Tootfinder

pySpainMobility: a Python Package to Access and Manage Spanish Open Mobility Data
Ciro Beneduce, Tania Gull\'on Mu\~noz-Repiso, Bruno Lepri, Massimiliano Luca
https://arxiv.org/abs/2506.13385

pySpainMobility: a Python Package to Access and Manage Spanish Open Mobility Data
Mobility patterns play a critical role in a wide range of societal challenges, from epidemic modeling and emergency response to transportation planning and regional development. Yet, access to high-quality, timely, and openly available mobility data remains limited. In response, the Spanish Ministry of Transportation and Sustainable Mobility has released daily mobility datasets based on anonymized mobile phone data, covering districts, municipalities, and greater urban areas from February 2020 …

The Daily Star drops its page three with pictures of scantily dressed women as part of a print and online redesign, with a new strapline "News with a wink" (Charlotte Tobitt/Press Gazette)
https://pressgazette.co.uk/publishers/

Daily Star drops page three pictures of scantily dressed women in redesign
The Daily Star has ditched page three models amid a wider redesign which includes a new masthead, new website/app platforms and new strapline

Couple of notes, #DailyMail
1. Obviously the story's overblown scaremongering, used here purely as a stick to beat the gov with;
2. IDK the details, but it sounds very much like the standard NHS protocol for anonymised mass data, it's not "Let's look up Jonathan Harmsworth's doctors notes, so as to embarrass, understand & predict, blackmail or manipulate…

This https://arxiv.org/abs/2409.03788 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

HSF: Defending against Jailbreak Attacks with Hidden State Filtering
With the growing deployment of LLMs in daily applications like chatbots and content generation, efforts to ensure outputs align with human values and avoid harmful content have intensified. However, increasingly sophisticated jailbreak attacks threaten this alignment, aiming to induce unsafe outputs. Current defense efforts either focus on prompt rewriting or detection, which are limited in effectiveness due to the various design of jailbreak prompts, or on output control and detection, which a…

One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture
Marcos Abel Zuzu\'arregui, Mustafa Melih Toslak, Stefano Carpin
https://arxiv.org/abs/2506.10106

One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture
Artificial intelligence is transforming precision agriculture, offering farmers new tools to streamline their daily operations. While these technological advances promise increased efficiency, they often introduce additional complexity and steep learning curves that are particularly challenging for non-technical users who must balance tech adoption with existing workloads. In this paper, we present a natural language (NL) robotic mission planner that enables non-specialists to control heterogen…

Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Eun Som Jeon, Sinjini Mitra, Jisoo Lee, Omik M. Save, Ankita Shukla, Hyunglae Lee, Pavan Turaga
https://arxiv.org/abs/2506.10265

Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Human gait analysis with wearable sensors has been widely used in various applications, such as daily life healthcare, rehabilitation, physical therapy, and clinical diagnostics and monitoring. In particular, ground reaction force (GRF) provides critical information about how the body interacts with the ground during locomotion. Although instrumented treadmills have been widely used as the gold standard for measuring GRF during walking, their lack of portability and high cost make them impracti…

Nowcasting the euro area with social media data
Konstantin Boss, Luigi Longo, Luca Onorante
https://arxiv.org/abs/2506.10546 https://…

Nowcasting the euro area with social media data
Using a state-of-the-art large language model, we extract forward-looking and context-sensitive signals related to inflation and unemployment in the euro area from millions of Reddit submissions and comments. We develop daily indicators that incorporate, in addition to posts, the social interaction among users. Our empirical results show consistent gains in out-of-sample nowcasting accuracy relative to daily newspaper sentiment and financial variables, especially in unusual times such as the (p…

Regenerating Daily Routines for Young Adults with Depression through User-Led Indoor Environment Modifications Using Local Natural Materials
Ziqun Hua, Ao Jiang, Haoling Yang, Hao Fan, Huizhong Hu, Bernard Foing
https://arxiv.org/abs/2506.05729

Regenerating Daily Routines for Young Adults with Depression through User-Led Indoor Environment Modifications Using Local Natural Materials
Young adults with depression often experience prolonged indoor stays, limiting their access to natural environments and exacerbating mental health challenges. While nature therapy is recognized for its psychological benefits, existing interventions frequently require outdoor engagement, which may not be accessible for all individuals. This study explores the potential of user-led indoor modifications using local natural materials as a mental health intervention. A qualitative approach wasemploy…

TESTQUEST: A Web Gamification Tool to Improve Locators and Page Objects Quality
Dario Olianas, Diego Clerissi, Maurizio Leotta, Filippo Ricca
https://arxiv.org/abs/2505.24756

