Miracle-dz/UniFAR-FTUs huggingface dataset:
Lipidsenkende Therapie bei chronischer Nierenerkrankung im Erwachsenenalter

ZUSAMMENFASSUNG Die chronische Nierenerkrankung (CKD: „chronic kidney disease“) wird entsprechend der Leitlinien des Netzwerks Kidney Disease: Improving Global Outcomes (KDIGO) unabhängig von der Ätiologie definiert als mindestens 3-monatige Einschränkung der Nierenfunktion mit meist irreversibler, struktureller Schädigung der Nieren, die bis zur terminalen Niereninsuffizienz fortschreiten kann. Kardiovaskuläre Erkrankungen stellen die Haupttodesursache bei Patienten mit CKD dar. Mit abnehmender glomerulärer Filtrationsrate (GFR) steigt die Bedeutung nicht atherosklerotischer Risikofaktoren. Diese komplexe Risikokonstellation führt zu den 2 allgemeinen Therapiezielen: die Verhinderung oder Verlangsamung der Progredienz der CKD unter Einschluss der möglichst effektiven Behandlung der Grunderkrankung und der kardiovaskulären Prävention. Hinsichtlich der Atherosklerose ist die lipidsenkende Therapie (LLT: „lipid-lowering therapy“) ein wesentliches Element mit den Empfehlungen des KDIGO-Netzwerks und den Leitlinien der European Society of Cardiology (ESC) als Grundlagen.
A Dual-branch Enhanced Multi-task Learning Network for Multimodal Sentiment Analysis

Multimodal sentiment analysis is a complex research problem. Firstly, current multimodal approaches fail to adequately consider the intricate multi-level correspondence between modalities and the unique contextual information within each modality; secondly, cross-modal fusion methods for inter-modal fusion somewhat weaken the mode-specific internal features, which is a limitation of the traditional single-branch model. To this end, we proposes a dual-branch enhanced multi-task learning network (DBEM), a new architecture that considers both the multiple dependencies of sequences and the heterogeneity of multimodal data, for better multimodal sentiment analysis. The global-local branch takes into account the intra-modal dependencies of different length time subsequences and aggregates global and local features to enrich the feature diversity. The cross-refine branch considers the difference in information density of different modalities and adopts coarse-to-fine fusion learning to model the inter-modal dependencies. Coarse-grained fusion achieves low-level feature reinforcement of audio and visual modalities, and fine-grained fusion improves the ability to integrate information complementarity between different levels of modalities. Finally, multi-task learning is carried out to improve the generalization and performance of the model based on the enhanced fusion features obtained from the dual-branch network. Compared with the single branch network (SBEM, variant of DBEM model) and SOTA methods, the experimental results on the two datasets CH-SIMS and CMU-MOSEI validate the effectiveness of the DBEM model.
ASCS-Reinforcement Learning: A Cascaded Framework for Accurate 3D Hand Pose Estimation

3D hand pose estimation can be achieved by cascading a feature extraction module and a feature exploitation module, where reinforcement learning (RL) is proved to be an effective way to perform feature exploitation. This paper points out the prospects of improving accuracy using better exploitation strategy, and proposes an Adaptive Step-Critic Shared RL (ASCS-RL) strategy for accurate feature exploitation in 3D hand pose estimation. Hand joint features are exploited in a multi-task manner, and divided into two groups according to the distributions of estimation error. An RL-based adaptive-step (AS-RL) strategy is then used to obtain the optimal step size for better exploitation. The exploitation process are finally performed using a critic-shared RL (CS-RL) strategy, where both groups share a universal critic mechanism. Ablation studies and extensive experiments are carried out to evaluate the performance of ASCS-RL on ICVL and NYU datasets. The results show the strategy achieves the state-of-the-art accuracy in monocular depth-based 3D hand pose estimation, especially the best on ICVL. Experiments also validates that ASCS-RL realizes better tradeoff between accuracy and running rapidity.
Algorithm for directly computing 7Pelliptic curves and its application

