Miracle-dz/UniFAR-FTUs huggingface dataset:
Scoring systems for the management of oncological hepato-pancreato-biliary patients

Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong’s score), pancreas (Genç’s score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.
Urine Biochemistry of a Human Fetus with Urinary Tract Obstruction – A Case Report

Abstract Introduction Fetal lower urinary tract obstruction (LUTO) can be mild or severe with oligohydramnios, renal dysplasia and pulmonary hypoplasia. Fetal urine biochemical markers correlate with fetal prognosis and, if favorable, surgical intervention is feasible. Methods We report a patient in her 18th gestational week whose fetus was diagnosed with LUTO and underwent fetal urine sampling for calcium, sodium, chloride, beta2-microglobulin and total protein of the routine LUTO panel, with the addition of creatinine, glucose, phosphate, urea, ammonia, albumin, and NGAL. Results Although the routine fetal urine biochemistry seemed to be favorably trending favorably, sodium, beta2-microglobulin, glucose, and urea did not decrease to the reference ranges, and ammonia and creatinine were lower than the reference ranges. Ultrasound demonstrated no improvement of the obstruction. Conclusions This case highlights the need to acquire further experience with biochemical fetal urine markers in order to better manage LUTO.
Assessment of mitophagy in human iPSC-derived cardiomyocytes

ABSTRACT Defective mitophagy contributes to normal aging and various neurodegenerative and cardiovascular diseases. The newly developed methodologies to visualize and quantify mitophagy allow for additional progress in defining the pathophysiological significance of mitophagy in various model organisms. However, current knowledge regarding mitophagy relevant to human physiology is still limited. Model organisms such as mice might not be optimal models to recapitulate all the key aspects of human disease phenotypes. The development of the human-induced pluripotent stem cells (hiPSCs) may provide an exquisite approach to bridge the gap between animal mitophagy models and human physiology. To explore this premise, we take advantage of the pH-dependent fluorescent mitophagy reporter, mt-Keima, to assess mitophagy in hiPSCs and hiPSC-derived cardiomyocytes (hiPSC-CMs). We demonstrate that mt-Keima expression does not affect mitochondrial function or cardiomyocytes contractility. Comparison of hiPSCs and hiPSC-CMs during different stages of differentiation revealed significant variations in basal mitophagy. In addition, we have employed the mt-Keima hiPSC-CMs to analyze how mitophagy is altered under certain pathological conditions including treating the hiPSC-CMs with doxorubicin, a chemotherapeutic drug well known to cause life-threatening cardiotoxicity, and hypoxia that stimulates ischemia injury. We have further developed a chemical screening to identify compounds that modulate mitophagy in hiPSC-CMs. The ability to assess mitophagy in hiPSC-CMs suggests that the mt-Keima hiPSCs should be a valuable resource in determining the role mitophagy plays in human physiology and hiPSC-based disease models. The mt-Keima hiPSCs could prove a tremendous asset in the search for pharmacological interventions that promote mitophagy as a therapeutic target. Abbreviations: AAVS1: adeno-associated virus integration site 1; AKT/protein kinase B: AKT serine/threonine kinase; CAG promoter: cytomegalovirus early enhancer, chicken ACTB/β-actin promoter; CIS: cisplatin; CRISPR: clustered regularly interspaced short palindromic repeats; FACS: fluorescence-activated cell sorting; FCCP: carbonyl cyanide p-trifluoromethoxyphenylhydrazone; hiPSC: human induced pluripotent stem cell; hiPSC-CMs: human induced pluripotent stem cell-derived cardiomyocytes; ISO: isoproterenol; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MTOR: mechanistic target of rapamycin kinase; PI3K: phosphoinositide 3-kinase; PINK1: PTEN induced kinase 1; PRKN: parkin RBR E3 ubiquitin protein ligase; RT: room temperature; SB: SBI-0206965; ULK1: unc-51 like autophagy activating kinase 1.
EMLA (lidocaine–prilocaine) cream for pain relief during hysterosalpingography: a systematic review and meta-analysis of randomised placebo-controlled trials

Abstract We systematically investigated the efficacy and safety of EMLA (5% lidocaine–prilocaine cream) versus placebo for pain relief among infertile patients undergoing hysterosalpingography (HSG). We screened four databases from inception until 25 November 2020. We included only randomised placebo-controlled trials (RCTs) and assessed their risk of bias. The main efficacy outcomes included safety and pain scores during the different stages of HSG. The pooled outcomes were summarised as mean difference (MD) with 95% confidence interval (CI). Three RCTs were included, comprising 258 patients (131 and 127 patients received EMLA and placebo, respectively). All RCTs revealed an overall low risk of bias. EMLA significantly reduced pain perception during cervical instrumentation of tenaculum and cannula (MD = −1.53, 95% CI [−2.59, −0.47], p = 0.005) and at 24 h after completion of HSG (MD = −1.30, 95% CI [−2.57, −0.03], p = 0.04). Despite EMLA decreased pain perception during the other procedural stages of HSG, the differences were not statistically significant compared with placebo. EMLA was safe and free of local and systemic adverse reactions. This meta-analysis advocates that topical application of 5% EMLA cream is safe and correlates with decreased pain perception during HSG, particularly during the cervical instrumentation step and at 24 h after HSG completion.
Hierarchical Policy Learning for Mechanical Search

