Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Functional and Dysfunctional Perceived Threat of Hate Crimes Among Persons With and Without Disability

This study examined the relationship between disability status and perceived threat of hate crimes. Building on existing conceptual frameworks, first we differentiated between dysfunctional perceived threat that damages quality of life and functional perceived threat that has the capacity to be motivational and precautionary. We then examined how disability status predicts individuals’ threat memberships across dysfunctional and functional perceived threats of hate crimes. Results—based on a survey of 1,824 adults recruited from Amazon’s Mechanical Turk—indicate that persons with a disability are more likely than those without a disability to exhibit dysfunctional perceived threat (vs. functional or no perceived threat) of hate crime across different bias motivations. This relationship was evident even when accounting for those most at risk for each type of hate crime (e.g., persons of color for anti-race/ethnicity hate crime). Further, persons with cognitive and physical disabilities were associated with higher odds of dysfunctional perceived threat of all types of hate crimes when compared to persons without a disability. Overall, the present study highlights that persons with a disability may experience exacerbated consequences of subjective threat of hate crimes. Findings also suggest the importance of an intersectional approach to hate crime by considering how disability may intersect with other forms of oppression in relation to perceived threat of hate crime. Implications and future directions, especially as they relate to measurement, are discussed.
Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification

Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging task, suffering from two limitations of inferior identity-relevant features and limited training samples. Existing methods mainly leverage auxiliary information to facilitate discriminative feature learning, including soft-biometrics features of shapes and gaits, and additional labels of clothing. However, these information may be unavailable in real-world applications. In this paper, we propose a novel FIne-grained Representation and Recomposition (FIRe$^{2}$) framework to tackle both limitations without any auxiliary information. Specifically, we first design a Fine-grained Feature Mining (FFM) module to separately cluster images of each person. Images with similar so-called fine-grained attributes (e.g., clothes and viewpoints) are encouraged to cluster together. An attribute-aware classification loss is introduced to perform fine-grained learning based on cluster labels, which are not shared among different people, promoting the model to learn identity-relevant features. Furthermore, by taking full advantage of the clustered fine-grained attributes, we present a Fine-grained Attribute Recomposition (FAR) module to recompose image features with different attributes in the latent space. It can significantly enhance representations for robust feature learning. Extensive experiments demonstrate that FIRe$^{2}$ can achieve state-of-the-art performance on five widely-used cloth-changing person Re-ID benchmarks.
Experimental Investigations of Wave Runup Downstream of Nappe Impact: Applications to Flood Wave Resulting from Dam Overtopping and Tsunami Wave Runup

Flood waves resulting from dam overtopping have been responsible for numerous losses of life (e.g. Vajont dam catastrophe). A related form of flooding is the wave runup generated by a tsunami reaching the shoreline. When dam overtopping or tsunami wave breaking occur, the process is often associated with a free-falling nappe and plunging jet, some energy dissipation at nappe impact and the downstream propagation of the surge. The dominant characteristic of the advancing bore is its high initial momentum resulting from the free-falling nappe or the breaker plunging jet. The wave front usually travels faster than a 'classical' dam break wave because of the higher momentum of the wave.New experiments were performed in a large-size facility (15-m long 0.8-m wide channel). The experimental data highlight the large wave celerity during the initial stage (i.e. x/do 30), the bore propagates at a speed similar to that of a 'classical' dam break wave. New correlations were presented to estimate roughly the wave celerity for x/do < 20 to 25. Altogether the results show a reduced warning time and a greater risk for significant damage and losses of life. The same results are later applied to dam overtopping cases and to tsunami runup applications. It is believed that the proposed correlations provide a more accurate estimate of the horizontal runup close to the nappe impact : i.e., x/do < 30 or x/(H1+h) < 10.
Engaged and Affective Virtual Agents: Their Impact on Social Presence, Trustworthiness, and Decision-Making in the Group Discussion

This study investigates how different virtual agent (VA) behaviors influence subjects' perceptions and group decision-making. Participants carried out experimental group discussions with a VA exhibiting varying levels of engagement and affective behavior. Engagement refers to the VA's focus on the group task, whereas affective behavior reflects the VA's emotional state. The findings revealed that VA's engagements effectively captured participants' attention even in the group setting and enhanced group synergy, thereby facilitating more in-depth discussion and producing better consensus. On the other hand, VA's affective behavior negatively affected the perceived social presence and trustworthiness. Consequently, in the context of group discussion, participants preferred the engaged and non-affective VA to the non-engaged and affective VA. The study provides valuable insights for improving the VA's behavioral design as a team member for collaborative tasks.
Towards Grounded Visual Spatial Reasoning in Multi-Modal Vision Language Models

