Miracle-dz/UniFAR-FTUs huggingface dataset:
Density Field Reconstruction of an Overexpanded Supersonic Jet using Tomographic Background-Oriented Schlieren

A Tomographic Background-Oriented Schlieren (TBOS) technique is developed to aid in the visualization of compressible flows. An experimental setup was devised around a sub-scale rocket nozzle, in which four cameras were set up in a circular configuration with 30{\deg} angular spacing in azimuth. Measurements were taken of the overexpanded supersonic jet plume at various nozzle pressure ratios (NPR), corresponding to different flow regimes during the start-up and shut-down of rocket nozzles. Measurements were also performed for different camera parameters using different exposure times and f-stops in order to study the effect of measurement accuracy. Density gradients and subsequently two-dimensional line-of-sight integrated density fields for each of the camera projections are recovered from the index of refraction field by solving a Poisson equation. The results of this stage are then used to reconstruct two-dimensional slices of the (time-averaged) density field using a tomographic reconstruction algorithm employing the filtered back-projection and the simultaneous algebraic reconstruction technique. By stacking these two-dimensional slices, the (quasi-) three-dimensional density field is obtained. The accuracy of the implemented method with a relatively low number of sparse cameras is briefly assessed and basic flow features are extracted such as the shock spacing in the overexpanded jet plume.
Optimal recovery by maximum and integrated conditional likelihood in the general Stochastic Block Model

In this paper, we obtain new results on the weak and strong consistency of the maximum and integrated conditional likelihood estimators for the community detection problem in the Stochastic Block Model with k communities. In particular, we show that maximum conditional likelihood achieves the optimal known threshold for exact recovery in the logarithmic degree regime. For the integrated conditional likelihood, we obtain a sub-optimal constant in the same regime. Both methods are shown to be weakly consistent in the divergent degree regime. These results confirm the optimality of maximum likelihood on the task of community detection, something that has remained as an open problem until now.
Learning Agile Locomotion on Risky Terrains

Quadruped robots have shown remarkable mobility on various terrains through reinforcement learning. Yet, in the presence of sparse footholds and risky terrains such as stepping stones and balance beams, which require precise foot placement to avoid falls, model-based approaches are often used. In this paper, we show that end-to-end reinforcement learning can also enable the robot to traverse risky terrains with dynamic motions. To this end, our approach involves training a generalist policy for agile locomotion on disorderly and sparse stepping stones before transferring its reusable knowledge to various more challenging terrains by finetuning specialist policies from it. Given that the robot needs to rapidly adapt its velocity on these terrains, we formulate the task as a navigation task instead of the commonly used velocity tracking which constrains the robot's behavior and propose an exploration strategy to overcome sparse rewards and achieve high robustness. We validate our proposed method through simulation and real-world experiments on an ANYmal-D robot achieving peak forward velocity of>= 2.5 m/s on sparse stepping stones and narrow balance beams. Video: youtu.be/Z5X0J8OH6z4
Scalable Edge Clustering of Dynamic Graphs via Weighted Line Graphs

Timestamped relational datasets consisting of records between pairs of entities are ubiquitous in data and network science. For applications like peer-to-peer communication, email, social network interactions, and computer network security, it makes sense to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies the complexity of building and utilizing the line graph is prohibitive. We present an algorithm to cluster the edges of a dynamic graph via the associated line graph without forming it explicitly. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This work draws particular inspiration from HDBSCAN. We present a parallel algorithm and show that it is able to break billion-scale dynamic graphs into small sets that correlate in topology and time. The entire clustering process for a graph with $O(10 \text{ billion})$ edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and/or the non-relational metadata. Finally, we demonstrate its use on a real-world large-scale directed dynamic graph and describe how it can be extended to dynamic hypergraphs and graphs with unstructured data living on vertices and edges.
Superradiance in the Kerr-Taub-NUT spacetime

Superradiance is the effect of field waves being amplified during reflection from a charged or rotating black hole. In this paper, we study the low-energy dynamics of super-radiant scattering of massive scalar and massless higher spin field perturbations in a generic axisymmetric stationary Kerr-Taub-NUT (Newman-Unti-Tamburino) spacetime, which represents sources with both gravitomagnetic monopole moment (magnetic mass) and gravitomagnetic dipole moment (angular momentum). We obtain a generalized Teukolsky master equation for all spin perturbation fields. The equations are separated into their angular and radial parts. The angular equations lead to spin-weighted spheroidal harmonic functions that generalize those in Kerr spacetime. We identify an effective spin as a coupling between frequency (or energy) and the NUT parameter. The behaviors of the radial wave function near the horizon and at the infinite boundary are studied. We provide analytical expressions for low-energy observables such as emission rates and cross sections of all massless fields with spin, including scalar, neutrino, electromagnetic, Rarita-Schwinger, and gravitational waves.
CV-Attention UNet: Attention-based UNet for 3D Cerebrovascular Segmentation of Enhanced TOF-MRA Images

