arXiv subjects

Yun Feng

Publications and source records attributed to Yun Feng.

8 recordsLinked to original sources

arXiv2026

Effective-potential classification of kink-antikink collision channels in the $\phi^8$ scalar field theory

We study kink-antikink collisions in a $(1+1)$-dimensional $\phi^8$ scalar field theory with multiple degenerate vacua. We derive soliton solutions for different vacuum structures labeled by $n=p_2/p_1$, and focus on the cases $n=2$ and $n=3$. We perform numerical simulations in all topological sectors and for both kink-antikink ($K\bar K$) and $\bar K K$ orderings. In the $(-1/2,1/2)$ sector, the kink-antikink pair annihilates for all initial velocities. To the best of our knowledge, this full-velocity annihilation regime has not been reported in $\phi^8$ kink collisions. We also find fractal multi-bounce windows in the $(-1,-1/2)$, $(-1,-1/3)$, and $(-1/3,1/3)$ sectors. Our main result indicates an effective-potential classification of these collision outcomes. We show that the shape of the effective potential is closely related to the final channel. It determines whether the pair escapes, forms a bion, annihilates, or changes sector. When the solitons pass through each other, the effective potential can change suddenly. This gives a possible mechanism for annihilation and sector change. Our results establish connections among topological structure, spectrum and effective potentials in higher-order scalar field theories.

hep-th↗

arXiv2025

More than a feeling: Expressive style influences cortical speech tracking in subjective cognitive decline

Subjective cognitive decline (SCD) doubles dementia risk. This study investigates how self-perceived cognitive worsening shapes neural dynamics during naturalistic speech perception. EEG was collected from 60 cognitively normal older adults as they listened to speech varied in prosodic contexts, categorized by expressive style (scrambled, descriptive, dialogue, exciting). Encoding models mapping three speech representations -- acoustic, subsyllabic segmentation and phonotactic features -- to ongoing EEG signals were built. Cortical tracking strength (CTS) showed that models fitted with subsyllabic linguistic features outperformed acoustic ones. Crucially, greater SCD severity was associated with weaker CTS of (1) subsyllabic linguistic but not acoustic features, and (2) prosodically flat speech (scrambled and descriptive). Thus, the CTS of higher-level linguistic features while listening to prosodically flat speech may serve as a potential neural marker for early-stage cognitive decline.

q-bio.NC↗

arXiv2025

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application

Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing evaluations of LLMs in finance remain text-only, monolingual, and largely saturated by current models. To bridge these gaps, we present MultiFinBen, the first expert-annotated multilingual (five languages) and multimodal (text, vision, audio) benchmark for evaluating LLMs in realistic financial contexts. MultiFinBen introduces two new task families: multilingual financial reasoning, which tests cross-lingual evidence integration from filings and news, and financial OCR, which extracts structured text from scanned documents containing tables and charts. Rather than aggregating all available datasets, we apply a structured, difficulty-aware selection based on advanced model performance, ensuring balanced challenge and removing redundant tasks. Evaluating 21 leading LLMs shows that even frontier multimodal models like GPT-4o achieve only 46.01% overall, stronger on vision and audio but dropping sharply in multilingual settings. These findings expose persistent limitations in multilingual, multimodal, and expert-level financial reasoning. All datasets, evaluation scripts, and leaderboards are publicly released.

cs.CL↗

arXiv2021

Comparing Broadband ISP Performance using Big Data from M-Lab

Comparing ISPs on broadband speed is challenging, since measurements can vary due to subscriber attributes such as operation system and test conditions such as access capacity, server distance, TCP window size, time-of-day, and network segment size. In this paper, we draw inspiration from observational studies in medicine, which face a similar challenge in comparing the effect of treatments on patients with diverse characteristics, and have successfully tackled this using "causal inference" techniques for {\em post facto} analysis of medical records. Our first contribution is to develop a tool to pre-process and visualize the millions of data points in M-Lab at various time- and space-granularities to get preliminary insights on factors affecting broadband performance. Next, we analyze 24 months of data pertaining to twelve ISPs across three countries, and demonstrate that there is observational bias in the data due to disparities amongst ISPs in their attribute distributions. For our third contribution, we apply a multi-variate matching method to identify suitable cohorts that can be compared without bias, which reveals that ISPs are closer in performance than thought before. Our final contribution is to refine our model by developing a method for estimating speed-tier and re-apply matching for comparison of ISP performance. Our results challenge conventional rankings of ISPs, and pave the way towards data-driven approaches for unbiased comparisons of ISPs world-wide.

