Miracle-dz/UniFAR-FTUs huggingface dataset:
Image or Information? Examining the Nature and Impact of Visualization Perceptual Classification

How do people internalize visualizations: as images or information? In this study, we investigate the nature of internalization for visualizations (i.e., how the mind encodes visualizations in memory) and how memory encoding affects its retrieval. This exploratory work examines the influence of various design elements on a user's perception of a chart. Specifically, which design elements lead to perceptions of visualization as an image (aims to provide visual references, evoke emotions, express creativity, and inspire philosophic thought) or as information (aims to present complex data, information, or ideas concisely and promote analytical thinking)? Understanding how design elements contribute to viewers perceiving a visualization more as an image or information will help designers decide which elements to include to achieve their communication goals. For this study, we annotated 500 visualizations and analyzed the responses of 250 online participants, who rated the visualizations on a bilinear scale as ‘image’ or ‘information. ’ We then conducted an in-person study ($n = 101$) using a free recall task to examine how the image/information ratings and design elements impacted memory. The results revealed several interesting findings: Image-rated visualizations were perceived as more aesthetically ‘appealing,’ ‘enjoyable,’ and ‘pleasing. ’ Information-rated visualizations were perceived as less ‘difficult to understand’ and more aesthetically ‘likable’ and ‘nice,’ though participants expressed higher ‘positive’ sentiment when viewing image-rated visualizations and felt less ‘guided to a conclusion. ’ The presence of axes and text annotations heavily influenced the likelihood of participants rating the visualization as ‘information. ’ We also found different patterns among participants that were older. Importantly, we show that visualizations internalized as ‘images’ are less effective in conveying trends and messages, though they elicit a more positive emotional judgment, while ‘informative’ visualizations exhibit annotation focused recall and elicit a more positive design judgment. We discuss the implications of this dissociation between aesthetic pleasure and perceived ease of use in visualization design.
A complete tree-level dictionary between simplified BSM models and SMEFT (d $\leq$ 7) operators

Finding all possible UV resonances of effective operators is an important task in the bottom-up approach of effective field theory. We present all the tree-level UV resonances for the dimension-5, -6 and -7 operators in the Standard Model effective field theory (SMEFT), and then obtain the correspondence between the UV resonances and the effective operators from the relations among their Wilson coefficients, through the functional matching and operator reduction procedure. This provides a cross-dimension UV/IR dictionary for the SMEFT at tree-level, and the methods used here, especially the on-shell construction of general UV Lagrangian and the systematic reduction of operators, are extendable for UV resonances of $d \geq 8$ operators in SMEFT and other EFTs.
Neural self-organization for muscle-driven robots

We present self-organizing control principles for simulated robots actuated by synthetic muscles. Muscles correspond to linear motors exerting force only when contracting, but not when expanding, with joints being actuated by pairs of antagonistic muscles. Individually, muscles are connected to a controller composed of a single neuron with a dynamical threshold that generates target positions for the respective muscle. A stable limit cycle is generated when the embodied feedback loop is closed, giving rise to regular locomotive patterns. In the absence of direct couplings between neurons, we show that force-mediated intra- and inter-leg couplings between muscles suffice to generate stable gaits.
Intelligent model for offshore China sea fog forecasting

Accurate and timely prediction of sea fog is very important for effectively managing maritime and coastal economic activities. Given the intricate nature and inherent variability of sea fog, traditional numerical and statistical forecasting methods are often proven inadequate. This study aims to develop an advanced sea fog forecasting method embedded in a numerical weather prediction model using the Yangtze River Estuary (YRE) coastal area as a case study. Prior to training our machine learning model, we employ a time-lagged correlation analysis technique to identify key predictors and decipher the underlying mechanisms driving sea fog occurrence. In addition, we implement ensemble learning and a focal loss function to address the issue of imbalanced data, thereby enhancing the predictive ability of our model. To verify the accuracy of our method, we evaluate its performance using a comprehensive dataset spanning one year, which encompasses both weather station observations and historical forecasts. Remarkably, our machine learning-based approach surpasses the predictive performance of two conventional methods, the weather research and forecasting nonhydrostatic mesoscale model (WRF-NMM) and the algorithm developed by the National Oceanic and Atmospheric Administration (NOAA) Forecast Systems Laboratory (FSL). Specifically, in regard to predicting sea fog with a visibility of less than or equal to 1 km with a lead time of 60 hours, our methodology achieves superior results by increasing the probability of detection (POD) while simultaneously reducing the false alarm ratio (FAR).
Combining Vision and EMG-Based Hand Tracking for Extended Reality Musical Instruments

