Miracle-dz/UniFAR-FTUs huggingface dataset:
HQP: A Human-Annotated Dataset for Detecting Online Propaganda

Online propaganda poses a severe threat to the integrity of societies. However, existing datasets for detecting online propaganda have a key limitation: they were annotated using weak labels that can be noisy and even incorrect. To address this limitation, our work makes the following contributions: (1) We present HQP: a novel dataset (N=30,000) for detecting online propaganda with high-quality labels. To the best of our knowledge, HQP is the first dataset for detecting online propaganda that was created through human annotation. (2) We show empirically that state-of-the-art language models fail in detecting online propaganda when trained with weak labels (AUC: 64.03). In contrast, state-of-the-art language models can accurately detect online propaganda when trained with our high-quality labels (AUC: 92.25), which is an improvement of ~44%. (3) To address the cost of labeling, we extend our work to few-shot learning. Specifically, we show that prompt-based learning using a small sample of high-quality labels can still achieve a reasonable performance (AUC: 80.27). Finally, we discuss implications for the NLP community to balance the cost and quality of labeling. Crucially, our work highlights the importance of high-quality labels for sensitive NLP tasks such as propaganda detection.
NONLINEAR VIBRATIONS OF CANTILEVER TIMOSHENKO BEAMS: A HOMOTOPY ANALYSIS

THIS STUDY ANALYZES THE FOURTH-ORDER NONLINEAR FREE VIBRATION OF A TIMOSHENKO BEAM. WE DISCRETIZE THE GOVERNING DIFFERENTIAL EQUATION BY GALERKINÂS PROCEDURE AND THEN APPLY THE HOMOTOPY ANALYSIS METHOD (HAM) TO THE OBTAINED ORDINARY DIFFERENTIAL EQUATION OF THE GENERALIZED COORDINATE. WE DERIVE NOVEL ANALYTICAL SOLUTIONS FOR THE NONLINEAR NATURAL FREQUENCY AND DISPLACEMENT TO INVESTIGATE THE EFFECTS OF ROTARY INERTIA, SHEAR DEFORMATION, PRE-TENSILE LOADS AND SLENDERNESS RATIOS ON THE BEAM. IN COMPARISON TO RESULTS ACHIEVED BY PERTURBATION TECHNIQUES, THIS STUDY DEMONSTRATES THAT A FIRST-ORDER APPROXIMATION OF HAM LEADS TO HIGHLY ACCURATE SOLUTIONS, VALID FOR A WIDE RANGE OF AMPLITUDE VIBRATIONS, OF A HIGH-ORDER STRONGLY NONLIN-EAR PROBLEM.
MUDiff: Unified Diffusion for Complete Molecule Generation

Molecule generation is a very important practical problem, with uses in drug discovery and material design, and AI methods promise to provide useful solutions. However, existing methods for molecule generation focus either on 2D graph structure or on 3D geometric structure, which is not sufficient to represent a complete molecule as 2D graph captures mainly topology while 3D geometry captures mainly spatial atom arrangements. Combining these representations is essential to better represent a molecule. In this paper, we present a new model for generating a comprehensive representation of molecules, including atom features, 2D discrete molecule structures, and 3D continuous molecule coordinates, by combining discrete and continuous diffusion processes. The use of diffusion processes allows for capturing the probabilistic nature of molecular processes and exploring the effect of different factors on molecular structures. Additionally, we propose a novel graph transformer architecture to denoise the diffusion process. The transformer adheres to 3D roto-translation equivariance constraints, allowing it to learn invariant atom and edge representations while preserving the equivariance of atom coordinates. This transformer can be used to learn molecular representations robust to geometric transformations. We evaluate the performance of our model through experiments and comparisons with existing methods, showing its ability to generate more stable and valid molecules. Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
Thermophysical properties of argon gas from improved two-body interaction potential

A new ab initio interaction potential for the electronic ground state of the argon dimer has been developed. The new potential uses previously calculated accurate Born--Oppenheimer potential while significantly improving the description of relativistic effects by including the two-electron Darwin and orbit-orbit corrections. Moreover, retardation of the electromagnetic interactions is taken into account in the long-range part of the potential and leading-order quantum electrodynamics correction is calculated. Spectroscopic properties of the argon dimer such as positions of vibrational levels, bond-dissociation energy, and rotational and centrifugal-distortion constants are reported. We show that the inclusion of the two-electron relativistic terms results in a destabilization of the previously discovered weakly bound ninth vibrational state. Finally, thermophysical properties of the argon gas including pressure and acoustic virial coefficients, as well as transport properties -- viscosity and thermal conductivity -- are evaluated using the new potential. For the thermophysical properties, the obtained ab initio values are somewhat less accurate than the most recent experimental results. However, the opposite is true for the transport properties, where the theoretical results calculated in this work have significantly smaller uncertainties than the data derived from measurements.
SFD2: Semantic-Guided Feature Detection and Description

