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Graphs are ubiquitous in real-world applications, ranging from social networks to biological systems, and have inspired the development of Graph Neural Networks (GNNs) for learning expressive representations. While most research has centered on static graphs, many real-world scenarios involve dynamic, temporally evolving graphs, motivating the need for Continuous-Time Dynamic Graph (CTDG) models. This paper provides a comprehensive review of Graph Representation Learning (GRL) on CTDGs with a focus on Self-Supervised Representation Learning (SSRL). We introduce a novel theoretical framework that analyzes the expressivity of CTDG models through an Information-Flow (IF) lens, quantifying their ability to propagate and encode temporal and structural information. Leveraging this framework, we categorize existing CTDG methods based on their suitability for different graph types and application scenarios. Within the same scope, we examine the design of SSRL methods tailored to CTDGs, such as predictive and contrastive approaches, highlighting their potential to mitigate the reliance on labeled data. Empirical evaluations on synthetic and real-world datasets validate our theoretical insights, demonstrating the strengths and limitations of various methods across long-range, bi-partite and community-based graphs. This work offers both a theoretical foundation and practical guidance for selecting and developing CTDG models, advancing the understanding of GRL in dynamic settings., Comment: 12-page main paper + 8-page appendix

A standard model that arises in several applications in sequential decision making is partially observable Markov decision processes (POMDPs) where a decision-making agent interacts with an uncertain environment. A basic objective in such POMDPs is the reachability objective, where given a target set of states, the goal is to eventually arrive at one of them. The limit-sure problem asks whether reachability can be ensured with probability arbitrarily close to 1. In general, the limit-sure reachability problem for POMDPs is undecidable. However, in many practical cases the most relevant question is the existence of policies with a small amount of memory. In this work, we study the limit-sure reachability problem for POMDPs with a fixed amount of memory. We establish that the computational complexity of the problem is NP-complete.

This study investigated the factors contributing to the failure of augmented reality (AR) and virtual reality (VR) startups in the emerging metaverse landscape. Through an in-depth analysis of 29 failed AR/VR startups from 2016 to 2022, key pitfalls were identified, such as a lack of scalability, poor usability, unclear value propositions, and the failure to address specific user problems. Grounded in these findings, we developed the Metaverse Innovation Canvas (MIC) a tailored business ideation framework for XR products and services. The canvas guides founders to define user problems, articulate unique XR value propositions, evaluate usability factors such as the motion-based interaction load, consider social/virtual economy opportunities, and plan for long term scalability. Unlike generalized models, specialized blocks prompt the consideration of critical XR factors from the outset. The canvas was evaluated through expert testing with startup consultants on five failed venture cases. The results highlighted the tool's effectiveness in surfacing overlooked usability issues and technology constraints upfront, enhancing the viability of future metaverse startups.

Theoretical quantum memory design often involves selectively focusing on certain energy levels to mimic an ideal $\Lambda$-configuration, a common approach that may unintentionally overlook the impact of neighboring levels or undesired couplings. While this simplification may be justified in certain protocols or platforms, it can significantly distort the achievable memory performance. Through numerical semi-classical analysis, we show that the presence of unwanted energy levels and undesired couplings in an NV-center-based absorptive memory can significantly amplify the signal, resulting in memory efficiencies exceeding unity, a clear indication of unwanted noise at the quantum level. This effect occurs even when the apparent noise i.e., output in the absence of an input field, is negligible. We then use semi-analytical estimates to analyze the amplification and propose a strategy for reducing it. Our results are potentially relevant for other memory platforms beyond the example of NV centers., Comment: 9 pages, 7 figures

Liquid crystal (LC) technology enables low-power and cost-effective solutions for implementing the reconfigurable intelligent surface (RIS). However, the phase-shift response of LC-RISs is temperature-dependent, which, if unaddressed, can degrade the performance. This issue is particularly critical in applications such as secure communications, where variations in phase-shift response may lead to significant information leakage. In this paper, we consider secure communication through an LC-RIS and developed a temperature-aware algorithm adapting the RIS phase shifts to thermal conditions. Our simulation results demonstrate that the proposed algorithm significantly improves the secure data rate compared to scenarios where temperature variations are not accounted for.

