Miracle-dz/UniFAR-FTUs huggingface dataset:
Near-Horizon Collisions around Near-Extremal Black Holes

Black holes have sometimes been described as astrophysical particle accelerators because finite energy particles can collide near the horizon with divergent center-of-mass (CM) energy. The collisions are classified by the radial motion of the constituent particles at the moment of collision, with each class exhibiting a distinct near-horizon behavior. Divergence in the CM energy is sourced by the difference in the rate at which the collision radius approaches the horizon and the rate at which a particle's angular momentum is tuned to a critical value set by the superradiant bound. To produce a high energy collision around a near-extremal BH, at least one particle must approach criticality slower than the collision radius approaches the horizon. When both particles are ingoing or outgoing, it is additionally required that the particles approach criticality at different rates. Using a novel multi-scaling limit, we calculate the explicit form for the divergent CM energy.
Hysteresis and chaos in anomalous Josephson junctions without capacitance

Usually, overdamped Josephson junctions do not exhibit chaotic behaviour in their phase dynamics, either because the phase space dimension is less than three (as in the case of a single overdamped ac-driven junction) or due to the general tendency of systems to become less chaotic with increasing dissipation (as in the case of coupled overdamped junctions). Here we consider the so-called $\varphi_0$ S/F/S Josephson junction in which the current flowing through the junction may induce magnetization dynamics in the ferromagnetic interlayer, between the two superconducting layers. We find that, due to the induced magnetization dynamics, even in the overdamped limit, i.e. for a junction without capacitance, the junction may exhibit chaos and hysteresis that in some cases leads to multiple branches in its current-voltage characteristics. We also show that pulsed current signals can result in switching between different voltage states, a result which is potentially useful for superconducting memory applications.
Loss Modeling for Multi-Annotator Datasets

Accounting for the opinions of all annotators of a dataset is critical for fairness. However, when annotating large datasets, individual annotators will frequently provide thousands of ratings which can lead to fatigue. Additionally, these annotation processes can occur over multiple days which can lead to an inaccurate representation of an annotator's opinion over time. To combat this, we propose to learn a more accurate representation of diverse opinions by utilizing multitask learning in conjunction with loss-based label correction. We show that using our novel formulation, we can cleanly separate agreeing and disagreeing annotations. Furthermore, we demonstrate that this modification can improve prediction performance in a single or multi-annotator setting. Lastly, we show that this method remains robust to additional label noise that is applied to subjective data.
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization

Decentralized and asynchronous communications are two popular techniques to speedup communication complexity of distributed machine learning, by respectively removing the dependency over a central orchestrator and the need for synchronization. Yet, combining these two techniques together still remains a challenge. In this paper, we take a step in this direction and introduce Asynchronous SGD on Graphs (AGRAF SGD) -- a general algorithmic framework that covers asynchronous versions of many popular algorithms including SGD, Decentralized SGD, Local SGD, FedBuff, thanks to its relaxed communication and computation assumptions. We provide rates of convergence under much milder assumptions than previous decentralized asynchronous works, while still recovering or even improving over the best know results for all the algorithms covered.
Statistical Network Analysis: Past, Present, and Future

This article provides a brief overview of statistical network analysis, a rapidly evolving field of statistics, which encompasses statistical models, algorithms, and inferential methods for analyzing data in the form of networks. Particular emphasis is given to connecting the historical developments in network science to today's statistical network analysis, and outlining important new areas for future research. This invited article is intended as a book chapter for the volume"Frontiers of Statistics and Data Science"edited by Subhashis Ghoshal and Anindya Roy for the International Indian Statistical Association Series on Statistics and Data Science, published by Springer. This review article covers the material from the short course titled"Statistical Network Analysis: Past, Present, and Future"taught by the author at the Annual Conference of the International Indian Statistical Association, June 6-10, 2023, at Golden, Colorado.
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis

