Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics

Trajectory Representation Learning (TRL) is a powerful tool for spatial-temporal data analysis and management. TRL aims to convert complicated raw trajectories into low-dimensional representation vectors, which can be applied to various downstream tasks, such as trajectory classification, clustering, and similarity computation. Existing TRL works usually treat trajectories as ordinary sequence data, while some important spatial-temporal characteristics, such as temporal regularities and travel semantics, are not fully exploited. To fill this gap, we propose a novel Self-supervised trajectory representation learning framework with TemporAl Regularities and Travel semantics, namely START. The proposed method consists of two stages. The first stage is a Trajectory Pattern-Enhanced Graph Attention Network (TPE-GAT), which converts the road network features and travel semantics into representation vectors of road segments. The second stage is a Time-Aware Trajectory Encoder (TAT-Enc), which encodes representation vectors of road segments in the same trajectory as a trajectory representation vector, meanwhile incorporating temporal regularities with the trajectory representation. Moreover, we also design two self-supervised tasks, i.e., span-masked trajectory recovery and trajectory contrastive learning, to introduce spatial-temporal characteristics of trajectories into the training process of our START framework. The effectiveness of the proposed method is verified by extensive experiments on two large-scale real-world datasets for three downstream tasks. The experiments also demonstrate that our method can be transferred across different cities to adapt heterogeneous trajectory datasets.
Prompt Tuning for Parameter-efficient Medical Image Segmentation

Neural networks pre-trained on a self-supervision scheme have become the standard when operating in data rich environments with scarce annotations. As such, fine-tuning a model to a downstream task in a parameter-efficient but effective way, e.g. for a new set of classes in the case of semantic segmentation, is of increasing importance. In this work, we propose and investigate several contributions to achieve a parameter-efficient but effective adaptation for semantic segmentation on two medical imaging datasets. Relying on the recently popularized prompt tuning approach, we provide a prompt-able UNETR (PUNETR) architecture, that is frozen after pre-training, but adaptable throughout the network by class-dependent learnable prompt tokens. We pre-train this architecture with a dedicated dense self-supervision scheme based on assignments to online generated prototypes (contrastive prototype assignment, CPA) of a student teacher combination. Concurrently, an additional segmentation loss is applied for a subset of classes during pre-training, further increasing the effectiveness of leveraged prompts in the fine-tuning phase. We demonstrate that the resulting method is able to attenuate the gap between fully fine-tuned and parameter-efficiently adapted models on CT imaging datasets. To this end, the difference between fully fine-tuned and prompt-tuned variants amounts to 7.81 pp for the TCIA/BTCV dataset as well as 5.37 and 6.57 pp for subsets of the TotalSegmentator dataset in the mean Dice Similarity Coefficient (DSC, in %) while only adjusting prompt tokens, corresponding to 0.51% of the pre-trained backbone model with 24.4M frozen parameters. The code for this work is available on https://github.com/marcdcfischer/PUNETR.
More Effective Centrality-Based Attacks on Weighted Networks

Only when understanding hackers” tactics, can we thwart their attacks. With this spirit, this paper studies how hackers can effectively launch the so-called ‘targeted node attacks”, in which iterative attacks are staged on a network, and in each iteration the most important node is removed. In the existing attacks for weighted networks, the node importance is typically measured by the centralities related to shortest paths, and the attack effectiveness is also measured mostly by shortest-path-related metrics. However, this paper argues that flows can better reflect network functioning than shortest paths for those networks with carrying traffic as the main functionality. Thus, this paper proposes metrics based on flows for measuring the node importance and the attack effectiveness, respectively. Our node importance metrics include three flow-based centralities (flow betweenness, current-flow betweenness and current-flow closeness), which have not been proposed for use in the attacks on weighted networks yet. Our attack effectiveness metric is a new one proposed by us based on average network flow. Extensive experiments on both artificial and real-world networks show that the attack methods with our three suggested centralities are more effective than the existing attack methods when evaluated under our proposed attack effectiveness metric.
Study of Lithium Rich Giants with the GALAH Spectroscopic Survey