TESTQUEST: A Web Gamification Tool to Improve Locators and Page Objects Quality
Web applications play a crucial role in our daily lives, making it essential to employ testing methods that ensure their quality. Typically, Web testing automation frameworks rely on locators to interact with the graphical user interface, acting as connection points to the elements on a Web page. Nevertheless, locators are widely recognized as a major vulnerability in Web testing, as they are highly sensitive to the frequent changes in Web page structures caused by rapid software evolution. The…

Strain dependent viscous response describes the mechanics of cohesionless soft granular materials
Chandan Shakya, Luca Placidi, Anil Misra, Lars Kool, Anke Lindner, Jasper van der Gucht, Joshua A. Dijksman
https://arxiv.org/abs/2506.08855

Strain dependent viscous response describes the mechanics of cohesionless soft granular materials
Granular materials are ubiquitous in nature and are used extensively in daily life and in industry. The modeling of these materials remains challenging; therefore, finding models with acceptable predictive accuracy that at the same time also reflect the complexity of the granular dynamics is a central research theme in the field. Soft particle packings present additional modeling challenges, as it has become clear that soft particles also have particle-level relaxation timescales that affect th…

A Spectral Energy Distribution Variability Study of the Eclipsing AGN NGC 6814
L. K. Pothier-Bogoslowski, L. C. Gallo, A. G. Gonzalez, M. Z. Buhariwalla, J. M. Miller
https://arxiv.org/abs/2506.07212

A Spectral Energy Distribution Variability Study of the Eclipsing AGN NGC 6814
The local Seyfert 1.5 active galactic nucleus (AGN), NGC 6814, is known to exhibit complex variability, eclipses, and even changing-look behaviour. In this work, we utilize optical-to-X-ray data obtained over 10-years with the Neil Gehrels Swift Observatory to examine the short-term (i.e. daily) and long-term (yearly) variations in the spectral energy distribution (SED). This includes three epochs of high-cadence monitoring (2012, 2016, and 2022), as well as two single observations (2019 and 20…

Generalizable Articulated Object Reconstruction from Casually Captured RGBD Videos
Weikun Peng, Jun Lv, Cewu Lu, Manolis Savva
https://arxiv.org/abs/2506.08334

Generalizable Articulated Object Reconstruction from Casually Captured RGBD Videos
Articulated objects are prevalent in daily life. Understanding their kinematic structure and reconstructing them have numerous applications in embodied AI and robotics. However, current methods require carefully captured data for training or inference, preventing practical, scalable, and generalizable reconstruction of articulated objects. We focus on reconstruction of an articulated object from a casually captured RGBD video shot with a hand-held camera. A casually captured video of an interac…

This https://arxiv.org/abs/2410.21801 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

PerSRV: Personalized Sticker Retrieval with Vision-Language Model
Instant Messaging is a popular means for daily communication, allowing users to send text and stickers. As the saying goes, "a picture is worth a thousand words", so developing an effective sticker retrieval technique is crucial for enhancing user experience. However, existing sticker retrieval methods rely on labeled data to interpret stickers, and general-purpose Vision-Language Models (VLMs) often struggle to capture the unique semantics of stickers. Additionally, relevant-based sticker retr…

Joint space-time modelling for upper daily maximum and minimum temperature record-breaking
Jorge Castillo-Mateo (University of Zaragoza), Zeus Gracia-Tabuenca (University of Zaragoza), Jes\'us As\'in (University of Zaragoza), Ana C. Cebri\'an (University of Zaragoza), Alan E. Gelfand (Duke University)
https://arxiv.o…

Joint space-time modelling for upper daily maximum and minimum temperature record-breaking
Record-breaking temperature events are now frequently in the news, proffered as evidence of climate change, and often bring significant economic and human impacts. Our previous work undertook the first substantial spatial modelling investigation of temperature record-breaking across years for any given day within the year, employing a dataset consisting of over sixty years of daily maximum temperatures across peninsular Spain. That dataset also supplies daily minimum temperatures (which, in fac…

This https://arxiv.org/abs/2404.15483 has been replaced.
link: https://scholar.google.com/scholar?q=a

Strategy Complexity of Büchi Objectives in Concurrent Stochastic Games
We study 2-player zero-sum concurrent (i.e., simultaneous move) stochastic Büchi games and Transience games on countable graphs. Two players, Max and Min, seek respectively to maximize and minimize the probability of satisfying the game objective. The Büchi objective is to visit a given set of target states infinitely often. This can be seen as a special case of maximizing the expected $\limsup$ of the daily rewards, where all daily rewards are in $\{0,1\}$. The Transience objective is to vis…

