Miracle-dz/UniFAR-FTUs huggingface dataset:
Multicolor Photoluminescent Carbon Dots à La Carte for Biomedical Applications

Dual-emission fluorescence probes that provide high sensitivity are key for biomedical diagnostic applications. Nontoxic carbon dots (CDs) are an emerging alternative to traditional fluorescent probes; however, robust and reproducible synthetic strategies are still needed to access materials with controlled emission profiles and improved fluorescence quantum yields (FQYs). Herein, we report a practical and general synthetic strategy to access dual-emission CDs with FQYs as high as 0.67 and green/blue, yellow/blue, or red/blue excitation-dependent emission profiles using common starting materials such as citric acid, cysteine, and co-dopants to bias the synthetic pathway. Structural and physicochemical analysis using nuclear magnetic resonance, absorbance and fluorescence spectroscopy, Fourier-transform infrared spectroscopy, and X-ray photoelectron spectroscopy in addition to transmission electron and atomic force microscopy (TEM and AFM) is used to elucidate the material’s composition which is responsible for the unique observed photoluminescence properties. Moreover, the utility of the probes is demonstrated in the clinical setting by the synthesis of green/blue emitting antibody-CD conjugates which are used for the immunohistochemical staining of human brain tissues of glioblastoma patients, showing detection under two different emission channels.
Uncertainty in surface wind speed projections over the Iberian Peninsula: CMIP6 GCMs versus a WRF‐RCM

This study assessed the projected near‐surface wind speed (SWS) changes and variability over the Iberian Peninsula for the 21st century. Here, we compared Coupled Model Intercomparison Project Phase 6 global climate models (GCMs) with a higher spatial resolution regional climate model (RCM; ∼20 km), known as WRF‐CESM2, which was created by a dynamic downscaling of the Community Earth System Model version 2 (CESM2) using the Weather Research and Forecasting (WRF) model. Our analysis found that the GCMs tended to overestimate observed SWS for 1985–2014, while the higher spatial resolution of the WRF‐CESM2 did not improve the accuracy and underestimated the SWS magnitude. GCMs project a decline of SWS under high shared socioeconomic pathways (SSPs) greenhouse concentrations, such as SSP370 and SSP585, while an interdecadal oscillation appears in SSP126 and SSP245 for the end of the century. The WRF‐CESM2 under SSP585 predicts the opposite increasing SWS. Our results suggest that 21st‐century projections of SWS are uncertain even for regionalized products and should be taken with caution.
Viewpoint Integration and Registration with Vision Language Foundation Model for Image Change Understanding

Recently, the development of pre-trained vision language foundation models (VLFMs) has led to remarkable performance in many tasks. However, these models tend to have strong single-image understanding capability but lack the ability to understand multiple images. Therefore, they cannot be directly applied to cope with image change understanding (ICU), which requires models to capture actual changes between multiple images and describe them in language. In this paper, we discover that existing VLFMs perform poorly when applied directly to ICU because of the following problems: (1) VLFMs generally learn the global representation of a single image, while ICU requires capturing nuances between multiple images. (2) The ICU performance of VLFMs is significantly affected by viewpoint variations, which is caused by the altered relationships between objects when viewpoint changes. To address these problems, we propose a Viewpoint Integration and Registration method. Concretely, we introduce a fused adapter image encoder that fine-tunes pre-trained encoders by inserting designed trainable adapters and fused adapters, to effectively capture nuances between image pairs. Additionally, a viewpoint registration flow and a semantic emphasizing module are designed to reduce the performance degradation caused by viewpoint variations in the visual and semantic space, respectively. Experimental results on CLEVR-Change and Spot-the-Diff demonstrate that our method achieves state-of-the-art performance in all metrics.
Classical shadows meet quantum optimal mass transport

Classical shadows constitute a protocol to estimate the expectation values of a collection of M observables acting on O(1) qubits of an unknown n-qubit state with a number of measurements that is independent of n and that grows only logarithmically with M. We propose a local variant of the quantum Wasserstein distance of order 1 of [De Palma et al., IEEE Trans. Inf. Theory 67, 6627 (2021)] and prove that the classical shadow obtained measuring O(log n) copies of the state to be learned constitutes an accurate estimate with respect to the proposed distance. We apply the results to quantum generative adversarial networks, showing that quantum access to the state to be learned can be useful only when some prior information on such state is available.
Differentiable Resolution Compression and Alignment for Efficient Video Classification and Retrieval