This paper presents the results of a novel scoping review on the practical models for generating three different types of synthetic health records (SHRs): medical text, time series, and longitudinal data. The innovative aspects of the review, which incorporate study objectives, data modality, and research methodology of the reviewed studies, uncover the importance and the scope of the topic for the digital medicine context. In total, 52 publications met the eligibility criteria for generating medical time series (22), longitudinal data (17), and medical text (13). Privacy preservation was found to be the main research objective of the studied papers, along with class imbalance, data scarcity, and data imputation as the other objectives. The adversarial network-based, probabilistic, and large language models exhibited superiority for generating synthetic longitudinal data, time series, and medical texts, respectively. Finding a reliable performance measure to quantify SHR re-identification risk is the major research gap of the topic., Comment: 27 pages, 3 figures

This systematic literature review paper explores perspectives on the ideal metaverse from user experience, business, and national levels, considering both academic and industry viewpoints. The study examines the metaverse as a sociotechnical imaginary, enabled collectively by virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies. Through a systematic literature review, n=144 records were included and by employing grounded theory for analysis of data, we developed three research models, which can guide researchers in examining the metaverse as a sociotechnical future of information technology. Designers can apply the metaverse user experience maturity model to develop more user-friendly services, while business strategists can use the metaverse business readiness model to assess their firms' current state and prepare for transformation. Additionally, policymakers and policy analysts can utilize the metaverse national competitiveness model to track their countries' competitiveness during this paradigm shift. The synthesis of the results also led to the development of practical assessment tools derived from these models that can guide researchers

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Traditional natural disaster response involves significant coordinated teamwork where speed and efficiency are key. Nonetheless, human limitations can delay critical actions and inadvertently increase human and economic losses. Agentic Large Vision Language Models (LVLMs) offer a new avenue to address this challenge, with the potential for substantial socio-economic impact, particularly by improving resilience and resource access in underdeveloped regions. We introduce DisasTeller, the first multi-LVLM-powered framework designed to automate tasks in post-disaster management, including on-site assessment, emergency alerts, resource allocation, and recovery planning. By coordinating four specialised LVLM agents with GPT-4 as the core model, DisasTeller autonomously implements disaster response activities, reducing human execution time and optimising resource distribution. Our evaluations through both LVLMs and humans demonstrate DisasTeller's effectiveness in streamlining disaster response. This framework not only supports expert teams but also simplifies access to disaster management processes for non-experts, bridging the gap between traditional response methods and LVLM-driven efficiency., Comment: 13 pages, 4 figures

We identify symmetries in a broad class of vector-like confining dark sectors that forbid the leading electromagnetic moments that would ordinarily mediate dark baryon scattering with the Standard Model. The absence of these operators implies dark baryon dark matter has much smaller cross sections for elastic scattering off nuclei, leading to suppressed direct detection signals. In the confined description, we identify an ``$\mathcal{H}$-parity'' symmetry that exists in any dark sector with dark quarks transforming under a vector-like representation of a new confining SU($N_c$) gauge theory as well as a vector-like representation of the electroweak group SU(2)$_L$. The parity is independent of $N_c$ and $N_f$, though it is essential that the dark quarks are neutral under hypercharge. This parity forbids dark hadron electric and magnetic dipole moments, charge radius, and anapole moment, while permitting dimension-7 operators that include polarizability, electroweak loop-induced interactions, as well as lower dimensional electromagnetic $\textit{transition}$ moments between different neutral dark baryon states. We work out an explicit example, $N_c=N_f=3$, that is the most minimal theory with fermionic dark baryons. In this specific model, we use the non-relativistic quark model to show the magnetic dipole moment and charge radius vanish while the transition moments are non-zero, consistent with $\mathcal{H}$-parity. We discuss the implications of a suppressed direct detection signal, emphasizing that this broad class of models provide a well-motivated target for future colliders., Comment: 28+14 pages, 1+1 figures, 2 tables

In this paper, we investigate three schemes for implementing Controlled-NOT (CNOT) gates between individual ytterbium (Yb) rare-earth ions doped into yttrium orthovanadate (YVO$_4$ or YVO). Specifically, we investigate the CNOT gates based on magnetic dipolar interactions between Yb ions, photon scattering off a cavity, and a photon interference-based protocol, with and without an optical cavity. We introduce a theoretical framework for precise computations of gate infidelity, accounting for noise effects. We then compute the gate fidelity of each scheme to evaluate the feasibility of their experimental implementation. Based on these results, we compare the performance of the gate schemes and discuss their respective advantages and disadvantages. We conclude that the probabilistic photon interference-based scheme offers the best fidelity scaling with cooperativity and is superior with the current technology of Yb values, while photon scattering is nearly deterministic but slower with less favourable fidelity scaling as a function of cooperativity. The cavityless magnetic dipolar scheme provides a fast, deterministic gate with high fidelity if close ion localization can be realized., Comment: 18 pages, 11 figures

