Miracle-dz/UniFAR-FTUs huggingface dataset:
Reading Radiology Imaging Like The Radiologist

Automated radiology report generation aims to generate radiology reports that contain rich, fine-grained descriptions of radiology imaging. Compared with image captioning in the natural image domain, medical images are very similar to each other, with only minor differences in the occurrence of diseases. Given the importance of these minor differences in the radiology report, it is crucial to encourage the model to focus more on the subtle regions of disease occurrence. Secondly, the problem of visual and textual data biases is serious. Not only do normal cases make up the majority of the dataset, but sentences describing areas with pathological changes also constitute only a small part of the paragraph. Lastly, generating medical image reports involves the challenge of long text generation, which requires more expertise and empirical training in medical knowledge. As a result, the difficulty of generating such reports is increased. To address these challenges, we propose a disease-oriented retrieval framework that utilizes similar reports as prior knowledge references. We design a factual consistency captioning generator to generate more accurate and factually consistent disease descriptions. Our framework can find most similar reports for a given disease from the CXR database by retrieving a disease-oriented mask consisting of the position and morphological characteristics. By referencing the disease-oriented similar report and the visual features, the factual consistency model can generate a more accurate radiology report.
Self-consistent interaction of linear gravitational and electromagnetic waves in non-magnetized plasma

This paper explores the hybridization of linear metric perturbations with linear electromagnetic (EM) perturbations in non-magnetized plasma for a general background metric. The local wave properties are derived from first principles for inhomogeneous plasma, without assuming any symmetries of the background metric. First, we derive the effective (“oscillation-center”) Hamiltonian that governs the average dynamics of plasma particles in a prescribed quasimonochromatic wave that involves metric perturbations and EM fields simultaneously. Then, using this Hamiltonian, we derive the backreaction of plasma particles on the wave itself and obtain gauge-invariant equations that describe the resulting self-consistent gravito-electromagnetic (GEM) waves in a plasma. The transverse tensor modes of gravitational waves are found to have no interaction with the plasma and the EM modes in the geometrical-optics limit. However, for longitudinal GEM modes with large values of the refraction index, the interplay between gravitational and EM interactions in plasma can have a strong effect. In particular, the dispersion relation of the Jeans mode is significantly affected by electrostatic interactions. As a spin-off, our calculation also provides an alternative resolution of the so-called Jeans swindle.
Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering

One of the main issues of the current tractography methods is their high false-positive rate. Tractogram filtering is an option to remove false-positive streamlines from tractography data in a post-processing step. In this paper, we train a deep neural network for filtering tractography data in which every streamline of a tractogram is classified as {\em plausible, implausible}, or {\em inconclusive}. For this, we use four different tractogram filtering strategies as supervisors: TractQuerier, RecobundlesX, TractSeg, and an anatomy-inspired filter. Their outputs are combined to obtain the classification labels for the streamlines. We assessed the importance of different types of information along the streamlines for performing this classification task, including the coordinates of the streamlines, diffusion data, landmarks, T1-weighted information, and a brain parcellation. We found that the streamline coordinates are the most relevant followed by the diffusion data in this particular classification task.
The Spatial and Emission Properties of the Large [O iii] Emission Nebula Near M31

