Miracle-dz/UniFAR-FTUs huggingface dataset:
A phase space localization operator in negative binomial states

We are dealing with some spectral properties of a phase space localization operator PR corresponding to the indicator function of a disk of radius R<1. The localization procedure is achieved with respect to a set of negative binomial states (NBS) labeled by points of the complex unit disk D and depending on a parameter 2B>1. We derive a formula expressing PR as function of the pseudo harmonic oscillator whose potential function depends on B. The phase space content outside the localization domain is estimated in terms of the photon counting probability distribution associated with the NBS. By using the coherent states transform attached to NBS, we transfer the action of the operator PR to a Bergman space AB(D) of analytic functions on D satisfying a growth condition depending on B and we explicitly give its integral kernel whose limit as R goes to 1 coincides with the reproducing kernel of AB(D). This leads to a natural generalization of this Hilbert space with respect to the parameter R.
Framing Analysis of Health-Related Narratives: Conspiracy versus Mainstream Media

Understanding how online media frame issues is crucial due to their impact on public opinion. Research on framing using natural language processing techniques mainly focuses on specific content features in messages and neglects their narrative elements. Also, the distinction between framing in different sources remains an understudied problem. We address those issues and investigate how the framing of health-related topics, such as COVID-19 and other diseases, differs between conspiracy and mainstream websites. We incorporate narrative information into the framing analysis by introducing a novel frame extraction approach based on semantic graphs. We find that health-related narratives in conspiracy media are predominantly framed in terms of beliefs, while mainstream media tend to present them in terms of science. We hope our work offers new ways for a more nuanced frame analysis.
Exploiting Hierarchical Interactions for Protein Surface Learning

Predicting interactions between proteins is one of the most important yet challenging problems in structural bioinformatics. Intrinsically, potential function sites in protein surfaces are determined by both geometric and chemical features. However, existing works only consider handcrafted or individually learned chemical features from the atom type and extract geometric features independently. Here, we identify two key properties of effective protein surface learning: 1) relationship among atoms: atoms are linked with each other by covalent bonds to form biomolecules instead of appearing alone, leading to the significance of modeling the relationship among atoms in chemical feature learning. 2) hierarchical feature interaction: the neighboring residue effect validates the significance of hierarchical feature interaction among atoms and between surface points and atoms (or residues). In this paper, we present a principled framework based on deep learning techniques, namely Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet), for protein surface analysis by bridging chemical and geometric features with hierarchical interactions. Extensive experiments demonstrate that our method outperforms the prior state-of-the-art method by 2.3% in site prediction task and 3.2 available at https://github.com/lyqun/HCGNet.
Influence of the Jovian Current Sheet Models on the Mapping of the UV Auroral Footprints of Io, Europa, and Ganymede

The in situ characterization of moon‐magnetosphere interactions at Jupiter and the mapping of moon auroral footpaths require accurate global models of the magnetospheric magnetic field. In this study, we compare the ability of two widely‐used current sheet models, Khurana‐2005 (KK2005) and Connerney‐2020 (CON2020) combined with the most recent internal magnetic field model of Jupiter (JRM33) to match representative Galileo and Juno measurements acquired at low, medium, and high latitudes. With the adjustments of the KK2005 model to JRM33, we show that in the outer and middle magnetosphere (R > 15RJ), JRM33 + KK2005 is found to be the best model to reproduce the magnetic field observations of Galileo and Juno as it accounts for local time effects. JRM33 + CON2020 gives the most accurate representation of the inner magnetosphere. This finding is drawn from comparisons with Juno in situ magnetic field measurements and confirmed by contrasting the timing of the crossings of the Io, Europa, and Ganymede flux tubes identified in the Juno particles data with the two model estimates. JRM33 + CON2020 also maps more accurately the UV auroral footpath of Io, Europa, and Ganymede observed by Juno than JRM33 + KK2005. The JRM33 + KK2005 model predicts a local time asymmetry in position of the moons' footprints, which is however not detected in Juno's UV measurements. This could indicate that local time effects on the magnetic field are marginal at the orbital locations of Io, Europa, and Ganymede. Finally, the accuracy of the models and their predictions as a function of hemisphere, local time, and longitude is explored.
A Review on the Scientific and Technological Breakthroughs in Thermal Emission Engineering

