr−1. Most LISA detections should originate from 106 to 106.8 M ⊙ MBHs in gas-rich galaxies at redshifts 1.6 ≤ z ≤ 2.4 and have a characteristic signal-to-noise ratio S/N ∼100. We however find a dramatic reduction in the coalescence and detection rates, as well as the average S/N, if the effects of radiative feedback from accreting MBHs are taken into account. In this case, the MBH coalescence rate is reduced by 78% (to ≲0.1 yr−1), and the LISA detection rate is reduced by 94% (to 0.02 yr−1), whereas the average S/N is ∼10. We emphasize that our model provides a conservative estimate of the LISA detection rates, due to the limited MBH mass range in TNG50-3, consistent with other works in the literature that draw their MBH pairs from cosmological simulations.
Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression

In this work, we explore the relationship between depression and manifestations of happiness in social media. While the majority of works surrounding depression focus on symptoms, psychological research shows that there is a strong link between seeking happiness and being diagnosed with depression. We make use of Positive-Unlabeled learning paradigm to automatically extract happy moments from social media posts of both controls and users diagnosed with depression, and qualitatively analyze them with linguistic tools such as LIWC and keyness information. We show that the life of depressed individuals is not always bleak, with positive events related to friends and family being more noteworthy to their lives compared to the more mundane happy events reported by control users.
TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction

Knowledge graph embedding methods are important for the knowledge graph completion (or link prediction) task.One state-of-the-art method, PairRE, leverages two separate vectors to model complex relations (i.e., 1-to-N, N-to-1, and N-to-N) in knowledge graphs. However, such a method strictly restricts entities on the hyper-ellipsoid surfaces which limits the optimization of entity distribution, leading to suboptimal performance of knowledge graph completion. To address this issue, we propose a novel score function TranSHER, which leverages relation-specific translations between head and tail entities to relax the constraint of hyper-ellipsoid restrictions. By introducing an intuitive and simple relation-specific translation, TranSHER can provide more direct guidance on optimization and capture more semantic characteristics of entities with complex relations. Experimental results show that TranSHER achieves state-of-the-art performance on link prediction and generalizes well to datasets in different domains and scales. Our codes are public available athttps://github.com/yizhilll/TranSHER.
Accretion Scenario of MAXI J1820+070 during 2018 Outbursts with Multi-mission Observations

We present a comprehensive spectral and temporal study of the black hole X-ray transient MAXI J1820 + 070 during its outbursts in 2018 using Swift / XRT, NICER, NuSTAR and AstroSat observations. The Swift / XRT and NICER spectral study shows a plateau in the light curve with spectral softening (hardness changes from ∼ 2 . 5 to 2) followed by a gradual decline without spectral softening during the ﬁrst outburst. Also, spectral modelling suggests that the ﬁrst outburst is in the low / hard state throughout with a truncated disk whereas the thermal disk emission dominates during the second outburst. During the entire outburst, strong reﬂection signature (reﬂection fraction varies between ∼ 0 . 38 − 3 . 8) is observed in the simultaneous wideband (NICER-NuSTAR, XRT-NuSTAR, AstroSat) data due to the presence of a dynamically evolving corona. The NICER timing analysis shows Quasi-periodic Oscillation (QPO) signatures and the characteristic frequency increases (decreases) in the plateau (decline) phase with time during the ﬁrst outburst. We understand that the reduction of the electron cooling timescale in the corona due to spectral softening and the resonance oscillation with the local dynamical timescale may explain the above behavior of the source during the outburst. Also, we propose a possible scenario of outburst triggering and the associated accretion geometry of the source. - stars: individual: MAXI J1820 + 070
Phase transition of eigenvalues in deformed Ginibre ensembles