Drechsler et al. reported the unexpected discovery of a 1. °5 long [O iii] emission nebula 1. °2 southeast of the M31 nucleus. Here we present additional images of this large emission arc, called the Strottner–Drechsler–Sainty Object (SDSO), along with radial velocity and flux measurements from low-dispersion spectra. Independent sets of [O iii] images show SDSO to be composed of broad streaks of diffuse emission aligned northeast–southwest. Deep Hα images reveal no strong coincident emission suggesting a high [O iii]/Hα ratio. We also find no other [O iii] emission nebulosity as bright as SDSO within several degrees of M31 and no filamentary Hα emission connected to SDSO. Optical spectra taken along the nebula’s northern limb reveal [O iii] λ λ4959, 5007 emissions matching the location and extent seen in our [O iii] images. The heliocentric velocity of this [O iii] nebulosity is −9.8 ± 6.8 km s−1 with a peak surface brightness of (4 ± 2) × 10−18 erg s−1 cm−2 arcsec−2 (∼0.55 Rayleigh). We discuss SDSO as a possible unrecognized supernova remnant, a large and unusually nearby planetary nebula, a stellar bow shock nebula, or an interaction of M31's outer halo with Local Group circumgalactic gas. We conclude that galactic origins for SDSO are unlikely and favor instead an extragalactic M31 halo–circumgalactic cloud interaction scenario, despite the nebula’s low radial velocity. We then describe new observations that may help resolve the nature and origin of this large nebulosity so close to M31 in the sky.
Operational Support Estimator Networks

In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the locations of non-zero elements in a sparse signal. By its very nature, the mapping between the measurement and sparse signal is a non-linear operation. Traditional support estimators rely on computationally expensive iterative signal recovery techniques to achieve such non-linearity. Contrary to the convolution layers, the proposed OSEN approach consists of operational layers that can learn such complex non-linearities without the need for deep networks. In this way, the performance of the non-iterative support estimation is greatly improved. Moreover, the operational layers comprise so-called generative \textit{super neurons} with non-local kernels. The kernel location for each neuron/feature map is optimized jointly for the SE task during the training. We evaluate the OSENs in three different applications: i. support estimation from Compressive Sensing (CS) measurements, ii. representation-based classification, and iii. learning-aided CS reconstruction where the output of OSENs is used as prior knowledge to the CS algorithm for an enhanced reconstruction. Experimental results show that the proposed approach achieves computational efficiency and outperforms competing methods, especially at low measurement rates by a significant margin. The software implementation is publicly shared at https://github.com/meteahishali/OSEN.
B-CLEAN-SC: CLEAN-SC for broadband sources

This paper presents B-CLEAN-SC, a variation of CLEAN-SC for broadband sources. In contrast to CLEAN-SC, which "deconvolves" the beamforming map for each frequency individually, B-CLEAN-SC processes frequency intervals. Instead of performing a deconvolution iteration at the location of the maximum level, B-CLEAN-SC performs it at the location of the over-frequency-averaged maximum to improve the location estimation. The method is validated and compared to standard CLEAN-SC on synthetic cases and real-world experiments for broad- and narrowband sources. It improves the source reconstruction at low and high frequencies and suppresses noise, while it only increases the need for memory and not computational effort.
Prototyping Theories with ChatGPT: Experiment with the Technology Acceptance Model

This research paper presents the findings of two experimental studies that explore the use of ChatGPT as a tool for theory prototyping. The objective of the studies is to assess ChatGPT's ability to comprehend theoretical concepts and differentiate between constructs. During the experiments, duplicated responses were identified in both Study 1 and Study 2, with duplicate response rates of 26.25% and 40% respectively. The results of the experiments indicate that ChatGPT can generate responses aligned with the constructs of the Technology Acceptance Model (TAM). The loading and reliability coefficients demonstrate the validity of the models, with Study 1 achieving an R-squared value of 82% and Study 2 achieving 71%. In Study 2, two items with negative wording exhibited low loadings and were subsequently removed from the model. Both studies exhibit reasonable discriminant validity despite high correlations among the TAM constructs. The experiments reveal potential biases in the generated samples, particularly regarding gender and usage experiences. These biases may impact the responses of constructs and should be considered when interpreting ChatGPT's conceptual capabilities. In sum, ChatGPT shows promise as a tool for theory prototyping, generating relevant responses aligned with theoretical constructs. However, further investigation is needed to address limitations such as duplicated responses, variations in prompts, and the generalizability of findings to different contexts.
A hybrid Krasnosel'ski\u{i}-Schauder fixed point theorem for systems

