Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer learning from a sparsely annotated dataset of 3D medical images

Transfer learning leverages pre-trained model features from a large dataset to save time and resources when training new models for various tasks, potentially enhancing performance. Due to the lack of large datasets in the medical imaging domain, transfer learning from one medical imaging model to other medical imaging models has not been widely explored. This study explores the use of transfer learning to improve the performance of deep convolutional neural networks for organ segmentation in medical imaging. A base segmentation model (3D U-Net) was trained on a large and sparsely annotated dataset; its weights were used for transfer learning on four new down-stream segmentation tasks for which a fully annotated dataset was available. We analyzed the training set size's influence to simulate scarce data. The results showed that transfer learning from the base model was beneficial when small datasets were available, providing significant performance improvements; where fine-tuning the base model is more beneficial than updating all the network weights with vanilla transfer learning. Transfer learning with fine-tuning increased the performance by up to 0.129 (+28\%) Dice score than experiments trained from scratch, and on average 23 experiments increased the performance by 0.029 Dice score in the new segmentation tasks. The study also showed that cross-modality transfer learning using CT scans was beneficial. The findings of this study demonstrate the potential of transfer learning to improve the efficiency of annotation and increase the accessibility of accurate organ segmentation in medical imaging, ultimately leading to improved patient care. We made the network definition and weights publicly available to benefit other users and researchers.
Moir\'e Fractional Chern Insulators I: First-principles calculations and Continuum Models of Twisted Bilayer MoTe$_2$

Recent experiments observed fractional Chern insulators (FCI) in twisted bilayer MoTe$_2$ at zero magnetic field, yet even the single-particle model of this material is controversial, leading to unreliable predictions of the experimental phase diagram as discussed in [Yu et al., 2023]. In this light, we revisit the single-particle model of twisted bilayer MoTe$_2$. Utilizing large-scale density functional theory, we calculate the band structure of twisted AA-stacked bilayer MoTe$_2$ at various twist angles relevant to experiment. We find that a band inversion occurs near $4.41^\circ$ between the second and third bands. Our ab initio band structure is in qualitative agreement with [Wang et al., 2023], but shows important differences in the remote bands and in the $\Gamma$ valley. We incorporate two higher harmonic terms into the continuum model to capture the highest 3 valence bands per valley. We confirm that the two highest valence bands per valley have opposite Chern numbers with $|C|=1$ for small angles, and also use our model to predict a variety of Chern states in the remote bands accessible by displacement field. Finally, we perform DFT calculations and build models for the AB stacking configuration. Our work serves as a foundation for accurate determination of the correlated phases in twisted bilayer MoTe$_2$.
Which Black Hole Is Spinning? Probing the Origin of Black Hole Spin with Gravitational Waves

Theoretical studies of angular momentum transport suggest that isolated stellar-mass black holes are born with negligible dimensionless spin magnitudes χ ≲ 0.01. However, recent gravitational-wave observations indicate ≳40% of binary black hole systems contain at least one black hole with a nonnegligible spin magnitude. One explanation is that the firstborn black hole spins up the stellar core of what will become the second-born black hole through tidal interactions. Typically, the second-born black hole is the “secondary” (less massive) black hole though it may become the “primary” (more massive) black hole through a process known as mass-ratio reversal. We investigate this hypothesis by analyzing data from the third gravitational-wave transient catalog using a “single-spin” framework in which only one black hole may spin in any given binary. Given this assumption, we show that at least 28% (90% credibility) of the LIGO–Virgo–KAGRA binaries contain a primary with significant spin, possibly indicative of mass-ratio reversal. We find no evidence for binaries that contain a secondary with significant spin. However, the single-spin framework is moderately disfavored (natural log Bayes factor ln B = 3.1 ) when compared to a model that allows both black holes to spin. If future studies can firmly establish that most merging binaries contain two spinning black holes, it may call into question our understanding of formation mechanisms for binary black holes or the efficiency of angular momentum transport in black hole progenitors.
Just-in-time Quantization with Processing-In-Memory for Efficient ML Training