Large vision-and-language models (VLMs) trained to match images with text on large-scale datasets of image-text pairs have shown impressive generalization ability on several vision and language tasks. Several recent works, however, showed that these models lack fine-grained understanding, such as the ability to count and recognize verbs, attributes, or relationships. The focus of this work is to study the understanding of spatial relations. This has been tackled previously using image-text matching (e.g., Visual Spatial Reasoning benchmark) or visual question answering (e.g., GQA or VQAv2), both showing poor performance and a large gap compared to human performance. In this work, we show qualitatively (using explainability tools) and quantitatively (using object detectors) that the poor object localization"grounding"ability of the models is a contributing factor to the poor image-text matching performance. We propose an alternative fine-grained, compositional approach for recognizing and ranking spatial clauses that combines the evidence from grounding noun phrases corresponding to objects and their locations to compute the final rank of the spatial clause. We demonstrate the approach on representative VLMs (such as LXMERT, GPV, and MDETR) and compare and highlight their abilities to reason about spatial relationships.
Directed percolation criticality in eternal inflation

False-vacuum eternal inflation can be described as a random walk on the network of vacua of the string landscape. In this paper we show that the problem can be mapped naturally to a problem of directed percolation. The mapping relies on two general and well-justified approximations for transition rates: 1. ~the downward approximation, which neglects ``upward"transitions, as these are generally exponentially suppressed; 2. the dominant decay channel approximation, which capitalizes on the fact that tunneling rates are exponentially staggered. Lacking detailed knowledge of the string landscape, we model the network of vacua as random graphs with arbitrary degree distribution, including Erd\"os-R\'enyi and scale-free graphs. As a complementary approach, we also model regions of the landscape as regular lattices, specifically Bethe lattices. We find that the uniform-in-time probabilities proposed in our previous work favor regions of the landscape poised at the directed percolation phase transition. This raises the tantalizing prospect of deriving universal statistical distributions for physical observables, characterized by critical exponents that are insensitive to the details of the underlying landscape. We illustrate this with the cosmological constant, and show that the resulting distribution peaks as a power-law for small positive vacuum energy, with a critical exponent uniquely determined by the random graph universality class.
Unveiling the Collaborative Patterns of Artificial Intelligence Applications in Human Resource Management: A Social Network Analysis Approach

The integration of artificial intelligence (AI) into human resource management (HRM) strategies has become increasingly common due to technological advancements. This has spurred a new field of research focused on evaluating the impact of AI adoption on business and individual outcomes, as well as how to evaluate AI-enabled HRM practices. However, there is limited cross-disciplinary research in this area, causing a fragmented body of knowledge. To address this issue, social network analysis has been recognized as a tool for analyzing and researching large-scale social phenomena in HRM. The study of scientific co-authorship networks is one application of social network analysis that can help identify the main components and trends in this field. Using social network analysis indicators, the current study examined the AI&HRM co-authorship network, which consists of 43,789 members and 81,891 scientific collaborations. The study analyzed articles related to AI&HRM published between 2000 and 2023 extracted from the WOS citation database. Through centrality measures, the most important members of the"AI&HRM"co-authorship network were identified using the TOPSIS method, which identified twenty prominent researchers in this field. The study also examined the keywords"AI&HRM"and the scientific cooperation network of nations, universities, and communities. Overall, this study highlights the importance of cross-disciplinary research and social network analysis in understanding the implications of AI adoption in HRM.
Reflection-driven turbulence in the super-Alfv\'enic solar wind

In magnetized, stratified astrophysical environments such as the Sun's corona and solar wind, Alfv\'enic fluctuations ''reflect'' from background gradients, enabling nonlinear interactions and thus dissipation of their energy into heat. This process, termed ''reflection-driven turbulence,'' is thought to play a crucial role in coronal heating and solar-wind acceleration, explaining a range of observational correlations and constraints. Building on previous works focused on the inner heliosphere, here we study the basic physics of reflection-driven turbulence using reduced magnetohydrodynamics in an expanding box -- the simplest model that can capture the local turbulent plasma dynamics in the super-Alfv\'enic solar wind. Although idealized, our high-resolution simulations and simple theory reveal a rich phenomenology that is consistent with a diverse range of observations. Outwards-propagating fluctuations, which initially have high imbalance, decay nonlinearly to heat the plasma, becoming more balanced and magnetically dominated. Despite the high imbalance, the turbulence is strong because Els\"asser collisions are suppressed by reflection, leading to ''anomalous coherence'' between the two Els\"asser fields. This coherence, together with linear effects, causes the turbulence to anomalously grow the ''anastrophy'' (squared magnetic potential) as it decays, forcing the energy to rush to larger scales and forming a ''$1/f$-range'' energy spectrum as it does so. At late times, the expansion overcomes the nonlinear and Alfv\'enic physics, forming isolated, magnetically dominated ''Alfv\'en vortex'' structures that minimize their nonlinear dissipation. These results can plausibly explain the observed radial and wind-speed dependence of turbulence imbalance, residual energy, plasma heating, and fluctuation spectra, as well as making testable predictions for future observations.
WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning

Watermarking serves as a widely adopted approach to safeguard media copyright. In parallel, the research focus has extended to watermark removal techniques, offering an adversarial means to enhance watermark robustness and foster advancements in the watermarking field. Existing watermark removal methods mainly rely on UNet with task-specific decoder branches--one for watermark localization and the other for background image restoration. However, watermark localization and background restoration are not isolated tasks; precise watermark localization inherently implies regions necessitating restoration, and the background restoration process contributes to more accurate watermark localization. To holistically integrate information from both branches, we introduce an implicit joint learning paradigm. This empowers the network to autonomously navigate the flow of information between implicit branches through a gate mechanism. Furthermore, we employ cross-channel attention to facilitate local detail restoration and holistic structural comprehension, while harnessing nested structures to integrate multi-scale information. Extensive experiments are conducted on various challenging benchmarks to validate the effectiveness of our proposed method. The results demonstrate our approach's remarkable superiority, surpassing existing state-of-the-art methods by a large margin.
LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds

Online multi-object tracking (MOT) plays a pivotal role in autonomous systems. The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role. This paper proposes a learning and graph-optimized (LEGO) modular tracker to improve data association performance in the existing literature. The proposed LEGO tracker integrates graph optimization and self-attention mechanisms, which efficiently formulate the association score map, facilitating the accurate and efficient matching of objects across time frames. To further enhance the state update process, the Kalman filter is added to ensure consistent tracking by incorporating temporal coherence in the object states. Our proposed method utilizing LiDAR alone has shown exceptional performance compared to other online tracking approaches, including LiDAR-based and LiDAR-camera fusion-based methods. LEGO ranked 1st at the time of submitting results to KITTI object tracking evaluation ranking board and remains 2nd at the time of submitting this paper, among all online trackers in the KITTI MOT benchmark for cars1
Monitoring a developing pandemic with available data (preprint)

This paper addresses pandemic statistics from a management perspective. Both input and output are easy to understand. Focus is on operations and cross border communication. To be able to work with simple available data some new missing data issues have to be solved from a mathematical statistical point of view. We illustrate our approach with data from France collected during the recent Covid-19 pandemic. Our new benchmark method also introduces a potential new division of labour while working with pandemic statistics allowing crucial input to be fed to the model via prior knowledge from external experts.
Derived $p$-adic heights and the leading coefficient of the Bertolini--Darmon--Prasanna $p$-adic $L$-function

Let $E/\mathbf{Q}$ be an elliptic curve and let $p$ be an odd prime of good reduction for $E$. Let $K$ be an imaginary quadratic field satisfying the classical Heegner hypothesis and in which $p$ splits. In a previous work, Agboola--Castella formulated an analogue of the Birch--Swinnerton-Dyer conjecture for the $p$-adic $L$-function $L_{\mathfrak{p}}^{\rm BDP}$ of Bertolini--Darmon--Prasanna attached to $E/K$, assuming the prime $p$ to be ordinary for $E$. The goal of this paper is two-fold: (1) We formulate a $p$-adic BSD conjecture for $L_{\mathfrak{p}}^{\rm BDP}$ for all odd primes $p$ of good reduction. (2) For an algebraic analogue $F_{\overline{\mathfrak{p}}}^{\rm BDP}$ of $L_{\mathfrak{p}}^{\rm BDP}$, we show that the ``leading coefficient'' part of our conjecture holds, and that the ``order of vanishing'' part follows from the expected ``maximal non-degeneracy'' of an anticyclotomic $p$-adic height. In particular, when the Iwasawa--Greenberg Main Conjecture $(F_{\overline{\mathfrak{p}}}^{\rm BDP})=(L_{\mathfrak{p}}^{\rm BDP})$ is known, our results determine the leading coefficient of $L_{\mathfrak{p}}^{\rm BDP}$ at $T=0$ up to a $p$-adic unit. Moreover, by adapting the approach of Burungale--Castella--Kim in the $p$-ordinary case, we prove the main conjecture for supersingular primes $p$ under mild hypotheses.
On the Inconsistency of Cluster-Robust Inference and How Subsampling Can Fix It