Not All Jokes Land: Evaluating Large Language Models Understanding of Workplace Humor
Moahmmadamin Shafiei, Hamidreza Saffari
https://arxiv.org/abs/2506.01819

Not All Jokes Land: Evaluating Large Language Models Understanding of Workplace Humor
With the recent advances in Artificial Intelligence (AI) and Large Language Models (LLMs), the automation of daily tasks, like automatic writing, is getting more and more attention. Hence, efforts have focused on aligning LLMs with human values, yet humor, particularly professional industrial humor used in workplaces, has been largely neglected. To address this, we develop a dataset of professional humor statements along with features that determine the appropriateness of each statement. Our ev…

Predicting Realized Variance Out of Sample: Can Anything Beat The Benchmark?
Austin Pollok
https://arxiv.org/abs/2506.07928 https://a…

Predicting Realized Variance Out of Sample: Can Anything Beat The Benchmark?
The discrepancy between realized volatility and the market's view of volatility has been known to predict individual equity options at the monthly horizon. It is not clear how this predictability depends on a forecast's ability to predict firm-level volatility. We consider this phenomenon at the daily frequency using high-dimensional machine learning models, as well as low-dimensional factor models. We find that marginal improvements to standard forecast error measurements can lead to economica…

Exploring the Convergence of HCI and Evolving Technologies in Information Systems
Rajan Das Gupta, Ashikur Rahman, Md Imrul Hasan Showmick, Md. Yeasin Rahat, Md. Jakir Hossen
https://arxiv.org/abs/2506.08549

Exploring the Convergence of HCI and Evolving Technologies in Information Systems
Modern technology driven information systems are part of our daily lives. However, this deep integration poses new challenges to the human computer interaction (HCI) professionals. With the rapid growth of mobile and cloud computing and the Internet of Things (IoT), the demand for HCI specialists to design user-friendly and adaptable interfaces has never been more pressing. Especially for diverse user groups such as children, the elderly and people with disabilities who need interfaces tailored…

Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia
Katelyn Xiaoying Mei, Anna Seo Gyeong Choi, Hilke Schellmann, Mona Sloane, Allison Koenecke
https://arxiv.org/abs/2506.08846

Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia
Automatic Speech Recognition (ASR) has transformed daily tasks from video transcription to workplace hiring. ASR systems' growing use warrants robust and standardized auditing approaches to ensure automated transcriptions of high and equitable quality. This is especially critical for people with speech and language disorders (such as aphasia) who may disproportionately depend on ASR systems to navigate everyday life. In this work, we identify three pitfalls in existing standard ASR auditing pro…

Can Artificial Intelligence Trade the Stock Market?
J\k{e}drzej Maskiewicz, Pawe{\l} Sakowski
https://arxiv.org/abs/2506.04658 https://

Can Artificial Intelligence Trade the Stock Market?
The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorab…

This https://arxiv.org/abs/2412.11369 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

PSGraph: Differentially Private Streaming Graph Synthesis by Considering Temporal Dynamics
Streaming graphs are ubiquitous in daily life, such as evolving social networks and dynamic communication systems. Due to the sensitive information contained in the graph, directly sharing the streaming graphs poses significant privacy risks. Differential privacy, offering strict theoretical guarantees, has emerged as a standard approach for private graph data synthesis. However, existing methods predominantly focus on static graph publishing, neglecting the intrinsic relationship between adjac…

HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction
Xiao-Qi Han, Ze-Feng Gao, Xin-De Wang, Zhenfeng Ouyang, Peng-Jie Guo, Zhong-Yi Lu
https://arxiv.org/abs/2506.03837

HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction
The discovery of high-temperature superconducting materials holds great significance for human industry and daily life. In recent years, research on predicting superconducting transition temperatures using artificial intelligence~(AI) has gained popularity, with most of these tools claiming to achieve remarkable accuracy. However, the lack of widely accepted benchmark datasets in this field has severely hindered fair comparisons between different AI algorithms and impeded further advancement of…

Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100
Sukru Selim Calik, Andac Akyuz, Zeynep Hilal Kilimci, Kerem Colak
https://arxiv.org/abs/2506.06345

Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100
Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer-based time series models with explainable artificial intelligence (XAI) to enhance the interpretability and accuracy of stock price predictions. The analysis focuses on the daily stock prices of the five highest-volume banks listed in the BIST100 index, along with XBANK and XU100 in…