Reconfigurable intelligent surfaces (RISs) offer enhanced control over propagation through phase and amplitude manipulation but face practical challenges like cost and power usage, especially at high frequencies. This is specifically a major problem at high frequencies (Ka- and V-band) where the high cost of semiconductor components (i.e., diodes, varactors, MEMSs) can make RISs prohibitively costly. In recent years, it is shown that liquid crystals (LCs) are low-cost and low-energy alternative which can address the aforementioned challenges but at the cost of lower response time. In LiquiRIS, we enable leveraging LC-based RIS in mobile networks. Specifically, we devise techniques that minimize the beam switching time of LC-based RIS by tapping into the physical properties of LCs and the underlying mathematical principles of beamforming. We achieve this by modeling and optimizing the beamforming vector to account for the rotation characteristics of LC molecules to reduce their transition time from one state to another. In addition to prototyping the proposed system, we show via extensive experimental analysis that LiquiRIS substantially reduces the response time (up to 70.80%) of liquid crystal surface (LCS).

The Gulf Cooperation Council countries -- Oman, Bahrain, Kuwait, UAE, Qatar, and Saudi Arabia -- holds strategic significance due to its large oil reserves. However, these nations face considerable challenges in shifting from oil-dependent economies to more diversified, knowledge-based systems. This study examines the progress of Gulf Cooperation Council (GCC) countries in achieving economic diversification and social development, focusing on the Social Progress Index (SPI), which provides a broader measure of societal well-being beyond just economic growth. Using data from the World Bank, covering 2010 to 2023, the study employs the XGBoost machine learning model to forecast SPI values for the period of 2024 to 2026. Key components of the methodology include data preprocessing, feature selection, and the simulation of independent variables through ARIMA modeling. The results highlight significant improvements in education, healthcare, and women's rights, contributing to enhanced SPI performance across the GCC countries. However, notable challenges persist in areas like personal rights and inclusivity. The study further indicates that despite economic setbacks caused by global disruptions, including the COVID-19 pandemic and oil price volatility, GCC nations are expected to see steady improvements in their SPI scores through 2027. These findings underscore the critical importance of economic diversification, investment in human capital, and ongoing social reforms to reduce dependence on hydrocarbons and build knowledge-driven economies. This research offers valuable insights for policymakers aiming to strengthen both social and economic resilience in the region while advancing long-term sustainable development goals.

This pilot study explores the application of language models (LMs) to model game event sequences, treating them as a customized natural language. We investigate a popular mobile game, transforming raw event data into textual sequences and pretraining a Longformer model on this data. Our approach captures the rich and nuanced interactions within game sessions, effectively identifying meaningful player segments. The results demonstrate the potential of self-supervised LMs in enhancing game design and personalization without relying on ground-truth labels., Comment: published in Workshop on Customizable NLP at EMNLP 2024

Global disparities in neurosurgical care necessitate innovations addressing affordability and accuracy, particularly for critical procedures like ventriculostomy. This intervention, vital for managing life-threatening intracranial pressure increases, is associated with catheter misplacement rates exceeding 30% when using a freehand technique. Such misplacements hold severe consequences including haemorrhage, infection, prolonged hospital stays, and even morbidity and mortality. To address this issue, we present a novel, stand-alone mobile-based augmented reality system (iSurgARy) aimed at significantly improving ventriculostomy accuracy, particularly in resource-limited settings such as those in low- and middle-income countries. iSurgARy uses landmark based registration by taking advantage of Light Detection and Ranging (LiDaR) to allow for accurate surgical guidance. To evaluate iSurgARy, we conducted a two-phase user study. Initially, we assessed usability and learnability with novice participants using the System Usability Scale (SUS), incorporating their feedback to refine the application. In the second phase, we engaged human-computer interaction (HCI) and clinical domain experts to evaluate our application, measuring Root Mean Square Error (RMSE), System Usability Scale (SUS) and NASA Task Load Index (TLX) metrics to assess accuracy usability, and cognitive workload, respectively

The generalization error (risk) of a supervised statistical learning algorithm quantifies its prediction ability on previously unseen data. Inspired by exponential tilting, Li et al. (2021) proposed the tilted empirical risk as a non-linear risk metric for machine learning applications such as classification and regression problems. In this work, we examine the generalization error of the tilted empirical risk. In particular, we provide uniform and information-theoretic bounds on the tilted generalization error, defined as the difference between the population risk and the tilted empirical risk, with a convergence rate of $O(1/\sqrt{n})$ where $n$ is the number of training samples. Furthermore, we study the solution to the KL-regularized expected tilted empirical risk minimization problem and derive an upper bound on the expected tilted generalization error with a convergence rate of $O(1/n)$., Comment: New results are added

