Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpretable learning of effective dynamics for multiscale systems

The modeling and simulation of high-dimensional multiscale systems is a critical challenge across all areas of science and engineering. It is broadly believed that even with today's computer advances resolving all spatiotemporal scales described by the governing equations remains a remote target. This realization has prompted intense efforts to develop model order reduction techniques. In recent years, techniques based on deep recurrent neural networks have produced promising results for the modeling and simulation of complex spatiotemporal systems and offer large flexibility in model development as they can incorporate experimental and computational data. However, neural networks lack interpretability, which limits their utility and generalizability across complex systems. Here we propose a novel framework of Interpretable Learning Effective Dynamics (iLED) that offers comparable accuracy to state-of-the-art recurrent neural network-based approaches while providing the added benefit of interpretability. The iLED framework is motivated by Mori-Zwanzig and Koopman operator theory, which justifies the choice of the specific architecture. We demonstrate the effectiveness of the proposed framework in simulations of three benchmark multiscale systems. Our results show that the iLED framework can generate accurate predictions and obtain interpretable dynamics, making it a promising approach for solving high-dimensional multiscale systems.
Radiology-Llama2: Best-in-Class Large Language Model for Radiology

This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning. Radiology-Llama2 is based on the Llama2 architecture and further trained on a large dataset of radiology reports to generate coherent and clinically useful impressions from radiological findings. Quantitative evaluations using ROUGE metrics on the MIMIC-CXR and OpenI datasets demonstrate that Radiology-Llama2 achieves state-of-the-art performance compared to other generative language models, with a Rouge-1 score of 0.4834 on MIMIC-CXR and 0.4185 on OpenI. Additional assessments by radiology experts highlight the model's strengths in understandability, coherence, relevance, conciseness, and clinical utility. The work illustrates the potential of localized language models designed and tuned for specialized domains like radiology. When properly evaluated and deployed, such models can transform fields like radiology by automating rote tasks and enhancing human expertise.
Using Reed-Muller Codes for Classification with Rejection and Recovery

When deploying classifiers in the real world, users expect them to respond to inputs appropriately. However, traditional classifiers are not equipped to handle inputs which lie far from the distribution they were trained on. Malicious actors can exploit this defect by making adversarial perturbations designed to cause the classifier to give an incorrect output. Classification-with-rejection methods attempt to solve this problem by allowing networks to refuse to classify an input in which they have low confidence. This works well for strongly adversarial examples, but also leads to the rejection of weakly perturbed images, which intuitively could be correctly classified. To address these issues, we propose Reed-Muller Aggregation Networks (RMAggNet), a classifier inspired by Reed-Muller error-correction codes which can correct and reject inputs. This paper shows that RMAggNet can minimise incorrectness while maintaining good correctness over multiple adversarial attacks at different perturbation budgets by leveraging the ability to correct errors in the classification process. This provides an alternative classification-with-rejection method which can reduce the amount of additional processing in situations where a small number of incorrect classifications are permissible.
Supernovae Ia and Gamma-Ray Bursts together shed new lights on the Hubble constant tension and cosmology

