ethicalabs/Research-Intent-Collab huggingface dataset:
Positive Affect and Chronic Pain:A Preregistered Systematic Review and Meta-Analysis  Health Sciences and Evidence Synthesis Librarian

Importance: Chronic non-cancer pain (CNCP) is a significant health burden among adults. Standard behavioral therapies typically focus on targeting negative affect and yield only modest treatment effects. Objective: To systematically review and investigate the association between positive affect (PA) and pain severity among adults with chronic non-cancer pain. Data Sources: Databases search included MEDLINE (PubMed), PsycINFO, CINAHL, ProQuest Dissertations and Theses, OLASTER, Open Grey, PeerJ, and PsyArXiv (inception to September 24, 2018). Study Selection: We analyzed studies that: 1) employed a daily diary, longitudinal, laboratory, or intervention study design; 2) enrolled individuals with CNCP (pain ≥ 12 weeks); and 3) reported full quantitative results on outcomes. Data Extraction and Synthesis: At least 2 researchers independently screened articles, extracted data, and assessed the risk of bias. The main meta-analysis was followed by subgroup analyses. All analyses were performed using random-effects models. Formal tests for heterogeneity (Q-statistic; I2) and publication bias (p-curve and p-uniform*) were performed. Main Outcome: Pain severity. Results: We analyzed 26 studies with 2702 participants. Positive affect was associated with reduced pain severity in both observational and experimental studies. In subgroup analyses, only gender demonstrated a consistent effect: Higher PA was associated with lower pain in observational studies that had higher percentages of female participants. Conclusion and Relevance: Positive affect is associated with a reduction in pain severity, with the strongest association observed among women with CNCP.
Ethical AI Regulatory Sandboxes: Insights from cyberspace regulation and Internet governance

This paper explores how theories of cyberspace regulation and Internet governance can provide important lessons for the implementation of ethical-oriented AI regulatory sandboxes, that is, sandboxes that consider ethical aspects in their implementation. The first part provides a brief overview of the challenges in defining the concept of artificial intelligence, and the principles developed to mitigate social impacts arising from the autonomy of AI systems. In the second, the regulatory sandbox is presented as a strategic tool to promote multidisciplinary and multistakeholder engagement. To support this argument, the views of authors who were essential for developing concepts of cyberspace regulation and Internet governance will be looked at, such as Lawrence Lessig, Jeanette Hoffman, Andrew Murray, and Julia Black. Finally, the third part focuses on the Brazilian regulatory environment and its maturity for implementing AI regulatory sandboxes. The article concludes by summarizing the lessons that can be retrieved from the theories analysed for the development of regulatory sandboxes for Artificial Intelligence.
Benchmarking Deep Learning Architectures for Radio Modulation Classification Using the Radioml Dataset in Wireless Network

Automatic Modulation Classification (AMC) is an essential element of the current wireless communication system, which allows the proper recognition of modulation schemes in the dynamic conditions on the channel and changing signal-to-noise ratios (SNRs). This study presents the results of DL architectural benchmarking using RadioML 2016. The architectures included in the research are CNN+BiLSTM, deep 1D ResNet, and LSTM.data set 10a. The approaches that are integrated into the proposed framework include the critical signal preprocessing steps, including the high-SNR filtering, power normalization, label encoding, and systematic split of training and testing data to get a better quality of data and better generalization of the model. The convolutional layers are used to achieve effective space features of raw I/Q samples and residual links within the ResNet framework to train the network deeper and prevent the vanishing gradient issue. Model performance is evaluated using accuracy (ACC), precision (PRE), recall (REC), F1-score (F1), and Matthews Correlation Coefficient (MCC). The experimental such as, the proposed ResNet model, which scores test ACC of 92.63%, with lower test loss (0.2903), along with higher PRE (0.9424), REC (0.9263), F1 (0.9225), and MCC (0.9213), compared with existing models, including HMF (90.70%), DenseNet (0.868), GRU (0.7891), and DRMM (73.31%) the robustness and usefulness of the ResNet model in the classification of test modulations in wireless networks.
Open Graph Benchmark: Datasets for Machine Learning on Graphs

We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders, evaluation scripts, baseline code, and leaderboards are publicly available at https://ogb.stanford.edu .
Information Design for Cultural Heritage. Data-Driven Dashboard Prototype - Ferrovie dello Stato Italiane Case