Data format innovations have been critical for machine learning (ML) scaling, which in turn fuels ground-breaking ML capabilities. However, even in the presence of low-precision formats, model weights are often stored in both high-precision and low-precision during training. Furthermore, with emerging directional data formats (e.g., MX9, MX6, etc.) multiple low-precision weight copies can be required. To lower memory capacity needs of weights, we explore just-in-time quantization (JIT-Q) where we only store high-precision weights in memory and generate low-precision weights only when needed. To perform JIT-Q efficiently, in this work, we evaluate emerging processing-in-memory (PIM) technology to execute quantization. With PIM, we can offload quantization to in-memory compute units enabling quantization to be performed without incurring costly data movement while allowing quantization to be concurrent with accelerator computation. Our proposed PIM-offloaded quantization keeps up with GPU compute and delivers considerable capacity savings (up to 24\%) at marginal throughput loss (up to 2.4\%). Said memory capacity savings can unlock several benefits such as fitting larger model in the same system, reducing model parallelism requirement, and improving overall ML training efficiency.
Legal-HNet: Mixing Legal Long-Context Tokens with Hartley Transform

Since its introduction, the transformers architecture has seen great adoption in NLP applications, but it also has limitations. Although the self-attention mechanism allows for generating very rich representations of the input text, its effectiveness may be limited in specialized domains such as legal, where, for example, language models often have to process very long texts. In this paper, we explore alternatives to replace the attention-based layers with simpler token-mixing mechanisms: Hartley and Fourier transforms. Using these non-parametric techniques, we train models with long input documents from scratch in the legal domain setting. We also introduce a new hybrid Seq2Seq architecture, a no-attention-based encoder connected with an attention-based decoder, which performs quite well on existing summarization tasks with much less compute and memory requirements. We believe that similar, if not better performance, as in the case of long correlations of abstractive text summarization tasks, can be achieved by adopting these simpler infrastructures. This not only makes training models from scratch accessible to more people, but also contributes to the reduction of the carbon footprint during training.
Dark dimension and decaying dark matter gravitons

We explore the cosmology of the Dark Dimension scenario taking into account perturbations in the linear regime. In the context of the Dark Dimension scenario, a natural candidate for dark matter in our universe is the excitations of a tower of massive spin-2 KK gravitons. These dark gravitons are produced in the early universe and decay to lighter KK gravitons during the course of cosmological evolution. The decay causes the average dark matter mass to decrease as the universe evolves. In addition, the kinetic energy liberated in each decay leads to a kick velocity for the dark matter particles leading to a suppression of structure formation. Using current CMB (Planck), BAO and cosmic shear (KiDS-1000) data, we put a bound on the dark matter kick velocity today $v_\mathrm{today} \leq 2.2 \times 10^{-4} c$ at 95\% CL. This leads to rather specific regions of parameter space for the dark dimension scenario. The combination of the experimental bounds from cosmology, astrophysics and table-top experiments lead to the range $l_5\sim 1- 10 \, \mu m$ for the size of the Dark Dimension. The Dark Dimension scenario is found to be remarkably consistent with current observations and provides signatures that are within reach of near-future experiments.
The Impact of Changes to Daylight Illumination level on Architectural experience in Offices Based on VR and EEG

This study investigates the influence of varying illumination levels on architectural experiences by employing a comprehensive approach that combines self-reported assessments and neurophysiological measurements. Thirty participants were exposed to nine distinct illumination conditions in a controlled virtual reality environment. Subjective assessments, collected through questionnaires in which participants were asked to rate how pleasant, interesting, exciting, calming, complex, bright and spacious they found the space. Objective measurements of brain activity were collected by electroencephalogram (EEG). Data analysis demonstrated that illumination levels significantly influenced cognitive engagement and different architectural experience indicators. This alignment between subjective assessment and EEG data underscores the relationship between illuminance and architectural experiences. The study bridges the gap between quantitative and qualitative assessments, providing a deeper understanding of the intricate connection between lighting conditions and human responses. These findings contribute to the enhancement of environmental design based on neuroscientific insights, emphasizing the critical role of well-considered daylighting design in positively influencing occupants' cognitive and emotional states within built environments.
Accuracy of a Vision-Language Model on Challenging Medical Cases

Background: General-purpose large language models that utilize both text and images have not been evaluated on a diverse array of challenging medical cases. Methods: Using 934 cases from the NEJM Image Challenge published between 2005 and 2023, we evaluated the accuracy of the recently released Generative Pre-trained Transformer 4 with Vision model (GPT-4V) compared to human respondents overall and stratified by question difficulty, image type, and skin tone. We further conducted a physician evaluation of GPT-4V on 69 NEJM clinicopathological conferences (CPCs). Analyses were conducted for models utilizing text alone, images alone, and both text and images. Results: GPT-4V achieved an overall accuracy of 61% (95% CI, 58 to 64%) compared to 49% (95% CI, 49 to 50%) for humans. GPT-4V outperformed humans at all levels of difficulty and disagreement, skin tones, and image types; the exception was radiographic images, where performance was equivalent between GPT-4V and human respondents. Longer, more informative captions were associated with improved performance for GPT-4V but similar performance for human respondents. GPT-4V included the correct diagnosis in its differential for 80% (95% CI, 68 to 88%) of CPCs when using text alone, compared to 58% (95% CI, 45 to 70%) of CPCs when using both images and text. Conclusions: GPT-4V outperformed human respondents on challenging medical cases and was able to synthesize information from both images and text, but performance deteriorated when images were added to highly informative text. Overall, our results suggest that multimodal AI models may be useful in medical diagnostic reasoning but that their accuracy may depend heavily on context.
Dynamical Masses for the Hyades Binary System vB 120