Due to the lack of automated methods, to diagnose cerebrovascular disease, time-of-flight magnetic resonance angiography (TOF-MRA) is assessed visually, making it time-consuming. The commonly used encoder-decoder architectures for cerebrovascular segmentation utilize redundant features, eventually leading to the extraction of low-level features multiple times. Additionally, convolutional neural networks (CNNs) suffer from performance degradation when the batch size is small, and deeper networks experience the vanishing gradient problem. Methods: In this paper, we attempt to solve these limitations and propose the 3D cerebrovascular attention UNet method, named CV-AttentionUNet, for precise extraction of brain vessel images. We proposed a sequence of preprocessing techniques followed by deeply supervised UNet to improve the accuracy of segmentation of the brain vessels leading to a stroke. To combine the low and high semantics, we applied the attention mechanism. This mechanism focuses on relevant associations and neglects irrelevant anatomical information. Furthermore, the inclusion of deep supervision incorporates different levels of features that prove to be beneficial for network convergence. Results: We demonstrate the efficiency of the proposed method by cross-validating with an unlabeled dataset, which was further labeled by us. We believe that the novelty of this algorithm lies in its ability to perform well on both labeled and unlabeled data with image processing-based enhancement. The results indicate that our method performed better than the existing state-of-the-art methods on the TubeTK dataset. Conclusion: The proposed method will help in accurate segmentation of cerebrovascular structure leading to stroke
Fast Estimations of Hitting Time of Elitist Evolutionary Algorithms from Fitness Levels

The fitness level method is an easy-to-use tool for estimating the hitting time of elitist EAs. Recently, general linear lower and upper bounds from fitness levels have been constructed. However, the construction of these bounds requires recursive computation, which makes them difficult to use in practice. We address this shortcoming with a new directed graph (digraph) method that does not require recursive computation and significantly simplifies the calculation of coefficients in linear bounds. In this method, an EA is modeled as a Markov chain on a digraph. Lower and upper bounds are directly calculated using conditional transition probabilities on the digraph. This digraph method provides straightforward and explicit expressions of lower and upper time bound for elitist EAs. In particular, it can be used to derive tight lower bound on both fitness landscapes without and with shortcuts. This is demonstrated through four examples: the (1+1) EA on OneMax, FullyDeceptive, TwoMax1 and Deceptive. Our work extends the fitness level method from addressing simple fitness functions without shortcuts to more realistic functions with shortcuts.
Bayesian mechanics of self-organising systems

Bayesian mechanics is a framework that addresses dynamical systems that can be conceptualised as Bayesian inference. However, the elucidation of requisite generative models is required for empirical applications to realistic self-organising systems. This work shows that the Hamiltonian of generic dynamical systems constitutes a class of generative models, thus rendering their Helmholtz energy naturally equivalent to variational free energy under the identified generative model. The self-organisation that minimises the Helmholtz energy entails matching the system's Hamiltonian with that of the environment, leading to an ensuing emergence of their generalised synchrony. In short, these self-organising systems can be read as performing variational Bayesian inference of the interacting environment. These properties have been demonstrated with coupled oscillators, simulated and living neural networks, and quantum computers. This notion offers foundational characterisations and predictions regarding asymptotic properties of self-organising systems exchanging with the environment, providing insights into potential mechanisms underlying emergence of intelligence.
Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models

Modeling evolving knowledge over temporal knowledge graphs (TKGs) has become a heated topic. Various methods have been proposed to forecast links on TKGs. Most of them are embedding-based, where hidden representations are learned to represent knowledge graph (KG) entities and relations based on the observed graph contexts. Although these methods show strong performance on traditional TKG forecasting (TKGF) benchmarks, they face a strong challenge in modeling the unseen zero-shot relations that have no prior graph context. In this paper, we try to mitigate this problem as follows. We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods. LLM-empowered representations can capture the semantic information in the relation descriptions. This makes the relations, whether seen or unseen, with similar semantic meanings stay close in the embedding space, enabling TKGF models to recognize zero-shot relations even without any observed graph context. Experimental results show that our approach helps TKGF models to achieve much better performance in forecasting the facts with previously unseen relations, while still maintaining their ability in link forecasting regarding seen relations.
Brillouin laser-driven terahertz oscillator up to 3 THz with femtosecond-level timing jitter