To raise the efficiency of scalar multiplication on elliptic curve,based on the idea of trading inversions for multiplications,two efficient algorithms were proposed to compute 7P directly over binary field F2n in terms of affine coordinates.The common divisor and division polynomial were respectively introduced to compute 7P in two algorithms,their computational complexity were 2I+7S+14M and I+6S+20M,saving one inversion and two inversions respectively,compared with the Purohit′s method(PUROHIT G N,RAWAT S A,KUMAR M.Elliptic curve point multiplication using MBNR and Point halving.International Journal of Advanced Networking and Applications,2012,3(5): 1329-1337). Moreover,a new method was given to compute 7kP directly,which was more efficient than computing 7P for k times.Finally,these new algorithms were applied to scalar multiplication combined with point halving and extended MBNS(Multi-Base Number Representation). The experimental results show that the efficiency of the new method is improved about 30%-37% over the Purohit's method and about 9%-13% over the Hong's method(HONG Y F,GUI F,DING Y.Extended algorithm for scalar multiplication based on point halving and MBNS.Computer Engineering,2011,37(4): 163-165) on the elliptic curves recommended by NIST(National Institute of Standards and Technology),when the number of pre-storages points is 2 and 5.The new method can reduce the computational complexity of scalar multiplication efficiently with a few more pre-computation storage.
Cross-Language Music Recommendation Exploration

Recommendation systems are essential for music platforms to drive exploration and discovery for users. Little work has been done in exploring cross-language music recommendation systems, which represent another avenue for music exploration. In this paper, we collected and created a database of over 200,000 artists, which includes subsets of artists that sing in 8 different languages other than English. Our goal was to recommend artists in those 8 other language subsets for a given English-speaking artist. Using Spotify’s API-related artists feature, we implemented two approaches: a matrix factorization model using alternating least squares and a breadth-first search system. Both systems perform significantly better than a random baseline based on accuracy of the base artist’s genre with the breadth-first search model outperforming the matrix factorization technique. We conclude with suggestions for improving the performance and reach of cross-language music recommendation systems.
Risk assessment of Highway Safety Based on Artificial Intelligence: A New Method of Highway Risk Assessment

A new risk assessment method is proposed which combines deep learning, computer vision and traffic safety. The method consists of several models. The highway risk factors identification model, extracts risk elements from video images. The vehicle velocity speculation model is built for the driving speeds calculation of different vehicle types. The Road alignment computational model is built for calculating highway alignment according to GPS. The highway risk management model for evaluating highway risk grades and factors is given. The new method greatly accelerates the speed of highway risk assessment and reduces the human cost. For the overall risk assessment, its accuracy depends on the results of each module. Through experiments and engineering applications, the new method can achieve the same or even better effect as the expert manpower.
A Dynamic 3D Point Cloud Dataset for Immersive Applications

Motion estimation in a 3D point cloud sequence is a fundamental operation with many applications, including compression, error concealment, and temporal upscaling. While there have been multiple research contributions toward estimating the motion vector of points between frames, there is a lack of a dynamic 3D point cloud dataset with motion ground truth to benchmark against. In this paper, we present an open dynamic 3D point cloud dataset to fill this gap. Our dataset consists of synthetically generated objects with pre-determined motion patterns, allowing us to generate the motion vectors for the points. Our dataset contains nine objects in three categories (shape, avatar, and textile) with different animation patterns. We also provide semantic segmentation of each avatar object in the dataset. Our dataset can be used by researchers who need temporal information across frames. As an example, we present an evaluation of two motion estimation methods using our dataset.
A First Look at Adaptive Video Streaming over Multipath QUIC with Shared Bottleneck Detection