We report spectroscopic observations of vB 120 (HD 30712), a 5.7 yr astrometric–spectroscopic binary system in the Hyades cluster. We combine our radial velocities with others from the literature, and with existing speckle interferometry measurements, to derive an improved 3D orbit for the system. We infer component masses of M 1 = 1.065 ± 0.018 M ☉ and M 2 = 1.008 ± 0.016 M ☉, and an orbital parallax of 21.86 ± 0.15 mas, which we show to be more accurate than the parallax from Gaia DR3. This is the ninth binary or multiple system in the Hyades with dynamical mass determinations, and one of the examples with the highest precision. An analysis of its spectral energy distribution yields the absolute radii of the stars, R 1 = 0.968 ± 0.012 R ☉ and R 2 = 0.878 ± 0.013 R ☉, and effective temperatures of 5656 ± 56 K and 5489 ± 60 K for the primary and secondary, respectively. A comparison of these properties with the predictions of current stellar evolution models for the known age and metallicity of the cluster shows only minor differences.
FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment

Deep learning techniques have greatly enhanced the performance of fire detection in videos. However, video-based fire detection models heavily rely on labeled data, and the process of data labeling is particularly costly and time-consuming, especially when dealing with videos. Considering the limited quantity of labeled video data, we propose a semi-supervised fire detection model called FireMatch, which is based on consistency regularization and adversarial distribution alignment. Specifically, we first combine consistency regularization with pseudo-label. For unlabeled data, we design video data augmentation to obtain corresponding weakly augmented and strongly augmented samples. The proposed model predicts weakly augmented samples and retains pseudo-label above a threshold, while training on strongly augmented samples to predict these pseudo-labels for learning more robust feature representations. Secondly, we generate video cross-set augmented samples by adversarial distribution alignment to expand the training data and alleviate the decline in classification performance caused by insufficient labeled data. Finally, we introduce a fairness loss to help the model produce diverse predictions for input samples, thereby addressing the issue of high confidence with the non-fire class in fire classification scenarios. The FireMatch achieved an accuracy of 76.92% and 91.81% on two real-world fire datasets, respectively. The experimental results demonstrate that the proposed method outperforms the current state-of-the-art semi-supervised classification methods.
On the Fibonacci tiling and its modern ramifications

In the last 30 years, the mathematical theory of aperiodic order has developed enormously. Many new tilings and properties have been discovered, few of which are covered or anticipated by the early papers and books. Here, we start from the well-known Fibonacci chain to explain some of them, with pointers to various generalisations as well as to higher-dimensional phenomena and results. This should give some entry points to the modern literature on the subject.
A critical evaluation of the added value of increased horizontal resolution in the hectometric range on the simulation of the mountain boundary layer

The horizontal grid spacing of numerical weather prediction models keeps decreasing towards the hectometric range. Although topography, land-use, and other static parameters are well-resolved, models have to be evaluated, because it cannot be assumed that higher horizontal resolution automatically yields better model performance. In this study, we perform limited-area simulations with the ICON model across horizontal grid spacings (1 km, 500 m, 250 m, 125 m) in the Inn Valley, Austria. Simulations are ran with two turbulence schemes - a 1D parameterization and a 3D Smagorinsky-type scheme. We evaluate the model across scales with observations of the valley boundary layer from the CROSSINN measurement campaign. This allows us to investigate whether increasing the horizontal resolution automatically improves the representation of the thermally-induced circulation, surface exchange, and other mountain boundary layer processes. The two turbulence schemes produce quite similar results across grid spacings, while differences are mostly found in the representation of the thermal structure of the valley atmosphere. The model keeps facing challenges for the correct simulation of scale interactions, and the 3D Smagorinsky scheme simulates a delayed evening transition of the up-valley flow. It is argued that the major difference between schemes actually emerges from the different surface transfer schemes, and the choice of boundary layer parameterization is secondary.
Limited quantum advantage for stellar interferometry via continuous-variable teleportation