The NetMob25 Dataset: A High-resolution Multi-layered View of Individual Mobility in Greater Paris Region
Alexandre Chasse, Anne J. Kouam, Aline C. Viana, Razvan Stanica, Wellington V. Lobato, Geymerson Ramos, Geoffrey Deperle, Abdelmounaim Bouroudi, Suzanne Bussod, Fernando Molano
https://arxiv.org/abs/2506.05903

The NetMob25 Dataset: A High-resolution Multi-layered View of Individual Mobility in Greater Paris Region
High-quality mobility data remains scarce despite growing interest from researchers and urban stakeholders in understanding individual-level movement patterns. The Netmob25 Data Challenge addresses this gap by releasing a unique GPS-based mobility dataset derived from the EMG 2023 GNSS-based mobility survey conducted in the Ile-de-France region (Greater Paris area), France. This dataset captures detailed daily mobility over a full week for 3,337 volunteer residents aged 16 to 80, collected betw…

This https://arxiv.org/abs/2412.00411 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Seismocardiography for Emotion Recognition: A Study on EmoWear with Insights from DEAP
Emotions have a profound impact on our daily lives, influencing our thoughts, behaviors, and interactions, but also our physiological reactions. Recent advances in wearable technology have facilitated studying emotions through cardio-respiratory signals. Accelerometers offer a non-invasive, convenient, and cost-effective method for capturing heart- and pulmonary-induced vibrations on the chest wall, specifically Seismocardiography (SCG) and Accelerometry-Derived Respiration (ADR). Their afforda…

Temac: Multi-Agent Collaboration for Automated Web GUI Testing
Chenxu Liu, Zhiyu Gu, Guoquan Wu, Ying Zhang, Jun Wei, Tao Xie
https://arxiv.org/abs/2506.00520

Temac: Multi-Agent Collaboration for Automated Web GUI Testing
Quality assurance of web applications is critical, as web applications play an essential role in people's daily lives. To reduce labor costs, automated web GUI testing (AWGT) is widely adopted, exploring web applications via GUI actions such as clicks and text inputs. However, these approaches face limitations in generating continuous and meaningful action sequences capable of covering complex functionalities. Recent work incorporates large language models (LLMs) for GUI testing. However, these…

NextQuill: Causal Preference Modeling for Enhancing LLM Personalization
Xiaoyan Zhao, Juntao You, Yang Zhang, Wenjie Wang, Hong Cheng, Fuli Feng, See-Kiong Ng, Tat-Seng Chua
https://arxiv.org/abs/2506.02368

NextQuill: Causal Preference Modeling for Enhancing LLM Personalization
Personalizing large language models (LLMs) for individual users has become increasingly important as they are progressively integrated into real-world applications to support users' daily lives. However, existing personalization approaches often fail to distinguish which components of model predictions and training data truly reflect user preferences, leading to superficial personalization alignment. In this paper, we introduce NextQuill, a novel LLM personalization alignment framework grounded…

SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting
Shengjie Lin, Jiading Fang, Muhammad Zubair Irshad, Vitor Campagnolo Guizilini, Rares Andrei Ambrus, Greg Shakhnarovich, Matthew R. Walter
https://arxiv.org/abs/2506.03594

SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting
Reconstructing articulated objects prevalent in daily environments is crucial for applications in augmented/virtual reality and robotics. However, existing methods face scalability limitations (requiring 3D supervision or costly annotations), robustness issues (being susceptible to local optima), and rendering shortcomings (lacking speed or photorealism). We introduce SplArt, a self-supervised, category-agnostic framework that leverages 3D Gaussian Splatting (3DGS) to reconstruct articulated ob…

Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking
Soumyakanti Pan, Sudipto Banerjee
https://arxiv.org/abs/2505.24397 ht…

Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking
Air pollution remains a major environmental risk factor that is often associated with adverse health outcomes. However, quantifying and evaluating its effects on human health is challenging due to the complex nature of exposure data. Recent technological advances have led to the collection of various indicators of air pollution at increasingly high spatial-temporal resolutions (e.g., daily averages of pollutant levels at spatial locations referenced by latitude-longitude). However, health outco…

A Sinusoidal Hull-White Model for Interest Rate Dynamics: Capturing Long-Term Periodicity in U.S. Treasury Yields
Amit Kumar Jha
https://arxiv.org/abs/2506.06317

A Sinusoidal Hull-White Model for Interest Rate Dynamics: Capturing Long-Term Periodicity in U.S. Treasury Yields
This study is motivated by empirical observations of periodic fluctuations in interest rates, notably long-term economic cycles spanning decades, which the conventional Hull-White short-rate model fails to adequately capture. To address this limitation, we propose an extension that incorporates a sinusoidal, time-varying mean reversion speed, allowing the model to reflect cyclic interest rate dynamics more effectively. The model is calibrated using a comprehensive dataset of daily U.S. Treasu…