Memory access efficiency is significantly enhanced by caching recent address translations in the CPUs' Translation Lookaside Buffers (TLBs). However, since the operating system is not aware of which core is using a particular mapping, it flushes TLB entries across all cores where the application runs whenever addresses are unmapped, ensuring security and consistency. These TLB flushes, known as TLB shootdowns, are costly and create a performance and scalability bottleneck. A key contributor to TLB shootdowns is memory-mapped I/O, particularly during mmap-munmap cycles and page cache evictions. Often, the same physical pages are reassigned to the same process post-eviction, presenting an opportunity for the operating system to reduce the frequency of TLB shootdowns. We demonstrate, that by slightly extending the mmap function, TLB shootdowns for these "recycled pages" can be avoided. Therefore we introduce and implement the "fast page recycling" (FPR) feature within the mmap system call. FPR-mmaps maintain security by only triggering TLB shootdowns when a page exits its recycling cycle and is allocated to a different process. To ensure consistency when FPR-mmap pointers are used, we made minor adjustments to virtual memory management to avoid the ABA problem. Unlike previous methods to mitigate shootdown effects, our approach does not require any hardware modifications and operates transparently within the existing Linux virtual memory framework. Our evaluations across a variety of CPU, memory, and storage setups, including persistent memory and Optane SSDs, demonstrate that FPR delivers notable performance gains, with improvements of up to 28% in real-world applications and 92% in micro-benchmarks. Additionally, we show that TLB shootdowns are a significant source of bottlenecks, previously misattributed to other components of the Linux kernel.

The proliferation of complex online media has accelerated the process of ideology formation, influenced by stakeholders through advertising channels. The media channels, which vary in cost and effectiveness, present a dilemma in prioritizing optimal fund allocation. There are technical challenges in describing the optimal budget allocation between channels over time, which involves defining the finite vector structure of controls on the chart. To enhance marketing productivity, it's crucial to determine how to distribute a budget across all channels to maximize business outcomes like revenue and ROI. Therefore, the strategy for media budget allocation is primarily an exercise focused on cost and achieving goals, by identifying a specific framework for a media program. Numerous researchers optimize the achievement and frequency of media selection models to aid superior planning decisions amid complexity and vast information availability. In this study, we present a planning model using the media mix model for advertising construction campaigns. Additionally, a decision-making strategy centered on FMEA identifies and prioritizes financial risk factors of the media system in companies. Despite some limitations, this research proposes a decision-making approach based on Z-number theory. To address the drawbacks of the RPN score, the suggested decision-making methodology integrates Z-SWARA and Z-WASPAS techniques with the FMEA method.

This study examines several renowned gradient-based optimization techniques and focuses on their computational efficiency and precision. In the study, the steepest descent, conjugate gradient (Fletcher-Reeves and Polak-Ribiere variants), Newton-Raphson, quasi-Newton (BFGS), and Levenberg-Marquardt techniques were evaluated. These methods were benchmarked using Rosenbrock's, Spring Force Vanderplaats', Ackley's, and Himmelblau's functions. We emphasize the critical role that initial point selection plays in optimizing optimization outcomes in our analysis. It is also important to distinguish between local and global optima since gradient-based methods may have difficulties dealing with nonlinearity and multimodality. We illustrate optimization trajectories using 3D surface visualizations in order to increase understanding. While gradient-based methods have been demonstrated to be effective, they may be limited by computational constraints and by the nature of the objective functions, necessitating the use of heuristic and metaheuristic algorithms in more complex situations., Comment: 25 pages

We have studied the entanglement of purification $E_p$ in a non-conformal holographic model which is a 5- dimensional Einstein gravity coupled to a scalar field $\phi$ with a non-trivial potential $V(\phi)$. The dual 4-dimensional gauge theory is not conformal and exhibits a FG flow between two different fixed points. There are three parameters including energy scale $\Lambda$, model parameter $\phi_M$ and temperature $T$ which control the behavior of the model. Interestingly, we have found that $E_p$ can be used as a measure to probe the non-conformal behavior of the theory at both zero and finite temperature. Furthermore, we have found that if one considers two different mixed states characterized by distinct values of $\frac{\Lambda}{T}$, then the correlation between the subsystems of these states can be the same independent of $\frac{\Lambda}{T}$., Comment: 10 pages, 11 figures