We consider stellar interferometry in the continuous-variable (CV) quantum information formalism and use the quantum Fisher information (QFI) to characterize the performance of three key strategies: direct interferometry (DI), local heterodyne measurement, and a CV teleportation-based strategy. In the lossless regime, we show that a squeezing parameter of $r\approx 2$ (18 dB) is required to reach $\approx$ 95\% of the QFI achievable with DI; such a squeezing level is beyond what has been achieved experimentally. In the low-loss regime, the CV teleportation strategy becomes inferior to DI, and the performance gap widens as loss increases. Curiously, in the high-loss regime, a small region of loss exists where the CV teleportation strategy slightly outperforms both DI and local heterodyne, representing a transition in the optimal strategy. We describe this advantage as limited because it occurs for a small region of loss, and the magnitude of the advantage is also small. We argue that practical difficulties further impede achieving any quantum advantage, limiting the merits of a CV teleportation-based strategy for stellar interferometry.
Towards adiabatic quantum computing using compressed quantum circuits

We describe tensor network algorithms to optimize quantum circuits for adiabatic quantum computing. To suppress diabatic transitions, we include counterdiabatic driving in the optimization and utilize variational matrix product operators to represent adiabatic gauge potentials. Traditionally, Trotter product formulas are used to turn adiabatic time evolution into quantum circuits and the addition of counterdiabatic driving increases the circuit depth per time step. Instead, we classically optimize a parameterized quantum circuit of fixed depth to simultaneously capture adiabatic evolution together with counterdiabatic driving over many time steps. The methods are applied to the ground state preparation of quantum Ising chains with transverse and longitudinal fields. We show that the classically optimized circuits can significantly outperform Trotter product formulas. Additionally, we discuss how the approach can be used for combinatorial optimization.
Functional central limit theorem and Marcinkiewicz strong law of large numbers for Hilbert-valued U-statistics of absolutely regular data

In this paper, we investigate the functional central limit theorem and the Marcinkiewicz strong law of large numbers for U-statistics having absolutely regular data and taking value in a separable Hilbert space. The novelty of our approach consists in using coupling in order to formulate a deviation inequality for original $U$-statistic, where the upper bound involves the mixing coefficient and the tail of several U-statistics of i.i. d. data. The presented results improve the known results in several directions: the case of metric space valued data is considered as well as Hilbert space valued, and the mixing rates are less restrictive in a wide range of parameters.
Exotic potentials and Bianchi identities in SL(5) exceptional field theory

Tensor hierarchy of Exceptional Field Theories contains gauge fields satisfying certain Bianchi identities. We define the full set of fluxes of the SL(5) exceptional field theory containing known gauge field strengths, generalized anholonomy coefficients and two new fluxes. It is shown that the full SL(5) ExFT Lagrangian can be written in terms of the listed fluxes. We derive the complete set of Bianchi identities and identify magnetic potentials of the theory and the corresponding (wrapped) membranes of M-theory.
Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies

Electron tomography has become a commonly used tool to investigate the three-dimensional (3D) structure of nanomaterials, including colloidal nanoparticle assemblies. However, electron microscopy is typically carried out under high vacuum conditions. Therefore, pre-treatment sample preparation is needed for assemblies obtained by (wet) colloid chemistry methods, including solvent evaporation and deposition on a solid TEM support. As a result of this procedure, changes are consistently imposed on the actual nanoparticle organization. Therefore, we propose herein the application of electron tomography of nanoparticle assemblies while in their original colloidal liquid environment. To address the challenges related to electron tomography in liquid, we devised a method that combines fast data acquisition in a commercial liquid-TEM cell, with a dedicated alignment and reconstruction workflow. We present the application of this method to two different systems, which exemplify the difference between conventional and liquid tomography, depending on the nature of the protecting ligands. 3D reconstructions of assemblies comprising polystyrene-capped Au nanoparticles encapsulated in polymeric shells revealed less compact and more distorted configurations for experiments performed in a liquid medium compared to their dried counterparts. On the other hand, quantitative analysis of the surface-to-surface distance of self-assembled Au nanorods in water agrees with previously reported dimensions of the ligand layers surrounding the nanorods, which are in much closer contact when in similar but dried assemblies. This study, therefore, emphasizes the importance of developing high-resolution characterization tools that preserve the native environment of colloidal nanostructures.