cs.PF↗

arXiv2019

Abnormal source identification for parabolic distributed parameter systems

Identification of abnormal source hidden in distributed parameter systems (DPSs) belongs to the category of inverse source problems. It is important in industrial applications but seldom studied. In this paper, we make the first attempt to investigate the abnormal spatio-temporal (S-T) source identification for a class of DPSs. An inverse S-T model for abnormal source identification is developed for the first time. It consists of an adaptive state observer for source identification and an adaptive source estimation algorithm. One major advantage of the proposed inverse S-T model is that only the system output is utilized, without any state measurement. Theoretic analysis is conducted to guarantee the convergence of the estimation error. Finally, the performance of the proposed method is evaluated on a heat transfer rod with an abnormal S-T source.

eess.SY↗

arXiv2019

Estimating Residential Broadband Capacity using Big Data from M-Lab

Knowing residential broadband capacity profiles across a population is of interest to both consumers and regulators who want to compare or audit performance of various broadband service offerings. Unfortunately, extracting broadband capacity from speed tests in public datasets like M-Lab is challenging because tests are indexed by client IP address which can be dynamic and/or obfuscated by NAT, and variable network conditions can affect measurements. This paper presents the first systematic effort to isolate households and extract their broadband capacity using 63 million speed test measurements recorded over a 12 month period in the M-Lab dataset. We first identify a key parameter, the correlation between measured speed and congestion count for a specific client IP address, as an indicator of whether the IP address represents a single house, or a plurality of houses that may be dynamically sharing addresses or be aggregated behind a NAT. We then validate our approach by comparing to ground truth taken from a few known houses, and at larger scale by checking internal consistency across ISPs and across months. Lastly, we present results that isolate households and estimate their broadband capacity based on measured data, and additionally reveal insights into the prevalence of NAT and variations in service capacity tiers across ISPs.

cs.NI↗

arXiv2019

A unified framework of epidemic spreading prediction by empirical mode decomposition based ensemble learning techniques

In this paper, a unified susceptible-exposed-infected-susceptible-aware (SEIS-A) framework is proposed to combine epidemic spreading with individuals' on-line self-consultation behaviors. An epidemic spreading prediction model is established based on the SEIS-A framework. The prediction process contains two phases. In phase I, the time series data of disease density are decomposed through the empirical mode decomposition (EMD) method to obtain the intrinsic mode functions (IMFs). In phase II, the ensemble learning techniques which use the on-line query data as an additional input are applied to these IMFs. Finally, experiments for prediction of weekly consultation rates of Hand-foot-and-mouth disease (HFMD) in Hong Kong are conducted to validate the effectiveness of the proposed method. The main advantage of this method is that it outperforms other methods on fluctuating complex data.

cs.CE↗

arXiv2019

Weights Adaptation Optimization of Heterogeneous Epidemic Spreading Networks: A Constrained Cooperative Coevolution Strategy

In this paper, the dynamic constrained optimization problem of weights adaptation for heterogeneous epidemic spreading networks is investigated. Due to the powerful ability of searching global optimum, evolutionary algorithms are employed as the optimizers. One major difficulty is that the dimension of the problem is increasing exponentially with the network size and most existing evolutionary algorithms cannot achieve satisfiable performance on large-scale optimization problems. To address this issue, a novel constrained cooperative coevolution ($C^3$) strategy, which can separate the original large-scale problem into different subcomponents, is employed to achieve the trade-off between the constraint and objective function.

cs.NE↗