The promises of multipath transport is to aggregate bandwidth and improve resource utilisation and reliability. We demonstrate in this paper that the way multipath coupled congestion control is defined today RFC6359 leads to a sub-optimal resource utilisation when network paths are mainly disjoint, i.e., they do not share a bottleneck. With growing interest to standardise Multipath QUIC (MPQUIC), we implement the practical shared bottleneck detection (SBD) algorithm from RFC8382 in MPQUIC, namely MPQUIC-SBD. We evaluate MPQUIC-SBD through extensive emulation experiments in the context of video streaming. We show that MPQUIC-SBD is able to correctly detect shared bottlenecks over 90% of the time as the video segments' size increase depending on the Adaptive Bitrate (ABR) algorithm. In non-shared bottleneck scenarios, MPQUIC-SBD results in video throughput gains of more than 13% compared to MPQUIC, which directly translates into better video quality metrics.
RIP-NeRF: Learning Rotation-Invariant Point-based Neural Radiance Field for Fine-grained Editing and Compositing

Neural Radiance Field (NeRF) shows dramatic results in synthesising novel views. However, existing controllable and editable NeRF methods are still incapable of both fine-grained editing and cross-scene compositing, greatly limiting their creative editing as well as potential applications. When the radiance field is fine-grained edited and composited, a severe drawback is that varying the orientation of the corresponding explicit scaffold, such as point, mesh, volume, etc., may lead to the degradation of rendering quality. In this work, by taking the respective strengths of the implicit NeRF-based representation and the explicit point-based representation, we present a novel Rotation-Invariant Point-based NeRF (RIP-NeRF) for both fine-grained editing and cross-scene compositing of the radiance field. Specifically, we introduce a novel point-based radiance field representation to replace the Cartesian coordinate as the network input. This rotation-invariant representation is met by carefully designing a Neural Inverse Distance Weighting Interpolation (NIDWI) module to aggregate neural points, significantly improving the rendering quality for fine-grained editing. To achieve cross-scene compositing, we disentangle the rendering module and the neural point-based representation in NeRF. After simply manipulating the corresponding neural points, a cross-scene neural rendering module is applied to achieve controllable cross-scene compositing without retraining. The advantages of our RIP-NeRF on editing quality and capability are demonstrated by extensive editing and compositing experiments on room-scale real scenes and synthetic objects with complex geometry.
Learning Visual Navigation System in Simulation for Autonomous Ground Vehicles in Real World

Navigation for autonomous ground vehicles (AGV) should be accurate and quick. Traditional navigation systems, consisting of perception, planning, and control, are unable to use noisy visual images efficiently on a power-limited computation unit. These systems also require lots of parameter-tuning work when deployed on a new robot. By contrast, end-to-end approaches, that directly map sensor information and robot state to planned trajectories, have the potential to navigate autonomous ground vehicles on edge computation devices and possess far fewer manually-tuned parameters. However, collecting data on real robots and labeling the data for training is time-consuming and costly. Therefore, many approaches turn to automatic data labeling and collection in the simulation environment. Motivated by a learning-based navigation system for drones, we present a sim-to-real learning-based navigation pipeline for AGVs where the model is solely trained in simulation environments (Gazebo and UE4) and directly deployed to a real AGV. Results show that after training, the system achieves a high success rate in both simulation and real-world cases, indicating the great potential of this learning pipeline.
EVASR: Edge-Based Video Delivery with Salience-Aware Super-Resolution

With the rapid growth of video content consumption, it is important to deliver high-quality streaming videos to users even under limited available network bandwidth. In this paper, we propose EVASR, a system that performs edge-based video delivery to clients with salience-aware super-resolution. We select patches with higher saliency score to perform super-resolution while applying the simple yet efficient bicubic interpolation for the remaining patches in the same video frame. To efficiently use the computation resources available at the edge server, we introduce a new metric called "saliency visual quality" and formulate patch selection as an optimization problem to achieve the best performance when an edge server is serving multiple users. We implement EVASR based on the FFmpeg framework and conduct extensive experiments for evaluation. Results show that EVASR outperforms baseline approaches in both resource efficiency and visual quality metrics including PSNR, saliency visual quality (SVQ), and VMAF.
Knowledge Graph Based Recommendation by Adversarial Learning Algorithm in Application of Lifelong Education Data Classification

Students can improve their capacity to learn continuously and work together to achieve a common goal through cooperative and explorative coursework in personalized learning. This article presents an effective method for clustering people by preference and a strategy for developing course suggestions for different organizations. This lets us consider student characteristics and courses in a statistically and semantically clear way. First, this article uses specific word articles and Word2Vec to extract factors efficiently. Optimizing efficiency. After that, a slightly modified K-means algorithm and perceptron adversarial learning method classify students into interest-based study clusters. The knowledge graph is created and saved to achieve this. In conclusion, the opinion-based deep learning algorithm used for subject recommendation system design provides advice for appropriate and high-quality results based on the degree of similarity between recommendation results and expert scoring. To do this, the proposed method is approximated against existing machine learning methods and compared to their prediction performance metrics.
AI-powered Human Digital Twins in Virtual Therapeutic Sessions

When patients are unwilling to speak openly, therapists miss valuable information and may not be able to provide best possible care. This study investigated the idea of human Digital Twins (DT) of patients and therapists as a novel solution to encourage them to speak openly. We proposed a methodology to create highly realistic human DT using computer vision, 3D scanning, Metahumans tool, and Unreal game engine. Based on this methodology, we created two versions of virtual therapeutic session: a) video-based virtual sessions with real human and b) virtual sessions with DT of patients and therapists. Twelve therapists then were asked to evaluate technology acceptance of each virtual session. Participants reported significantly lower trust to virtual sessions with highly realistic digital human compared to video-based one. Findings from this study may provide insights regarding opportunities and challenges of virtual therapy using highly realistic digital human technologies. This work also contributes to the virtual therapeutic research field by proposing an AI-powered methodology to create human DT of patients and therapists. Further studies are needed to provide more validation of the efficiency of this approach in virtual sessions for mental health support.
Aging and Visual Presentations in MRI Environments

As individuals age, their vision tends to decline. These changes are natural, but often neglected when designing tasks for both older and younger adults. For instance, many MRI machine set-ups include in-bore displays. Ongoing work in our lab suggests that some older adults have issues seeing stimuli on these displays if there is a visual noise component. This is a problem that did not occur with younger adult samples. Therefore, this work provides an example of how this concern was addressed using a psychophysical thresholding technique. We hope that our experience will inform others who are facing similar issues and/or seeking suggestions for improving their patient and participants’ scanning experiences.
Modified Gauss-Newton full-waveform inversion explained — Why sparsity-promoting updates do matter

ABSTRACTFull-waveform inversion (FWI) can be formulated as a nonlinear least-squares optimization problem. This nonconvex problem can be computationally expensive because it requires repeated solutions of the wave equation. Randomized subsampling techniques allow us to work with small subsets of (monochromatic) source experiments, reducing the computational cost. However, this subsampling may weaken subsurface illumination or introduce subsampling-related incoherent artifacts. These subsampling-related artifacts — in conjunction with the desire to obtain high-fidelity inversion results — motivate us to come up with a technique to regularize this inversion problem. Following earlier work, we have taken advantage of the fact that curvelets represent subsurface models and model perturbations parsimoniously. At first impulse, promoting sparsity on the model directly seemed the most natural way to proceed, but we have determined that in certain cases it can be advantageous to promote sparsity on the Gauss-Newt.
Lessons Learned Using Rapid Human Factors Methods: Condensed Timeline with Effective and Impactful Results