The LambdaCDM model is the most commonly accepted framework in modern cosmology. However, the local measurements of the Hubble constant, H0, via the Supernovae Type Ia (SNe Ia) calibrated on Cepheids provide a value which is in significant disagreement, from 4 to 6 sigma, with the value of H0 inferred from the Cosmic Microwave Background (CMB) observed by Planck. This disagreement is the so-called Hubble constant tension. To find out the reason for this discrepancy, we analyze the behaviour of the H0 in the Pantheon sample of SNe Ia through a binning approach: we divide the Pantheon into 3 and 4 bins ordered with redshift (z), and for each of them, we estimate the H0. After the H0 estimation, we fit the H0 values with a decreasing function of z, finding out that H0 undergoes a slow decreasing trend compatible with the evolution scenario in 2.0 sigma. [. ] Together with SNe Ia, more astrophysical probes such as quasars (QSO) [. ] and Gamma-Ray Bursts (GRBs) [. ], are needed to tackle the H0 tension. In the realm of GRB-cosmology, one of the most promising correlations is the fundamental plane relation [. ]. In the context of applying this relation as a cosmological tool, we also compute how many GRBs must be gathered to reach the same precision as the SNe Ia. Since we are about two decades away from reaching such precision, we also attempt to find additional correlations for the GRBs associated with SNe Ibc that could be exploited to standardize the class of GRB-SNe Ibc in the future. We find a hint of a correlation between the GRBs' end-of-plateau optical luminosity and the SNe's rest-frame peak time, suggesting that the GRBs with the most luminous optical plateau emission are associated with SNe with the most delayed peaks in their light curves. So far, it is the fundamental plane relation to be the most promising candle for exploring the high-z universe.
Dual-Path Temporal Map Optimization for Make-up Temporal Video Grounding

Make-up temporal video grounding (MTVG) aims to localize the target video segment which is semantically related to a sentence describing a make-up activity, given a long video. Compared with the general video grounding task, MTVG focuses on meticulous actions and changes on the face. The make-up instruction step, usually involving detailed differences in products and facial areas, is more fine-grained than general activities (e.g, cooking activity and furniture assembly). Thus, existing general approaches cannot locate the target activity effectually. More specifically, existing proposal generation modules are not yet fully developed in providing semantic cues for the more fine-grained make-up semantic comprehension. To tackle this issue, we propose an effective proposal-based framework named Dual-Path Temporal Map Optimization Network (DPTMO) to capture fine-grained multimodal semantic details of make-up activities. DPTMO extracts both query-agnostic and query-guided features to construct two proposal sets and uses specific evaluation methods for the two sets. Different from the commonly used single structure in previous methods, our dual-path structure can mine more semantic information in make-up videos and distinguish fine-grained actions well. These two candidate sets represent the cross-modal makeup video-text similarity and multi-modal fusion relationship, complementing each other. Each set corresponds to its respective optimization perspective, and their joint prediction enhances the accuracy of video timestamp prediction. Comprehensive experiments on the YouMakeup dataset demonstrate our proposed dual structure excels in fine-grained semantic comprehension.
Batch Implicit Neural Representation for MRI Parallel Reconstruction

Magnetic resonance imaging (MRI) always suffered from the problem of long acquisition time. MRI reconstruction is one solution to reduce scan time by skipping certain phase-encoding lines and then restoring high-quality images from undersampled measurements. Recently, implicit neural representation (INR) has emerged as a new deep learning method that represents an object as a continuous function of spatial coordinates, and this function is normally parameterized by a multilayer perceptron (MLP). In this paper, we propose a novel MRI parallel reconstruction method based on INR, which represents the fully-sampled images as the function of voxel coordinates and prior feature vectors of undersampled images for overcoming the generalization problem of INR. Specifically, we introduce a scale-embedded encoder to produce scale-independent voxel-specific features from MR images with different undersampled scales and then concatenate with coordinates vectors to recover fully-sampled MR images via an MLP, thus achieving arbitrary scale reconstruction. The performance of the proposed method was assessed by experimenting on publicly available MRI datasets and compared with other reconstruction methods. Our quantitative evaluation demonstrates the superiority of the proposed method over alternative reconstruction methods.
Efficient two-qutrit gates in superconducting circuits using parametric coupling

Recently, significant progress has been made in the demonstration of single qutrit and coupled qutrit gates with superconducting circuits. Coupled qutrit gates have significantly lower fidelity than single qutrit gates, owing to long implementation times. We present a protocol to implement the CZ universal gate for two qutrits based on a decomposition involving two partial state swaps and local operations. The partial state swaps can be implemented effectively using parametric coupling, which is fast and has the advantage of frequency selectivity. We perform a detailed analysis of this protocol in a system consisting of two fixed-frequency transmons coupled by a flux-tunable transmon. The application of an AC flux in the tunable transmon controls the parametric gates. This protocol has the potential to lead to fast and scalable two-qutrit gates in superconducting circuit architectures.
Monte Carlo Simulation for Trading Under a Lévy-Driven Mean-Reverting Framework