Visual localization is a fundamental task for various applications including autonomous driving and robotics. Prior methods focus on extracting large amounts of often redundant locally reliable features, resulting in limited efficiency and accuracy, especially in large-scale environments under challenging conditions. Instead, we propose to extract globally reliable features by implicitly embedding high-level semantics into both the detection and description processes. Specifically, our semantic-aware detector is able to detect keypoints from reliable regions (e.g. building, traffic lane) and suppress unreliable areas (e.g. sky, car) implicitly instead of relying on explicit semantic labels. This boosts the accuracy of keypoint matching by reducing the number of features sensitive to appearance changes and avoiding the need of additional segmentation networks at test time. Moreover, our descriptors are augmented with semantics and have stronger discriminative ability, providing more inliers at test time. Particularly, experiments on long-term large-scale visual localization Aachen Day Night and RobotCar-Seasons datasets demonstrate that our model outperforms previous local features and gives competitive accuracy to advanced matchers but is about 2 and 3 times faster when using 2k and 4k keypoints, respectively. Code is available at https://github.com/feixue94/sfd2.
SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis

Text-conditioned image generation has made significant progress in recent years with generative adversarial networks and more recently, diffusion models. While diffusion models conditioned on text prompts have produced impressive and high-quality images, accurately representing complex text prompts such as the number of instances of a specific object remains challenging.To address this limitation, we propose a novel guidance approach for the sampling process in the diffusion model that leverages bounding box and segmentation map information at inference time without additional training data. Through a novel loss in the sampling process, our approach guides the model with semantic features from CLIP embeddings and enforces geometric constraints, leading to high-resolution images that accurately represent the scene. To obtain bounding box and segmentation map information, we structure the text prompt as a scene graph and enrich the nodes with CLIP embeddings. Our proposed model achieves state-of-the-art performance on two public benchmarks for image generation from scene graphs, surpassing both scene graph to image and text-based diffusion models in various metrics. Our results demonstrate the effectiveness of incorporating bounding box and segmentation map guidance in the diffusion model sampling process for more accurate text-to-image generation. Project Page: scenegenie.github. io/SceneGenie/
Magnetized (
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)-dimensional Gross-Neveu model at finite density

We perform a lattice study of the ($2+1$)-dimensional Gross-Neveu model in a background magnetic field $B$ and at non-zero chemical potential $\mu$. The complex-action problem arising in our simulations using overlap fermions is under control. For $B=0$ we observe a first-order phase transition in $\mu$ even at non-vanishing temperatures. Our main finding, however, is that the rich phase structure found in the limit of infinite flavor number $N_\mathrm{f}$ is washed out by the fluctuations present at $N_\mathrm{f}=1$. We find no evidence for inverse magnetic catalysis, i.e., the decrease of the order parameter of chiral symmetry breaking with $B$ for $\mu$ close to the chiral phase transition. Instead, the magnetic field tends to enhance the breakdown of chiral symmetry for all values of $\mu$ below the transition. Moreover, we find no trace of spatial inhomogeneities in the order parameter. We briefly comment on the potential relevance of our results for QCD.
Fusion is Not Enough: Single-Modal Attacks to Compromise Fusion Models in Autonomous Driving

—Multi-sensor fusion (MSF) is widely adopted for perception in autonomous vehicles (AVs), particularly for the task of 3D object detection with camera and LiDAR sensors. The rationale behind fusion is to capitalize on the strengths of each modality while mitigating their limitations. The exceptional and leading performance of fusion models has been demonstrated by advanced deep neural network (DNN)- based fusion techniques. Fusion models are also perceived as more robust to attacks compared to single-modal ones due to the redundant information in multiple modalities. In this work, we challenge this perspective with single-modal attacks that targets the camera modality, which is considered less signiﬁcant in fusion but more affordable for attackers. We argue that the weakest link of fusion models depends on their most vulnerable modality, and propose an attack framework that targets advanced camera-LiDAR fusion models with adversarial patches. Our approach employs a two-stage optimization-based strategy that ﬁrst comprehensively assesses vulnerable image areas under adversarial attacks, and then applies customized attack strategies to different fusion models, generating deployable patches. Evaluations with ﬁve state-of-the-art camera-LiDAR fusion models on a real-world dataset show that our attacks successfully compromise all models. Our approach can either reduce the mean average precision (mAP) of detection performance from 0.824 to 0.353 or degrade the detection score of the target object from 0.727 to 0.151 on average, demonstrating the effectiveness and practicality of our proposed attack framework.
Mind the Gap. I. Hα Activity of M Dwarfs Near the Partially/Fully Convective Boundary and a New Hα Emission Deficiency Zone on the Main Sequence

Since identifying the gap in the H-R Diagram (HRD) marking the transition between partially and fully-convective interiors, a unique type of slowly pulsating M dwarf has been proposed. These unstable M dwarfs provide new laboratories in which to understand how changing interior structures can produce potentially observable activity at the surface. In this work, we report the results of the largest high-resolution spectroscopic Hα emission survey to date spanning this transition region, including 480 M dwarfs observed using the CHIRON spectrograph at CTIO/SMARTS 1.5 m. We find that M dwarfs with Hα in emission are almost entirely found 0–0.5 mag above the top edge of the gap in the HRD, whereas effectively no stars in and below the gap show emission. Thus, the top edge of the gap marks a relatively sharp activity transition, and there is no anomalous Hα activity for stars in the gap. We also identify a new region at 10.3 < M G < 10.8 on the main sequence where fewer M dwarfs exhibit Hα emission compared to M dwarfs above and below this magnitude range. Careful evaluation of the results in the literature indicates that (1) rotation and Hα activity distributions on the main-sequence are closely related, and (2) fewer stars in this absolute magnitude range rotate in less than ∼13 days than populations surrounding this region. This result suggests that the most massive fully-convective stars lose their angular momentum faster than both partially convective stars and less massive fully-convective stars.
Hydrodynamics and Nucleosynthesis of Jet-driven Supernovae. I. Parameter Study of the Dependence on Jet Energetics

Rotating massive stars with initial progenitor masses M prog ∼ 25–140 M ⊙ can leave rapidly rotating black holes to become collapsars. The black holes and the surrounding accretion disks may develop powerful jets by magnetohydrodynamics instabilities. The propagation of the jet in the stellar envelope provides the necessary shock heating for triggering nucleosynthesis unseen in canonical core-collapse supernovae. However, the energy budget of the jet and its effects on the final chemical abundance pattern are unclear. In this exploratory work, we present a survey on the parameter dependence of collapsar nucleosynthesis on jet energetics. We use the zero-metallicity star with M prog ∼ 40 M ⊙ as the progenitor. The parameters include the jet duration, its energy deposition rate, deposited energy, and the opening angle. We examine the correlations of the following observables: (1) the ejecta and remnant masses; (2) the energy deposition efficiency; (3) the 56Ni production and its correlation with the ejecta velocity, deposited energy, and the ejected mass; (4) the Sc–Ti–V correlation as observed in metal-poor stars; and (5) the [Zn/Fe] ratio as observed in some metal-poor stars. We also provide the chemical abundance table of these explosion models for the use of the galactic chemical evolution and stellar archeology.
Spectral density of the Dirac-Ginsparg-Wilson operator, chiral 
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 anom

Using general properties of the $Q=0$ topological sector we previously argued that a vector-like theory, with chiral $U(1)_A$ anomaly, and exact non-Abelian chiral symmetry, should exhibit divergent susceptibilities in the chiral limit, the two-flavor Schwinger model being a paradigmatic example of the realization of this scenario. Two flavor $QCD$ at $T>T_c$ satisfies all the above conditions, and it is also expected that the $U(1)_A$ axial symmetry remains effectively broken in its high temperature phase. Therefore we would expect a non-analyticity in the quark mass dependence of the free energy density, in contrast with the Dilute Instanton Gas Approximation (DIGA) prediction. We investigate in this work whether the aforementioned results can also be reproduced making only use of standard properties of the spectral density of the Dirac operator, without having to resort to general properties of the $Q=0$ topological sector. We show that the only way to derive a non-trivial $\theta$-dependence, and an analytical free energy density in $QCD$ with two degenerate flavors is that the spectral density, $\rho\left(\lambda,m\right)$, of the absolute value of the non-zero modes of the Dirac-Ginsparg-Wilson operator develops a $m^2\delta(\lambda)$ function in the thermodynamic limit. This is the expected result in the DIGA, where interactions between instantons in the dilute gas are fully neglected. However, at temperatures close to $T_c$ the interaction between instantons should become non-negligible, and the splitting from zero of the near-zero modes, which has been neglected in the DIGA, should be taken into account. Therefore we expect that the $m^2\delta(\lambda)$ contribution to the spectral density is no longer correct at these temperatures, and that the free energy density becomes a non-analytic function of the quark mass.some clarifications added
Does the speed of gravitational waves depend on the source velocity?