Contextual inquiry and discount usability represent a powerful combination of human factors methods as a responsive and rapid approach to better understand end users’ interactions with a new electronic health record (EHR) system as an enterprise capability. Contextual inquiry is an approach to conducting ethnographic field studies and discount usability is a simplified and efficient technique to identify usability issues. This paper describes how the Veterans Health Administration (VHA) Office of Health Informatics Human Factors Engineering (OHI HFE) responded to a request from VHA leadership to provide rapid support to assess EHR post-implementation system performance to discover if there are barriers or shortcomings. The team utilized contextual inquiry methods and principles of discount usability to assess system usability and effectiveness in meeting end-user needs. OHI and the Electronic Health Record Modernization Integration Office (OEHRM IO) formed a team to conduct semi-structured interviews and observations with health care providers at a VHA facility to gather qualitative data showcasing representative users’ experiences with the EHR. The team analyzed the data collected to identify themes and created an affinity map and two service blueprints. It then used this information to produce a report of findings and recommendations. The study identified areas that required remediation, actions that would facilitate success, and opportunities for improvement. Key contributors to the success of this rapid and important effort included the team’s ability to accommodate a compressed schedule by carefully targeting scope; focusing on a smaller number of participants than originally planned; asking direct, targeted questions developed in advance; and capturing data during short interview and observation windows. The data collected also highlights opportunities to continue the use of discount usability methods, when necessary, to continuously improve OHI HFE data collection and achieve targeted quality improvement in a condensed timeframe.
Rough Set Knowledge Reduction Approach Based on GA

A kind of knowledge relative reduction Algorithm was proposed. With decision attribute support degree applied in knowledge express system, the support degree of the knowledge supplied by condition attribute for the whole decision was described and relative importance degree and relative core was obtained and relative core was obtained and as initial population in GA in order to accelerate convergence. Punishing function was used in fitness function to assuring reduction have fewer attributes and stronger support and search effect is very good. The practical results showed that the approach was effective in solving knowledge reduction.
Thermoelectric characteristics of uniaxially deformed n‐Si in the temperature range 85–355 K

The temperature dependences of thermoelectromotive force (thermo‐emf), tenso‐thermo‐emf (at X∥∇T∥[001] , X = 1.2 GPa), thermo‐emf anisotropy Δα=α||ph−α⊥ph , anisotropy parameter of drag thermo‐emf M=α||ph/α⊥ph and thermoelectric figure of merit Za of n‐Si⟨P⟩ crystals with charge carrier concentration ne≈1.75×1014 cm−3 were investigated in the temperature range 85 K ≤ T ≤ 355 K. It was found that with increasing temperature the anisotropy parameter of drag thermo‐emf M decreases 2.4‐fold, and the anisotropy parameter of mobility K, 2.3‐fold. It is shown that the anisotropy parameter of drag thermo‐emf M decreases with temperature much slower than thermo‐emf anisotropy Δα. It is also shown that within the entire temperature range, the thermo‐emf anisotropy Δα exceeds the thermo‐emf anisotropy, typical for the conventional thermoelectric anisotropic materials in the absence of deformation.
An obligate intracellular bacterial pathogen forms a direct, interkingdom membrane contact site

Interorganelle communication regulates cellular homeostasis through the formation of tightly-associated membrane contact sites 1–3. Prior work has identified several ways that intracellular pathogens alter contacts between eukaryotic membranes 4–6, but there is no existing evidence for contact sites spanning eukaryotic and prokaryotic membranes. Here, using a combination of live-cell microscopy and transmission and focused-ion-beam scanning electron microscopy, we demonstrate that the intracellular bacterial pathogen Rickettsia parkeri forms a direct membrane contact site between its bacterial outer membrane and the rough endoplasmic reticulum (ER), with tethers that are approximately 55 nm apart. Depletion of the ER-specific tethers VAPA and VAPB reduced the frequency of rickettsia-ER contacts, suggesting these interactions mimic organelle-ER contacts. Overall, our findings illuminate a direct, interkingdom membrane contact site uniquely mediated by rickettsia that seems to mimic traditional host MCSs.