Understanding in-context learning (ICL) capability that enables large language models (LLMs) to excel in proficiency through demonstration examples is of utmost importance. This importance stems not only from the better utilization of this capability across various tasks, but also from the proactive identification and mitigation of potential risks, including concerns regarding truthfulness, bias, and toxicity, that may arise alongside the capability. In this paper, we present a thorough survey on the interpretation and analysis of in-context learning. First, we provide a concise introduction to the background and definition of in-context learning. Then, we give an overview of advancements from two perspectives: 1) a theoretical perspective, emphasizing studies on mechanistic interpretability and delving into the mathematical foundations behind ICL; and 2) an empirical perspective, concerning studies that empirically analyze factors associated with ICL. We conclude by highlighting the challenges encountered and suggesting potential avenues for future research. We believe that our work establishes the basis for further exploration into the interpretation of in-context learning. Additionally, we have created a repository containing the resources referenced in our survey.
Weak-lensing Analysis of the Complex Cluster Merger A746 with Subaru/Hyper Suprime-Cam

The galaxy cluster A746 (z = 0.214), featuring a double radio relic system, two isolated radio relics, a possible radio halo, disturbed V-shaped X-ray emission, and intricate galaxy distributions, is a unique and complex merging system. We present a weak-lensing analysis of A746 based on wide-field imaging data from Subaru/Hyper Suprime-Cam observations. The mass distribution is characterized by a main peak, which coincides with the center of the X-ray emission. At this main peak, we detect two extensions toward the north and west tracing the cluster galaxy and X-ray distributions. Despite the ongoing merger, our estimate of the A746 global mass M 500 = 4.4 ± 1.0 × 1014 M ⊙ is consistent with the previous results from Sunyaev-Zel'dovich and X-ray observations. We conclude that reconciling the distributions of mass, galaxies, and intracluster medium with the double radio relic system and other radio features remains challenging.
On the Gaussian product inequality conjecture for disjoint principal minors of Wishart random matrices

This paper extends various results related to the Gaussian product inequality (GPI) conjecture to the setting of disjoint principal minors of Wishart random matrices. This includes product-type inequalities for matrix-variate analogs of completely monotone functions and Bernstein functions of Wishart disjoint principal minors, respectively. In particular, the product-type inequalities apply to inverse determinant powers. Quantitative versions of the inequalities are also obtained when there is a mix of positive and negative exponents. Furthermore, an extended form of the GPI is shown to hold for the eigenvalues of Wishart random matrices by virtue of their law being multivariate totally positive of order~2 ($\mathrm{MTP}_2$). A new, unexplored avenue of research is presented to study the GPI from the point of view of elliptical distributions.
Dynamical characterization of $Z_{2}$ Floquet topological phases via quantum quenches

The complete characterization of a generic $d$-dimensional Floquet topological phase is usually hard for the requirement of information about the micromotion throughout the entire driving period. In a recent work [L. Zhang et al., Phys. Rev. Lett. 125, 183001 (2020)], an experimentally feasible dynamical detection scheme was proposed to characterize the integer Floquet topological phases using quantum quenches. However, this theory is still far away from completion, especially for free-fermion Floquet topological phases, where the states can also be characterized by $Z_{2}$ invariants. Here we develop the first full and unified dynamical characterization theory for the $Z_{2}$ Floquet topological phases of different dimensionality and tenfold-way symmetry classes by quenching the system from a trivial and static initial state to the Floquet topological regime through suddenly changing the parameters and turning on the periodic driving. By measuring the minimal information of Floquet bands via the stroboscopic time-averaged spin polarizations, we show that the topological spin texture patterns emerging on certain discrete momenta of Brillouin zone called the $0$ or $\pi$ gap highest-order band-inversion surfaces provide a measurable dynamical $Z_{2}$ Floquet invariant, which uniquely determines the Floquet boundary modes in the corresponding quasienergy gap and characterizes the $Z_{2}$ Floquet topology. The applications of our theory are illustrated via one- and two-dimensional models that are accessible in current quantum simulation experiments. Our work provides a highly feasible way to detect the $Z_{2}$ Floquet topology and completes the dynamical characterization for the full tenfold classes of Floquet topological phases, which shall advance the research in theory and experiments.
Constructing the Fulde-Ferrell-Larkin-Ovchinnikov state in antiferromagnetic insulator CrOCl