We present a Monte Carlo approach to pairs trading on mean-reverting spreads modeled by L\'evy-driven Ornstein-Uhlenbeck processes. Specifically, we focus on using a variance gamma driving process, an infinite activity pure jump process to allow for more flexible models of the price spread than is available in the classical model. However, this generalization comes at the cost of not having analytic formulas, so we apply Monte Carlo methods to determine optimal trading levels and develop a variance reduction technique using control variates. Within this framework, we numerically examine how the optimal trading strategies are affected by the parameters of the model. In addition, we extend our method to bivariate spreads modeled using a weak variance alpha-gamma driving process, and explore the effect of correlation on these trades.
Aperiodicity is all you need: Aperiodic monotiles for high-performance composites

This study introduces a novel approach to composite design by employing aperiodic monotiles, shapes that cover surfaces without translational symmetry. Using a combined computational and experimental approach, we study the fracture behavior of composites crafted with these monotiles, and compared their performance against conventional honeycomb patterns. Remarkably, our aperiodic monotile-based composites exhibited superior stiffness, strength, and toughness in comparison to honeycomb designs. This study suggests that leveraging the inherent disorder of aperiodic structures can usher in a new generation of robust and resilient materials.
Downward Propagation of the Arctic Oscillation from the Stratosphere to the Troposphere

Geopotential anomalies ranging from the Earth's surface to the middle stratosphere in the northern hemisphere are dominated by a mode of variability known as the Arctic Oscillation (AO). The AO is represented herein by the leading mode (the first empirical orthogonal function) of low-frequency variability of wintertime geopotential between 1000 and 10 hPa. In the middle stratosphere the signature of the AO is a nearly zonally symmetric pattern representing a strong or weak polar vortex. At 1000 hPa the AO is similar to the North Atlantic Oscillation, but with more zonal symmetry, especially at high latitudes. In zonal-mean zonal wind the AO is seen as a north-south dipole centered on 40°–45°N; in zonal-mean temperature it is seen as a deep warm or cold polar anomaly from the upper troposphere to ∼10 hPa. The association of the AO pattern in the troposphere with modulation of the strength of the stratospheric polar vortex provides perhaps the best measure of coupling between the stratosphere and the troposphere. By examining separately time series of AO signatures at tropospheric and stratospheric levels, it is shown that AO anomalies typically appear first in the stratosphere and propagate downward. The midwinter correlation between the 90-day low-pass-filtered 10-hPa anomaly and the 1000-hPa anomaly exceeds 0.65 when the surface anomaly time series is lagged by about three weeks. The tropospheric signature of the AO anomaly is characterized by substantial changes to the storm tracks and strength of the midtropospheric flow, especially over the North Atlantic and Europe. The implications of large stratospheric anomalies as precursors to changes in tropospheric weather patterns are discussed.
GA-NIFS: Early-stage feedback in a heavily obscured active galactic nucleus at z=4.76