Hand tracking is a critical component of natural user interactions in extended reality (XR) environments, including extended reality musical instruments (XRMIs). However, self-occlusion remains a significant challenge for vision-based hand tracking systems, leading to inaccurate results and degraded user experiences. In this paper, we propose a multimodal hand tracking system that combines vision-based hand tracking with surface electromyography (sEMG) data for finger joint angle estimation. We validate the effectiveness of our system through a series of hand pose tasks designed to cover a wide range of gestures, including those prone to self-occlusion. By comparing the performance of our multimodal system to a baseline vision-based tracking method, we demonstrate that our multimodal approach significantly improves tracking accuracy for several finger joints prone to self-occlusion. These findings suggest that our system has the potential to enhance XR experiences by providing more accurate and robust hand tracking, even in the presence of self-occlusion.
Sub-nanosecond resolution electric field measurements during ns pulse breakdown in ambient air

Electric field during ns pulse discharge breakdown in ambient air has been measured by ps four-wave mixing, with temporal resolution of 0.2 ns. The measurements have been performed in a diffuse plasma generated in a dielectric barrier discharge, in plane-to-plane geometry. Absolute calibration of the electric field in the plasma is provided by the Laplacian field measured before breakdown. Sub-nanosecond time resolution is obtained by using a 150 ps duration laser pulse, as well as by monitoring the timing of individual laser shots relative to the voltage pulse, and post-processing four-wave mixing signal waveforms saved for each laser shot, placing them in the appropriate ‘time bins’. The experimental data are compared with the analytic solution for time-resolved electric field in the plasma during pulse breakdown, showing good agreement on ns time scale. Qualitative interpretation of the data illustrates the effects of charge separation, charge accumulation/neutralization on the dielectric surfaces, electron attachment, and secondary breakdown. Comparison of the present data with more advanced kinetic modeling is expected to provide additional quantitative insight into air plasma kinetics on ~ 0.1–100 ns scales.
Edge-set reduction to efficiently solve the graph partitioning problem with the genetic algorithm

The graph partitioning problem (GPP) is among the most challenging models in optimization. Because of its NP-hardness, the researchers directed their interest towards approximate methods such as the genetic algorithms (GA). The edge-based GA has shown promising results when solving GPP. However, for big dense instances, the size of the encoding representation becomes too huge and affects GA's efficiency. In this paper, we investigate the impact of modifying the size of the chromosomes on the edge based GA by reducing the GPP edge set. We study the GA performance with different levels of reductions, and we report the obtained results.
Applications of Bell-polynomial Scheme in Constructing the Conversation Laws of the Nonlinear Evolution Equations Admitting Two-field Bilinear Forms

Abstract: In this paper, the Bell-polynomial scheme is applied to obtain the infinite conservation laws for the nonlinear evolution equations (NLEEs) admitting two-field bilinear forms. Bell-polynomial scheme has been used to obtain certain bilinear Bäcklund transformations (BTs), Lax pairs and infinite conservation laws for the NLEEs with one-field bilinear forms. Based on the two-field bilinear forms and four-field bilinear BTs in the binary-Bell-polynominal form, the infinite conversation laws for a variable-coefficient modified Korteweg-de Vries equation arising in fluid and plasma physics as well as the Boussinesq-Burgers equations for the shallow water waves are construed systematically. Such procedure can be applied to other NLEEs admitting two-field bilinear forms.
An IPW-based Unbiased Ranking Metric in Two-sided Markets

In modern recommendation systems, unbiased learning-to-rank (LTR) is crucial for prioritizing items from biased implicit user feedback, such as click data. Several techniques, such as Inverse Propensity Weighting (IPW), have been proposed for single-sided markets. However, less attention has been paid to two-sided markets, such as job platforms or dating services, where successful conversions require matching preferences from both users. This paper addresses the complex interaction of biases between users in two-sided markets and proposes a tailored LTR approach. We first present a formulation of feedback mechanisms in two-sided matching platforms and point out that their implicit feedback may include position bias from both user groups. On the basis of this observation, we extend the IPW estimator and propose a new estimator, named two-sided IPW, to address the position bases in two-sided markets. We prove that the proposed estimator satisfies the unbiasedness for the ground-truth ranking metric. We conducted numerical experiments on real-world two-sided platforms and demonstrated the effectiveness of our proposed method in terms of both precision and robustness. Our experiments showed that our method outperformed baselines especially when handling rare items, which are less frequently observed in the training data.
Efficient selective attention LSTM for well log curve synthesis