We provide new results regarding the localization of the solutions of nonlinear operator systems. We make use of a combination of Krasnosel'ski\u{\i} cone compression-expansion type methodologies and Schauder-type ones. In particular we establish a localization of the solution of the system within the product of a conical shell and of a closed convex set. By iterating this procedure we prove the existence of multiple solutions. We illustrate our theoretical results by applying them to the solvability of systems of Hammerstein integral equations. In the case of two specific boundary value problems and with given nonlinearities, we are also able to obtain a numerical solution, consistent with our theoretical results.
High-energy X-ray spectrum reconstruction: solving the inverse problem from optimized multi-material transmission measurements

Reconstructing the unknown spectrum of a given X-ray source is a common problem in a wide range of X-ray imaging tasks. For high-energy sources, transmission measurements are mostly used to recover the X-ray spectrum, as a solution to an inverse problem. While this inverse problem is usually under-determined, ill-posedness can be reduced by improving the choice of transmission measurements. A recently proposed approach optimizes custom thicknesses of calibration materials used to generate transmission measurements, employing a genetic algorithm to minimize the condition number of the system matrix before inversion. In this paper, we generalize the proposed approach to multiple calibration materials and show a much larger decrease of the condition number of the system matrix than thickness-only optimization. Additionally, the spectrum reconstruction pipeline is tested in a simulation study with a challenging high-energy Bremsstrahlung X-ray source encountered in Linear Induction Accelerators with strong scatter noise. Using this approach, a realistic noise level is obtained on measurements. A generic anti-scatter grid is designed to reduce noise to an acceptable -yet still high-noise range. A novel noise-robust reconstruction method is then presented, which shows much less sensitive to initialization than common expectation-maximization approaches, enables a precise choice of spectrum resolution and a controlled injection of prior knowledge of the X-ray spectrum.
Theory of Elastic Microphase Separation

Elastic microphase separation refers to equilibrium patterns that form by phase separation in elastic gels. Recent experiments revealed a continuous phase transition from the homogeneous phase to a regularly patterned phase, whose period decreased for stiffer systems. We here propose a model that captures these observations. The model combines a continuous field of the elastic component to describe phase separation with nonlocal elasticity theory to capture the gel's microstructure. Analytical approximations unveil that the pattern period is determined by the geometric mean between the elasto-capillary length and a microscopic length scale of the gel. Our theory highlights the importance of nonlocal elasticity in soft matter systems, reveals the mechanism of elastic microphase separation, and will improve the engineering of such systems.
The Horton-Strahler number of Galton-Watson trees with possibly infinite variance

The Horton-Strahler number, also known as the register function, provides a tool for quantifying the branching complexity of a rooted tree. We consider the Horton-Strahler number of critical Galton-Watson trees conditioned to have size $n$ and whose offspring distribution is in the domain of attraction of an $\alpha$-stable law with $\alpha\in [1, 2]$. We give tail estimates and when $\alpha\neq 1$, we prove that it grows as $\frac{1}{\alpha}\log_{\alpha/(\alpha-1)} n$ in probability. This extends the result in Brandenberger, Devroye \&Reddad [6] dealing with the finite variance case for which $\alpha=1$. We also characterize the cases where $\alpha=1$, namely the spectrally positive Cauchy regime, which exhibits more complex behaviors.
A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes

The task of inductive link prediction in (discrete) attributed multigraphs infers missing attributed links (relations) between nodes in new test multigraphs. Traditional relational learning methods face the challenge of limited generalization to test multigraphs containing both novel nodes and novel relation types not seen in training. Recently, under the only assumption that all relation types share the same structural predictive patterns (single task), Gao et al. (2023) proposed a link prediction method using the theoretical concept of double equivariance (equivariance for nodes&relation types), in contrast to the (single) equivariance (only for nodes) used to design Graph Neural Networks (GNNs). In this work we further extend the double equivariance concept to multi-task double equivariance, where we define link prediction in attributed multigraphs that can have distinct and potentially conflicting predictive patterns for different sets of relation types (multiple tasks). Our empirical results on real-world datasets demonstrate that our approach can effectively generalize to test graphs with multi-task structures without access to additional information.
Building Persuasive Robots with Social Power Strategies