The emission of thermal radiation is a physical process of fundamental and technological interest. From different approaches, thermal radiation can be regarded either as one of the basic mechanisms of heat transfer, as a fundamental quantum phenomenon of photon production, or as the propagation of electromagnetic waves. However, unlike light emanating from conventional photonic sources, such as lasers or antennas, thermal radiation is characterized for being broadband, omnidirectional, and unpolarized. Due to these features, ultimately tied to its inherently incoherent nature, taming the thermal radiation constitutes a challenging issue. Latest advances in the field of nanophotonics have led to a whole set of artificial platforms, ranging from spatially structured materials, and much more recently, time-modulated media, offering promising avenues for enhancing the control and manipulation of electromagnetic waves, from far to near-field regimes. Given the ongoing parallelism between the fields of nanophotonics and thermal emission, these recent developments have been harnessed to deal with radiative thermal processes, thereby conforming the current basis of thermal emission engineering. In this review, we survey some of the main breakthroughs carried out in this burgeoning research field, from fundamental aspects, theoretical limits, the emergence of new phenomena, practical applications, challenges, and future prospects.
A method for characterizing disease emergence curves from paired pathogen detection and serology data

Wildlife disease surveillance programs and research studies track infection and identify risk factors for wild populations, humans, and agriculture. Often, several types of samples are collected from individuals to provide more complete information about an animal's infection history. Methods that jointly analyze multiple data streams to study disease emergence and drivers of infection via epidemiological process models remain underdeveloped. Joint-analysis methods can more thoroughly analyze all available data, more precisely quantifying epidemic processes, outbreak status, and risks. We contribute a paired data modeling approach that analyzes multiple samples from individuals. We use"characterization maps"to link paired data to epidemiological processes through a hierarchical statistical observation model. Our approach can provide both Bayesian and frequentist estimates of epidemiological parameters and state. We motivate our approach through the need to use paired pathogen and antibody detection tests to estimate parameters and infection trajectories for the widely applicable susceptible, infectious, recovered (SIR) model. We contribute general formulas to link characterization maps to arbitrary process models and datasets and an extended SIR model that better accommodates paired data. We find via simulation that paired data can more efficiently estimate SIR parameters than unpaired data, requiring samples from 5-10 times fewer individuals. We then study SARS-CoV-2 in wild White-tailed deer (Odocoileus virginianus) from three counties in the United States. Estimates for average infectious times corroborate captive animal studies. Our methods use general statistical theory to let applications extend beyond the SIR model we consider, and to more complicated examples of paired data.
Universal contributions to charge fluctuations in spin chains at finite temperature

At finite temperature, conserved charges undergo thermal fluctuations in a quantum many-body system in the grand canonical ensemble. The full structure of the fluctuations of the total U(1) charge $Q$ can be succinctly captured by the generating function $G(\theta)=\left\langle e^{i \theta Q}\right\rangle$. For a 1D translation-invariant spin chain, in the thermodynamic limit the magnitude $|G(\theta)|$ scales with the system size $L$ as $\ln |G(\theta)|=-\alpha(\theta)L+\gamma(\theta)$, where $\gamma(\theta)$ is the scale-invariant contribution and may encode universal information about the underlying system. In this work we investigate the behavior and physical meaning of $\gamma(\theta)$ when the system is periodic. We find that $\gamma(\theta)$ only takes non-zero values at isolated points of $\theta$, which is $\theta=\pi$ for all our examples. In two exemplary lattice systems we show that $\gamma(\pi)$ takes quantized values when the U(1) symmetry exhibits a specific type of 't Hooft anomaly with other symmetries. In other cases, we investigate how $\gamma(\theta)$ depends on microscopic conditions (such as the filling factor) in field theory and exactly solvable lattice models.
Dense Hadronic Matter in Neutron Stars

In this lecture we discuss the properties of dense hadronic matter inside neutron stars. In particular, we pay attention to the role of strangeness in the core of neutron stars, by analysing the presence of baryons and mesons with strangeness. We consider two interesting possible scenarios in their interior, that is, the existence of hyperons leading to the so-called hyperon puzzle and the presence of a kaon condensed phase inside neutron stars.
Automatic 3D Multi-modal Ultrasound Segmentation of Human Placenta using Fusion Strategies and Deep Learning

Purpose: Ultrasound is the most commonly used medical imaging modality for diagnosis and screening in clinical practice. Due to its safety profile, noninvasive nature and portability, ultrasound is the primary imaging modality for fetal assessment in pregnancy. Current ultrasound processing methods are either manual or semi-automatic and are therefore laborious, time-consuming and prone to errors, and automation would go a long way in addressing these challenges. Automated identification of placental changes at earlier gestation could facilitate potential therapies for conditions such as fetal growth restriction and pre-eclampsia that are currently detected only at late gestational age, potentially preventing perinatal morbidity and mortality. Methods: We propose an automatic three-dimensional multi-modal (B-mode and power Doppler) ultrasound segmentation of the human placenta using deep learning combined with different fusion strategies.We collected data containing Bmode and power Doppler ultrasound scans for 400 studies. Results: We evaluated different fusion strategies and state-of-the-art image segmentation networks for placenta segmentation based on standard overlap- and boundary-based metrics. We found that multimodal information in the form of B-mode and power Doppler scans outperform any single modality. Furthermore, we found that B-mode and power Doppler input scans fused at the data level provide the best results with a mean Dice Similarity Coefficient (DSC) of 0.849. Conclusion: We conclude that the multi-modal approach of combining B-mode and power Doppler scans is effective in segmenting the placenta from 3D ultrasound scans in a fully automated manner and is robust to quality variation of the datasets.
Boosting Few-Shot Semantic Segmentation Via Segment Anything Model