The second postulate of special relativity states that the speed of light in vacuum is independent of the emitter's motion. The test of this postulate so far remains unexplored for gravitational radiation. We analyze data from the LIGO-Virgo detectors to test this postulate within the ambit of a model where the speed of the emitted GWs ($c'$) from a binary depends on a characteristic velocity $\tilde{v}$ proportional to that of the reduced one-body system as $c' = c + k\, \tilde{v}$, where $k$ is a constant. We have estimated the upper bound on the 90\% credible interval over $k$ to be ${k \leq 8.3 \times {10}^{-18}}$, which is several orders of magnitude more stringent compared to previous bounds obtained from electromagnetic observations. The Bayes' factor supports the second postulate with a strong evidence that the data is consistent with the null hypothesis $k = 0$, upholding the principle of relativity for gravitational interactions.
Semisupervised RF Fingerprinting With Consistency-Based Regularization

As a promising non-password authentication technology, radio frequency (RF) fingerprinting can greatly improve wireless security. Recent work has shown that RF fingerprinting based on deep learning significantly outperforms conventional approaches. However, this superiority relies largely on using plenty of labeled data for supervised learning, whereas training deep neural networks on a small dataset generally falls into overfitting, resulting in performance degradation. Considering that it is often easier to obtain enough unlabeled data in practice, we leverage deep semisupervised learning for RF fingerprinting, which largely relies on a composite data augmentation scheme specifically designed for wireless communication signals, combined with two popular techniques: 1) consistency-based regularization and 2) pseudo-labeling. Experimental results on both simulated and real-world datasets demonstrate that our proposed method for semisupervised RF fingerprinting is far superior to other competing ones, and it achieves remarkable performance almost close to that of fully supervised learning, with a very limited number of examples available.
Discourse over Discourse: The Need for an Expanded Pragmatic Focus in Conversational AI

The summarization of conversation, that is, discourse over discourse, elevates pragmatic considerations as a pervasive limitation of both summarization and other applications of contemporary conversational AI. Building on impressive progress in both semantics and syntax, pragmatics concerns meaning in the practical sense. In this paper, we discuss several challenges in both summarization of conversations and other conversational AI applications, drawing on relevant theoretical work. We illustrate the importance of pragmatics with so-called star sentences, syntactically acceptable propositions that are pragmatically inappropriate in conversation or its summary. Because the baseline for quality of AI is indistinguishability from human behavior, we draw heavily on the psycho-linguistics literature, and label our complaints as"Turing Test Triggers"(TTTs). We discuss implications for the design and evaluation of conversation summarization methods and conversational AI applications like voice assistants and chatbots
Enhancement of performance of AlGaN/GaN high-electron-mobility transistors by transfer from sapphire to a copper plate

We transferred AlGaN/GaN high-electron-mobility transistors (HEMTs) from a sapphire substrate to a copper plate using the hexagonal boron nitride epitaxial lift-off technique. After transfer, the negative slope in the drain current Id decreased owing to the suppression of the self-heating effect. The significant increase in Id and the negative shift of threshold voltage indicate an increase in two-dimensional electron gas (2DEG) density. The increase in 2DEG density is at least partially caused by the reduction in compressive stress in the GaN layer after the transfer, which is revealed from the E2 peak shifts of −1.3 cm−1 in Raman spectroscopy measurements. We also estimated the temperature in the active region of HEMTs by micro-Raman spectroscopy. For the transferred HEMT, the temperature at the gate edge on the drain side was 100 °C at a power dissipation of 0.9 W. In contrast, the temperature reached 240 °C at a power dissipation of only 0.7 W for the HEMT on the sapphire substrate. This indicates that the transfer technique can enhance the performance of AlGaN/GaN HEMTs.
Generating Images of Rare Concepts Using Pre-trained Diffusion Models

Text-to-image diffusion models can synthesize high quality images, but they have various limitations. Here we highlight a common failure mode of these models, namely, generating uncommon concepts and structured concepts like hand palms. We show that their limitation is partly due to the long-tail nature of their training data: web-crawled data sets are strongly unbalanced, causing models to under-represent concepts from the tail of the distribution. We characterize the effect of unbalanced training data on text-to-image models and offer a remedy. We show that rare concepts can be correctly generated by carefully selecting suitable generation seeds in the noise space, using a small reference set of images, a technique that we call SeedSelect. SeedSelect does not require retraining or finetuning the diffusion model. We assess the faithfulness, quality and diversity of SeedSelect in creating rare objects and generating complex formations like hand images, and find it consistently achieves superior performance. We further show the advantage of SeedSelect in semantic data augmentation. Generating semantically appropriate images can successfully improve performance in few-shot recognition benchmarks, for classes from the head and from the tail of the training data of diffusion models.
Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction

Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives.