Efficient 2.8-µm Er-doped all-fiber laser based on a low optical feedback cavity.

Mid-infrared (mid-IR) fiber lasers have faced significant challenges in achieving all-fiberized structure with high efficiency and high output power due to the limitations of conventional cavity designs. In this paper, we introduce what we believe to be a novel 2.8 µm all-fiber laser configuration with a low optical feedback linear cavity, effectively eliminating the need for traditional high-reflector components.

Dubbing Movies via Hierarchical Phoneme Modeling and Acoustic Diffusion Denoising.

Given a piece of text, a video clip, and reference audio, the movie dubbing (also known as Visual Voice Cloning, V2C) task aims to generate speeches that clone reference voice and align well with the video in both emotion and lip movement, which is more challenging than conventional text-to-speech synthesis tasks. To align the generated speech with the inherent lip motion of the given silent video, most existing works utilize each video frame to query textual phonemes. However, such an attention operation usually leads to mumble speech because different phonemes are fused for video frames corresponding to one phoneme (video frames are finer-grained than phonemes).

4.5 W Er-doped ZBLAN fiber laser at 3.78 μm.

In this work, we demonstrate an efficient long-wavelength operation of an Er-doped ZBLAN fiber laser in the 3.5 μm band with F→I transition. A high-transmission bandpass filter (BPF) is employed in the laser cavity to select a specific lasing wavelength of around 3.

Accurate POI recommendation for random groups with improved graph neural networks and a multi-negotiation model.

In recent years, the growing prevalence of group activities has brought increased interest in Point of Interest (POI) recommendations for groups. While significant progress has been made in recommending POIs for fixed groups, research on personality-aware recommendations for random groups has been still largely untouched. Moreover, existing works recommend a POI list for a group and the group makes further choice of the optimal POI, which results in poor user experience.

Structured training on gross motor skills and physical fitness in 4-5-year-old children.

Objective: Preschool children are in a period of rapid physical development, and improving their gross motor skills and physical fitness is quite important for their health. This study aimed to investigate the effectiveness of a structured physical training program in improving Chinese preschool children's gross motor development and physical fitness.

Method: A sample of 80 children aged 4 to 5 from Fujian, China, were randomly assigned to the intervention group ( = 41), which received a 15-week structured physical training, while the control group ( = 39) continued with their daily physical activity.

Large-mode-area Nd-doped anti-resonant phosphate fiber for high-power single-mode 900 nm laser generation.

In this work, we propose an Nd-doped double-layer anti-resonant phosphate fiber with a core diameter of 50 µm for high-power single-mode 900 nm laser generation. Double-layer interlaced anti-resonant elements were designed here to enhance the fundamental mode confinement capability of the large-mode-area Nd-doped fiber core. Moreover, a double-layer F-P etalon formed between the anti-resonant elements and the inner cladding was analyzed for the first time for fiber loss manipulation.

Largescale multicenter study of a serum metabolite biomarker panel for the diagnosis of breast cancer.

Breast cancer (BC) is currently the most prevalent malignancy worldwide, and finding effective non-invasive biomarkers for routine clinical detection of BC remains a significant challenge. Here, we performed non-targeted and targeted metabolomics analysis on the screening, training and validation cohorts of serum samples from 1,947 participants. A metabolite biomarker model including glutamate, erythronate, docosahexaenoate, propionylcarnitine, and patient's age was established for detecting BC.

Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale.

Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions.

Deep learning for computer-aided abnormalities classification in digital mammogram: A data-centric perspective.

Breast cancer is the most common type of cancer in women, and early abnormality detection using mammography can significantly improve breast cancer survival rates. Diverse datasets are required to improve the training and validation of deep learning (DL) systems for autonomous breast cancer diagnosis. However, only a small number of mammography datasets are publicly available.

Efficient continuous-wave Nd:YVO/KGW intracavity Raman laser.

We demonstrate an efficient Nd:YVO/KGW intracavity Raman laser in continuous-wave (CW) scheme. With a V-shaped fundamental laser cavity and a short Stokes cavity in it, the oscillating beam sizes are designed to alleviate the thermal effect and to enhance the Raman gain for efficient CW operation. The output power of CW Stokes wave at 1177 nm reached 9.

Widely tunable single-frequency Er-doped ZBLAN fiber laser with emission from 3.37 to 3.72 µm.

We demonstrate a widely tunable single-frequency Er-doped ZBLAN fiber laser operating on a F→I transition band. An uncoated germanium (Ge) plate serves as a narrow-bandwidth etalon and is inserted in the cavity to achieve a single longitudinal mode selection. Wavelength tuning from 3373.

1.2 W single-frequency Tm/Ho co-doped fiber oscillator at 2050 nm.

In this Letter, a watt-level single-frequency fiber oscillator at 2050 nm was demonstrated for the first time, to the best of our knowlegde, in a linear laser cavity with a piece of an un-pumped Tm/Ho co-doped fiber serving as a saturable absorber. With delicate optimization of mode filtering effect of the dynamic gratings formed in the saturable absorber, a maximum single-frequency laser output power of 1.2 W was achieved under a total bidirectional pump power of 5.

Integrative proteomics and metabolomics study reveal enhanced immune responses by COVID-19 vaccine booster shot against Omicron SARS-CoV-2 infection.

Since its outbreak in late 2021, the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widely reported to be able to evade neutralizing antibodies, becoming more transmissible while causing milder symptoms than previous SARS-CoV-2 strains. Understanding the underlying molecular changes of Omicron SARS-CoV-2 infection and corresponding host responses are important to the control of Omicron COVID-19 pandemic. In this study, we report an integrative proteomics and metabolomics investigation of serum samples from 80 COVID-19 patients infected with Omicron SARS-CoV-2, as well as 160 control serum samples from 80 healthy individuals and 80 patients who had flu-like symptoms but were negative for SARS-CoV-2 infection.

Pump quantum efficiency optimization of 3.5 μm Er-doped ZBLAN fiber laser for high-power operation.

976 nm + 1976 nm dual-wavelength pumped Er-doped ZBLAN fiber lasers are generally accepted as the preferred solution for achieving 3.5 μm lasing. However, the 2 μm band excited state absorption from the upper lasing level (F → F) depletes the Er ions population inversion, reducing the pump quantum efficiency and limiting the power scaling.

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles.

The rapid proliferation of the emerging yet promising notion of the Internet-of-Vehicles (IoV) has led to the development of a variety of conventional trust assessment schemes to tackle insider attackers. The primary reliance of these frameworks is on the accumulation of individual trust attributes. While aggregating these influential parameters, weights are often associated with each individual attribute to reflect its impact on the final trust score.

High-power, high-efficiency single-frequency DBR fiber laser at 1064 nm based on Yb-doped silica fiber.

A high-power, high-efficiency single-frequency fiber laser at 1064 nm was demonstrated based on a distributed Bragg reflector (DBR) all-silica-fiber configuration. A single-frequency laser with an output power of 642 mW and slope efficiency of 66.4% with respect to absorbed pump power was achieved from a 1.

Perturb and optimize users' location privacy using geo-indistinguishability and location semantics.

Location-based services (LBS) are capable of providing location-based information retrieval, traffic navigation, entertainment services, emergency rescues, and several similar services primarily on the premise of the geographic location of users or mobile devices. However, in the process of introducing a new user experience, it is also easy to expose users' specific location which can result in more private information leakage. Hence, the protection of location privacy remains one of the critical issues of the location-based services.

Deep graph level anomaly detection with contrastive learning.

Graph level anomaly detection (GLAD) aims to spot anomalous graphs that structure pattern and feature information are different from most normal graphs in a graph set, which is rarely studied by other researchers but has significant application value. For instance, GLAD can be used to distinguish some different characteristic molecules in drug discovery and chemical analysis. However, GLAD mainly faces the following three challenges: (1) learning more comprehensive graph level representations to differ normal graphs and abnormal graphs, (2) designing an effective graph anomaly evaluation paradigm to capture graph anomalies from the local and global graph perspectives, (3) overcoming the number imbalance problem of normal and abnormal graphs.

Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation.

Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on convolutional neural networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation. This is because, although the human visual system can detect symmetries in 2-D images effectively, regular CNNs can only exploit translation invariance, overlooking further inherent symmetries existing in medical images, such as rotations and reflections.

Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients.

The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV to predict clinical scores of cognitive functions in ESRD patients.

Single-frequency Tm-doped fiber laser with 215 mW at 2.05 µm based on a Tm/Ho-codoped fiber saturable absorber.

We demonstrate an efficient single-frequency Tm-doped fiber laser at 2050 nm. A ring cavity scheme is employed to boost the net gain at the wavelength. A piece of Tm/Ho-codoped fiber with an absorption coefficient at 2050 nm higher than that of a Tm-doped fiber is used to establish the dynamic Bragg grating for enhancing the frequency selection capability.

Erbium-doped ZBLAN fiber laser pumped at 1.7 µm for emission at 2.8 µm.

In this paper, a novel, to the best of our knowledge, efficient pump scheme for an erbium-doped fluoride fiber laser with emission at 2.8 µm in the mid-infrared region is proposed and demonstrated. A singular pump source at 1.

Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems.

Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems.