Miracle-dz/UniFAR-FTUs huggingface dataset:
DOCTOR: Dynamic On-Chip Remediation Against Temporally-Drifting Thermal Variations Toward Self-Corrected Photonic Tensor Accelerators

Photonic computing has emerged as a promising solution for accelerating computation-intensive artificial intelligence (AI) workloads, offering unparalleled speed and energy efficiency, especially in resource-limited, latency-sensitive edge computing environments. However, the deployment of analog photonic tensor accelerators encounters reliability challenges due to hardware noises and environmental variations. While off-chip noise-aware training and on-chip training have been proposed to enhance the variation tolerance of optical neural accelerators with moderate, static noises, we observe a notable performance degradation over time due to temporally drifting variations, which requires a real-time, in-situ calibration mechanism. To tackle this challenging reliability issues, for the first time, we propose a lightweight dynamic on-chip remediation framework, dubbed DOCTOR, providing adaptive, in-situ accuracy recovery against temporally drifting noises. The DOCTOR framework intelligently monitors the chip status using adaptive probing and performs fast in-situ training-free calibration to restore accuracy when necessary. Recognizing nonuniform spatial variation distributions across devices and tensor cores, we also propose a variation-aware architectural remapping strategy to avoid executing critical tasks on noisy devices. Extensive experiments show that our proposed framework can guarantee sustained performance under drifting variations with 34% higher accuracy and 2-3 orders-of-magnitude lower overhead compared to state-of-the-art on-chip training methods.
Anodization-free fabrication process for high-quality cross-type Josephson tunnel junctions based on a Nb/Al-AlO$_x$/Nb trilayer

Josephson tunnel junctions form the basis for various superconducting electronic devices. For this reason, enormous efforts are routinely taken to establish and later on maintain a scalable and reproducible wafer-scale manufacturing process for high-quality Josephson junctions. Here, we present an anodization-free fabrication process for Nb/Al-AlO$_x$/Nb cross-type Josephson junctions that requires only a small number of process steps and that is intrinsically compatible with wafer-scale fabrication. We show that the fabricated junctions are of very high-quality and, compared to other junction types, exhibit not only a significantly reduced capacitance but also an almost rectangular critical current density profile. Our process hence enables the usage of low capacitance Josephson junctions for superconducting electronic devices such as ultra-low noise dc-SQUIDs, microwave SQUID multiplexers based on non-hysteretic rf-SQUIDs and RFSQ circuits.
Impact of (magneto-)thermoelectric effect on diffusion of conserved charges in hot and dense hadronic matter

We investigate the thermoelectric effect, which describes the generation of an electric field induced by temperature and conserved charge chemical potential gradients, in the hot and dense hadronic matter created in heavy-ion collisions. Utilizing the Boltzmann kinetic theory within the repulsive mean-field hadron resonance gas model, we evaluate both the diffusion thermopower matrix and diffusion coefficient matrix for the baryon number ($B$), electric charge ($Q$), and strangeness ($S$). The Landau-Lifshitz frame is enforced in the derivation. We find that the thermoelectric effect hinders the diffusion processes of multiple conserved charges, particularly reducing the coupling between electric charge and baryon number (strangeness) in baryon (strangeness) diffusion. Based on the fact that the repulsive mean-field interactions between hadrons have a significant effect on the diffusion thermopower matrix and diffusion coefficient matrix in the baryon-rich region, we extend the investigation to include the impact of the magnetic field, analyzing the magneto-thermoelectric effect on both the diffusion coefficient matrix and the Hall-like diffusion coefficient matrix. The sensitivity of the magnetic field-dependent diffusion thermopower matrix and magneto-thermoelectric modified diffusion coefficient matrix to the choices of various transverse conditions is also studied.
Graph neural network for in-network placement of real-time metaverse tasks in next-generation network

This study addresses the challenge of real-time metaverse applications by proposing an in-network placement and task-offloading solution for delay-constrained computing tasks in next-generation networks. The metaverse, envisioned as a parallel virtual world, requires seamless real-time experiences across diverse applications. The study introduces a software-defined networking (SDN)-based architecture and employs graph neural network (GNN) techniques for intelligent and adaptive task allocation in in-network computing (INC). Considering time constraints and computing capabilities, the proposed model optimally decides whether to offload rendering tasks to INC nodes or edge server. Extensive experiments demonstrate the superior performance of the proposed GNN model, achieving 97% accuracy compared to 72% for multilayer perceptron (MLP) and 70% for decision trees (DTs). The study fills the research gap in in-network placement for real-time metaverse applications, offering insights into efficient rendering task handling.
Energy fluctuations of a Brownian particle freely moving in a liquid

We study the statistical properties of the variation of the kinetic energy of a spherical Brownian particle that freely moves in an incompressible fluid at constant temperature. Based on the underdamped version of the generalized Langevin equation that includes the inertia of both the particle and the displaced fluid, we derive an analytical expression for the probability density function of such a kinetic energy variation during an arbitrary time interval, which exactly amounts to the energy exchanged with the fluid in absence of external forces. We also determine all the moments of this probability distribution, which can be fully expressed in terms of a function that is proportional to the velocity autocorrelation function of the particle. The derived expressions are verified by means of numerical simulations of the stochastic motion of a particle in a viscous liquid with hydrodynamic backflow for representative values of the time-scales of the system. Furthermore, we also investigate the effect of viscoelasticity on the statistics of the kinetic energy variation of the particle, which reveals the existence of three distinct regimes of the energy exchange process depending on the values of the viscoelastic parameters of the fluid.
DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs

The Synchronization Signal Block (SSB) is a fundamental component of the 5G New Radio (NR) air interface, crucial for the initial access procedure of Connected and Automated Vehicles (CAVs), and serves several key purposes in the network's operation. However, due to the predictable nature of SSB transmission, including the Primary and Secondary Synchronization Signals (PSS and SSS), jamming attacks are critical threats. These attacks, which can be executed without requiring high power or complex equipment, pose substantial risks to the 5G network, particularly as a result of the unencrypted transmission of control signals. Leveraging RF domain knowledge, this work presents a novel deep learning-based technique for detecting jammers in CAV networks. Unlike the existing jamming detection algorithms that mostly rely on network parameters, we introduce a double-threshold deep learning jamming detector by focusing on the SSB. The detection method is focused on RF domain features and improves the robustness of the network without requiring integration with the pre-existing network infrastructure. By integrating a preprocessing block to extract PSS correlation and energy per null resource elements (EPNRE) characteristics, our method distinguishes between normal and jammed received signals with high precision. Additionally, by incorporating of Discrete Wavelet Transform (DWT), the efficacy of training and detection are optimized. A double-threshold double Deep Neural Network (DT-DDNN) is also introduced to the architecture complemented by a deep cascade learning model to increase the sensitivity of the model to variations of signal-to-jamming noise ratio (SJNR). Results show that the proposed method achieves 96.4% detection rate in extra low jamming power, i.e., SJNR between 15 to 30 dB. Further, performance of DT-DDNN is validated by analyzing real 5G signals obtained from a practical testbed.
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning

For image super-resolution (SR), bridging the gap between the performance on synthetic datasets and real-world degradation scenarios remains a challenge. This work introduces a novel"Low-Res Leads the Way"(LWay) training framework, merging Supervised Pre-training with Self-supervised Learning to enhance the adaptability of SR models to real-world images. Our approach utilizes a low-resolution (LR) reconstruction network to extract degradation embeddings from LR images, merging them with super-resolved outputs for LR reconstruction. Leveraging unseen LR images for self-supervised learning guides the model to adapt its modeling space to the target domain, facilitating fine-tuning of SR models without requiring paired high-resolution (HR) images. The integration of Discrete Wavelet Transform (DWT) further refines the focus on high-frequency details. Extensive evaluations show that our method significantly improves the generalization and detail restoration capabilities of SR models on unseen real-world datasets, outperforming existing methods. Our training regime is universally compatible, requiring no network architecture modifications, making it a practical solution for real-world SR applications.
Towards Fair and Efficient Learning-based Congestion Control

Recent years have witnessed a plethora of learning-based solutions for congestion control (CC) that demonstrate better performance over traditional TCP schemes. However, they fail to provide consistently good convergence properties, including {\em fairness}, {\em fast convergence} and {\em stability}, due to the mismatch between their objective functions and these properties. Despite being intuitive, integrating these properties into existing learning-based CC is challenging, because: 1) their training environments are designed for the performance optimization of single flow but incapable of cooperative multi-flow optimization, and 2) there is no directly measurable metric to represent these properties into the training objective function. We present Astraea, a new learning-based congestion control that ensures fast convergence to fairness with stability. At the heart of Astraea is a multi-agent deep reinforcement learning framework that explicitly optimizes these convergence properties during the training process by enabling the learning of interactive policy between multiple competing flows, while maintaining high performance. We further build a faithful multi-flow environment that emulates the competing behaviors of concurrent flows, explicitly expressing convergence properties to enable their optimization during training. We have fully implemented Astraea and our comprehensive experiments show that Astraea can quickly converge to fairness point and exhibit better stability than its counterparts. For example, \sys achieves near-optimal bandwidth sharing (i.e., fairness) when multiple flows compete for the same bottleneck, delivers up to 8.4$\times$ faster convergence speed and 2.8$\times$ smaller throughput deviation, while achieving comparable or even better performance over prior solutions.
A reproducible 3D convolutional neural network with dual attention module (3D-DAM) for Alzheimer's disease classification

— Alzheimer's disease is one of the most common types of neurodegenerative disease, characterized by the accumulation of amyloid-beta plaque and tau tangles. Recently, deep learning approaches have shown promise in Alzheimer's disease diagnosis. In this study, we propose a reproducible model that utilizes a 3D convolutional neural network with a dual attention module for Alzheimer's disease classification. We trained the model in the ADNI database and verified the generalizability of our method in two independent datasets (AIBL and OASIS1). Our method achieved state-of-the-art classification performance, with an accuracy of 91.94% for MCI progression classification and 96.30% for Alzheimer's disease classification on the ADNI dataset. Furthermore, the model demonstrated good generalizability, achieving an accuracy of 86.37% on the AIBL dataset and 83.42% on the OASIS1 dataset. These results indicate that our proposed approach has competitive performance and generalizability when compared to recent studies in the field.
METTL3 as a master regulator of translation in cancer: mechanisms and implications

Abstract Translational regulation is an important step in the control of gene expression. In cancer cells, the orchestration of both global control of protein synthesis and selective translation of specific mRNAs promote tumor cell survival, angiogenesis, transformation, invasion and metastasis. N6-methyladenosine (m6A), the most prevalent mRNA modification in higher eukaryotes, impacts protein translation. Over the past decade, the development of m6A mapping tools has facilitated comprehensive functional investigations, revealing the involvement of this chemical mark, together with its writer METTL3, in promoting the translation of both oncogenes and tumor suppressor transcripts, with the impact being context-dependent. This review aims to consolidate our current understanding of how m6A and METTL3 shape translation regulation in the realm of cancer biology. In addition, it delves into the role of cytoplasmic METTL3 in protein synthesis, operating independently of its catalytic activity. Ultimately, our goal is to provide critical insights into the interplay between m6A, METTL3 and translational regulation in cancer, offering a deeper comprehension of the mechanisms sustaining tumorigenesis.
Influence of transient emotional episodes on affective and cognitive theory of mind

Abstract Our emotions may influence how we interact with others. Previous studies have shown an important role of emotion induction in generating empathic reactions towards others’ affect. However, it remains unclear whether (and to which extent) our own emotions can influence the ability to infer people’s mental states, a process associated with Theory of Mind (ToM) and implicated in the representation of both cognitive (e.g. beliefs and intentions) and affective conditions. We engaged 59 participants in two emotion-induction experiments where they saw joyful, neutral and fearful clips. Subsequently, they were asked to infer other individuals’ joy, fear (affective ToM) or beliefs (cognitive ToM) from verbal scenarios. Using functional magnetic resonance imaging, we found that brain activity in the superior temporal gyrus, precuneus and sensorimotor cortices were modulated by the preceding emotional induction, with lower response when the to-be-inferred emotion was incongruent with the one induced in the observer (affective ToM). Instead, we found no effect of emotion induction on the appraisal of people’s beliefs (cognitive ToM). These findings are consistent with embodied accounts of affective ToM, whereby our own emotions alter the engagement of key brain regions for social cognition, depending on the compatibility between one’s own and others’ affect.
Open government data use: The Brazilian states and federal district cases

Purpose This paper presents the results of an online survey and subsequent interviews investigating whether, how, and why public administrations of Brazilian states and the federal district (Federation Units) use open government data. According to the literature reviewed, the questions were categorized into four big groups: benefits, barriers, enablers, and drivers. Design/Methodology/Approach The Survey method, based on a questionnaire followed by interviews, was used to collect and analyze data from the open data officers of 26 Brazilian Federation Units. Findings The use of open government data is controversial as responses from the questionnaires and interviews do not match and raise questions about how well-represented each Federation Unit was. Evidence of open government data use was found. Among others, findings showed that political leadership committed to using open data facilitates and motivates public agents to use these data. Additionally, interviews indicated that the lack of human resources with the knowledge, skills, and capabilities to use open data is a relevant barrier to data use. Findings also revealed that open government data mainly support policy and decision-making processes. Practical implications This research contributed to the open data and public administration fields. It portrays diverse realities of open government data use and institutionalization in Brazilian state and district public administrations. In addition, it provides lists of open government data use benefits, barriers, drivers, and enablers from the perspective of these administrations so that they can benchmark against each other and improve their OGD use. Originality and research implications For academia, this research provides empirical evidence of the factors influencing public administrations’ use of open government data at the subnational level in Brazil. Even though Brazil ranks high on OGD global assessments, few studies on its use and reuse in the public sector were identified. This is one of the first academic studies focusing on open government data use in the country. It also contributes by offering to the academic community two instruments, a questionnaire and an interview protocol, which can be applied to other public settings to expand this study’s results or open new research paths by applying them to other contexts.
Evaluation and estimation of compressive strength of concrete masonry prism using gradient boosting algorithm

The compressive strength (CS) of the hollow concrete masonry prism is known as an important parameter for designing masonry structures. In general, the CS is determined using laboratory tests, however, laboratory tests are time-consuming and high-cost. Thus, it is necessary to evaluate and estimate the CS using different methods, for example, machine learning techniques. This study employed Gradient Boosting (GB) to evaluate and predict the CS of hollow masonry prism. The database consists of 102 hollow concrete specimens taken from different previous published literature used for modeling. The output is the CS of the hollow masonry prism, while the inputs include the compressive strength of mortar (fm), the compressive strength of blocks (fb), height-to-thickness ratio (h/t), the ratio of fm/fb. To reduce the overfitting problem, this study used K-Fold cross-validation, then particle swarm optimization (PSO) was employed to obtain the optimum hyperparameter. The GB model then was modeled using the optimum hyperparameters. The results showed that the GB model performed very well in evaluating and predicting the CS of the hollow masonry prims with a high prediction accuracy, the values of R2, RMSE, MAE, and MAPE are 0.977, 0.803 MPa, 0.612 MPa, and 0.036%, respectively. The performance of the GB model in this study outperformed in comparison to six different machine learning models (decision tree, linear regression, random forest regression, ridge regression, Artificial Neural network, and Extreme Gradient Boosting) used in previous studies. The results of sensitivity analysis using SHAP and PDP-2D indicate that the CS is strongly dependent on the fb (with a mean SHAP value of 3.2), h/t (with a mean SHAP value of 1.63), while the fm/fb (with a mean SHAP value of 0.57) had a small effect on the CS. Thus, it can be stated that this research provides a good method to evaluate and predict the CS of the hollow masonry prism, which can bring good knowledge for practical application in this field.
Nocardia rubra cell-wall skeleton mitigates whole abdominal irradiation-induced intestinal injury via regulating macrophage function

Abstract Background Ionizing radiation (IR)-induced intestinal injury is a major side effect and dose-limiting toxicity in patients receiving radiotherapy. There is an urgent need to identify an effective and safe radioprotectant to reduce radiation-induced intestinal injury. Immunoregulation is considered an effective strategy against IR-induced injury. The purpose of this article was to investigate the protective effect of Nocardia rubra cell wall skeleton (Nr-CWS), an immunomodulator, on radiation-induced intestinal damage and to explore its potential mechanism. Methods C57BL/6 J male mice exposed to 12 Gy whole abdominal irradiation (WAI) were examined for survival rate, morphology and function of the intestine and spleen, as well as the gut microbiota, to comprehensively evaluate the therapeutic effects of Nr-CWS on radiation-induced intestinal and splenetic injury. To further elucidate the underlying mechanisms of Nr-CWS-mediated intestinal protection, macrophages were depleted by clodronate liposomes to determine whether Nr-CWS-induced radioprotection is macrophage dependent, and the function of peritoneal macrophages stimulated by Nr-CWS was detected in vitro. Results Our data showed that Nr-CWS promoted the recovery of intestinal barrier function, enhanced leucine-rich repeat-containing G protein-coupled receptor 5+ intestinal stem cell survival and the regeneration of intestinal epithelial cells, maintained intestinal flora homeostasis, protected spleen morphology and function, and improved the outcome of mice exposed to 12 Gy WAI. Mechanistic studies indicated that Nr-CWS recruited macrophages to reduce WAI-induced intestinal damage. Moreover, macrophage depletion by clodronate liposomes blocked Nr-CWS-induced radioprotection. In vitro, we found that Nr-CWS activated the nuclear factor kappa-B signaling pathway and promoted the phagocytosis and migration ability of peritoneal macrophages. Conclusions Our study suggests the therapeutic effect of Nr-CWS on radiation-induced intestinal injury, and provides possible therapeutic strategy and potential preventive and therapeutic drugs to alleviate it.
Underwater image enhancement using Divide-and-Conquer network

Underwater image enhancement has become the requirement for more people to have a better visual experience or to extract information. However, underwater images often suffer from the mixture of color distortion and blurred quality degradation due to the external environment (light attenuation, background noise and the type of water). To solve the above problem, we design a Divide-and-Conquer network (DC-net) for enhancing underwater image, which mainly consists of a texture network, a color network and a refinement network. Specifically, the multi-axis attention block is presented in the texture network, which combine different region/channel features into a single stream structure. And the color network employs an adaptive 3D look-up table method to obtain the color enhanced results. Meanwhile, the refinement network is presented to focus on image features of ground truth. Compared to state-of-the-art (SOTA) underwater image enhance methods, our proposed method can obtain the better visual quality of underwater images and better qualitative and quantitative performance. The code is publicly available at https://github.com/zhengshijian1993/DC-Net.