Abstract

Following up on our earlier study [J. Bardhan et al., Phys. Rev. D 107, 115001 (2023)], we investigate the LHC prospects of pair-produced vectorlike 𝐵 quarks decaying exotically to a new gauge-singlet (pseudo)scalar field Φ and a 𝑏 quark. After the electroweak symmetry breaking, the Φ decays predominantly to 𝑔⁡𝑔⁡/𝑏⁢𝑏 final states, leading to a fully hadronic 2⁢𝑏 +4⁢𝑗 or 6⁢𝑏 signature. Because of the large Standard Model background and the lack of leptonic handles, it is a difficult channel to probe. To overcome the challenge, we employ a hybrid deep learning model containing a graph neural network followed by a deep neural network. We estimate that such a state-of-the-art deep learning analysis pipeline can lead to a performance comparable to that in the semileptonic mode, taking the discovery (exclusion) reach up to about 𝑀_𝐵=1.8⁢(2.4)  TeV at HL-LHC when 𝐵 decays fully exotically, i.e., BR⁡(𝐵→𝑏⁢Φ)=100%.

Abstract

This study advances Kai Katahira’s Speculation Game, an agent-based model (ABM) for financial markets, by addressing its limitation in capturing order flow imbalance, a critical indicator of herd behavior. Although the original model successfully replicated key stylized facts of financial markets, it did not account for the persistence of order imbalance observed in real-world trading. Through a comprehensive analysis of two decades of BSE Sensex data, we establish the prevalence of order imbalance and its correlation with price fluctuations. To bridge this gap, we propose an extended model, Speculation Game with Information Cascade (SGIC), which integrates Self-Organized Criticality (SOC) through a sand-pile model, enabling agents to interact within a small-world network. Our proposed model not only reproduces the stylized facts captured by the original Speculation Game, but also successfully generates the additional stylized fact of order flow imbalance. These advances enhance the realism of ABMs in financial markets, providing deeper insights into the mechanisms driving herding and market fluctuations.

Abstract

The color gamut of visual scenes can encompass a multitude of hues, but how many distinct hues are individuals aware of? We examined this question in the context of texture perception, with a grid of stimuli composed of random colors chosen from color sets varying from two to six different hues or saturations. In a behavioral experiment, participants had to discriminate between differences in the number of hues present by identifying which of four color grids included a larger number of different colors. Color number discrimination was also assessed neurally, using electroencephalogram (EEG) frequency tagging, wherein a texture with an extra hue was shown as an “oddball,” once per second, in a stream of textures presented at six images per second. In both experiments, two versus three texture contrasts were readily distinguished, yet performance fell rapidly when the set size increased further. The results suggest that sensitivity to the density of the color gamut defining a texture is very restricted.

Abstract

Understanding user intent is essential for build- ing better human interaction agents, as it enables personalization, co-creation, and contextual adap- tation. However, existing approaches are either restricted to text environments, use human anno- tation, or just predict future user actions lacking the ability to reason explicitly about user goals. In this work, we introduce EARL (Early Action Reasoning for Latent intent), a theory of mind inspired inference-time algorithm that models user intent as an inverse planning problem, in- ferring latent goals from observed user actions. EARL hypothesizes potential user intent at mul- tiple stages during the course of task execution, enabling timely intervention and personalization. Evaluated on three diverse benchmarks namely Mind2Web, AiTz, and VideoGUI, and using two strong LLMs (Gemini-1.5-Pro and GPT-4o), we show that EARL consistently outperforms CoT- based LLM baselines in accurately deciphering user intent, especially under partial observations

Abstract

Centrality measures offer a mechanism to quantify various notions of the importance of vertices in a network. These measures find application in social network analysis, the study of disease spread, and the like. Since the networks of interest are usually large, parallelism has become a powerful tool for computing these measures. However, for some of these applications, it is crucial to study how these centrality values change when there are changes to the underlying network. As a result, parallel algorithms that examine how to update these values due to changes to the graph are gaining research interest. In this paper, we study the update of the percolation centrality measure in a dynamic graph. We present parallel algorithms to handle changes to the percolation value of a batch of vertices and the addition and deletion of a batch of edges to the graph. We leverage the graphs' structural properties to improve our algorithms' performance. To our knowledge, we are the first to propose such algorithms for percolation centrality. We implement and benchmark our dynamic algorithms on a server with two AMD EPYC CPUs and an Nvidia A100 GPU. Our experiments on a collection of real-world graphs indicate that our algorithms achieve speedups of 8.79x and 2.82x on CPU and GPU, respectively, for edge updates, and 309.44x and 18.71x on CPU and GPU, respectively, for vertex percolation updates over state-of-the-art static algorithms for a batch of 10000 edges and vertices, respectively.

Abstract

Efficient algorithms for graph connected components and minimum spanning tree (MST) computations are vital in diverse domains, from medical diagnostics to chip design. Yet, as graph sizes grow to billions of edges, existing approaches often fail due to limited device memory. This challenge demands innovative rethinking of both algorithmic design and implementation strategies. We address this need by presenting a novel Parallel Heterogeneous External Memory (PHEM) framework, which harnesses both multicore CPU and GPU resources to tackle large-scale graph processing. Our method minimizes communication overhead while maximizing computational throughput through efficient copy- computation strategies. Experiments on a variety of real-world and synthetic benchmarks demonstrate that our algorithms for connected components and minimum spanning tree in the PHEM model outperform current state-of-the-art solutions, significantly advancing the capability to process massive graphs.

Abstract

Globesity has widely affected the world population. It is a multifactorial health problem. Obesity can be caused by high-fat intake, high-calorie intake, physical inactivity, age, gender, or hormonal issues that can induce many health issues, such as cardiovascular disorders, diabetes, and metabolic disorders. Health facilities are insufficient to provide services to the whole population. In such a case, introducing Artificial Intelligence (AI) in the health sector is one of the most significant steps that can be taken. With the help of AI, the prognosis, diagnosis, and treatment can be provided to individuals within their homes and setups without any physical interactions. Assembling the data collected with AI can help to give more information on the type of obesity prevailing in a particular society so that it could be prevented in the population. Since AI learning is relatively novel within the existing system of medicine, enabling …

Abstract

Instruction fine-tuning of large language models (LLMs) is a powerful method for improving task-specific performance, but it can inadvertently lead to a phenomenon where models generate harmful responses when faced with malicious prompts. In this paper, we explore Low-Rank Adapter Fusion (LoRA) as a means to mitigate these risks while preserving the model’s ability to handle diverse instructions effectively. Through an extensive comparative analysis against established baselines using recognized benchmark datasets, we demonstrate a 42% reduction in the harmfulness rate by leveraging LoRA fusion between a task adapter and a safety adapter, the latter of which is specifically trained on our safety dataset. In addition, we made noteworthy observations related to exaggerated safety behavior, where the model rejects safe prompts that closely resemble unsafe ones.

Abstract

Recent advancements in multimodal large language models (MLLMs) have driven researchers to explore how well these models read data visualizations, e.g., bar charts, scatter plots. More recently, attention has shifted to visual question answering with maps (Map-VQA). However, Map-VQA research has primarily focused on choropleth maps, which cover only a limited range of thematic categories and visual analytical tasks. To address these gaps, we introduce MapIQ, a benchmark dataset comprising 14,706 question-answer pairs across three map types: choropleth maps, cartograms, and proportional symbol maps spanning topics from six distinct themes (e.g., housing, crime). We evaluate multiple MLLMs using six visual analytical tasks, comparing their performance against one another and a human baseline. An additional experiment examining the impact of map design changes (e.g., altered color schemes, modified legend designs, and removal of map elements) provides insights into the robustness and sensitivity of MLLMs, their reliance on internal geographic knowledge, and potential avenues for improving Map-VQA performance.

Abstract

Accurate and complete product descriptions are crucial for e-commerce, yet seller-provided information often falls short. Customer reviews offer valuable details but are laborious to sift through manually. We present PRAISE: Product Review Attribute Insight Structuring Engine, a novel system that uses Large Language Models (LLMs) to automatically extract, compare, and structure insights from customer reviews and seller descriptions. PRAISE provides users with an intuitive interface to identify missing, contradictory, or partially matching details between these two sources, presenting the discrepancies in a clear, structured format alongside supporting evidence from reviews. This allows sellers to easily enhance their product listings for clarity and persuasiveness, and buyers to better assess product reliability. Our demonstration showcases PRAISE's workflow, its effectiveness in generating actionable structured insights from unstructured reviews, and its potential to significantly improve the quality and trustworthiness of e-commerce product catalogs.