Time reversal symmetry breaking in superconductors, resulting from external magnetic fields or spontaneous magnetization, often leads to unconventional superconducting properties. In this way, a conventional Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state, characterized by the Cooper pairs with nonzero total momentum, may be realized by the Zeeman effect caused from external magnetic fields. Here, we report the observation of superconductivity in a few-layer antiferromagnetic insulator CrOCl by utilizing superconducting proximity effect with NbSe2 flakes. The superconductivity demonstrates a considerably weak gap of about 0.12 meV and the in-plane upper critical field reveals as behavior of the FFLO state at low temperature. Our first-principles calculations indicate that the proximitized superconductivity may exist in the CrOCl layer with Cr vacancies or line-defects. Moreover, the FFLO state could be induced by the inherent larger spin splitting in the CrOCl layer. Our findings not only demonstrate the fascinating interaction between superconductivity and magnetism, but also provide a possible path to construct FFLO state by intrinsic time reversal symmetry breaking and superconducting proximity effect.
A Unified Framework for the Error Analysis of Physics-Informed Neural Networks

We prove a priori and a posteriori error estimates for physics-informed neural networks (PINNs) for linear PDEs. We analyze elliptic equations in primal and mixed form, elasticity, parabolic, hyperbolic and Stokes equations; and a PDE constrained optimization problem. For the analysis, we propose an abstract framework in the common language of bilinear forms, and we show that coercivity and continuity lead to error estimates. The obtained estimates are sharp and reveal that the $L^2$ penalty approach for initial and boundary conditions in the PINN formulation weakens the norm of the error decay. Finally, utilizing recent advances in PINN optimization, we present numerical examples that illustrate the ability of the method to achieve accurate solutions.
Catchment transit times and landscape controls—does scale matter?

Mean transit times (MTTs) can give useful insights into the internal processes of hydrological systems. However, our understanding of how they vary and scale remains unclear. We used MTT estimates obtained from δ18O data from 20, mostly nested, contrasting catchments in North East Scotland, ranging from 1 to 1700 km2. The estimated MTTs ranged between 270 and 1170 days and were used to test a previously developed multiple linear regression (MLR) model for MTT prediction based on metrics of soil cover, landscape organization and climate. We show that the controls on MTT identified by the MLR model hold with the independent data from these 20 sites and that the MLR can be used to predict MTT in ungauged montane catchments. The dominant controls also remain unchanged over four orders of magnitude of catchment size, suggesting no major change of dominant flow paths and mixing processes at larger scales. This is consistent with the fact that only the variance of MTT, rather than MTT, showed a scaling relationship. MTTs appeared to converge with increasing catchment scale, apparently due to the integration of heterogeneous headwater responses in larger downstream catchments. Copyright © 2009 John Wiley & Sons, Ltd.
Multi-task Representation Learning for Pure Exploration in Bilinear Bandits

We study multi-task representation learning for the problem of pure exploration in bilinear bandits. In bilinear bandits, an action takes the form of a pair of arms from two different entity types and the reward is a bilinear function of the known feature vectors of the arms. In the \textit{multi-task bilinear bandit problem}, we aim to find optimal actions for multiple tasks that share a common low-dimensional linear representation. The objective is to leverage this characteristic to expedite the process of identifying the best pair of arms for all tasks. We propose the algorithm GOBLIN that uses an experimental design approach to optimize sample allocations for learning the global representation as well as minimize the number of samples needed to identify the optimal pair of arms in individual tasks. To the best of our knowledge, this is the first study to give sample complexity analysis for pure exploration in bilinear bandits with shared representation. Our results demonstrate that by learning the shared representation across tasks, we achieve significantly improved sample complexity compared to the traditional approach of solving tasks independently.
Observing periodic gap variations in cuprates