In semantic segmentation, accurate prediction masks are crucial for downstream tasks such as medical image analysis and image editing. Due to the lack of annotated data, few-shot semantic segmentation (FSS) performs poorly in predicting masks with precise contours. Recently, we have noticed that the large foundation model segment anything model (SAM) performs well in processing detailed features. Inspired by SAM, we propose FSS-SAM to boost FSS methods by addressing the issue of inaccurate contour. The FSS-SAM is training-free. It works as a post-processing tool for any FSS methods and can improve the accuracy of predicted masks. Specifically, we use predicted masks from FSS methods to generate prompts and then use SAM to predict new masks. To avoid predicting wrong masks with SAM, we propose a prediction result selection (PRS) algorithm. The algorithm can remarkably decrease wrong predictions. Experiment results on public datasets show that our method is superior to base FSS methods in both quantitative and qualitative aspects.
Flow structure near three phase contact line of low-contact-angle evaporating droplets

Flow structure near three phase contact line (TPCL) of evaporating liquids plays a significant role in liquid wetting and dewetting, liquid film evaporation, and boiling. Despite the wide focus it receives, the interacting mechanisms therein remain elusive and in specific cases, controversial. Here, we reveal the profile of internal flow and elucidate the dominating mechanisms near TPCL of evaporating droplets, using mathematical modeling, trajectory analysis, and infrared thermography. We indicate that for less volatile liquids such as butanol, the flow pattern is dominated by capillary flow. With increasing liquid volatility, e.g., alcohol, the effect of evaporation cooling, under conditions, induces interfacial temperature gradient with cold droplet apex and warm edge. The temperature gradient leads to Marangoni flow that competes with outwarding capillary flow, resulting in the reversal of interfacial flow and the formation of a stagnation point near TPCL. The spatiotemporal variations of capillary velocity and Marangoni velocity are further quantified by mathematically decomposing the tangential velocity of interfacial flow. The conclusions can serve as a theoretical base for explaining deposition patterns from colloidal suspensions and can be utilized as a benchmark in analyzing more complex liquid systems.
CT Liver Segmentation via PVT-based Encoding and Refined Decoding

Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning. Computer-aided diagnosis systems promise to improve the precision of liver disease diagnosis, disease progression, and treatment planning. In response to the need, we propose a novel deep learning approach, \textit{\textbf{PVTFormer}}, that is built upon a pretrained pyramid vision transformer (PVT v2) combined with advanced residual upsampling and decoder block. By integrating a refined feature channel approach with a hierarchical decoding strategy, PVTFormer generates high quality segmentation masks by enhancing semantic features. Rigorous evaluation of the proposed method on Liver Tumor Segmentation Benchmark (LiTS) 2017 demonstrates that our proposed architecture not only achieves a high dice coefficient of 86.78\%, mIoU of 78.46\%, but also obtains a low HD of 3.50. The results underscore PVTFormer's efficacy in setting a new benchmark for state-of-the-art liver segmentation methods. The source code of the proposed PVTFormer is available at \url{https://github.com/DebeshJha/PVTFormer}.
Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstruction

Spectral imaging enables the analysis of optical material properties that are invisible to the human eye. Different spectral capturing setups, e.g., based on filter-wheel, push-broom, line-scanning, or mosaic cameras, have been introduced in the last years to support a wide range of applications in agriculture, medicine, and industrial surveillance. However, these systems often suffer from different disadvantages, such as lack of real-time capability, limited spectral coverage or low spatial resolution. To address these drawbacks, we present a novel approach combining two calibrated multispectral real-time capable snapshot cameras, covering different spectral ranges, into a stereo-system. Therefore, a hyperspectral data-cube can be continuously captured. The combined use of different multispectral snapshot cameras enables both 3D reconstruction and spectral analysis. Both captured images are demosaicked avoiding spatial resolution loss. We fuse the spectral data from one camera into the other to receive a spatially and spectrally high resolution video stream. Experiments demonstrate the feasibility of this approach and the system is investigated with regard to its applicability for surgical assistance monitoring.
L-values of elliptic curves twisted by cubic characters