Conventional methods of cluster-robust inference are inconsistent in the presence of unignorably large clusters. We formalize this claim by establishing a necessary and sufficient condition for the consistency of the conventional methods. We find that this condition for the consistency is rejected for a majority of empirical research papers. In this light, we propose a novel score subsampling method that achieves uniform size control over a broad class of data generating processes, covering that fails the conventional method. Simulation studies support these claims. With real data used by an empirical paper, we showcase that the conventional methods conclude significance while our proposed method concludes insignificance.
Quasi-finite modules over extended affine Lie algebras

In this paper, we consider irreducible quasi-finite (or equivalently weakly integrable) modules, with non-trivial action of the core, over the extended affine Lie algebras (EALAs) whose centerless cores are multiloop algebras. The centerless cores of all but one family of EALAs having nullity greater than 1 are known to admit such multiloop realizations. For any such (untwisted) EALA, we show that the irreducible quasi-finite modules are either integrable with the center of the underlying core acting trivially, or restricted generalized highest weight (GHW) modules. We further prove that in the nullity 2 case, these irreducible restricted GHW modules turn out to be highest weight type modules, thereby classifying the irreducible quasi-finite modules over all such EALAs. In particular, we obtain the classification of irreducible quasi-finite modules over toroidal Lie algebras, minimal EALAs and toroidal EALAs of nullity 2. Along the way, we completely classify the irreducible weakly integrable modules over affine Kac-Moody algebras (studied by Rao-Futorny [Trans. Amer. Math. Soc. 2009] for non-zero level modules). Our results generalize the well-known work of Chari [Invent. Math. 1986] and Chari-Pressley [Math. Ann. 1986] concerning the classification of irreducible integrable modules over (nullity 1) affine Kac--Moody algebras.
Simple Cycle Reservoirs are Universal

Reservoir computation models form a subclass of recurrent neural networks with fixed non-trainable input and dynamic coupling weights. Only the static readout from the state space (reservoir) is trainable, thus avoiding the known problems with propagation of gradient information backwards through time. Reservoir models have been successfully applied in a variety of tasks and were shown to be universal approximators of time-invariant fading memory dynamic filters under various settings. Simple cycle reservoirs (SCR) have been suggested as severely restricted reservoir architecture, with equal weight ring connectivity of the reservoir units and input-to-reservoir weights of binary nature with the same absolute value. Such architectures are well suited for hardware implementations without performance degradation in many practical tasks. In this contribution, we rigorously study the expressive power of SCR in the complex domain and show that they are capable of universal approximation of any unrestricted linear reservoir system (with continuous readout) and hence any time-invariant fading memory filter over uniformly bounded input streams.
On the radius of self-repellent fractional Brownian motion

We study the radius $R_T$ of a self-repellent fractional Brownian motion $\left\{B^H_t\right\}_{0\le t\le T}$ taking values in $\mathbb{R}^d$. Our sharpest result is for $d=1$, where we find that with high probability, \begin{equation*} R_T \asymp T^\nu, \quad \text{with $\nu=\frac{2}{3}\left(1+H\right)$. } \end{equation*} For $d>1$, we provide upper and lower bounds for the exponent $\nu$, but these bounds do not match.
VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control

As the model size of pre-trained language models (PLMs) grows rapidly, full fine-tuning becomes prohibitively expensive for model training and storage. In vision-and-language (VL), parameter-efficient tuning (PET) techniques are proposed to integrate modular modifications (e.g., Adapter and LoRA) into encoder-decoder PLMs. By tuning a small set of trainable parameters, these techniques perform on par with full fine-tuning. However, excessive modular modifications and neglecting the functionality gap between the encoders and decoders can lead to performance degradation, while existing PET techniques (e.g., VL-Adapter) overlook these critical issues. In this paper, we propose a Vision-and-Language Parameter-Efficient Tuning (VL-PET) framework to impose effective control over modular modifications via a novel granularity-controlled mechanism. Considering different granularity-controlled matrices generated by this mechanism, a variety of model-agnostic VL-PET modules can be instantiated from our framework for better efficiency and effective-ness trade-offs. We further propose lightweight PET module designs to enhance VL alignment and modeling for the encoders and maintain text generation for the decoders. Extensive experiments conducted on four image-text tasks and four video-text tasks demonstrate the efficiency, effectiveness and transferability of our VL-PET framework. In particular, our VL-PETlarge with lightweight PET module designs significantly outperforms VL-Adapter by 2.92% (3.41%) and LoRA by 3.37% (7.03%) with BART-base (T5-base) on image-text tasks. Furthermore, we validate the enhanced effect of employing our VL-PET designs on existing PET techniques, enabling them to achieve significant performance improvements. Our code is available at https://github.com/HenryHZY/VL-PET.