In automatic speech recognition, any factor that alters the acoustic properties of speech can pose a challenge to the system's performance. This paper presents a novel approach for automatic whispered speech recognition in the Irish dialect using the self-supervised WavLM model. Conventional automatic speech recognition systems often fail to accurately recognise whispered speech due to its distinct acoustic properties and the scarcity of relevant training data. To address this challenge, we utilized a pre-trained WavLM model, fine-tuned with a combination of whispered and normal speech data from the wTIMIT and CHAINS datasets, which include the English language in Singaporean and Irish dialects, respectively. Our baseline evaluation with the OpenAI Whisper model highlighted its limitations, achieving a Word Error Rate (WER) of 18.8% and a Character Error Rate (CER) of 4.24% on whispered speech. In contrast, the proposed WavLM-based system significantly improved performance, achieving a WER of 9.22% and a CER of 2.59%. These results demonstrate the efficacy of our approach in recognising whispered speech and underscore the importance of tailored acoustic modeling for robust automatic speech recognition systems. This study provides valuable insights into developing effective automatic speech recognition solutions for challenging speech affected by whisper and dialect. The source codes for this paper are freely available., Comment: This paper was accepted at the ICCKE2024 conference

One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving. Although physiological signals have been extensively used in driving-related research to assess cognitive load, only a few studies have specifically focused on high cognitive load scenarios. Most existing studies tend to examine moderate or low levels of cognitive load In this study, we adopted an auditory version of the n-back task of three levels as a cognitively loading secondary task while driving in a driving simulator. During the simultaneous execution of driving and the n-back task, we recorded fNIRS, eye-tracking, and driving behavior data to predict cognitive load at three different levels. To the best of our knowledge, this combination of data sources has never been used before. Un-like most previous studies that utilize binary classification of cognitive load and driving in conditions without traffic, our study involved three levels of cognitive load, with drivers operating in normal traffic conditions under low visibility, specifically during nighttime and rainy weather. We proposed a hybrid neural network combining a 1D Convolutional Neural Network and a Recurrent Neural Network to predict cognitive load. Our experimental re-sults demonstrate that the proposed model, with fewer parameters, increases accuracy from 99.82% to 99.99% using physiological data, and from 87.26% to 92.02% using driving behavior data alone. This significant improvement highlights the effectiveness of our hybrid neural network in accurately pre-dicting cognitive load during driving under challenging conditions., Comment: 17 pages

Motion simulators allow researchers to safely investigate the interaction of drivers with a vehicle. However, many studies that use driving simulator data to predict cognitive load only employ two levels of workload, leaving a gap in research on employing deep learning methodologies to analyze cognitive load, especially in challenging low-light conditions. Often, studies overlook or solely focus on scenarios in bright daylight. To address this gap and understand the correlation between performance and cognitive load, this study employs functional near-infrared spectroscopy (fNIRS) and eye-tracking data, including fixation duration and gaze direction, during simulated driving tasks in low visibility conditions, inducing various mental workloads. The first stage involves the statistical estimation of useful features from fNIRS and eye-tracking data. ANOVA will be applied to the signals to identify significant channels from fNIRS signals. Optimal features from fNIRS, eye-tracking and vehicle dynamics are then combined in one chunk as input to the CNN and LSTM model to predict workload variations. The proposed CNN-LSTM model achieved 99% accuracy with neurological data and 89% with vehicle dynamics to predict cognitive load, indicating potential for real-time assessment of driver mental state and guide designers for the development of safe adaptive systems., Comment: 10 pages

Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in the concentrations of oxygenated haemoglobin (HbO) and deoxygenated haemo-globin (HbR). Various machine learning classification techniques have been utilized to distinguish cognitive states. However, conventional machine learning methods, although simpler to implement, undergo a complex pre-processing phase before network training and demonstrate reduced accuracy due to inadequate data preprocessing. Additionally, previous research in cog-nitive load assessment using fNIRS has predominantly focused on differ-sizeentiating between two levels of mental workload. These studies mainly aim to classify low and high levels of cognitive load or distinguish between easy and difficult tasks. To address these limitations associated with conven-tional methods, this paper conducts a comprehensive exploration of the im-pact of Long Short-Term Memory (LSTM) layers on the effectiveness of Convolutional Neural Networks (CNNs) within deep learning models. This is to address the issues related to spatial features overfitting and lack of tem-poral dependencies in CNN in the previous studies. By integrating LSTM layers, the model can capture temporal dependencies in the fNIRS data, al-lowing for a more comprehensive understanding of cognitive states. The primary objective is to assess how incorporating LSTM layers enhances the performance of CNNs. The experimental results presented in this paper demonstrate that the integration of LSTM layers with Convolutional layers results in an increase in the accuracy of deep learning models from 97.40% to 97.92%., Comment: conference

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

Convolutional neural networks (CNNs) and Transformers have shown advanced accuracy in crack detection under certain conditions. Yet, the fixed local attention can compromise the generalisation of CNNs, and the quadratic complexity of the global self-attention restricts the practical deployment of Transformers. Given the emergence of the new-generation architecture of Mamba, this paper proposes a Vision Mamba (VMamba)-based framework for crack segmentation on concrete, asphalt, and masonry surfaces, with high accuracy, generalisation, and less computational complexity. Having 15.6% - 74.5% fewer parameters, the encoder-decoder network integrated with VMamba could obtain up to 2.8% higher mDS than representative CNN-based models while showing about the same performance as Transformer-based models. Moreover, the VMamba-based encoder-decoder network could process high-resolution image input with up to 90.6% lower floating-point operations., Comment: 23 pages, 9 figures