This research report documents a project developed for the Information Design & Data Research + Storytelling course. Using a corporate archive as the primary source, the study explores the historical evolution of a selected topic through archival and secondary research, situating it within the broader economic, territorial, and social transformations of its time.  Through a structured research process — including archive exploration, data collection, and narrative development — the team identified key patterns and stories embedded in the archival materials, which informed both the content and the design of the final output.  The result is a digital storytelling platform that translates archival complexity into an interactive, multi-layered experience. The platform combines data visualizations, maps, timelines, and narrative nodes to allow users to explore the topic at different levels of depth, balancing historical context with visual clarity.
Thermodynamic Uncertainty Relation and Thermodynamic Speed Limit in Deterministic Chemical Reaction Networks

We generalize the thermodynamic uncertainty relation (TUR) and thermodynamic speed limit (TSL) for deterministic chemical reaction networks (CRNs). The scaled diffusion coefficient derived by considering the connection between macro- and mesoscopic CRNs plays an essential role in our results. The TUR shows that the product of the entropy production rate and the ratio of the scaled diffusion coefficient to the square of the rate of concentration change is bounded below by two. The TSL states a trade-off relation between speed and thermodynamic quantities, the entropy production, and the time-averaged scaled diffusion coefficient. The results are proved under the general setting of open and nonideal CRNs.
Protecting Hamiltonian-based quantum computation using error suppression and error correction

In this dissertation, we present general conditions for quantum error suppression for Hamiltonian-based quantum computation using subsystem codes. This involves encoding the Hamiltonian performing the computation using an error detecting subsystem code and the addition of a penalty term that commutes with the encoded Hamiltonian. We illustrate the power of subsystem-based error suppression with several examples of two-local constructions for protection against local errors, which circumvent an earlier no-go theorem about two-local commuting Hamiltonians. ? We also discuss the generalization of the quantum error suppression results of Jordan, Farhi, and Shor to arbitrary Markovian dynamics. In this setting, we show that it is possible to suppress the initial decay out of the encoded ground state with an energy penalty strength that grows only logarithmically in the system size, at a fixed temperature. ? Finally, we discuss how to simulate high-weight Hamiltonians. Such a simulation can convert local noise on the original Hamiltonian into undesirable non-local noise on the simulated Hamiltonian. We show how starting from two-local Hamiltonian in the presence of non-Markovian noise, a desired computation can be simulated as well as protected using fast pulses, while maintaining an energy gap against the errors created in the process.
Study of muons from ultra-high energy cosmic ray air showers measured with the Telescope Array experiment

One of the uncertainties in ultrahigh energy cosmic ray (UHECR) observation derives from the hadronic interaction model used for air shower Monte-Carlo (MC) simulations. One may test the hadronic interaction models by comparing the measured number of muons observed at the ground from UHECR induced air showers with the MC prediction. The Telescope Array (TA) is the largest experiment in the northern hemisphere observing UHECR in Utah, USA. It aims to reveal the origin of UHECRs by studying the energy spectrum, mass composition and anisotropy of cosmic rays by utilizing an array of surface detectors (SDs) and fluorescence detectors. We studied muon densities in the UHE extensive air showers by analyzing the signal of TA SD stations for highly inclined showers. On condition that the muons contribute about 65% of the total signal, the number of particles from air showers is typically 1.88 ± 0.08 (stat.) ± 0.42 (syst.) times larger than the MC prediction with the QGSJET II-03 model for proton-induced showers. The same feature was also obtained for other hadronic interaction models, such as QGSJET II-04.
Trastuzumab Accessibility and Utilization for Breast Cancer Treatment in an eastern prefecture-level city in China: Does health insurance coverage matter?