Dust-obscured galaxies are thought to represent an early evolutionary phase of massive galaxies in which the active galactic nucleus (AGN) is still deeply buried in significant amounts of dusty material and its emission is strongly suppressed. The unprecedented sensitivity of the James Webb Space Telescope (JWST) enabled us for the first time to detect the rest-frame optical emission of heavily obscured AGN and unveil the properties of the hidden accreting super-massive black holes (BHs). In this work, we present the JWST/NIRSpec integral field spectroscopy (IFS) data of a massive ($ star /M_ dusty, star-forming galaxy at $z = 4.755$ hosting an AGN at its center. The detection of a very broad ($>9000$ emission associated with the broad line region (BLR) confirms the presence of a BH ($ BH M_ accreting at less than 18 of its Eddington limit. The identification of the BLR classifies the target as a type 1 AGN despite the observed high column density of H $. The rest-frame optical emission lines also reveal a fast ($ ionized gas outflow marginally resolved in the galaxy center. The high sensitivity of NIRSpec allowed us to perform the kinematic analysis of the narrow component, which indicates that the warm ionized gas velocity field is consistent with disk rotation. Interestingly, we find that in the innermost nuclear regions ($< 1.5$ kpc), the intrinsic velocity dispersion of the disk reaches $ which is $ times higher than the velocity dispersion inferred from the 158mu m line tracing mostly cold gas. Since at large radii the velocity dispersion of the warm and cold gas are comparable, we conclude that the outflows are injecting turbulence in the warm ionized gas in the central region, but they are not sufficiently powerful to disrupt the dense gas and quench star formation. These findings support the scenario that dust-obscured galaxies represent the evolutionary stage preceding the unobscured quasar when all gas and dust are removed from the host.
Higman-Thompson groups and profinite properties of right-angled Coxeter groups

We prove that every right-angled Coxeter group (RACG) is profinitely rigid amongst all Coxeter groups. On the other hand we exhibit RACGs which have infinite profinite genus amongst all finitely generated residually finite groups. We also establish profinite rigidity results for graph products of finite groups. Along the way we prove that the Higman-Thompson groups $V_{n}$ are generated by $4$ involutions, generalising a classical result of Higman for Thompson's group $V$.
Modeling Reachability Types with Logical Relations

Reachability types are a recent proposal to bring Rust-style reasoning about memory properties to higher-level languages. While key type soundness results for reachability types have been established using syntactic techniques in prior work, stronger metatheoretic properties have so far been unexplored. This paper presents an alternative semantic model of reachability types using logical relations, providing a framework in which to study key properties of interest such as (1) semantic type soundness, including of not syntactically well-typed code fragments, (2) termination, especially in the presence of higher-order state, and (3) program equivalence, especially reordering of non-interfering expressions for parallelization or compiler optimization.
Interlacing Property of a Family of Generating Polynomials over Dyck Paths

In the study of a tantalizing symmetry on Catalan objects, Bóna et al. introduced a family of polynomials $\{W_{n,k}(x)\}_{n\geq k\geq 0}$ defined by$$W_{n,k}(x)=\sum_{m=0}^{k}w_{n,k,m}x^{m},$$where $w_{n,k,m}$ counts the number of Dyck paths of semilength $n$ with $k$ occurrences of $UD$ and $m$ occurrences of $UUD$. They proposed two conjectures on the interlacing property of these polynomials, one of which states that $\{W_{n,k}(x)\}_{n\geq k}$ is a Sturm sequence for any fixed $k\geq 1$, and the other states that $\{W_{n,k}(x)\}_{1\leq k\leq n}$ is a Sturm-unimodal sequence for any fixed $n\geq 1$. In this paper, we obtain certain recurrence relations for $W_{n,k}(x)$, and further confirm their conjectures.
Grain Growth and Dust Segregation Revealed by Multiwavelength Analysis of the Class I Protostellar Disk WL 17

The first step toward planet formation is grain growth from (sub)micrometer to millimeter/centimeter sizes. Grain growth has been reported not only in Class II protoplanetary disks but also in Class 0/I protostellar envelopes. However, early-stage grain growth occurring in Class 0/I stages has rarely been observed on the protostellar disk scale. Here we present the results from the Atacama Large Millimeter/submillimeter Array Band 3 (λ = 3.1 mm) and 7 (λ = 0.87 mm) archival data of the Class I protostellar disk WL 17 in the ρ Ophiuchus molecular cloud. Disk substructures are found in both bands, but they are different: while a central hole and a symmetric ring appear in Band 3, an off-center hole and an asymmetric ring are shown in Band 7. Furthermore, we obtain an asymmetric spectral index map with a low mean value of α = 2.28 ± 0.02, suggestive of grain growth and dust segregation on the protostellar disk scale. Our radiative transfer modeling verifies these two features by demonstrating that 10 cm sized large grains are symmetrically distributed, whereas 10 μm sized small grains are asymmetrically distributed. In addition, the analysis shows that the disk is expected to be massive and gravitationally unstable. We thus suggest a single Jupiter-mass protoplanet formed by gravitational instability as the origin of the ring-like structure, grain growth, and dust segregation identified in WL 17.
TransferDoc: A Self-Supervised Transferable Document Representation Learning Model Unifying Vision and Language