In this article, we speculate on the possible mechanisms for Li enhancement origin in RGB stars based on a large data set of around 340,299 stars collected from the GALAH survey combined with the Gaia astrometry. Data has 51,982 low mass (M$\leq$ 2M$_{\odot}$) RGB stars with reliable atmospheric parameters. The data set shows a well populated RGB with well-defined luminosity bump and red clump with significant number of stars at each of these two key phases. We found 335 new Li-rich RGB stars with Li abundance, A(Li) $\geq$ 1.80 $\pm$ 0.14 dex, of which 20 are super Li-rich with A(Li) $\geq$ 3.20~dex. Most of them appear to be in the red clump region which, when combined with stellar evolutionary timescales on RGB, indicates that the Li enhancement origin may lie at RGB tip during He-flash rather than by external source of merging of sub-stellar objects or during luminosity bump evolution. Kinematic properties of sample stars suggest that Li-rich giants are relatively more prevalent among giants of thin disk compared to thick disk and halo.
Continuous Gated First-Passage Processes

Gated first-passage processes, where completion depends on both hitting a target and satisfying additional constraints, are prevalent across various fields. Despite their significance, analytical solutions to basic problems remain unknown, e.g. the detection time of a diffusing particle by a gated interval, disk, or sphere. In this paper, we elucidate the challenges posed by continuous gated first-passage processes and present a renewal framework to overcome them. This framework offers a unified approach for a wide range of problems, including those with single-point, half-line, and interval targets. The latter have so far evaded exact solutions. Our analysis reveals that solutions to gated problems can be obtained directly from the ungated dynamics. This, in turn, reveals universal properties and asymptotic behaviors, shedding light on cryptic intermediate-time regimes and refining the notion of high-crypticity for continuous-space gated processes. Moreover, we extend our formalism to higher dimensions, showcasing its versatility and applicability. Overall, this work provides valuable insights into the dynamics of continuous gated first-passage processes and offers analytical tools for studying them across diverse domains.
Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models

Diffusion models have experienced a surge of interest as highly expressive yet efficiently trainable probabilistic models. We show that these models are an excellent fit for synthesising human motion that co-occurs with audio, e.g., dancing and co-speech gesticulation, since motion is complex and highly ambiguous given audio, calling for a probabilistic description. Specifically, we adapt the DiffWave architecture to model 3D pose sequences, putting Conformers in place of dilated convolutions for improved modelling power. We also demonstrate control over motion style, using classifier-free guidance to adjust the strength of the stylistic expression. Experiments on gesture and dance generation confirm that the proposed method achieves top-of-the-line motion quality, with distinctive styles whose expression can be made more or less pronounced. We also synthesise path-driven locomotion using the same model architecture. Finally, we generalise the guidance procedure to obtain product-of-expert ensembles of diffusion models and demonstrate how these may be used for, e.g., style interpolation, a contribution we believe is of independent interest.
Optimised graded metamaterials for energy harvesting via reinforcement learning

A reinforcement learning approach to design optimised graded metamaterials for energy harvesting is described. Through the proximal policy optimisation algorithm, the reinforcement agent is trained to optimally set the lengths and the spacing of an array of resonators. The design optimisation problem is formalised in a Markov decision problem by splitting the optimisation procedure into a discrete number of decisions. The a priori physical knowledge of the problem has been exploited to constrain the space of possible conﬁgurations by adopting few continuous variables to describe the resonator
A Unified Multimodal De- and Re-Coupling Framework for RGB-D Motion Recognition