Optimising cryptocurrency portfolios through stable clustering of price correlation networks
Ruixue Jing, Ryota Kobayashi, Luis Enrique Correa Rocha
https://arxiv.org/abs/2505.24831

Optimising cryptocurrency portfolios through stable clustering of price correlation networks
The emerging cryptocurrency market presents unique challenges for investment due to its unregulated nature and inherent volatility. However, collective price movements can be explored to maximise profits with minimal risk using investment portfolios. In this paper, we develop a technical framework that utilises historical data on daily closing prices and integrates network analysis, price forecasting, and portfolio theory to identify cryptocurrencies for building profitable portfolios under unc…

Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study
Yuke Zhang
https://arxiv.org/abs/2505.16136 https://arxiv…

Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study
This study introduces an interpretable machine learning (ML) framework to extract macroeconomic alpha from global news sentiment. We process the Global Database of Events, Language, and Tone (GDELT) Project's worldwide news feed using FinBERT -- a Bidirectional Encoder Representations from Transformers (BERT) based model pretrained on finance-specific language -- to construct daily sentiment indices incorporating mean tone, dispersion, and event impact. These indices drive an XGBoost classifier…

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
Dominik Stempie\'n, Robert \'Slepaczuk
https://arxiv.org/abs/2505.19617

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
This research systematically develops and evaluates various hybrid modeling approaches by combining traditional econometric models (ARIMA and ARFIMA models) with machine learning and deep learning techniques (SVM, XGBoost, and LSTM models) to forecast financial time series. The empirical analysis is based on two distinct financial assets: the S&P 500 index and Bitcoin. By incorporating over two decades of daily data for the S&P 500 and almost ten years of Bitcoin data, the study provides a comp…

Cryptocurrencies in the Balance Sheet: Insights from (Micro)Strategy -- Bitcoin Interactions
Sabrina Aufiero, Antonio Briola, Tesfaye Salarin, Fabio Caccioli, Silvia Bartolucci, Tomaso Aste
https://arxiv.org/abs/2505.14655

Cryptocurrencies in the Balance Sheet: Insights from (Micro)Strategy -- Bitcoin Interactions
This paper investigates the evolving link between cryptocurrency and equity markets in the context of the recent wave of corporate Bitcoin (BTC) treasury strategies. We assemble a dataset of 39 publicly listed firms holding BTC, from their first acquisition through April 2025. Using daily logarithmic returns, we first document significant positive co-movements via Pearson correlations and single factor model regressions, discovering an average BTC beta of 0.62, and isolating 12 companies, inclu…

Facial Appearance Capture at Home with Patch-Level Reflectance Prior
Yuxuan Han, Junfeng Lyu, Kuan Sheng, Minghao Que, Qixuan Zhang, Lan Xu, Feng Xu
https://arxiv.org/abs/2506.03478

Facial Appearance Capture at Home with Patch-Level Reflectance Prior
Existing facial appearance capture methods can reconstruct plausible facial reflectance from smartphone-recorded videos. However, the reconstruction quality is still far behind the ones based on studio recordings. This paper fills the gap by developing a novel daily-used solution with a co-located smartphone and flashlight video capture setting in a dim room. To enhance the quality, our key observation is to solve facial reflectance maps within the data distribution of studio-scanned ones. Spec…

This https://arxiv.org/abs/2501.16772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Trends and Reversion in Financial Markets on Time Scales from Minutes to Decades
We empirically analyze the reversion of financial market trends with time horizons ranging from minutes to decades. The analysis covers equities, interest rates, currencies and commodities and combines 14 years of futures tick data, 30 years of daily futures prices, 330 years of monthly asset prices, and yearly financial data since medieval times. Across asset classes, we find that markets are in a trending regime on time scales that range from a few hours to a few years, while they are in a …

I've been tracking #Hopetown 's Kickstarter as I've become interested in #discolike video games. I don't have skills in Kickstarter analytics, so I picked The Sinking City as a comparator:
<https://www.kicktraq.com/projects/frogwares/the-sinking-city-2/#chart-daily>
Hopetown's has fewer people interested:
<https://www.kicktraq.com/projects/longduegames/hopetown/#chart-daily>
Which may sadden the Hopetown devs, as the Kickstarter aims to show future investors there's interest in the game. They say the $$ won't help them. But I understand they already have at least 1 tech bro investor, so is Kickstarter really the venue? The Kickstarter page boasts so many industry credits & mentors, it feels uncool to ask for community help.