Computational notebooks have become the primary coding environment for data scientists. However, research on their code quality is still emerging, and the code shared is often of poor quality. Given the importance of maintenance and reusability, understanding the metrics that affect notebook code comprehensibility is crucial. Code understandability, a qualitative variable, is closely tied to user opinions. Traditional approaches to measuring it either use limited questionnaires to review a few code pieces or rely on metadata such as likes and votes in software repositories. Our approach enhances the measurement of Jupyter notebook understandability by leveraging user comments related to code understandability. As a case study, we used 542,051 Kaggle Jupyter notebooks from our previous research, named DistilKaggle. We employed a fine-tuned DistilBERT transformer to identify user comments associated with code understandability. We established a criterion called User Opinion Code Understandability (UOCU), which considers the number of relevant comments, upvotes on those comments, total notebook views, and total notebook upvotes. UOCU proved to be more effective than previous methods. Furthermore, we trained machine learning models to predict notebook code understandability based solely on their metrics. We collected 34 metrics for 132,723 final notebooks as features in our dataset, using UOCU as the label. Our predictive model, using the Random Forest classifier, achieved 89% accuracy in predicting the understandability levels of computational notebooks.

In predictive modeling, overfitting poses a significant risk, particularly when the feature count surpasses the number of observations, a common scenario in high-dimensional data sets. To mitigate this risk, feature selection is employed to enhance model generalizability by reducing the dimensionality of the data. This study focuses on evaluating the stability of feature selection techniques with respect to varying data volumes, particularly employing time series similarity methods. Utilizing a comprehensive dataset that includes the closing, opening, high, and low prices of stocks from 100 high-income companies listed in the Fortune Global 500, this research compares several feature selection methods including variance thresholds, edit distance, and Hausdorff distance metrics. The aim is to identify methods that show minimal sensitivity to the quantity of data, ensuring robustness and reliability in predictions, which is crucial for financial forecasting. Results indicate that among the tested feature selection strategies, the variance method, edit distance, and Hausdorff methods exhibit the least sensitivity to changes in data volume. These methods therefore provide a dependable approach to reducing feature space without significantly compromising the predictive accuracy. This study not only highlights the effectiveness of time series similarity methods in feature selection but also underlines their potential in applications involving fluctuating datasets, such as financial markets or dynamic economic conditions. The findings advocate for their use as principal methods for robust feature selection in predictive analytics frameworks., Comment: arXiv admin note: text overlap with arXiv:2406.03742

We focus on the task of learning the value function in the reinforcement learning (RL) setting. This task is often solved by updating a pair of online and target networks while ensuring that the parameters of these two networks are equivalent. We propose Lookahead-Replicate (LR), a new value-function approximation algorithm that is agnostic to this parameter-space equivalence. Instead, the LR algorithm is designed to maintain an equivalence between the two networks in the function space. This value-based equivalence is obtained by employing a new target-network update. We show that LR leads to a convergent behavior in learning the value function. We also present empirical results demonstrating that LR-based target-network updates significantly improve deep RL on the Atari benchmark., Comment: Accepted to International Conference on Machine Learning (ICML24)

Recent benchmarks found In-Context Learning (ICL) outperforms both deep learning and tree-based algorithms on small tabular datasets. However, on larger datasets, ICL for tabular learning cannot run without severely compromising performance, due to its quadratic space and time complexity w.r.t. dataset size. We propose MIXTUREPFN, which both extends nearest-neighbor sampling to the state-of-the-art ICL for tabular learning model and uses bootstrapping to finetune said model on the inference-time dataset. MIXTUREPFN is the Condorcet winner across 36 diverse tabular datasets against 19 strong deep learning and tree-based baselines, achieving the highest mean rank among Top-10 aforementioned algorithms with statistical significance., Comment: 32 pages, 16 figures

In this letter we report a proof-of-concept terahertz band-stop filter constructed from split-ring resonators that has a center frequency of 1.06 THz and a -3 dB bandwidth of 0.36 THz. The design consists of nine split ring resonators of varying radii (3 x 13 um, 3 x 14 um, 3 x 15 um) that are placed between the conductors of a coplanar stripline transmission line. The response of the filter is measured using a modified terahertz time-domain spectrometer and we find reasonable agreement between simulation and experiment. This work demonstrates the viability of using varying-radii split-ring resonators as discrete sub-wavelength filter elements for terahertz systems., Comment: 5 pages and 6 figures