Consider a random matrix of size N as an additive deformation of the complex Ginibre ensemble under a deterministic matrix X 0 with a ﬁnite rank, independent of N . When some eigenvalues of X 0 separate from the unit disk, outlier eigenvalues may appear asymptotically in the same locations, and their ﬂuctuations exhibit surprising phenomena that highly depend on the Jordan canonical form of X 0 . These ﬁndings are largely due to Benaych-Georges and Rochet [9], Bordenave and Capitaine [11], and Tao [49]. When all eigenvalues of X 0 lie inside the unit disk, we prove that local eigenvalue statistics at the spectral edge form a new class of determinantal point processes, for which correlation kernels are characterized in terms of the repeated erfc integrals. This thus completes a non-Hermitian analogue of the BBP phase transition in Random Matrix Theory. Similar results hold for the deformed quaternion Ginibre ensemble.
Resource-efficient domain adaptive pre-training for medical images

The deep learning-based analysis of medical images suffers from data scarcity because of high annotation costs and privacy concerns. Researchers in this domain have used transfer learning to avoid overfitting when using complex architectures. However, the domain differences between pre-training and downstream data hamper the performance of the downstream task. Some recent studies have successfully used domain-adaptive pre-training (DAPT) to address this issue. In DAPT, models are initialized with the generic dataset pre-trained weights, and further pre-training is performed using a moderately sized in-domain dataset (medical images). Although this technique achieved good results for the downstream tasks in terms of accuracy and robustness, it is computationally expensive even when the datasets for DAPT are moderately sized. These compute-intensive techniques and models impact the environment negatively and create an uneven playing field for researchers with limited resources. This study proposed computationally efficient DAPT without compromising the downstream accuracy and robustness. This study proposes three techniques for this purpose, where the first (partial DAPT) performs DAPT on a subset of layers. The second one adopts a hybrid strategy (hybrid DAPT) by performing partial DAPT for a few epochs and then full DAPT for the remaining epochs. The third technique performs DAPT on simplified variants of the base architecture. The results showed that compared to the standard DAPT (full DAPT), the hybrid DAPT technique achieved better performance on the development and external datasets. In contrast, simplified architectures (after DAPT) achieved the best robustness while achieving modest performance on the development dataset 1 .
The Effect of Preferences in Abstract Argumentation Under a Claim-Centric View

In this paper, we study the effect of preferences in abstract argumentation under a claim-centric perspective. Recent work has revealed that semantical and computational properties can change when reasoning is performed on claim-level rather than on the argument-level, while under certain natural restrictions (arguments with the same claims have the same outgoing attacks) these properties are conserved. We now investigate these effects when, in addition, preferences have to be taken into account and consider four prominent reductions to handle preferences between arguments. As we shall see, these reductions give rise to different classes of claim-augmented argumentation frameworks, and behave differently in terms of semantic properties and computational complexity. This strengthens the view that the actual choice for handling preferences has to be taken with care.
Keep the Caption Information: Preventing Shortcut Learning in Contrastive Image-Caption Retrieval

To train image-caption retrieval (ICR) methods, contrastive loss functions are a common choice for optimization functions. Unfortunately, contrastive ICR methods are vulnerable to learning short-cuts: decision rules that perform well on the training data but fail to transfer to other testing conditions. We introduce an approach to reduce shortcut feature representations for the ICR task: latent target decoding (LTD). We add an additional decoder to the learning framework to reconstruct the input caption, which prevents the image and caption encoder from learning shortcut features. Instead of reconstructing input captions in the input space, we decode the semantics of the caption in a latent space. We implement the LTD objective as an optimization constraint, to ensure that the reconstruction loss is below a threshold value while primarily optimizing for the contrastive loss. Importantly, LTD does not depend on additional training data or expensive (hard) negative mining strategies. Our experiments show that, unlike reconstructing the input caption, LTD reduces shortcut learning and improves generalizability by obtaining higher recall@k and r-precision scores. Additionally, we show that the evaluation scores benefit from implementing LTD as an optimization constraint instead of a dual loss.
The tropical and zonotopal geometry of periodic timetables