Central to the enigma of the cuprates is ubiquitous electronic inhomogeneity arising from a variety of electronic orders that coexist with superconductivity, the individual signatures of which have been impossible to disentangle despite four decades of intense research. This strong nanoscale inhomogeneity complicates interpretation of measurements both by probes which average over this inhomogeneity and those, like scanning tunneling microscopy (STM), which should be able to spatially resolve variations driven by both order and inhomogeneity. Here, we develop a novel technique that directly acknowledges this electronic inhomogeneity and extracts statistically significant features from scanning tunneling spectroscopic data. Applying our novel technique to single and bilayer Bi-based cuprates spanning a large doping range, we peer through local inhomogeneities and find that the gap breaks translational and rotational symmetries and varies periodically in a four-fold pattern. Our direct observation of a symmetry breaking gap in the single particle tunneling spectra adds strong credence to the pair density wave hypotheses supposed to exist in these materials. We also discuss various implications of our observations and, in particular, how they can explain the origin of the low energy checkerboard pattern.
Heavy Baryon Dark Matter from $SU(N)$ Confinement: Bubble Wall Velocity and Boundary Effects

Confinement in $SU(N_{\rm DC})$ Yang-Mills theories is known to proceed through first-order phase transition. The wall velocity is bounded by $v_w \lesssim 10^{-6}$ due to the needed time for the substantial latent heat released during the phase transition to dissipate through Hubble expansion. Quarks much heavier than the confinement scale can be introduced without changing the confinement dynamics. After they freeze-out, heavy quarks are squeezed into pockets of the deconfined phase until they completely annihilate with anti-quarks. We calculate the dark baryon abundance surviving annihilation, due to bound-state formation occurring both in the bulk and - for the first time - at the boundary. We find that dark baryons can be dark matter with a mass up to $10^3~\rm TeV$. We study indirect and direct detection, CMB and BBN probes, assuming portals to Higgs and neutrinos.
Enhancing Traffic Object Detection in Variable Illumination with RGB-Event Fusion

Traffic object detection under variable illumination is challenging due to the information loss caused by the limited dynamic range of conventional frame-based cameras. To address this issue, we introduce bio-inspired event cameras and propose a novel Structure-aware Fusion Network (SFNet) that extracts sharp and complete object structures from the event stream to compensate for the lost information in images through cross-modality fusion, enabling the network to obtain illumination-robust representations for traffic object detection. Specifically, to mitigate the sparsity or blurriness issues arising from diverse motion states of traffic objects in fixed-interval event sampling methods, we propose the Reliable Structure Generation Network (RSGNet) to generate Speed Invariant Frames (SIF), ensuring the integrity and sharpness of object structures. Next, we design a novel Adaptive Feature Complement Module (AFCM) which guides the adaptive fusion of two modality features to compensate for the information loss in the images by perceiving the global lightness distribution of the images, thereby generating illumination-robust representations. Finally, considering the lack of large-scale and high-quality annotations in the existing event-based object detection datasets, we build a DSEC-Det dataset, which consists of 53 sequences with 63,931 images and more than 208,000 labels for 8 classes. Extensive experimental results demonstrate that our proposed SFNet can overcome the perceptual boundaries of conventional cameras and outperform the frame-based method by 8.0% in mAP50 and 5.9% in mAP50:95. Our code and dataset will be available at https://github.com/YN-Yang/SFNet.
Parameterized covering in semi-ladder-free hypergraphs

In this article, we study the parameterized complexity of the Set Cover problem restricted to d-semi-ladder-free hypergraphs, a class defined by Fabianski et al. [Proceedings of STACS 2019]. We observe that two algorithms introduced by Langerman and Morin [Discrete \&Computational Geometry 2005] in the context of geometric covering problems can be adapted to this setting, yielding simple FPT and kernelization algorithms for Set Cover in d-semi-ladder-free hypergraphs. We complement our algorithmic results with a compression lower bound for the problem, that proves the tightness of our kernelization under standard complexity-theoretic assumptions.
Near-IR Spectral Observations of the Didymos System: Daily Evolution Before and After the DART Impact Indicates that Dimorphos Originated from Didymos