Lead halide perovskites have emerged as platforms for exciton-polaritonic studies at room temperature thanks to their excellent photoluminescence efficiency and synthetic versatility. In this work we find proof of strong exciton-photon coupling in cavities formed by the layered crystals themselves, a phenomenon known as self-hybridization effect. We use multi-layers of high quality Ruddlesden-Popper perovskites in their 2D crystalline form, benefitting from their quantum-well excitonic resonances and the strong Fabry-Perot cavity modes resulting from the total-internal-reflection at their smooth surfaces. Optical spectroscopy reveals bending of the cavity modes typical for exciton-polariton formation, and photoluminescence spectroscopy shows thickness dependent splitting of the excitonic resonance. Strikingly, local optical excitation with energy below the excitonic resonance of the flakes in photoluminescence measurements unveils coupling of light to in-plane polaritonic modes with directed propagation. These exciton-polaritons exhibit high coupling efficiencies and extremely low loss propagation mechanisms which is confirmed by finite difference time domain simulations. We therefore prove that mesoscopic 2D Ruddlesden-Popper perovskites flakes represent an effective but simple system to study the rich physics of exciton-polaritons at room temperature.

We study two-player zero-sum concurrent stochastic games with finite state and action space played for an infinite number of steps. In every step, the two players simultaneously and independently choose an action. Given the current state and the chosen actions, the next state is obtained according to a stochastic transition function. An objective is a measurable function on plays (or infinite trajectories) of the game, and the value for an objective is the maximal expectation that the player can guarantee against the adversarial player. We consider: (a) stateful-discounted objectives, which are similar to the classical discounted-sum objectives, but states are associated with different discount factors rather than a single discount factor; and (b) parity objectives, which are a canonical representation for $\omega$-regular objectives. For stateful-discounted objectives, given an ordering of the discount factors, the limit value is the limit of the value of the stateful-discounted objectives, as the discount factors approach zero according to the given order. The computational problem we consider is the approximation of the value within an arbitrary additive error. The above problem is known to be in EXPSPACE for the limit value of stateful-discounted objectives and in PSPACE for parity objectives. The best-known algorithms for both the above problems are at least exponential time, with an exponential dependence on the number of states and actions. Our main results for the value approximation problem for the limit value of stateful-discounted objectives and parity objectives are as follows: (a) we establish TFNP[NP] complexity; and (b) we present algorithms that improve the dependency on the number of actions in the exponent from linear to logarithmic. In particular, if the number of states is constant, our algorithms run in polynomial time.

Turn-based discounted-sum games are two-player zero-sum games played on finite directed graphs. The vertices of the graph are partitioned between player 1 and player 2. Plays are infinite walks on the graph where the next vertex is decided by a player that owns the current vertex. Each edge is assigned an integer weight and the payoff of a play is the discounted-sum of the weights of the play. The goal of player 1 is to maximize the discounted-sum payoff against the adversarial player 2. These games lie in NP and coNP and are among the rare combinatorial problems that belong to this complexity class and the existence of a polynomial-time algorithm is a major open question. Since breaking the general exponential barrier has been a challenging problem, faster parameterized algorithms have been considered. If the discount factor is expressed in unary, then discounted-sum games can be solved in polynomial time. However, if the discount factor is arbitrary (or expressed in binary), but the weights are in unary, none of the existing approaches yield a sub-exponential bound. Our main result is a new analysis technique for a classical algorithm (namely, the strategy iteration algorithm) that present a new runtime bound which is $n^{O ( W^{1/4} \sqrt{n} )}$, for game graphs with $n$ vertices and maximum absolute weight of at most $W$. In particular, our result yields a deterministic sub-exponential bound for games with weights that are constant or represented in unary.

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

The conditional disclosure of secrets (CDS) primitive is among the simplest cryptographic settings in which to study the relationship between communication, randomness, and security. CDS involves two parties, Alice and Bob, who do not communicate but who wish to reveal a secret $z$ to a referee if and only if a Boolean function $f$ has $f(x,y)=1$. Alice knows $x,z$, Bob knows $y$, and the referee knows $x,y$. Recently, a quantum analogue of this primitive called CDQS was defined and related to $f$-routing, a task studied in the context of quantum position-verification. CDQS has the same inputs, outputs, and communication pattern as CDS but allows the use of shared entanglement and quantum messages. We initiate the systematic study of CDQS, with the aim of better understanding the relationship between privacy and quantum resources in the information theoretic setting. We begin by looking for quantum analogues of results already established in the classical CDS literature. Doing so we establish a number of basic properties of CDQS, including lower bounds on entanglement and communication stated in terms of measures of communication complexity. Because of the close relationship to the $f$-routing position-verification scheme, our results have relevance to the security of these schemes.