The Periodic Event Scheduling Problem (PESP) is the standard mathematical tool for optimizing periodic timetabling problems in public transport. A solution to PESP consists of three parts: a periodic timetable, a periodic tension, and integer periodic offset values. While the space of periodic tension has received much attention in the past, we explore geometric properties of the other two components, establishing novel connections between periodic timetabling and discrete geometry. Firstly, we study the space of feasible periodic timetables, and decompose it into polytropes, i.e., polytopes that are convex both classically and in the sense of tropical geometry. We then study this decomposition and use it to outline a new heuristic for PESP, based on the tropical neighbourhood of the polytropes. Secondly, we recognize that the space of fractional cycle offsets is in fact a zonotope. We relate its zonotopal tilings back to the hyperrectangle of fractional periodic tensions and to the tropical neighbourhood of the periodic timetable space. To conclude we also use this new understanding to give tight lower bounds on the minimum width of an integral cycle basis.
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling

4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications. With the advances of new sensors and algorithms, there is an increasing demand for more versatile datasets. In this work, we contribute HuMMan, a large-scale multi-modal 4D human dataset with 1000 human subjects, 400k sequences and 60M frames. HuMMan has several appealing properties: 1) multi-modal data and annotations including color images, point clouds, keypoints, SMPL parameters, and textured meshes; 2) popular mobile device is included in the sensor suite; 3) a set of 500 actions, designed to cover fundamental movements; 4) multiple tasks such as action recognition, pose estimation, parametric human recovery, and textured mesh reconstruction are supported and evaluated. Extensive experiments on HuMMan voice the need for further study on challenges such as fine-grained action recognition, dynamic human mesh reconstruction, point cloud-based parametric human recovery, and cross-device domain gaps.
Attention Mechanism with Energy-Friendly Operations

Attention mechanism has become the dominant module in natural language processing models. It is computationally intensive and depends on massive power-hungry multiplications. In this paper, we rethink variants of attention mechanism from the energy consumption aspects. After reaching the conclusion that the energy costs of several energy-friendly operations are far less than their multiplication counterparts, we build a novel attention model by replacing multiplications with either selective operations or additions. Empirical results on three machine translation tasks demonstrate that the proposed model, against the vanilla one, achieves competitable accuracy while saving 99% and 66% energy during alignment calculation and the whole attention procedure. Our code will be released upon the acceptance.
Evidence for Anisotropic Superconductivity Beyond Pauli Limit in Infinite‐Layer Lanthanum Nickelates

After being expected to be a promising analog to cuprates for decades, superconductivity has recently been discovered in infinite‐layer nickelates, providing new opportunities to explore mechanisms of high‐temperature superconductivity. However, in sharp contrast to the single‐band and anisotropic superconductivity in cuprates, nickelates exhibit a multi‐band electronic structure and an unexpected isotropic superconductivity as reported recently, which challenges the cuprate‐like picture in nickelates. Here, it is shown that strong anisotropic magnetotransport behaviors exist in La‐based nickelate films with enhanced crystallinity and superconductivity ( Tconset$T_{\rm{c}}^{{\rm{onset}}}$ = 18.8 K, Tczero$T_{\rm{c}}^{{\rm{zero}}}$ = 16.5 K). The upper critical fields are anisotropic and violate the estimated Bardeen–Cooper–Schrieffer (BCS) Pauli limit ( HPauli,μ=1μB=1.86×Tc,H=0${H}_{\mathrm{Pauli},\mu =1{\mu}_{B}}=1.86\ensuremath{\times{}}{T}_{\mathrm{c},H=0}$ ) for in‐plane magnetic fields. Moreover, the anisotropic superconductivity is further manifested by the cusp‐like peak of the angle‐dependent Tc and the vortex motion anisotropy under external magnetic fields.
Toward Compositional Generalization in Object-Oriented World Modeling

Compositional generalization is a critical ability in learning and decision-making. We focus on the setting of reinforcement learning in object-oriented environments to study compositional generalization in world modeling. We (1) formalize the compositional generalization problem with an algebraic approach and (2) study how a world model can achieve that. We introduce a conceptual environment, Object Library, and two instances, and deploy a principled pipeline to measure the generalization ability. Motivated by the formulation, we analyze several methods with exact or no compositional generalization ability using our framework, and design a differentiable approach, Homomorphic Object-oriented World Model (HOWM), that achieves soft but more efficient compositional generalization.
Radio bounds on the mixed dark matter scenarios of primordial black holes and WIMPs