Optimizing video inference efficiency has become increasingly important with the growing demand for video analysis in various fields. Some existing methods achieve high efficiency by explicit discard of spatial or temporal information, which poses challenges in fast-changing and fine-grained scenarios. To address these issues, we propose an efficient video representation network with Differentiable Resolution Compression and Alignment mechanism, which compresses non-essential information in the early stage of the network to reduce computational costs while maintaining consistent temporal correlations. Specifically, we leverage a Differentiable Context-aware Compression Module to encode the saliency and non-saliency frame features, refining and updating the features into a high-low resolution video sequence. To process the new sequence, we introduce a new Resolution-Align Transformer Layer to capture global temporal correlations among frame features with different resolutions, while reducing spatial computation costs quadratically by utilizing fewer spatial tokens in low-resolution non-saliency frames. The entire network can be end-to-end optimized via the integration of the differentiable compression module. Experimental results show that our method achieves the best trade-off between efficiency and performance on near-duplicate video retrieval and competitive results on dynamic video classification compared to state-of-the-art methods. Code:https://github.com/dun-research/DRCA
Rhythm of Work: Mixed-methods Characterization of Information Workers Scheduling Preferences and Practices

As processes around hybrid work, spatially distant collaborations, and work-life boundaries grow increasingly complex, managing workers' schedules for synchronous meetings has become a critical aspect of building successful global teams. However, gaps remain in our understanding of workers' scheduling preferences and practices, which we aim to fill in this large-scale, mixed-methods study of individuals calendars in a multinational organization. Using interviews with eight participants, survey data from 165 respondents, and telemetry data from millions of meetings scheduled by 211 thousand workers, we characterize scheduling preferences, practices, and their relationship with each other and organizational factors. We find that temporal preferences can be broadly classified as either cyclical, such as suitability of certain days, or relational, such as dispersed meetings, at various time scales. Furthermore, our results suggest that these preferences are disconnected from actual practice--albeit with several notable exceptions--and that individual differences are associated with factors like meeting load, time-zones, importance of meetings to job function, and job titles. We discuss key themes for our findings, along with the implications for calendar and scheduling systems and socio-technical systems more broadly.
Advancing the Evaluation of Traditional Chinese Language Models: Towards a Comprehensive Benchmark Suite

The evaluation of large language models is an essential task in the field of language understanding and generation. As language models continue to advance, the need for effective benchmarks to assess their performance has become imperative. In the context of Traditional Chinese, there is a scarcity of comprehensive and diverse benchmarks to evaluate the capabilities of language models, despite the existence of certain benchmarks such as DRCD, TTQA, CMDQA, and FGC dataset. To address this gap, we propose a novel set of benchmarks that leverage existing English datasets and are tailored to evaluate language models in Traditional Chinese. These benchmarks encompass a wide range of tasks, including contextual question-answering, summarization, classification, and table understanding. The proposed benchmarks offer a comprehensive evaluation framework, enabling the assessment of language models' capabilities across different tasks. In this paper, we evaluate the performance of GPT-3.5, Taiwan-LLaMa-v1.0, and Model 7-C, our proprietary model, on these benchmarks. The evaluation results highlight that our model, Model 7-C, achieves performance comparable to GPT-3.5 with respect to a part of the evaluated capabilities. In an effort to advance the evaluation of language models in Traditional Chinese and stimulate further research in this field, we have open-sourced our benchmark and opened the model for trial.
The Effect of Smoothing on the Interpretation of Time Series Data: A COVID-19 Case Study

We conduct a controlled crowd-sourced experiment of COVID-19 case data visualization to study if and how different plotting methods, time windows, and the nature of the data influence people's interpretation of real-world COVID-19 data and people's prediction of how the data will evolve in the future. We find that a 7-day backward average smoothed line successfully reduces the distraction of periodic data patterns compared to just unsmoothed bar data. Additionally, we find that the presence of a smoothed line helps readers form a consensus on how the data will evolve in the future. We also find that the fixed 7-day smoothing window size leads to different amounts of perceived recurring patterns in the data depending on the time period plotted -- this suggests that varying the smoothing window size together with the plot window size might be a promising strategy to influence the perception of spurious patterns in the plot.
Casteist but Not Racist? Quantifying Disparities in Large Language Model Bias between India and the West

Large Language Models (LLMs), now used daily by millions of users, can encode societal biases, exposing their users to representational harms. A large body of scholarship on LLM bias exists but it predominantly adopts a Western-centric frame and attends comparatively less to bias levels and potential harms in the Global South. In this paper, we quantify stereotypical bias in popular LLMs according to an Indian-centric frame and compare bias levels between the Indian and Western contexts. To do this, we develop a novel dataset which we call Indian-BhED (Indian Bias Evaluation Dataset), containing stereotypical and anti-stereotypical examples for caste and religion contexts. We find that the majority of LLMs tested are strongly biased towards stereotypes in the Indian context, especially as compared to the Western context. We finally investigate Instruction Prompting as a simple intervention to mitigate such bias and find that it significantly reduces both stereotypical and anti-stereotypical biases in the majority of cases for GPT-3.5. The findings of this work highlight the need for including more diverse voices when evaluating LLMs.
Fully microscopic scission-point model to predict fission fragment observables