Given a rational elliptic curve $ E $ of analytic rank zero, its L-function can be twisted by an even primitive Dirichlet character $ \chi $ of order $ q $, and in many cases its associated central algebraic L-value $ \mathcal{L}(E, \chi) $ is known to be integral. This paper derives some arithmetic consequences from a congruence between $ \mathcal{L}(E, 1) $ and $ \mathcal{L}(E, \chi) $ arising from this integrality, with an emphasis on cubic characters $ \chi $. These include $ q $-adic valuations of the denominator of $ \mathcal{L}(E, 1) $, determination of $ \mathcal{L}(E, \chi) $ in terms of Birch--Swinnerton-Dyer invariants, and asymptotic densities of $ \mathcal{L}(E, \chi) $ modulo $ q $ by varying $ \chi $.
TEXterity: Tactile Extrinsic deXterity

We introduce a novel approach that combines tactile estimation and control for in-hand object manipulation. By integrating measurements from robot kinematics and an image-based tactile sensor, our framework estimates and tracks object pose while simultaneously generating motion plans to control the pose of a grasped object. This approach consists of a discrete pose estimator that uses the Viterbi decoding algorithm to find the most likely sequence of object poses in a coarsely discretized grid, and a continuous pose estimator-controller to refine the pose estimate and accurately manipulate the pose of the grasped object. Our method is tested on diverse objects and configurations, achieving desired manipulation objectives and outperforming single-shot methods in estimation accuracy. The proposed approach holds potential for tasks requiring precise manipulation in scenarios where visual perception is limited, laying the foundation for closed-loop behavior applications such as assembly and tool use. Please see supplementary videos for real-world demonstration at https://sites.google. com/view/texterity.
Marrying Adapters and Mixup to Efficiently Enhance the Adversarial Robustness of Pre-Trained Language Models for Text Classification

Existing works show that augmenting training data of neural networks using both clean and adversarial examples can enhance their generalizability under adversarial attacks. However, this training approach often leads to performance degradation on clean inputs. Additionally, it requires frequent re-training of the entire model to account for new attack types, resulting in significant and costly computations. Such limitations make adversarial training mechanisms less practical, particularly for complex Pre-trained Language Models (PLMs) with millions or even billions of parameters. To overcome these challenges while still harnessing the theoretical benefits of adversarial training, this study combines two concepts: (1) adapters, which enable parameter-efficient fine-tuning, and (2) Mixup, which train NNs via convex combinations of pairs data pairs. Intuitively, we propose to fine-tune PLMs through convex combinations of non-data pairs of fine-tuned adapters, one trained with clean and another trained with adversarial examples. Our experiments show that the proposed method achieves the best trade-off between training efficiency and predictive performance, both with and without attacks compared to other baselines on a variety of downstream tasks.
AutoFT: Learning an Objective for Robust Fine-Tuning

Foundation models encode rich representations that can be adapted to downstream tasks by fine-tuning. However, fine-tuning a model on one data distribution often degrades performance under distribution shifts. Current approaches to robust fine-tuning use hand-crafted regularization techniques to constrain the fine-tuning process towards the pretrained model. Yet, it is hard to specify how to adapt relevant characteristics of the foundation model during fine-tuning, as this depends on how the pre-training, fine-tuning, and test data distributions relate to each other. We propose AutoFT, a data-driven approach for robust fine-tuning. Given a task, AutoFT searches for a fine-tuning procedure that enhances out-of-distribution (OOD) generalization. Specifically, AutoFT uses bi-level optimization to search for an objective function and hyperparameters that maximize post-adaptation performance on a small OOD validation set. We evaluate AutoFT on nine natural distribution shifts. Our experiments show that AutoFT significantly improves generalization to OOD inputs, outperforming existing robust fine-tuning methods. Notably, AutoFT achieves a new state-of-the-art on the WILDS iWildCam and FMoW benchmarks, outperforming the previous best methods by $6.0\%$ and $1.5\%$, respectively.
Deep Back-Filling: a Split Window Technique for Deep Online Cluster Job Scheduling

Job scheduling is a critical component of workload management systems that can significantly influence system performance, e.g., in HPC clusters. The scheduling objectives are often mixed, such as maximizing resource utilization and minimizing job waiting time. An increasing number of researchers are moving from heuristic-based approaches to Deep Reinforcement Learning approaches in order to optimize scheduling objectives. However, the job scheduler's state space is partially observable to a DRL-based agent because the job queue is practically unbounded. The agent's observation of the state space is constant in size since the input size of the neural networks is predefined. All existing solutions to this problem intuitively allow the agent to observe a fixed window size of jobs at the head of the job queue. In our research, we have seen that such an approach can lead to “window staleness” where the window becomes full of jobs that can not be scheduled until the cluster has completed sufficient work. In this paper, we propose a novel general technique that we call split window, which allows the agent to observe both the head and tail of the queue. With this technique, the agent can observe all arriving jobs at least once, which completely eliminates the window staleness problem. By leveraging the split window, the agent can significantly reduce the average job waiting time and average queue length, alternatively allowing the use of much smaller windows and, therefore, faster training times. We show a range of simulation results using HPC job scheduling trace data that supports the effectiveness of our technique.