Can social power endow social robots with the capacity to persuade? This paper represents our recent endeavor to design persuasive social robots. We have designed and run three different user studies to investigate the effectiveness of different bases of social power (inspired by French and Raven's theory) on peoples' compliance to the requests of social robots. The results show that robotic persuaders that exert social power (specifically from expert, reward, and coercion bases) demonstrate increased ability to influence humans. The first study provides a positive answer and shows that under the same circumstances, people with different personalities prefer robots using a specific social power base. In addition, social rewards can be useful in persuading individuals. The second study suggests that by employing social power, social robots are capable of persuading people objectively to select a less desirable choice among others. Finally, the third study shows that the effect of power on persuasion does not decay over time and might strengthen under specific circumstances. Moreover, exerting stronger social power does not necessarily lead to higher persuasion. Overall, we argue that the results of these studies are relevant for designing human--robot-interaction scenarios especially the ones aiming at behavioral change.
Monotonicity formula and stratification of the singular set of perimeter minimizers in RCD spaces

The goal of this paper is to establish a monotonicity formula for perimeter minimizing sets in RCD(0,N) metric measure cones, together with the associated rigidity statement. The applications include sharp Hausdorff dimension estimates for the singular strata of perimeter minimizing sets in non collapsed RCD spaces and the existence of blow-down cones for global perimeter minimizers in Riemannian manifolds with nonnegative Ricci curvature and Euclidean volume growth.
On Maximal Functions Associated to Families of Curves in the Plane

We consider the $L^p$ mapping properties of maximal averages associated to families of curves, and thickened curves, in the plane. These include the (planar) Kakeya maximal function, the circular maximal functions of Wolff and Bourgain, and their multi-parameter analogues. We propose a framework that allows for a unified study of such maximal functions, and prove sharp $L^p\to L^p$ operator bounds in this setting. A key ingredient is an estimate from discretized incidence geometry that controls the number of higher order approximate tangencies spanned by a collection of plane curves. We discuss applications to the F\"assler-Orponen restricted projection problem, and the dimension of Furstenberg-type sets associated to families of curves.
The sharp turn: Backward rupture branching during the 2023 Mw 7.8 Kahramanmaraş (Türkiye) earthquake

Multiple lines of evidence indicate that the 2023 Mw 7.8 Kahramanmaraş (Türkiye) earthquake started on a splay fault, then branched bilaterally onto the nearby East Anatolian Fault (EAF). This rupture pattern includes one feature previously deemed implausible, called backward rupture branching: rupture propagating from the splay fault onto the SW EAF segment through a sharp corner (with an acute angle between the two faults). To understand this feature, we perform 2.5-D dynamic rupture simulations considering a large set of possible scenarios. We find that both subshear and supershear ruptures on the splay fault can trigger bilateral ruptures on the EAF, which themselves can be either subshear, supershear, or a mixture of the two. In most cases, rupture on the SW segment of the EAF starts after rupture onset on its NE segment: the SW rupture is triggered by the NE rupture. Only when the EAF has initial stresses very close to failure can its SW segment be directly triggered by the initial splay-fault rupture, earlier than the activation of the NE segment. These results advance our understanding of the mechanisms of multi-segment rupture and the complexity of rupture processes, paving the way for a more accurate assessment of earthquake hazards.
Stability Guarantees for Feature Attributions with Multiplicative Smoothing