The terahertz (THz) frequency range, spanning 0.1 to 10 THz, is a field ripe for innovation with vast, developing potential in areas like wireless communication and molecular spectroscopy. Our work introduces a dual-wavelength laser design that utilizes stimulated Brillouin scattering in an optical fiber cavity to effectively generate two highly coherent optical Stokes waves with differential phase noise inherently mitigated. To guarantee robust operation, the Stokes waves are optically injected into their respective pump lasers, which also serves to greatly improve the resulting coherence. The frequency difference between the two wavelengths is converted into THz waves through a uni-traveling-carrier photodiode. This innovative design facilitates the generation of THz waves with phase noise levels of less than -100 dBc/Hz, translating to timing noise below 10~$\mathrm{as} / \sqrt{\mathrm{Hz}}$ at 10 kHz Fourier frequency, over a carrier frequency range from 300 GHz to 3 THz. This development in phase noise reduction establishes a new benchmark in the spectral purity of tunable THz sources. Such advances are pivotal for applications to move beyond oscillator constraints.
SelfEval: Leveraging the discriminative nature of generative models for evaluation

In this work, we show that text-to-image generative models can be 'inverted' to assess their own text-image understanding capabilities in a completely automated manner. Our method, called SelfEval, uses the generative model to compute the likelihood of real images given text prompts, making the generative model directly applicable to discriminative tasks. Using SelfEval, we repurpose standard datasets created for evaluating multimodal text-image discriminative models to evaluate generative models in a fine-grained manner: assessing their performance on attribute binding, color recognition, counting, shape recognition, spatial understanding. To the best of our knowledge SelfEval is the first automated metric to show a high degree of agreement for measuring text-faithfulness with the gold-standard human evaluations across multiple models and benchmarks. Moreover, SelfEval enables us to evaluate generative models on challenging tasks such as Winoground image-score where they demonstrate competitive performance to discriminative models. We also show severe drawbacks of standard automated metrics such as CLIP-score to measure text faithfulness on benchmarks such as DrawBench, and how SelfEval sidesteps these issues. We hope SelfEval enables easy and reliable automated evaluation for diffusion models.
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks

Despite the increasing use of deep learning in medical image segmentation, acquiring sufficient training data remains a challenge in the medical field. In response, data augmentation techniques have been proposed; however, the generation of diverse and realistic medical images and their corresponding masks remains a difficult task, especially when working with insufficient training sets. To address these limitations, we present an end-to-end architecture based on the Hamiltonian Variational Autoencoder (HVAE). This approach yields an improved posterior distribution approximation compared to traditional Variational Autoencoders (VAE), resulting in higher image generation quality. Our method outperforms generative adversarial architectures under data-scarce conditions, showcasing enhancements in image quality and precise tumor mask synthesis. We conduct experiments on two publicly available datasets, MICCAI's Brain Tumor Segmentation Challenge (BRATS), and Head and Neck Tumor Segmentation Challenge (HECKTOR), demonstrating the effectiveness of our method on different medical imaging modalities.
Accommodating Missing Modalities in Time-Continuous Multimodal Emotion Recognition

Decades of research indicate that emotion recognition is more effective when drawing information from multiple modalities. But what if some modalities are sometimes missing? To address this problem, we propose a novel Transformer-based architecture for recognizing valence and arousal in a time-continuous manner even with missing input modalities. We use a coupling of cross-attention and self-attention mechanisms to emphasize relationships between modalities during time and enhance the learning process on weak salient inputs. Experimental results on the Ulm-TSST dataset show that our model exhibits an improvement of the concordance correlation coefficient evaluation of 37% when predicting arousal values and 30% when predicting valence values, compared to a late-fusion baseline approach.
Using linear initialisation to improve speed of convergence and fully-trained error in Autoencoders