The field of visual document understanding has witnessed a rapid growth in emerging challenges and powerful multi-modal strategies. However, they rely on an extensive amount of document data to learn their pretext objectives in a ``pre-train-then-fine-tune'' paradigm and thus, suffer a significant performance drop in real-world online industrial settings. One major reason is the over-reliance on OCR engines to extract local positional information within a document page. Therefore, this hinders the model's generalizability, flexibility and robustness due to the lack of capturing global information within a document image. We introduce TransferDoc, a cross-modal transformer-based architecture pre-trained in a self-supervised fashion using three novel pretext objectives. TransferDoc learns richer semantic concepts by unifying language and visual representations, which enables the production of more transferable models. Besides, two novel downstream tasks have been introduced for a ``closer-to-real'' industrial evaluation scenario where TransferDoc outperforms other state-of-the-art approaches.
Ancestral reproductive bias in continuous time branching trees under various sampling schemes

Cheek and Johnston (Journal of Mathematical Biology, 2023) consider a continuous-time Bienaym\'e-Galton-Watson tree conditioned on being alive at time $T$. They study the reproduction events along the ancestral lineage of an individual randomly sampled from all those alive at time $T$. We give a short proof of an extension of their main results to the more general case of Bellman-Harris processes. Our proof also sheds light onto the probabilistic structure of the rate of the reproduction events. A similar method will be applied to explain (i) the different ancestral reproduction bias appearing in work by Geiger (Journal of Applied Probability, 1999) and (ii) the fact that the sampling rule considered by Chauvin, Rouault and Wakolbinger (Stochastic Processes and their Applications, 1991) leads to a time homogeneous process along the ancestral lineage.
Cop-width, flip-width and strong colouring numbers

Cop-width and flip-width are new families of graph parameters introduced by Toru\'nczyk (2023) that generalise treewidth, degeneracy, generalised colouring numbers, clique-width and twin-width. In this paper, we bound the cop-width and flip-width of a graph by its strong colouring numbers. In particular, we show that for every $r\in \mathbb{N}$, every graph $G$ has $\text{copwidth}_r(G)\leq \text{scol}_{4r}(G)$. This implies that every class of graphs with linear strong colouring numbers has linear cop-width and linear flip-width. We use this result to deduce improved bounds for cop-width and flip-width for various sparse graph classes.
Multiplierless Design of High-Speed Very Large Constant Multiplications

In cryptographic algorithms, the constants to be multiplied by a variable can be very large due to security requirements. Thus, the hardware complexity of such algorithms heavily depends on the design architecture handling large constants. In this paper, we introduce an electronic design automation tool, called LEIGER, which can automatically generate the realizations of very large constant multiplications for low-complexity and high-speed applications, targeting the ASIC design platform. LEIGER can utilize the shift-adds architecture and use 3-input operations, i.e., carry-save adders (CSAs), where the number of CSAs is reduced using a prominent optimization algorithm. It can also generate constant multiplications under a hybrid design architecture, where 2-and 3-input operations are used at different stages. Moreover, it can describe constant multiplications under a design architecture using compressor trees. As a case study, high-speed Montgomery multiplication, which is a fundamental operation in cryptographic algorithms, is designed with its constant multiplication block realized under the proposed architectures. Experimental results indicate that LEIGER enables a designer to explore the trade-off between area and delay of the very large constant and Montgomery multiplications and leads to designs with area-delay product, latency, and energy consumption values significantly better than those obtained by a recently proposed algorithm.