Non-core drilling has gradually become the primary exploration method in geological exploration engineering, and well logging curves have increasingly gained importance as the main carriers of geological information. However, factors such as geological environment, logging equipment, borehole quality, and unexpected events can all impact the quality of well logging curves. Previous methods of re-logging or manual corrections have been associated with high costs and low efficiency. This paper proposes a machine learning method that utilizes existing data to predict missing data, and its effectiveness and feasibility have been validated through field experiments. The proposed method builds on the traditional Long Short-Term Memory (LSTM) neural network by incorporating a self-attention mechanism to analyze the sequential dependencies of the data. It selects the dominant computational results in the LSTM, reducing the computational complexity from O(n^2) to O(nlogn) and improving model efficiency. Experimental results demonstrate that the proposed method achieves higher accuracy compared to traditional curve synthesis methods based on Fully Connected Neural Networks (FCNN) and vanilla LSTM. This accurate, efficient, and cost-effective prediction method holds a practical value in engineering applications.
Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges

We introduce two novel visualization designs to support practitioners in performing identification and discrimination tasks on large value ranges (i.e., several orders of magnitude) in time-series data: (1) The <italic>order of magnitude horizon</italic> graph, which extends the classic horizon graph; and (2) the <italic>order of magnitude line</italic> chart, which adapts the log-line chart. These new visualization designs visualize large value ranges by explicitly splitting the mantissa <inline-formula><tex-math notation="LaTeX">$m$</tex-math><alternatives><inline-graphic xlink:href="tvcg-braun-3326576-eqinline-1-small.tif"/></alternatives></inline-formula> and exponent <inline-formula><tex-math notation="LaTeX">$e$</tex-math><alternatives><inline-graphic xlink:href="tvcg-braun-3326576-eqinline-2-small.tif"/></alternatives></inline-formula> of a value <inline-formula><tex-math notation="LaTeX">$v=m\cdot 10^{e}$</tex-math><alternatives><inline-graphic xlink:href="tvcg-braun-3326576-eqinline-3-small.tif"/></alternatives></inline-formula>. We evaluate our novel designs against the most relevant state-of-the-art visualizations in an empirical user study. It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection. For each task we analyze error, confidence, and response time. The new <italic>order of magnitude horizon</italic> graph performs better or equal to all other designs in identification, discrimination, and estimation tasks. Only for trend detection tasks, the more traditional horizon graphs reported better performance. Our results are domain-independent, only requiring time-series data with large value ranges.
Characterising Decision Theories with Mechanised Causal Graphs

How should my own decisions affect my beliefs about the outcomes I expect to achieve? If taking a certain action makes me view myself as a certain type of person, it might affect how I think others view me, and how I view others who are similar to me. This can influence my expected utility calculations and change which action I perceive to be best. Whether and how it should is subject to debate, with contenders for how to think about it including evidential decision theory, causal decision theory, and functional decision theory. In this paper, we show that mechanised causal models can be used to characterise and differentiate the most important decision theories, and generate a taxonomy of different decision theories.
Universality of Spectral Independence with Applications to Fast Mixing in Spin Glasses

We study Glauber dynamics for sampling from discrete distributions $\mu$ on the hypercube $\{\pm 1\}^n$. Recently, techniques based on spectral independence have successfully yielded optimal $O(n)$ relaxation times for a host of different distributions $\mu$. We show that spectral independence is universal: a relaxation time of $O(n)$ implies spectral independence. We then study a notion of tractability for $\mu$, defined in terms of smoothness of the multilinear extension of its Hamiltonian -- $\log \mu$ -- over $[-1,+1]^n$. We show that Glauber dynamics has relaxation time $O(n)$ for such $\mu$, and using the universality of spectral independence, we conclude that these distributions are also fractionally log-concave and consequently satisfy modified log-Sobolev inequalities. We sharpen our estimates and obtain approximate tensorization of entropy and the optimal $\widetilde{O}(n)$ mixing time for random Hamiltonians, i.e. the classically studied mixed $p$-spin model at sufficiently high temperature. These results have significant downstream consequences for concentration of measure, statistical testing, and learning.
Froude number scaling unifies impact trajectories into granular media across gravitational conditions