Good weight initialisation is an important step in successful training of Artificial Neural Networks. Over time a number of improvements have been proposed to this process. In this paper we introduce a novel weight initialisation technique called the Straddled Matrix Initialiser. This initialisation technique is motivated by our assumption that major, global-scale relationships in data are linear with only smaller effects requiring complex non-linearities. Combination of Straddled Matrix and ReLU activation function initialises a Neural Network as a de facto linear model, which we postulate should be a better starting point for optimisation given our assumptions. We test this by training autoencoders on three datasets using Straddled Matrix and seven other state-of-the-art weight initialisation techniques. In all our experiments the Straddeled Matrix Initialiser clearly outperforms all other methods.
Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification

The Segment Anything Model (SAM) has recently emerged as a groundbreaking foundation model for prompt-driven image segmentation tasks. However, both the original SAM and its medical variants require slice-by-slice manual prompting of target structures, which directly increase the burden for applications. Despite attempts of auto-prompting to turn SAM into a fully automatic manner, it still exhibits subpar performance and lacks of reliability especially in the field of medical imaging. In this paper, we propose UR-SAM, an uncertainty rectified SAM framework to enhance the reliability for auto-prompting medical image segmentation. Building upon a localization framework for automatic prompt generation, our method incorporates a prompt augmentation module to obtain a series of input prompts for SAM for uncertainty estimation and an uncertainty-based rectification module to further utilize the distribution of estimated uncertainty to improve the segmentation performance. Extensive experiments on two public 3D medical datasets covering the segmentation of 35 organs demonstrate that without supplementary training or fine-tuning, our method further improves the segmentation performance with up to 10.7 % and 13.8 % in dice similarity coefficient, demonstrating efficiency and broad capabilities for medical image segmentation without manual prompting.
Photo-Detection Efficiency measurement for FBK HD Near-UV sensitive SiPMs at 10 K temperature

We report the characterization of the FBK ``NUV-HD-Cryo'' SiPMs at 10 K temperature. With 405 nm and 530 nm light, we measured the photo-detection efficiency (PDE) at the bias voltages between 6 to 11 V overvoltage (OV). The PDE reaches $\sim$ 40\% for 405 nm and 530 nm light with a bias voltage of OV 9 V. A bias voltage higher than 9 V leads to a slightly greater PDE. We also measured the SiPMs' PDE at room temperature (RT). The results are consistent with the measurements on the similar model SiPMs by other groups. The I-V curve of the SiPMs differs significantly from the conventional one measured at RT. The dark current ratio (DCR) is tested to be $\sim$ 1 Hz for the 92 mm$^2$ SiPMs, or 0.01 Hz/mm$^2$, which is $\sim$ 7 orders lower than the ratio tested at RT. The SiPMs' performance at 10 K demonstrated that it could be equipped on a liquid helium detector as the photosensor to search for rare events, including but not limited to dark matter searches.
Archtree: on-the-fly tree-structured exploration for latency-aware pruning of deep neural networks

Deep neural networks (DNNs) have become ubiquitous in addressing a number of problems, particularly in computer vision. However, DNN inference is computationally intensive, which can be prohibitive e.g. when considering edge devices. To solve this problem, a popular solution is DNN pruning, and more so structured pruning, where coherent computational blocks (e.g. channels for convolutional networks) are removed: as an exhaustive search of the space of pruned sub-models is intractable in practice, channels are typically removed iteratively based on an importance estimation heuristic. Recently, promising latency-aware pruning methods were proposed, where channels are removed until the network reaches a target budget of wall-clock latency pre-emptively estimated on specific hardware. In this paper, we present Archtree, a novel method for latency-driven structured pruning of DNNs. Archtree explores multiple candidate pruned sub-models in parallel in a tree-like fashion, allowing for a better exploration of the search space. Furthermore, it involves on-the-fly latency estimation on the target hardware, accounting for closer latencies as compared to the specified budget. Empirical results on several DNN architectures and target hardware show that Archtree better preserves the original model accuracy while better fitting the latency budget as compared to existing state-of-the-art methods.
Groupoid intertwiner and twist for dynamical Yang--Baxter equation: part I

Intertwiner is a homomorphism between two existing dynamical R matrices, first introduced by Baxter in eight vertex-SOS correspondence, we develop certain equivalence relations among R matrices using intertwiners. Twist is a homomorphism that twist a dynamical R matrix to get a new dynamical R matrix, we introduce a kind of notion of twist that generalize classical Drinfeld twist in quasi-triangular Hopf algebra and some dynamical twist. As applications, we obtain some examples of twists from Ocneanu cell calculus and Fendley--Ginsparg orbifold constructions. The relations between intertwiner and twist are also discussed, the groupoid structures are emphasized.