The significance of last-mile logistics in the healthcare supply chain is growing steadily, especially in pharmacies where the growing prevalence of medication delivery to patients' homes is remarkable. This paper proposes a novel mathematical model for the last-mile logistics of the pharmaceutical supply chain and optimizes a pharmacy's logistical financial outcome while considering medication synchronization, different delivery modes, and temperature requirements of medicines. We propose a mathematical formulation of the problem using Mixed Integer Linear Programming (MILP) evolved from the actual problem of an outpatient pharmacy of a Dutch hospital. We create a case study by gathering, preparing, processing, and analyzing the associated data. We find the optimal solution, using Python MIP package and the Gurobi solver, which indicates the number of order batches, the composition of these batches, and the number of staff related to the preparation of the order batches. Our results show that our optimal solution increases the pharmacy's logistical financial outcome by 34 percent. Moreover, we propose other model variations and perform extensive scenario analysis to provide managerial insights applicable to other pharmacies and distributors in the last step of cold supply chains. Based on our scenario analysis, we conclude that improving medication synchronization can significantly enhance the pharmacy's logistical financial outcome.

Vision-based crack detection faces deployment challenges due to the size of robust models and edge device limitations. These can be addressed with lightweight models trained with knowledge distillation (KD). However, state-of-the-art (SOTA) KD methods compromise anti-noise robustness. This paper develops Robust Feature Knowledge Distillation (RFKD), a framework to improve robustness while retaining the precision of light models for crack segmentation. RFKD distils knowledge from a teacher model's logit layers and intermediate feature maps while leveraging mixed clean and noisy images to transfer robust patterns to the student model, improving its precision, generalisation, and anti-noise performance. To validate the proposed RFKD, a lightweight crack segmentation model, PoolingCrack Tiny (PCT), with only 0.5 M parameters, is also designed and used as the student to run the framework. The results show a significant enhancement in noisy images, with RFKD reaching a 62% enhanced mean Dice score (mDS) compared to SOTA KD methods., Comment: 24 pages, 13 figures

Methods for learning latent user representations from historical behavior logs have gained traction for recommendation tasks in e-commerce, content streaming, and other settings. However, this area still remains relatively underexplored in video and mobile gaming contexts. In this work, we present a novel method for overcoming this limitation by extending a long-range Transformer model from the natural language processing domain to player behavior data. We discuss specifics of behavior tracking in games and propose preprocessing and tokenization approaches by viewing in-game events in an analogous way to words in sentences, thus enabling learning player representations in a self-supervised manner in the absence of ground-truth annotations. We experimentally demonstrate the efficacy of the proposed approach in fitting the distribution of behavior events by evaluating intrinsic language modeling metrics. Furthermore, we qualitatively analyze the emerging structure of the learned embedding space and show its value for generating insights into behavior patterns to inform downstream applications.

In this paper, we present two datasets that we make publicly available for research. The data is collected in a testbed comprised of a custom-made Reconfigurable Intelligent Surface (RIS) prototype and two regular OFDM transceivers within an anechoic chamber. First, we discuss the details of the testbed and equipment used, including insights about the design and implementation of our RIS prototype. We further present the methodology we employ to gather measurement samples, which consists of letting the RIS electronically steer the signal reflections from an OFDM transmitter toward a specific location. To this end, we evaluate a suitably designed configuration codebook and collect measurement samples of the received power with an OFDM receiver. Finally, we present the resulting datasets, their format, and examples of exploiting this data for research purposes.

Agent-based simulation (ABS) models are potent tools for analyzing complex systems. However, understanding and validating ABS models can be a significant challenge. To address this challenge, cutting-edge data-driven techniques offer sophisticated capabilities for analyzing the outcomes of ABS models. One such technique is process mining, which encompasses a range of methods for discovering, monitoring, and enhancing processes by extracting knowledge from event logs. However, applying process mining to event logs derived from ABSs is not trivial, and deriving meaningful insights from the resulting process models adds an additional layer of complexity. Although process mining is invaluable in extracting insights from ABS models, there is a lack of comprehensive methodological guidance for its application in ABS evaluation in the research landscape. In this paper, we propose a methodology, based on the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, to assess ABS models using process mining techniques. We incorporate process mining techniques into the stages of the CRISP-DM methodology, facilitating the analysis of ABS model behaviors and their underlying processes. We demonstrate our methodology using an established agent-based model, Schelling model of segregation. Our results show that our proposed methodology can effectively assess ABS models through produced event logs, potentially paving the way for enhanced agent-based model validity and more insightful decision-making.