We study the synchrotron radio emission in the mixed dark matter scenarios consisting of the primordial black holes (PBHs) and the self-annihilating WIMPs (weakly interacting massive particles). The WIMPs can form the ultracompact minihalos around PBHs and the annihilation enhancement from these dense halos can lead to the efficient synchrotron radiation at the radio frequency in the presence of galactic magnetic fields. The upper bound of PBH fraction with respect to the total dark matter abundance is of order 10-8 ∼ 10-5 depending on the electroweak scale WIMP mass (mχ = 10 ∼ 1000 GeV) and the WIMP annihilation channel (e.g. a hadronic χχ ⟶ bb̅ or a leptonic χχ ⟶ e + e - channel). The PBH contribution to the total dark matter abundance is hence negligible when the other component of dark matter is composed of the conventional electroweak scale WIMPs.
Sums of distances on graphs and embeddings into Euclidean space

Let G=(V,E)$G=(V,E)$ be a finite, connected graph. We consider a greedy selection of vertices: given a list of vertices x1,⋯,xk$x_1, \dots , x_k$ , take xk+1$x_{k+1}$ to be any vertex maximizing the sum of distances to the vertices already chosen and iterate, keep adding the “most remote” vertex. The frequency with which the vertices of the graph appear in this sequence converges to a set of probability measures with nice properties. The support of these measures is, generically, given by a rather small number of vertices m≪|V|$m \ll |V|$ . We prove that this suggests that the graph G is, in a suitable sense, “m‐dimensional” by exhibiting an explicit 1‐Lipschitz embedding ϕ:V→ℓ1(Rm)$\phi : V \rightarrow \ell ^1(\mathbb {R}^m)$ with good properties.
Derivation of Learning Rules for Coupled Principal Component Analysis in a Lagrange-Newton Framework

We describe a Lagrange-Newton framework for the derivation of learning rules with desirable convergence properties and apply it to the case of principal component analysis (PCA). In this framework, a Newton descent is applied to an extended variable vector which also includes Lagrange multipliers introduced with constraints. The Newton descent guarantees equal convergence speed from all directions, but is also required to produce stable fixed points in the system with the extended state vector. The framework produces"coupled"PCA learning rules which simultaneously estimate an eigenvector and the corresponding eigenvalue in cross-coupled differential equations. We demonstrate the feasibility of this approach for two PCA learning rules, one for the estimation of the principal, the other for the estimate of an arbitrary eigenvector-eigenvalue pair (eigenpair).
On the Relationship Between Explanations, Fairness Perceptions, and Decisions

It is known that recommendations of AI-based systems can be incorrect or unfair. Hence, it is often proposed that a human be the final decision-maker. Prior work has argued that explanations are an essential pathway to help human decision-makers enhance decision quality and mitigate bias, i.e., facilitate human-AI complementarity. For these benefits to materialize, explanations should enable humans to appropriately rely on AI recommendations and override the algorithmic recommendation when necessary to increase distributive fairness of decisions. The literature, however, does not provide conclusive empirical evidence as to whether explanations enable such complementarity in practice. In this work, we (a) provide a conceptual framework to articulate the relationships between explanations, fairness perceptions, reliance, and distributive fairness, (b) apply it to understand (seemingly) contradictory research findings at the intersection of explanations and fairness, and (c) derive cohesive implications for the formulation of research questions and the design of experiments.
Bona Fide Riesz Projections for Density Estimation

The projection of sample measurements onto a reconstruction space represented by a basis on a regular grid is a powerful and simple approach to estimate a probability density function. In this paper, we focus on Riesz bases and propose a projection operator that, in contrast to previous works, guarantees the bona fide properties for the estimate, namely, non-negativity and total probability mass 1. Our bona fide projection is defined as a convex problem. We propose solution techniques and evaluate them. Results suggest an improved performance, specifically in circumstances prone to rippling effects.