Miracle-dz/UniFAR-FTUs huggingface dataset:
Carroll Hawking effect

Carroll black holes with an associated Carroll temperature were introduced recently. So far, it is unclear if they exhibit a Hawking-like effect. To solve this, we study scalar fields on Carroll black hole backgrounds. Inspired by anomaly methods, we derive a Hawking-like energy-momentum tensor compatible with the Carroll temperature and the Stefan-Boltzmann law. Key steps in our derivation are the finiteness of energy at the Carroll extremal surface and compatibility with the Carroll Ward identities, thereby eliminating, respectively, the Carroll-analogs of the Boulware and Unruh vacua.
Slender vortex filaments in the Boussinesq Approximation

A model for the motion of slender vortex filaments is extended to include the effect of gravity. The model, initially introduced by Callegari and Ting (SIAM, J. of App. Math., (1978), vol. 35, pp. 148-175), is based on a matched asymptotic expansion in which the outer solution, given by the Biot-Savart law, is matched with the inner solution derived from the Navier-Stokes equations. Building on recent work by Harikrishnan et al (Phys. of Fluids, (2023), vol. 35) the Boussinesq approximation is applied such that the density variations only enter in the gravity term. However, unlike Harikrishnan et al. (2023) the density variation enters at a lower order in the asymptotic expansion, and thus has a more significant impact on the self-induced velocity of the vortex filament. In this regime, which corresponds to the regime studied by Chang and Smith (J. of Fl. Mech., (2018), vol. 857), the effect of gravity is given by an alteration of the core constant, which couples the motion of the filament to the motion within the vortical core, in addition to a change in the compatability conditions (evolution equations) which determine the leading order azimuthal and tangential velocity fields in the vortex core. The results are used to explain certain properties of bouyant vortex rings, as well as qualitatively explore the impact of gravity on tornado type atmospheric vorticies.
Features of Tajikistan's past and future climate

Tajikistan is an exceptionally vulnerable Central Asian country in the face of climate change due to its' high exposure and sensitivity to climate change combined with poor adaptation capacity. Therefore, comprehensive climate data are needed for efficient planning of mitigation and adaptation. Here, the key spatio‐temporal features of Tajikistan's climate were analysed based on historical data from 52 stations with the longest time‐series covering nearly 80 years. In addition, bias‐corrected CMIP5 climate models were used to simulate future temperature and precipitation conditions across a range of time periods and greenhouse gas emission scenarios. The results show that the mean annual temperatures have risen in Tajikistan since the 1930s, average rate of change being approximately 0.1 °C decade−1. For precipitation, only a weak signal of change could be detected. The high‐resolution (30 arc seconds) spatial analyses reveal the strong control of topography on Tajikistan's climate with such coupling leading mean annual air temperatures to vary from −13.7 °C (eastern mountain regions) to 17.3 °C (low‐altitude plains). Similarly, the mean annual precipitation vary from less than 50 mm to over 1000 mm, spring being the wettest of the seasons. The future climate simulations suggest drastic increase in air temperatures; at the end of this century the temperature multi‐model mean can be approximately 2 °C (Representative Concentration Pathways, RCP2.6) to 7 °C (RCP8.5) higher compared to 1961–1990. Precipitation scenarios indicate increase in the eastern part of the country and decrease in the west. Finally, the applied spatial uncertainty analyses stress the need for the future development of the climate station network especially in high‐elevation areas associated with complex topography. These results show the pronounced climatic variation in Tajikistan and the expected near‐ and long‐term changes in air temperature and precipitation conditions, thus forming a sound basis for future planning of national mitigation and adaptation activities.
Exploring the dynamic interplay of cognitive load and emotional arousal by using multimodal measurements: Correlation of pupil diameter and emotional arousal in emotionally engaging tasks

Multimodal data analysis and validation based on streams from state-of-the-art sensor technology such as eye-tracking or emotion recognition using the Facial Action Coding System (FACTs) with deep learning allows educational researchers to study multifaceted learning and problem-solving processes and to improve educational experiences. This study aims to investigate the correlation between two continuous sensor streams, pupil diameter as an indicator of cognitive workload and FACTs with deep learning as an indicator of emotional arousal (RQ 1a), specifically for epochs of high, medium, and low arousal (RQ 1b). Furthermore, the time lag between emotional arousal and pupil diameter data will be analyzed (RQ 2). 28 participants worked on three cognitively demanding and emotionally engaging everyday moral dilemmas while eye-tracking and emotion recognition data were collected. The data were pre-processed in Phyton (synchronization, blink control, downsampling) and analyzed using correlation analysis and Granger causality tests. The results show negative and statistically significant correlations between the data streams for emotional arousal and pupil diameter. However, the correlation is negative and significant only for epochs of high arousal, while positive but non-significant relationships were found for epochs of medium or low arousal. The average time lag for the relationship between arousal and pupil diameter was 2.8 ms. In contrast to previous findings without a multimodal approach suggesting a positive correlation between the constructs, the results contribute to the state of research by highlighting the importance of multimodal data validation and research on convergent vagility. Future research should consider emotional regulation strategies and emotional valence.
FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition

Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. However, such a paradigm fails to comprehensively differentiate the fine-grained language and cognitive skills, rendering the lack of sufficient interpretation to LLMs' capabilities. In this paper, we present FAC$^2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation. Specifically, we formulate LLMs' evaluation in a multi-dimensional and explainable manner by dissociating the language-related capabilities and the cognition-related ones. Besides, through extracting the intermediate reasoning from LLMs, we further break down the process of applying a specific capability into three sub-steps: recalling relevant knowledge, utilizing knowledge, and solving problems. Finally, FAC$^2$E evaluates each sub-step of each fine-grained capability, providing a two-faceted diagnosis for LLMs. Utilizing FAC$^2$E, we identify a common shortfall in knowledge utilization among models and propose a straightforward, knowledge-enhanced method to mitigate this issue. Our results not only showcase promising performance enhancements but also highlight a direction for future LLM advancements.
Trustworthy Self-Attention: Enabling the Network to Focus Only on the Most Relevant References

The prediction of optical flow for occluded points is still a difficult problem that has not yet been solved. Recent methods use self-attention to find relevant non-occluded points as references for estimating the optical flow of occluded points based on the assumption of self-similarity. However, they rely on visual features of a single image and weak constraints, which are not sufficient to constrain the trained network to focus on erroneous and weakly relevant reference points. We make full use of online occlusion recognition information to construct occlusion extended visual features and two strong constraints, allowing the network to learn to focus only on the most relevant references without requiring occlusion ground truth to participate in the training of the network. Our method adds very few network parameters to the original framework, making it very lightweight. Extensive experiments show that our model has the greatest cross-dataset generalization. Our method achieves much greater error reduction, 18.6%, 16.2%, and 20.1% for all points, non-occluded points, and occluded points respectively from the state-of-the-art GMA-base method, MATCHFlow(GMA), on Sintel Albedo pass. Furthermore, our model achieves state-of-the-art performance on the Sintel bench-marks, ranking \#1 among all published methods on Sintel clean pass. The code will be open-source.
Hardware-Efficient Bosonic Quantum Computing with Photon-loss Detection Capability

Bosonic quantum systems offer the hardware-efficient construction of error detection/error correction codes by using the infinitely large Hilbert space. However, due to the encoding, arbitrary gate rotations usually require magic state teleportation or complicated optimized pulse sequences involving an ancilla qubit. Here, we propose a simple and hardware-efficient bosonic 02 error detection code that allows for the implementation of arbitrary X and Z rotations and a controlled phase gate by using a Kerr nonlinear resonator. Our code can detect a single photon loss, and we observe significant error suppression by simulating the frequently used hardware-efficient ansatz quantum circuit in near-term quantum computing.
Splitting links by integer homology spheres

For every $n \ge 3$, we construct 2-component links in $S^{n+1}$ that are a split by an integer homology $n$-sphere, but not by $S^n$. In the special case $n=3$, i.e. that of 2-links in $S^4$, we produce an infinite family of links $L_\ell$ and of integer homology spheres $Y_\ell$ such that the link $L_\ell$ is (topologically or smoothly) split by $Y_\ell$ and by no other integer homology sphere in the family.
Novel Pauli blocking method in quantum molecular dynamics type models

In this work, we propose a novel method for calculating the occupation probability in the Pauli blocking of the quantum molecular dynamics type models. This method refines the description of the Pauli blocking ratio in the nuclear matter and that in the finite nucleus. The influence of the new Pauli blocking method on the heavy ion collisions observables, such as the charge distribution, the free neutron to proton yield ratios, and the extracted physical quantities, such as the in-medium nucleon-nucleon cross sections, are investigated. For the extracted in-medium nucleon-nucleon cross sections, our results show that it will be enhanced 1.1$-$2.5 times than that with the conventional Pauli blocking method at the beam energy less than 150 MeV/u, which highlights the importance of a refined Pauli blocking method for developing an advanced transport model to describe complex heavy ion collisions.
CustomListener: Text-guided Responsive Interaction for User-friendly Listening Head Generation

Listening head generation aims to synthesize a non-verbal responsive listener head by modeling the correlation between the speaker and the listener in dynamic conversion.The applications of listener agent generation in virtual interaction have promoted many works achieving the diverse and fine-grained motion generation. However, they can only manipulate motions through simple emotional labels, but cannot freely control the listener's motions. Since listener agents should have human-like attributes (e.g. identity, personality) which can be freely customized by users, this limits their realism. In this paper, we propose a user-friendly framework called CustomListener to realize the free-form text prior guided listener generation. To achieve speaker-listener coordination, we design a Static to Dynamic Portrait module (SDP), which interacts with speaker information to transform static text into dynamic portrait token with completion rhythm and amplitude information. To achieve coherence between segments, we design a Past Guided Generation Module (PGG) to maintain the consistency of customized listener attributes through the motion prior, and utilize a diffusion-based structure conditioned on the portrait token and the motion prior to realize the controllable generation. To train and evaluate our model, we have constructed two text-annotated listening head datasets based on ViCo and RealTalk, which provide text-video paired labels. Extensive experiments have verified the effectiveness of our model.
Searching for magnetically hard monoborides (and finding a few): A first-principles investigation

New hard magnetic materials with zero or low rare earth content are in demand due to the high prices of the rare earth metals. Among the candidates for such materials, we consider MnB, FeB and their alloys, because previous experiments suggest that FeB has a relatively high magnetic hardness of about 0.83 at room temperature. Using first-principles calculations, we examine the full range of alloys from CrB, through MnB, FeB, to CoB. Furthrmore, we consider alloys of MnB and FeB with substitutions of 3$d$, 4$d$ and 5$d$ transition metals. For the above ninety compositions, we determine magnetic moment, magnetocrystalline anisotropy energy and magnetic hardness. For (Fe-Co)B alloys, the calculated values of magnetic hardness exceed five, which is an exceptionally high. While these values are inflated by the virtual crystal approximation used, we still expect actual magnetic hardnesses well above unity. Furthermore, we classify considered MnB alloys substituted with transition metals as magnetically soft or semi-hard and FeB alloys with Sc, Ti, V, Zr, Nb, Mo, Hf, Ta or W as magnetically hard (with magnetic hardness exceeding unity).
Rethinking Classifier Re-Training in Long-Tailed Recognition: A Simple Logits Retargeting Approach

In the long-tailed recognition field, the Decoupled Training paradigm has demonstrated remarkable capabilities among various methods. This paradigm decouples the training process into separate representation learning and classifier re-training. Previous works have attempted to improve both stages simultaneously, making it difficult to isolate the effect of classifier re-training. Furthermore, recent empirical studies have demonstrated that simple regularization can yield strong feature representations, emphasizing the need to reassess existing classifier re-training methods. In this study, we revisit classifier re-training methods based on a unified feature representation and re-evaluate their performances. We propose a new metric called Logits Magnitude as a superior measure of model performance, replacing the commonly used Weight Norm. However, since it is hard to directly optimize the new metric during training, we introduce a suitable approximate invariant called Regularized Standard Deviation. Based on the two newly proposed metrics, we prove that reducing the absolute value of Logits Magnitude when it is nearly balanced can effectively decrease errors and disturbances during training, leading to better model performance. Motivated by these findings, we develop a simple logits retargeting approach (LORT) without the requirement of prior knowledge of the number of samples per class. LORT divides the original one-hot label into small true label probabilities and large negative label probabilities distributed across each class. Our method achieves state-of-the-art performance on various imbalanced datasets, including CIFAR100-LT, ImageNet-LT, and iNaturalist2018.
EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation

The ability of zero-shot translation emerges when we train a multilingual model with certain translation directions; the model can then directly translate in unseen directions. Alternatively, zero-shot translation can be accomplished by pivoting through a third language (e.g., English). In our work, we observe that both direct and pivot translations are noisy and achieve less satisfactory performance. We propose EBBS, an ensemble method with a novel bi-level beam search algorithm, where each ensemble component explores its own prediction step by step at the lower level but they are synchronized by a"soft voting"mechanism at the upper level. Results on two popular multilingual translation datasets show that EBBS consistently outperforms direct and pivot translations as well as existing ensemble techniques. Further, we can distill the ensemble's knowledge back to the multilingual model to improve inference efficiency; profoundly, our EBBS-based distillation does not sacrifice, or even improves, the translation quality.
An Anatomical and Physiological Basis for Coincidence Detection Across Time Scales in the Auditory System

Coincidence detection is a common neural computation that identifies co-occurring stimuli by integration of inputs. In the auditory system, octopus cells act as coincidence detectors for complex sounds that include both synchronous and sequenced combinations of frequencies. Octopus cells must detect coincidence on both the millisecond and submillisecond time scale, unlike the average neuron, which integrates inputs over time on the order of tens of milliseconds. Here, we show that octopus cell computations in the cell body are shaped by inhibition in the dendrites, which adjusts the strength and timing of incoming signals to achieve submillisecond acuity. This mechanism is crucial for the fundamental process of integrating the synchronized frequencies of natural auditory signals over time.
ADAMTS7 Promotes Smooth Muscle Cell Foam Cell Expansion in Atherosclerosis

Human genetic studies have repeatedly associated SNPs near the gene ADAMTS7 with atherosclerotic cardiovascular disease. Subsequent investigations in mice demonstrated that ADAMTS7 is proatherogenic, induced in response to vascular injury, and alters smooth muscle cell function. However, the mechanisms governing this function and its relationship to atherosclerosis remain unclear. Here, we report the first conditional Adamts7 transgenic mouse in which the gene can be conditionally overexpressed in smooth muscle cells, mimicking its induction in atherosclerosis. We observed that smooth muscle cell Adamts7 overexpression results in a 3.5-fold increase in peripheral atherosclerosis, coinciding with an expansion of smooth muscle foam cells. RNA sequencing of Adamts7 overexpressed primary smooth muscle cells revealed an upregulation in the expression of lipid uptake genes. Subsequent experiments in primary smooth muscle cells demonstrated that increased Spi1 and Cd36 expression leads to increased smooth muscle cell oxLDL uptake. To uncover ADAMTS7 expression in human disease, we have interrogated the largest scRNA-seq dataset of human carotid atherosclerosis. This analysis discovered that endothelial cells had the highest expression level of ADAMTS7 with lesser expression in smooth muscle cells, fibroblasts, and mast cells. Subsequent conditional knockout studies in smooth muscle cells surprisingly showed no change in atherosclerosis, suggesting redundant expression of this secreted factor in the vessel wall. Finally, mice overexpressing Adamts7 in endothelial cells also exhibit increased atherosclerosis, suggesting that multiple vascular cell types can contribute to ADAMTS7-mediated foam cell expansion. In summary, Adamts7 is expressed by multiple vascular cell types in atherosclerosis, and ADAMTS7 promotes oxLDL uptake in smooth muscle cells, increasing smooth muscle foam cell formation and peripheral atherosclerosis in mice.
Improving Trigger Functions for Convective Parameterization Schemes Using GOAmazon Observations

AbstractUsing observations from the Green Ocean Amazon (GOAmazon) field campaign, this study aims to improve trigger functions of convection schemes. Results show that the CAPE generation rate (dCAPE)-type triggers are the first tier and that the Bechtold and heated condensation framework (HCF) triggers are a distant second tier. The composite analysis reveals that the undilute dCAPE trigger underpredicts convection when there is bottom-heavy upward motion but overpredicts convection with low-level downward and upper-level upward motions. The empirical orthogonal function (EOF) analysis on vertical velocity shows that EOF1 (62.65%) exhibits upward motion throughout the troposphere and that EOF2 (28.05%) has lower-level upward motion and upper-level downward motion. Both of them have close relationships with precipitation, indicating the role of vertical velocity in triggering convection. The skill sensitivity analysis shows that the inclusion of 700-hPa upward motion significantly enhances the undilute dC.
Awareness is determined by emotion and gender

Traditionally, consciousness studies focus on domain general cognitive processes rather than on specific information reaching subjective awareness. The present study (N = 45) used visual masking and whole-body images to investigate whether the specific emotional expression as well as gender of the stimuli and of the participants impact awareness. Our results show that participants’ awareness responses reflect differences in the specific emotion of the stimuli, that these differences are a function of the gender of the stimuli as well as the gender of the participants and that minimal awareness may be associated with emotion specific features of the body images. Overall, we observed that threatening expressions are more easily detected than fearful ones, especially by males presented with male stimuli. Our findings underscore the importance of affective factors for theories of consciousness and underscore the significance of gender differences in emotional processing, often overlooked in past face and body emotion recognition studies.
Long-term survival of asexual Zymoseptoria tritici spores in the environment

The fungal phytopathogen Zymoseptoria tritici, causal agent of the economically damaging Septoria tritici blotch of wheat, is different from most foliar fungal pathogens in that its germination occurs slowly and apparently randomly after arrival on the leaf surface and is followed by a potentially prolonged period of epiphytic growth and even reproduction, during which no feeding structures are formed by the fungus. Thus, understanding the cues for germination and the mechanisms that underpin survival in low-nutrient environments could provide key new avenues for disease control. In this work, we examine survival, culturability, and virulence of spores following transfer from a high nutrient environment to water. We find that a sub-population of Z. tritici spores can survive and remain virulent for at least 7 weeks in water alone, during which time multicellular structures split to single cells. The fungus relies heavily on stored lipids; however, if cell suspensions in water are dried, the cells survive without lipid utilisation. Changes in gene expression in the first hours after suspension in water reflect adaptation to stress, while longer term starvation (7 days) induces changes particularly in primary metabolism and cytochrome P450 (CYP) gene expression. Importantly, we also found that Z. tritici spores are equally or better able to survive in soil as in water, and that rain-splash occurring 49 days after soil inoculation can transfer cells to wheat seedlings growing in inoculated soil and cause Septoria leaf blotch disease.