Motion recognition is a promising direction in computer vision, but the training of video classification models is much harder than images due to insufficient data and considerable parameters. To get around this, some works strive to explore multimodal cues from RGB-D data. Although improving motion recognition to some extent, these methods still face sub-optimal situations in the following aspects: (i) Data augmentation, i.e., the scale of the RGB-D datasets is still limited, and few efforts have been made to explore novel data augmentation strategies for videos; (ii) Optimization mechanism, i.e., the tightly space-time-entangled network structure brings more challenges to spatiotemporal information modeling; And (iii) cross-modal knowledge fusion, i.e., the high similarity between multimodal representations leads to insufficient late fusion. To alleviate these drawbacks, we propose to improve RGB-D-based motion recognition both from data and algorithm perspectives in this article. In more detail, firstly, we introduce a novel video data augmentation method dubbed ShuffleMix, which acts as a supplement to MixUp, to provide additional temporal regularization for motion recognition. Secondly, a <bold>U</bold>nified <bold>M</bold>ultimodal <bold>D</bold>e-coupling and multi-stage <bold>R</bold>e-coupling framework, termed UMDR, is proposed for video representation learning. Finally, a novel cross-modal Complement Feature Catcher (CFCer) is explored to mine potential commonalities features in multimodal information as the auxiliary fusion stream, to improve the late fusion results. The seamless combination of these novel designs forms a robust spatiotemporal representation and achieves better performance than state-of-the-art methods on four public motion datasets. Specifically, UMDR achieves unprecedented improvements of <inline-formula><tex-math notation="LaTeX">$\uparrow 4.5\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>↑</mml:mo><mml:mn>4</mml:mn><mml:mo>. </mml:mo><mml:mn>5</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhou-ieq1-3274783.gif"/></alternatives></inline-formula> on the Chalearn IsoGD dataset.
Baryon and meson masses in the Nambu–Jona-Lasinio model: A Bayesian approach

We investigate the capabilities of the Nambu--Jona-Lasinio model to describe and reproduce fundamental vacuum properties of Quantum Chromodynamics, notably the hadronic spectrum. Mesons are described as quark-antiquark bound states at the level of the random phase approximation of the Bethe-Salpeter equation, while baryons are characterized as quark-diquark bound states within the static approximation of the Faddeev equation. Within a Bayesian framework, we constrain the model by phenomenologically known quantities and study the implications on its parameters and predictions in vacuum, as well as the correlations between the two. We find that within our framework, the vacuum masses of mesons and baryons can be reasonably well reproduced. Scalar diquarks need to be significantly bound in order to correctly reproduce the masses of the baryon octet, therefore enforcing values of the scalar diquark coupling larger than what is suggested by the canonical Fierz values. These findings could have important implications on the phenomenology of strongly-interacting matter at high temperature and density as well as of compact star physics.
Robust d-wave superconductivity from the Su-Schrieffer-Heeger-Hubbard model: possible route to high-temperature superconductivity

Increasing numerical studies showed that the simplest Hubbard model on the square lattice with strong repulsion may not exhibit high-temperature superconductivity (SC). It is desired to look for other possible microscopic mechanism of realizing high-temperature SC. Here, we explore the interplay between the Su-Schrieffer-Heeger (SSH) electron-phonon coupling (EPC) and the Hubbard repulsion by density-matrix-renormalization-group (DMRG) simulations. Our state-of-the-art DMRG study showed convincingly that the interplay between strong Hubbard $U$ and moderate Su-Schrieffer-Heeger EPC $\lambda$ can induce robust $d$-wave SC. The SSH-type EPC can generates effective antiferromagnetic spin-exchange interactions between neighboring sites, which plays a crucial role in the interplay of inducing robust $d$-wave SC. Specifically, for $U=8t$, we find that $d$-wave SC emerges when $\lambda>\lambda_c$ with a moderate critical value $\lambda_c=0.1\sim 0.2$. Our results might shed new light to understanding high-temperature SC in cuprates as well as pave a possible new route in looking for high-temperature SC in other quantum materials with both strong $U$ and moderate $\lambda$.
Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation

Evaluating automatically generated text is generally hard due to the inherently subjective nature of many aspects of the output quality. This difficulty is compounded in automatic consultation note generation by differing opinions between medical experts both about which patient statements should be included in generated notes and about their respective importance in arriving at a diagnosis. Previous real-world evaluations of note-generation systems saw substantial disagreement between expert evaluators. In this paper we propose a protocol that aims to increase objectivity by grounding evaluations in Consultation Checklists, which are created in a preliminary step and then used as a common point of reference during quality assessment. We observed good levels of inter-annotator agreement in a first evaluation study using the protocol; further, using Consultation Checklists produced in the study as reference for automatic metrics such as ROUGE or BERTScore improves their correlation with human judgements compared to using the original human note.
A Two-Stage Deep Representation Learning-Based Speech Enhancement Method Using Variational Autoencoder and Adversarial Training

This article focuses on leveraging deep representation learning (DRL) for speech enhancement (SE). In general, the performance of the deep neural network (DNN) is heavily dependent on the learning of data representation. However, the DRL's importance is often ignored in many DNN-based SE algorithms. To obtain a higher quality enhanced speech, we propose a two-stage DRL-based SE method through adversarial training. In the first stage, we disentangle different latent variables because disentangled representations can help DNN generate a better enhanced speech. Specifically, we use the $\beta$-variational autoencoder (VAE) algorithm to obtain the speech and noise posterior estimations and related representations from the observed signal. However, since the posteriors and representations are intractable and we can only apply a conditional assumption to estimate them, it is difficult to ensure that these estimations are always pretty accurate, which may potentially degrade the final accuracy of the signal estimation. To further improve the quality of enhanced speech, in the second stage, we introduce adversarial training to reduce the effect of the inaccurate posterior towards signal reconstruction and improve the signal estimation accuracy, making our algorithm more robust for the potentially inaccurate posterior estimations. As a result, better SE performance can be achieved. The experimental results indicate that the proposed strategy can help similar DNN-based SE algorithms achieve higher short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and scale-invariant signal-to-distortion ratio (SI-SDR) scores. Moreover, the proposed algorithm can also outperform recent competitive SE algorithms.
Interacting models for twisted bilayer graphene: A quantum chemistry approach

The nature of correlated states in twisted bilayer graphene (TBG) at the magic angle has received intense attention in recent years. We present a numerical study of an interacting Bistritzer-MacDonald (IBM) model of TBG using a suite of methods in quantum chemistry, including Hartree-Fock, coupled cluster singles, doubles (CCSD), and perturbative triples (CCSD(T)), as well as a quantum chemistry formulation of the density matrix renormalization group method (DMRG). Our treatment of TBG is agnostic to gauge choices, and hence we present a new gauge-invariant formulation to detect the spontaneous symmetry breaking in interacting models. To benchmark our approach, we focus on a simplified spinless, valleyless IBM model. At integer filling ($\nu=0$), all numerical methods agree in terms of energy and $C_{2z} \mathcal{T}$ symmetry breaking. Additionally, as part of our benchmarking, we explore the impact of different schemes for removing ``double-counting'' in the IBM model. Our results at integer filling suggest that cross-validation of different IBM models may be needed for future studies of the TBG system. After benchmarking our approach at integer filling, we perform the first systematic study of the IBM model near integer filling (for $|\nu|<0.2$). In this regime, we find that the ground state can be in a metallic and $C_{2z} \mathcal{T}$ symmetry breaking phase. The ground state appears to have low entropy, and therefore can be relatively well approximated by a single Slater determinant. Furthermore, we observe many low entropy states with energies very close to the ground state energy in the near integer filling regime.
DynaPicker: Dynamic Convolutional Neural Networks for Seismic Phase Classiﬁcation and Arrival-time Picking

—Seismic phase picking is at the core of earthquake monitoring. To date, the advancements in earthquake data collection have led to an exponential increase in the volume of available seismic waveform data. This highly necessitates a reliable solution for seismic phase classiﬁcation. Advanced technologies, such as convolutional neural networks, have been widely introduced to create earthquake catalogs from continuous waveforms produced using conventional methods. However, their performance is restricted by the static convolution kernels. To cope with this challenge, a dynamic convolutional neural network-based framework, termed DynaPicker, is proposed in this study for detecting seismic body wave phases that allows a dynamic inference with the deep learning architecture. We demonstrate the performance of our framework on two tasks: seismic phase classiﬁcation and arrival-time picking, both tested on four open-source seismic datasets. The results of our experiments show that DynaPicker can yield a testing accuracy of 98.82 % in seismic phase identiﬁcation. We demonstrate the robustness of this method’s ability in classifying seismic phases, even when the low-magnitude seismic data is polluted by noise. Moreover, DynaPicker can be extended to deal with input data of varying lengths for seismic phase detection. Given continuous seismic data, DynaPicker can correctly identify more seismic events and produce a lower arrival time picking error compared to the baseline methods.
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent

We provide the first finite-particle convergence rate for Stein variational gradient descent (SVGD), a popular algorithm for approximating a probability distribution with a collection of particles. Specifically, whenever the target distribution is sub-Gaussian with a Lipschitz score, SVGD with n particles and an appropriate step size sequence drives the kernel Stein discrepancy to zero at an order 1/sqrt(log log n) rate. We suspect that the dependence on n can be improved, and we hope that our explicit, non-asymptotic proof strategy will serve as a template for future refinements.
Spin-wave confinement and coupling in organic-based magnetic nanostructures

Vanadium tetracyanoethylene (V[TCNE]$_\text{x}$) is an organic-based ferrimagnet that exhibits robust magnetic ordering (T$_\text{C}$ of over 600 K), high quality-factor (high-Q) microwave resonance (Q up to 3,500), and compatibility with a wide variety of substrates and encapsulation technologies. Here, we substantially expand the potential scope and impact of this emerging material by demonstrating the ability to produce engineered nanostructures with tailored magnetic anisotropy that serve as a platform for the exploration of cavity magnonics, revealing strongly coupled quantum confined standing wave modes that can be tuned into and out of resonance with an applied magnetic field. Specifically, time-domain micromagnetic simulations of these nanostructures faithfully reproduce the experimentally measured spectra, including the quasi-uniform mode and higher-order spin-wave (magnon) modes. Finally, when the two dominant magnon modes present in the spectra are brought into resonance by varying the orientation of the in-plane magnetic field, we observe anti-crossing behavior indicating strong coherent coupling between these two magnon modes at room temperature. These results position V[TCNE]$_\text{x}$ as a leading candidate for the development of coherent magnonics, with potential applications ranging from microwave electronics to quantum information.
Supplementary Materials for: NULL-text Inversion for Editing Real Images using Guided Diffusion Models

Most of the presented results consist of applying our method with the editing technique of Prompt-to-Prompt [4]. However, we demonstrate that our method is not confined to a specific editing approach, by showing it improves the results of the SDEdit [7] editing technique. In Fig. 1 (top), we measure the fidelity to the original image using LPIPS perceptual distance [13] (lower is better), and the fidelity to the target text using CLIP similarity [8] (higher is better) over 100 examples. We use different values of the SDEdit parameter t0 (marked on the curve), i.e., we start the diffusion process from different t = t0 · T using a correspondingly noised input image. This parameter controls the trade-off between fidelity to the input image (low t0) and alignment to the text (high t0). We compare the standard SDEdit to first applying our inversion and then performing SDEdit while replacing the null-text embedding with our optimized embeddings. As shown, our inversion significantly improves the fidelity to the input image. This is visually demonstrated in Fig. 1 (bottom). Since the parameter t0 controls a reconstruction-editability tradeoff, we have used a different parameter for each method (SDEdit with and without our inversion) such that both achieve the same CLIP score. As can be seen, when using our method, the true identity of the baby is well preserved.