Abstract         Background  Trastuzumab, a monoclonal antibody for breast cancer, faces global accessibility challenges, especially in low- and middle-income countries, primarily due to high costs. This study examines the impact of medical insurance policy changes and price reductions on Trastuzumab utilization in China, using JX city as a case study, to provide insights into improving drug accessibility and affordability. Methods  This retrospective study investigated Trastuzumab utilization among HER2-positive breast cancer patients at a tertiary hospital from May 2013 to December 2022 in JX, a prefecture-level city located in eastern China. Patients were stratified based on their CerbB2 indicator. Patients with a 2 + CerbB2 status require further FISH testing to confirm HER2 expression for Trastuzumab eligibility, while patients with a 3 + CerbB2 status directly qualifies for the treatment. We analyzed the utilization rates of FISH test and Trastuzumab using logistic regressions, identifying factors that influence their usage. Results  766 patients were included in this study. The utilization rate of Trastuzumab among patients with CerbB2 3 + rose from 40.40–77.94% in September 2017 following the expansion of insurance coverage, and further increased to 90.41% after a price reduction in 2020. The FISH test was not covered by health insurance, and it was not available in the local hospital until Trastuzumab became available in JX city. For patients with CerbB2 2+, the proportion undergoing the FISH test increased dramatically from 8.89–82.08% after the price reduction in 2020. The mere inclusion into basic medical insurance, regardless of insurance types, significantly increased the utilization of Trastuzumab and the FISH test. However, rural patients in JX city are still facing financial burdens with Trastuzumab's out-of-pocket cost accounting for 62.9% of their annual disposable income in 2020. Conclusions  Our findings indicate that expanding insurance coverage and reducing the price significantly increased Trastuzumab utilization. Additionally, improving the accessibility of the FISH test plays a crucial role in enhancing the uptake of Trastuzumab among eligible patients. These insights are essential for informing policies in China and other low- and middle-income countries towards achieving universal health coverage.
Sub-pJ/MAC Silicon Photonic GeMM for Optical Neural Networks using a Time-Space Multiplexed Coherent Xbar

We present a time-space multiplexed Silicon Photonic Neural Network that acts as a General Matrix Multiply (GeMM) engine, using a 2×2 photonic Xbar prototype for demonstrating experimental results at 20GBd and an accuracy of 93.3% at an energy efficiency of 0.2pJ/MAC.
X-ray Spectroscopy of Accretion Disks and Stellar Winds in X-Ray Binaries

From hot, tenuous gas dominated by Compton processes, to warm, photoionized emission-line regions, to cold, optically thick fluorescing matter, accreting gas flows in X-ray binaries span a huge portion of the parameter space accessible to astrophysical plasmas. The coexistence of such diverse states of material within small volumes (10^33-10^36 cm^3) leaves X-ray spectroscopists with a challenging set of problems, since all such matter produces various X-ray spectral signatures when exposed to hard X rays. Emission-line regions in X-ray binaries are characterized by high radiation energy densities, relatively high particle densities, and velocities ~1000 km/s. In this article, we describe some recent efforts to generate detailed X-ray line spectra from models of X-ray binaries, whose aims are to reproduce spectra acquired with the ASCA, Chandra, and XMM-Newton observatories. With emphasis on the global nature of X-ray line emission in these systems, the article includes separate treatments of high-mass and low-mass systems, as well as summaries of continuum spectroscopy
Optical Bistability in Photonic Topological Hypercrystals and Its Applications in Photonic Neural Network

Optical bistability is a nonlinear phenomenon enabling stable switching between two optical states and has important applications in optical communication and photonic neural networks (PNNs). However, conventional bistable devices often suffer from fabrication imperfections and scattering losses, which limit their robustness and dispersionless performance. In this study, we numerically investigate optical bistability from a one-dimensional photonic topological hypercrystal (PhH) composed of alternating hyperbolic metamaterials (HMMs) and dielectric layers. By designing a center-inversed symmetric layered PhH structure and introducing Kerr nonlinearity into the localized dielectric region of maximum electric field intensity at the inversion center, we achieve a robust, angle-insensitive optical bistability for TM polarization through phase variation compensation mechanism. When applied as a nonlinear activation function in PNNs, the bistable PhH exhibits performance comparable to conventional digital activation functions such as ReLU and Sigmoid in image-recognition tasks. Our work paves the way for integrating topological bistable devices into next-generation PNNs.
Alexander Duality for Functions: the Persistent Behavior of Land and Water and Shore

This note contributes to the point calculus of persistent homology by extending Alexander duality to real-valued functions. Given a perfect Morse function $f: S^{n+1} \to [0,1]$ and a decomposition $S^{n+1} = U \cup V$ such that $M = \U \cap V$ is an $n$-manifold, we prove elementary relationships between the persistence diagrams of $f$ restricted to $U$, to $V$, and to $M$.
An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data--Extended Version

Time series anomaly detection is important in modern large-scale systems and is applied in a variety of domains to analyze and monitor the operation of diverse systems. Unsupervised approaches have received widespread interest, as they do not require anomaly labels during training, thus avoiding potentially high costs and having wider applications. Among these, autoencoders have received extensive attention. They use reconstruction errors from compressed representations to define anomaly scores. However, representations learned by autoencoders are sensitive to anomalies in training time series, causing reduced accuracy. We propose a novel encode-then-decompose paradigm, where we decompose the encoded representation into stable and auxiliary representations, thereby enhancing the robustness when training with contaminated time series. In addition, we propose a novel mutual information based metric to replace the reconstruction errors for identifying anomalies. Our proposal demonstrates competitive or state-of-the-art performance on eight commonly used multi- and univariate time series benchmarks and exhibits robustness to time series with different contamination ratios.
Establishment of video-assisted and robot-assisted thoracic surgery training platform with surgical simulations for the new generation of thoracic surgeons

It is indeed a special privilege and pleasure to introduce readers to this issue about current status of surgical simulation in minimally invasive surgery including video-assisted thoracic surgery (VATS) and robot-assisted thoracic surgery (RATS).
Quality of life and patient-reported outcomes in patients with osteosarcoma:

Patients diagnosed with osteosarcoma undergo intensive multimodality treatment that can lead to long-term adverse effects, significantly impacting various aspects of daily living. To objectively assess the Health-Related Quality of Life (HR-QoL) in pediatric and adult populations, several Patient-Reported Outcome Measurements (PROMs) are available. However, these questionnaires often exhibit substantial variability in the domains and items they encompass, frequently failing to address aspects that are particularly important after osteosarcoma treatment. A systematic review was conducted to identify the most frequently used questionnaires concerning QoL in pediatric and adult patients with osteosarcoma and to examine the diverse domains and subdomains of QoL assessed by these questionnaires to identify gaps in their coverage, to recommend suitable instruments for an upcoming European trial within the Fighting Osteosarcoma Through European Research (FOSTER) Consortium. English-language literature published since 1980 in PubMed was reviewed. One hundred twenty-eight articles were initially screened for eligibility. Sixty-three original articles were included in the qualitative synthesis. An overview from review articles was given. Selected studies displayed substantial heterogeneity in terms of their objectives, target populations, age ranges, follow-up time, and number of patients included. None of the questionnaires covered all age groups and addressed all important aspects following osteosarcoma treatment. To comprehensively address as many relevant aspects as possible, a combination of questionnaires is suggested. For the adult population, it is recommended to use the EORTC-QLQ-C30 questionnaire together with the Body Image Scale (BIS), while for pediatric patients, the PedsQl-generic and PedsQl-cancer-specific questionnaires and BIS (> 16 years) are suggested. The use of Patient-Reported Outcome Measurement Information Systems (PROMIS) can provide a comprehensive assessment of symptoms such as anxiety, pain, and fatigue. The development of new bone sarcoma-specific, pediatric and adult self-reported questionnaires, or the validation and translation of existing bone sarcoma-specific questionnaires, along with the utilization of new digital possibilities, holds great value for upcoming trials.
Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models

Abstract                                        Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called                     SurvivalNet                     that enables automatic training, evaluation and interpretation of deep survival models.
Assessing Conservation Values: Biodiversity and Endemicity in Tropical Land Use Systems

Despite an increasing amount of data on the effects of tropical land use on continental forest fauna and flora, it is debatable whether the choice of the indicator variables allows for a proper evaluation of the role of modified habitats in mitigating the global biodiversity crisis. While many single-taxon studies have highlighted that species with narrow geographic ranges especially suffer from habitat modification, there is no multi-taxa study available which consistently focuses on geographic range composition of the studied indicator groups. We compiled geographic range data for 180 bird, 119 butterfly, 204 tree and 219 understorey plant species sampled along a gradient of habitat modification ranging from near-primary forest through young secondary forest and agroforestry systems to annual crops in the southwestern lowlands of Cameroon. We found very similar patterns of declining species richness with increasing habitat modification between taxon-specific groups of similar geographic range categories. At the 8 km(2) spatial level, estimated richness of endemic species declined in all groups by 21% (birds) to 91% (trees) from forests to annual crops, while estimated richness of widespread species increased by +101% (trees) to +275% (understorey plants), or remained stable (-2%, butterflies). Even traditional agroforestry systems lost estimated endemic species richness by -18% (birds) to -90% (understorey plants). Endemic species richness of one taxon explained between 37% and 57% of others (positive correlations) and taxon-specific richness in widespread species explained up to 76% of variation in richness of endemic species (negative correlations). The key implication of this study is that the range size aspect is fundamental in assessments of conservation value via species inventory data from modified habitats. The study also suggests that even ecologically friendly agricultural matrices may be of much lower value for tropical conservation than indicated by mere biodiversity value.