We present an upgraded version of the SPY model, called SPY2 for version 2 of the scission point yield, to estimate mainly the yields and the kinetic energy distributions of ﬁssion fragments: The theoretical framework is similar to our previous version, i.e., a statistical scission point model, but this version is based on fully microscopic nuclear ingredients describing the fragments properties at the scission point. These include the static properties of some 7000 nuclei at 120 axial quadrupole deformations, such as binding energies, proton densities, single-particle level schemes, and states densities, coherently calculated within the constrained Hartree-Fock-Bogoliubov model on the basis of the Skyrme BSk27 interaction. The use of microscopic ingredients has been extended to the proton density distribution and the nuclear states densities. Considering realistic proton densities of fragments allows us to improve the deﬁnition of the scission point as well as the prediction of the kinetic energy distribution and to link the kinetic energy to the diffuseness of the fragments’ proton density. New microscopic nuclear states densities improve the general coherence of the model, in particular regarding the inclusion of the odd-even pairing effect. In this updated SPY2 version, the calculation of the ﬁssion yields and kinetic energy distributions is signiﬁcantly improved and found to be in relatively good agreement with experiments, at least qualitatively. A detailed study is performed for three well known ﬁssioning systems, namely, thermal neutron induced ﬁssion of 235 U and 239 Pu and spontaneous ﬁssion of 252 Cf. A systematic analysis of the ﬁssion mode as well as mean fragments deformation and total kinetic energies has been performed for some 2000 ﬁssioning nuclei with 78 (cid:2) Z (cid:2) 110 lying between the proton and neutron drip lines.
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach

Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a learning framework that can provide node privacy at the user level, while incurring low utility loss. We focus on a decentralized notion of Differential Privacy, namely Local Differential Privacy, and apply randomization mechanisms to perturb both feature and label data at the node level before the data is collected by a central server for model training. Specifically, we investigate the application of randomization mechanisms in high-dimensional feature settings and propose an LDP protocol with strict privacy guarantees. Based on frequency estimation in statistical analysis of randomized data, we develop reconstruction methods to approximate features and labels from perturbed data. We also formulate this learning framework to utilize frequency estimates of graph clusters to supervise the training procedure at a sub-graph level. Extensive experiments on real-world and semi-synthetic datasets demonstrate the validity of our proposed model.
Persuasion with Precision: Using Natural Language Processing to Improve Instrument Fidelity for Risk Communication Experimental Treatments

Instrument fidelity in message testing research hinges upon how precisely messages operationalize treatment conditions. However, numerous message testing studies have unmitigated threats to validity and reliability because no established procedures exist to guide construction of message treatments. Their construction typically occurs in a black box, resulting in suspect inferential conclusions about treatment effects. Because a mixed methods approach is needed to enhance instrument fidelity in message testing research, this article contributes to the field of mixed methods research by presenting an integrated multistage procedure for constructing precise message treatments using an exploratory sequential mixed methods design. This work harnesses the power of integration through crossover analysis to improve instrument fidelity in message testing research through the use of natural language processing (NLP).
Synchrotron Polarization Signatures of Surface Waves in Supermassive Black Hole Jets

Supermassive black holes in active galactic nuclei are known to launch relativistic jets, which are observed across the entire electromagnetic spectrum and thought to be efficient particle accelerators. Their primary radiation mechanism for radio emission is polarized synchrotron emission produced by a population of nonthermal electrons. In this Letter, we present a global general relativistic magnetohydrodynamical (GRMHD) simulation of a magnetically arrested disk (MAD). After the simulation reaches the MAD state, we show that waves are continuously launched from the vicinity of the black hole and propagate along the interface between the jet and the wind. At this interface, a steep gradient in velocity is present between the mildly relativistic wind and the highly relativistic jet. The interface is, therefore, a shear layer, and due to the shear, the waves generate roll-ups that alter the magnetic field configuration and the shear layer geometry. We then perform polarized radiation transfer calculations of our GRMHD simulation and find signatures of the waves in both total intensity and linear polarization, effectively lowering the fully resolved polarization fraction. The telltale polarization signatures of the waves could be observable by future very long baseline interferometric observations, e.g., the next-generation Event Horizon Telescope.
Risk factors for mortality in severe COVID-19: Exploring the interplay of immunomodulatory therapy and coinfection

Patients with severe clinical manifestations of coronavirus disease 2019 (COVID-19) present particular diagnostic and management challenges to critical care physicians, including identifying and responding to concurrent bacterial and fungal coinfections. This study evaluates risk factors for in-hospital mortality in patients admitted to the intensive care unit with severe COVID-19 during circulation of the B.1. 617.2 (Delta) variant, including the impact of immunomodulators and bacterial and/or fungal coinfection. This retrospective cohort study enrolled patients with severe COVID-19. A Cox proportional hazard ratio analysis identified risk factors for in-hospital mortality. Outcomes were also compared between patients receiving and not receiving immunomodulatory therapy alongside standard care. Ninety patients admitted to the intensive care unit were enrolled. On multivariate analysis, the greatest risk factors for in-hospital mortality were invasive mechanical ventilation (hazard ratio (HR) = 15.27; 95% confidence interval (CI) 3.29–71.0; P < 0.001), elevated body mass index (HR = 1.07 per unit; 95% CI 1.02–1.13; P = 0.007) and older age (HR = 1.53 per decade; 95% CI 1.05–2.24; P = 0.028). Bacterial and/or fungal coinfection occurred at equal frequency in patients receiving and not receiving immunomodulatory therapy. However, in patients receiving immunomodulators, coinfection carried a significantly higher mortality risk (63.0%) compared with those without coinfection (15.4%; P = 0.038). Mortality from severe COVID-19 is significantly higher in older patients and those with elevated body mass index and requiring mechanical ventilation. Immunomodulatory therapy necessitates vigilance towards evolving coinfection in the intensive care setting.
Denoising Diffusion Probabilistic Models for Hardware-Impaired Communications

Generative AI has received significant attention among a spectrum of diverse industrial and academic domains, thanks to the magnificent results achieved from deep generative models such as generative pre-trained transformers (GPT) and diffusion models. In this paper, we explore the applications of denoising diffusion probabilistic models (DDPMs) in wireless communication systems under practical assumptions such as hardware impairments (HWI), low-SNR regime, and quantization error. Diffusion models are a new class of state-of-the-art generative models that have already showcased notable success with some of the popular examples by OpenAI1 and Google Brain2. The intuition behind DDPM is to decompose the data generation process over small ``denoising'' steps. Inspired by this, we propose using denoising diffusion model-based receiver for a practical wireless communication scheme, while providing network resilience in low-SNR regimes, non-Gaussian noise, different HWI levels, and quantization error. We evaluate the reconstruction performance of our scheme in terms of mean-squared error (MSE) metric. Our results show that more than 25 dB improvement in MSE is achieved compared to deep neural network (DNN)-based receivers. We also highlight robust out-of-distribution performance under non-Gaussian noise.
How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent

We analyze the generalization properties of two-layer neural networks in the neural tangent kernel (NTK) regime, trained with gradient descent (GD). For early stopped GD we derive fast rates of convergence that are known to be minimax optimal in the framework of non-parametric regression in reproducing kernel Hilbert spaces. On our way, we precisely keep track of the number of hidden neurons required for generalization and improve over existing results. We further show that the weights during training remain in a vicinity around initialization, the radius being dependent on structural assumptions such as degree of smoothness of the regression function and eigenvalue decay of the integral operator associated to the NTK.
It is time to improve the diagnostic workup of oropharyngeal cancer with circulating tumor HPV DNA: Systematic review and meta‐analysis

The possibility of detecting circulating tumor HPV DNA (ctHPVDNA) in plasma in patients with oropharyngeal cancer has been demonstrated in several reports. However, these data are from small cohorts and available tests for detection of ctHPVDNA are not fully validated. The aim is to evaluate sensitivity, specificity, and accuracy of ctHPVDNA by ddPCR to define its efficacy in the clinical setting for the diagnosis of HPV + OPSCC. A comprehensive search of three different databases: MEDLINE, Embase, and Cochrane Library databases. A total of 998 patients were evaluated from the 13 studies. OPSSC p16+ were 729, while controls p16− were 269. The meta‐analytic study estimated the diagnostic performance of ctHPVDNA as follows: pooled sensitivity and specificity of 0.90 (95% CI: 0.82–0.94) and 0.94 (95% CI: 0.85–0.98), respectively; positive and negative likelihood ratios of 12.6 (95% CI: 4.9–32.1) and 0.05 (95% CI: 0.02–0.13), respectively. ddPCR for ctHPVDNA has good accuracy, sensitivity, and specificity for diagnosis of HPV + OPSCC. ctHPVDNA kinetic represents a great reliable opportunity to improve diagnostic and therapeutic management of cancer patients and could open new perspectives for understanding tumor biology.