arXiv subjects

Chenxu Hao

Publications and source records attributed to Chenxu Hao.

7 recordsLinked to original sources

arXiv2026

COSI-Lab: Conference Living Lab for Modeling Multi-Perspective Multimodal Social Intention

COSI-Lab presents a multimodal, multi-sensor dataset of an interdisciplinary scientific workshop containing 32 academics at an international conference. It captures ecologically valid social interactions in a weakly scripted setting consisting of two 30-minute mingling sessions with real professional and social consequences for the participants involved. We argue that future intelligent systems could be better equipped to handle subjective perceptions by modeling their multiplicity not as label noise but as a explainable perspective-driven reasoning process. We focus on the Apparent Intent Inference (AII) problem as determined by ex-situ observers and conceptualize intentions to be independent of manifest future outcomes. We contribute 1. a novel annotation process for AII that accounts for a perceiver's own interpretative tendencies, 2. quantitative and qualitative analyses of intent narratives with respect to diversity, grounding, and plausibility; 3. benchmark tasks for AII and surrounding relevant contextual factors such as social involvement; 4. speech quality audio for all participants as well as privacy preserving multi-modal data, enabling lexical and nonverbal behavior analysis; and 5. coupling of self-reported goals of each participant (30 minute to 3 hour) with annotated AII (seconds).

cs.HC↗

arXiv2026

The Exact Limsup Constant for Once-Visited Sites of One-Dimensional Simple Random Walk

For a one-dimensional simple random walk, let $g_1(n)$ denote the number of sites visited exactly once at time $n$. Major (1988) proved that \begin{equation*} \limsup_{n\to\infty}\frac{g_1(n)}{\log^2 n}=C\qquad a.s. \end{equation*} where $C$ is a positive and finite constant. While this result settled the question of existence, the exact value of $C$ remained unknown. In this paper, we determine that $C=1/16$. The main novelty of our work lies in introducing a self-boosting iterative framework for analysis.

math.PR↗

arXiv2025

Favorite sites for simple random walk in two and more dimensions

On the trace of a discrete-time simple random walk on $\mathbb{Z}^d$ for $d\geq 2$, we consider the evolution of favorite sites, i.e., sites that achieve the maximal local time at a certain time. For $d=2$, we show that almost surely three favorite sites occur simultaneously infinitely often and eventually there is no simultaneous occurrence of four favorite sites. For $d\geq 3$, we derive sharp asymptotics of the number of favorite sites. This answers an open question of Erdős and Révész (1987), which was brought up again in Dembo (2005).

math.PR↗

arXiv2025

Exact Limsup Growth of Rarely Visited Sites for One-Dimensional Simple Random Walk

We investigate the minimal local time $f(n)$ of a one-dimensional simple random walk up to time $n$, defined as the smallest number of visits to any site in the range. A conjecture formulated repeatedly by Erdős and Révész (1987, 1991) stated that $\limsup_{n\to\infty}f(n)=2$ almost surely, which was disproved by Tóth (1996) who showed $\limsup_{n\to\infty}f(n)=\infty$. Subsequently, Révész (2013) suggested studying the growth rate and established an upper bound of the order $\log n$. In this paper, we determine the precise asymptotic growth rate, proving that with probability one, $$ \limsup_{n\to\infty}\frac{f(n)}{\log\log n}=\frac{1}{\log 2}. $$ This result answers the open question posed in Section 13.2 of Révész (2013).

math.PR↗

arXiv2025

Multimodal Quantitative Measures for Multiparty Behaviour Evaluation

Digital humans are emerging as autonomous agents in multiparty interactions, yet existing evaluation metrics largely ignore contextual coordination dynamics. We introduce a unified, intervention-driven framework for objective assessment of multiparty social behaviour in skeletal motion data, spanning three complementary dimensions: (1) synchrony via Cross-Recurrence Quantification Analysis, (2) temporal alignment via Multiscale Empirical Mode Decompositionbased Beat Consistency, and (3) structural similarity via Soft Dynamic Time Warping. We validate metric sensitivity through three theory-driven perturbations -- gesture kinematic dampening, uniform speech-gesture delays, and prosodic pitch-variance reduction-applied to $\approx 145$ 30-second thin slices of group interactions from the DnD dataset. Mixed-effects analyses reveal predictable, joint-independent shifts: dampening increases CRQA determinism and reduces beat consistency, delays weaken cross-participant coupling, and pitch flattening elevates F0 Soft-DTW costs. A complementary perception study ($N=27$) compares judgments of full-video and skeleton-only renderings to quantify representation effects. Our three measures deliver orthogonal insights into spatial structure, timing alignment, and behavioural variability. Thereby forming a robust toolkit for evaluating and refining socially intelligent agents. Code available on \href{https://github.com/tapri-lab/gig-interveners}{GitHub}.

cs.HC↗

arXiv2025

A phase transition for the two-dimensional random field Ising/FK-Ising model

We study the total variation (TV) distance between the laws of the 2D Ising/FK-Ising model in a box of side-length $N$ with and without an i.i.d.\ Gaussian external field with variance $ε^2$. Letting the external field strength $ε= ε(N)$ depend on the size of the box, we derive a phase transition for each model depending on the order of $ε(N)$. For the random field Ising model, the critical order for $ε$ is $N^{-1}$. For the random field FK-Ising model, the critical order depends on the temperature regime: for $T>T_c$, $T=T_c$ and $T\in (0, T_c)$ the critical order for $ε$ is, respectively, $N^{-\frac{1}{2}}$, $N^{-\frac{15}{16}}$ and $N^{-1}$. In each case, as $N \to \infty$ the TV distance under consideration converges to $1$ when $ε$ is above the respective critical order and converges to $0$ when below.

math.PR↗

arXiv2025

Indeterminacy in Affective Computing: Considering Meaning and Context in Data Collection Practices

Automatic Affect Prediction (AAP) uses computational analysis of input data such as text, speech, images, and physiological signals to predict various affective phenomena (e.g., emotions or moods). These models are typically constructed using supervised machine-learning algorithms, which rely heavily on labeled training datasets. In this position paper, we posit that all AAP training data are derived from human Affective Interpretation Processes, resulting in a form of Affective Meaning. Research on human affect indicates a form of complexity that is fundamental to such meaning: it can possess what we refer to here broadly as Qualities of Indeterminacy (QIs) - encompassing Subjectivity (meaning depends on who is interpreting), Uncertainty (lack of confidence regarding meanings' correctness), Ambiguity (meaning contains mutually exclusive concepts) and Vagueness (meaning is situated at different levels in a nested hierarchy). Failing to appropriately consider QIs leads to results incapable of meaningful and reliable predictions. Based on this premise, we argue that a crucial step in adequately addressing indeterminacy in AAP is the development of data collection practices for modeling corpora that involve the systematic consideration of 1) a relevant set of QIs and 2) context for the associated interpretation processes. To this end, we are 1) outlining a conceptual model of AIPs and the QIs associated with the meaning these produce and a conceptual structure of relevant context, supporting understanding of its role. Finally, we use our framework for 2) discussing examples of context-sensitivity-related challenges for addressing QIs in data collection setups. We believe our efforts can stimulate a structured discussion of both the role of aspects of indeterminacy and context in research on AAP, informing the development of better practices for data collection and analysis.

cs.AI↗