Explanation methods for machine learning models tend not to provide any formal guarantees and may not reflect the underlying decision-making process. In this work, we analyze stability as a property for reliable feature attribution methods. We prove that relaxed variants of stability are guaranteed if the model is sufficiently Lipschitz with respect to the masking of features. We develop a smoothing method called Multiplicative Smoothing (MuS) to achieve such a model. We show that MuS overcomes the theoretical limitations of standard smoothing techniques and can be integrated with any classifier and feature attribution method. We evaluate MuS on vision and language models with various feature attribution methods, such as LIME and SHAP, and demonstrate that MuS endows feature attributions with non-trivial stability guarantees.
Impact of the $^6$Li asymptotic normalization constant onto $\alpha$-induced reactions of astrophysical interest

Indirect methods have become the predominant approach in experimental nuclear astrophysics for studying several low-energy nuclear reactions occurring in stars, as direct measurements of many of these relevant reactions are rendered infeasible due to their low reaction probability. Such indirect methods, however, require theoretical input that in turn can have significant poorly-quantified uncertainties, which can then be propagated to the reaction rates and have a large effect on our quantitative understanding of stellar evolution and nucleosynthesis processes. We present two such examples involving $\alpha$-induced reactions, $^{13}$C($\alpha,n)^{16}$O and $^{12}$C$(\alpha,\gamma)^{16}$O, for which the low-energy cross sections have been constrained with $(^6$Li$,d)$ transfer data. In this Letter, we discuss how a first-principle calculation of $^6$Li leads to a 21% reduction of the $^{12}$C$(\alpha,\gamma)^{16}$O cross sections with respect to a previous estimation. This calculation further resolves the discrepancy between recent measurements of the $^{13}$C$(\alpha,n)^{16}$O reaction and points to the need for improved theoretical formulations of nuclear reactions.
Can Large Language Models Aid in Annotating Speech Emotional Data? Uncovering New Frontiers

Despite recent advancements in speech emotion recognition (SER) models, state-of-the-art deep learning (DL) approaches face the challenge of the limited availability of annotated data. Large language models (LLMs) have revolutionised our understanding of natural language, introducing emergent properties that broaden comprehension in language, speech, and vision. This paper examines the potential of LLMs to annotate abundant speech data, aiming to enhance the state-of-the-art in SER. We evaluate this capability across various settings using publicly available speech emotion classification datasets. Leveraging ChatGPT, we experimentally demonstrate the promising role of LLMs in speech emotion data annotation. Our evaluation encompasses single-shot and few-shots scenarios, revealing performance variability in SER. Notably, we achieve improved results through data augmentation, incorporating ChatGPT-annotated samples into existing datasets. Our work uncovers new frontiers in speech emotion classification, highlighting the increasing significance of LLMs in this field moving forward.
Robust Impulse Responses using External Instruments: the Role of Information

External-instrument identification leads to biased responses when the shock is not invertible and the measurement error is present. We propose to use this identification strategy in a structural Dynamic Factor Model, which we call Proxy DFM. In a simulation analysis, we show that the Proxy DFM always successfully retrieves the true impulse responses, while the Proxy SVAR systematically fails to do so when the model is either misspecified, does not include all relevant information, or the measurement error is present. In an application to US monetary policy, the Proxy DFM shows that a tightening shock is unequivocally contractionary, with deteriorations in domestic demand, labor, credit, housing, exchange, and financial markets. This holds true for all raw instruments available in the literature. The variance decomposition analysis highlights the importance of monetary policy shocks in explaining economic fluctuations, albeit at different horizons.
Provably Faster Gradient Descent via Long Steps

This work establishes new convergence guarantees for gradient descent in smooth convex optimization via a computer-assisted analysis technique. Our theory allows nonconstant stepsize policies with frequent long steps potentially violating descent by analyzing the overall effect of many iterations at once rather than the typical one-iteration inductions used in most first-order method analyses. We show that long steps, which may increase the objective value in the short term, lead to provably faster convergence in the long term. A conjecture towards proving a faster $O(1/T\log T)$ rate for gradient descent is also motivated along with simple numerical validation.