The interactions of solid objects with granular media is countered by a resistance force that stems from frictional forces between the grains and the media's resistance to inertia imposed by the intruder. Earlier theories of granular intrusion have suggested an additive contribution of these two families of forces and had tremendous success in predicting resistive forces on arbitrary shaped objects. However, it remains unclear how these forces are influenced by gravitational conditions. We examine the role of gravity on surface impact behavior into cohesionless granular media using hundreds of soft-sphere discrete element simulations, we demonstrate that the outcome of impacts remain qualitatively similar under varying gravitational conditions if initial velocities are scaled with the Froude number, suggesting an underlying law. Using theoretical arguments, we provide reasoning for the observed universality and show that there is a hidden dependency in resistive forces into granular media on Froude number. Following the theoretical framework, we show that Froude number scaling precisely collapses impact trajectories across gravitational conditions, setting the foundation for explorations in granular behavior beyond Earth.
The Dutch chronic lower limb-threatening ischemia registry (THRILLER): A study protocol for popliteal and infrapopliteal endovascular interventions

Introduction Chronic limb-threatening ischemia (CLTI) is the end stage of peripheral arterial disease (PAD) and is associated with high amputation rates, mortality and disease-related health care costs. In infrapopliteal arterial disease (IPAD), endovascular revascularization should be considered for the majority of anatomical and clinical subgroups of CLTI. However, a gap of high-quality evidence exists in this field. The aim of the Dutch Chronic Lower Limb-Threatening Ischemia Registry (THRILLER) is to collect real world data on popliteal and infrapopliteal endovascular interventions. Methods THRILLER is a clinician-driven, prospective, multicenter, observational registry including all consecutive patients that undergo a popliteal or infrapopliteal endovascular intervention in seven Dutch hospitals. We estimate that THRILLER will include 400–500 interventions annually. Standardized follow-up visits with wound monitoring, toe pressure measurement and duplex ultrasonography will be scheduled at 6–8 weeks and 12 months after the intervention. The independent primary endpoints are primary patency, limb salvage and amputation free survival. Patients must give informed consent before participation and will be included according to predefined reporting standards. A data log of patients who meet the inclusion criteria but are not included in the registry will be maintained. We intend to conduct the first interim analysis two years after the start of inclusion. The results will be published in a scientific journal. Discussion Despite innovations in medical therapy and revascularization techniques, patients with CLTI undergoing endovascular revascularization still have a moderate prognosis. Previous prospective cohort studies were hampered by small sample sizes or heterogeneous reporting. Randomized controlled trials (RCTs) have high costs, potential conflicts of interest and give a limited reflection of daily practice. THRILLER aims to provide the largest prospective well phenotyped up-to-date dataset on treatment outcomes in CLTI patients to answer multiple underexplored research questions regarding diagnostics, medication, patient selection, treatment strategies and post intervention follow-up.
Effect of implantable cardiac monitors on preventing stroke: A systematic review and meta-analysis of randomized clinical trials

Background and aim Implantable cardiac monitors (ICM) can facilitate the detection of asymptomatic atrial fibrillation episodes. We performed a systematic review and meta-analysis to investigate whether ICM can prevent stroke in patients with prior stroke and risk factors for stroke. Methods This study included randomized controlled trials comparing ICM with conventional (non-ICM) external cardiac monitoring in patients with prior stroke and risk factors for stroke. We searched Medline, Embase, and CENTRAL from inception until January 5, 2022, without language restriction. Quantitative pooling of the data was undertaken using a random-effects model. The primary outcome was ischemic stroke at the longest follow-up. Results Four trials comprising 7237 patients were included. ICM was significantly associated with decreased risk of ischemic stroke (RR 0.76; 95% CI, 0.59–0.97; moderate-quality evidence) in patients with prior stroke and risk factors for stroke. ICM was associated with higher detection of atrial fibrillation (RR 4.21, 95% CI 2.26–7.85) and use of oral anticoagulants (RR 2.29, 95% CI 2.07–2.55). Conclusions ICM results in a significantly lower risk of ischemic stroke than conventional (non-ICM) external cardiac monitoring in patients with prior stroke and risk factors for stroke. Due to the clinical heterogeneity of study population and limited related studies, more trials were needed to furtherly explore the topic in patients with prior stroke or high risk of stroke.
A case report of abscopal toxicity in a patient with lung adenocarcinoma

Abstract The abscopal effect describes tumor responses outside the irradiated field. The literature shows increased overall survival and response rates in patients receiving immunotherapy and radiation, likely from exaggerated abscopal effects. We present a 57-year-old woman with stage 4 lung adenocarcinoma who received treatment with a combination of chemotherapy and immunotherapy. She had disease progression on maintenance immunotherapy, confirming resistance. Palliative radiation to the sternal bone lesion resulted in a significant response to all areas of cancer, confirming the abscopal effect. Unfortunately, she developed severe pneumonitis; to our knowledge, this is the first case of abscopal lung toxicity.