Ejecta from Dimorphos following the DART mission impact significantly increased the brightness of the Didymos–Dimorphos system, allowing us to examine subsurface material. We report daily near-IR spectroscopic observations of the Didymos system using NASA’s IRTF that follow the evolution of the spectral signature of the ejecta cloud over 1 week, from 1 day before the impact. Overall, the spectral features remained fixed (S-type classification) while the ejecta dissipated, confirming that both Didymos and Dimorphos are constructed from the same silicate material. This novel result strongly supports binary asteroid formation models that include the breaking up of a single body due to rotational breakup of kilometer-wide bodies. At impact time +14 and +38 hr, the spectral slope decreased, but the following nights presented an increasing spectral slope that almost returned to the preimpact slope. However, the parameters of the 1 μm band remained fixed, and no “fresh”/Q-type-like spectrum was measured. We interpret this as follows. (1) The ejecta cloud is the main contributor (60%–70%) to the overall light during the ∼40 hr after impact. (2) Coarser debris (≥100 μm) dominated the ejecta cloud, decreasing the spectral slope (after radiation pressure removed the fine grains ≤10 hr after impact). (3) After approximately 1 week, the ejecta cloud dispersed enough to make the fine grains on Didymos’s surface the dominant part of the light, increasing the spectral slope to the preimpact level. (4) A negligible amount of nonweathered material was ejected from Dimorphos’s subsurface, suggesting that Dimorphos was accumulated from weathered material ejected from Didymos’s surface.
Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection

Few-shot object detection, which focuses on detecting novel objects with few labels, is an emerging challenge in the community. Recent studies show that adapting a pre-trained model or modified loss function can improve performance. In this paper, we explore leveraging the power of Contrastive Language-Image Pre-training (CLIP) and hard negative classification loss in low data setting. Specifically, we propose Re-scoring using Image-language Similarity for Few-shot object detection (RISF) which extends Faster R-CNN by introducing Calibration Module using CLIP (CM-CLIP) and Background Negative Re-scale Loss (BNRL). The former adapts CLIP, which performs zero-shot classification, to re-score the classification scores of a detector using image-class similarities, the latter is modified classification loss considering the punishment for fake backgrounds as well as confusing categories on a generalized few-shot object detection dataset. Extensive experiments on MS-COCO and PASCAL VOC show that the proposed RISF substantially outperforms the state-of-the-art approaches. The code will be available.
Evaluation of the Influence of Geographical Location on Phytochemical Composition of Moringa oleifera Seeds

: Plants contain phytochemicals that are non-nutritive but have anti-oxidant activity, which protects the body cells from oxidative damage. An example is the seeds of Moringa oleifera which contain phytochemicals of great medicinal importance. Seed samples of Moringa oleifera were collected from three senatorial zones of Ebonyi State (Ebonyi North, Ebonyi Central and Ebonyi South Senatorial Zones) and from each senatorial zone, three local governments were chosen to serve as replicates. The seeds were sun-dried and pulverized to obtain 5.0 g powder which was defatted using 100 mL of petroleum ether for 24 hours. The extracts were quantitatively and qualitatively analyzed for flavonoid, alkaloid, saponin, tannin and glycoside. The soil analysis was also carried out on soil samples collected from each study site. Significant level was tested at p < 0.05 in which no significant difference was observed on seed’s phytochemical parameters analysed across the zones but on soil quality tested, there were significant difference on % clay, % silt and % coarse sand. The result showed that the phytochemical constitution of Moringa oleifera seeds from the senatorial zones varied in presence and concentration across the senatorial zones. Ebonyi South Senatorial Zone had best soil features and seeds from the Zone contained the phytochemicals studied at averagely high concentrations, which suggests that enhanced soil quality enhances availability of phytochemicals and their concentrations