Retrieving objects from clutters is a complex task, which requires multiple interactions with the environment until the target object can be extracted. These interactions involve executing action primitives like grasping or pushing as well as setting priorities for the objects to manipulate and the actions to execute. Mechanical Search (MS) [1] is a framework for object retrieval, which uses a heuristic algorithm for pushing and rule-based algorithms for high-level planning. While rule-based policies profit from human intuition in how they work, they usually perform sub-optimally in many cases. Deep reinforcement learning (RL) has shown great performance in complex tasks such as taking decisions through evaluating pixels, which makes it suitable for training policies in the context of object-retrieval. In this work, we first formulate the MS problem in a principled formulation as a hierarchical POMDP. Based on this formulation, we propose a hierarchical policy learning approach for the MS problem. For demonstration, we present two main parameterized sub-policies: a push policy and an action selection policy. When integrated into the hierarchical POMDP's policy, our proposed sub-policies increase the success rate of retrieving the target object from less than 32% to nearly 80%, while reducing the computation time for push actions from multiple seconds to less than 10 milliseconds.
Research on the Spray Cone's Characteristics of the Photoresist Saving-oriented Ultrasonic Atomizing Spraying Process

(cid:0)The photoresist is widely used in integrated circuit and micro electro-mechanical manufacturing systems processes, but traditional spin coating process will loss 95% of photoresist and cause negative environment impact for its high toxicity, which need higher disposing cost. Improving the utilization rate of the photoresist is one of the main objects of the coating process. Based on the self-developed 100 kHz ultrasonic nozzle, the film uniformity problem during the photoresist saving-oriented ultrasonic atomizing spraying process is studied. After analyzing the flow field distribution of carrier gas, a force and collision model is built to study behavioral characteristics of atomized droplets in rotating and symmetrical airflow field and then proposed a numerical simulation procedure to study transport processes of atomized droplets, which could analyze the impact of gas flow, fluid flow and spray cone cross-section height on atomized droplet’s particle size and concentration distribution effectively. The actual ultrasonic atomizing spraying experiment was carried out on 304.8 mm wafer using photoresist AZ4999. The results shows that the ultrasonic atomizing spraying nozzle can achieve high film uniformity and save about 80% photoresist when compared with spin coating process.
Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search

Decision-making under uncertainty (DMU) is present in many important problems. An open challenge is DMU in non-stationary environments, where the dynamics of the environment can change over time. Reinforcement Learning (RL), a popular approach for DMU problems, learns a policy by interacting with a model of the environment offline. Unfortunately, if the environment changes the policy can become stale and take sub-optimal actions, and relearning the policy for the updated environment takes time and computational effort. An alternative is online planning approaches such as Monte Carlo Tree Search (MCTS), which perform their computation at decision time. Given the current environment, MCTS plans using high-fidelity models to determine promising action trajectories. These models can be updated as soon as environmental changes are detected to immediately incorporate them into decision making. However, MCTS's convergence can be slow for domains with large state-action spaces. In this paper, we present a novel hybrid decision-making approach that combines the strengths of RL and planning while mitigating their weaknesses. Our approach, called Policy Augmented MCTS (PA-MCTS), integrates a policy's actin-value estimates into MCTS, using the estimates to seed the action trajectories favored by the search. We hypothesize that PA-MCTS will converge more quickly than standard MCTS while making better decisions than the policy can make on its own when faced with nonstationary environments. We test our hypothesis by comparing PA-MCTS with pure MCTS and an RL agent applied to the classical CartPole environment. We find that PC-MCTS can achieve higher cumulative rewards than the policy in isolation under several environmental shifts while converging in significantly fewer iterations than pure MCTS.
Over-the-Air Computation with Imperfect Channel State Information

This paper investigates the effect of imperfect channel state information (CSI) on the over-the-air computation (AirComp) system, in which multiple wireless devices (WDs) send individual messages to one access point (AP) for distributed functional computation. By particularly considering the channel estimation errors, we jointly design the transmit coefficients at the WDs and the receive strategy at the AP, for minimizing the computation mean squared error (MSE). First, we consider the single-input single-output (SISO) case with each WD and AP equipped with one single antenna, in which the globally optimal solution to the computation MSE minimization problem is obtained in closed form. Next, we consider the single-input multiple-output (SIMO) case with multiple receive antennas at the AP, in which a high-quality solution is obtained based on alternating optimization and convex optimization. For both cases, the optimized power control solution at the WDs follows a threshold-based regularized channel inversion structure; while for the SIMO case, the receive beamforming at the AP follows a sum-minimum MSE (MMSE) structure. It is shown that with finite receive antennas, a non-zero computation MSE is inevitable due to the channel estimation errors even when the WDs' transmit powers become infinity; while with massive receive antennas, a vanishing MSE is achievable when the channel vectors are independent and identically distributed. Finally, numerical results are provided to demonstrate the effectiveness of the proposed designs.
RONELDv2: A faster, improved lane tracking method

Lane detection is an integral part of control systems in autonomous vehicles and lane departure warning systems as lanes are a key component of the operating environment for road vehicles. In a previous paper, a robust neural network output enhancement for active lane detection (RONELD) method augmenting deep learning lane detection models to improve active, or ego, lane accuracy performance was presented. This paper extends the work by further investigating the lane tracking methods used to increase robustness of the method to lane changes and different lane dimensions (e.g. lane marking thickness) and proposes an improved, lighter weight lane detection method, RONELDv2. It improves on the previous RONELD method by detecting the lane point variance, merging lanes to find a more accurate set of lane parameters, and using an exponential moving average method to calculate more robust lane weights. Experiments using the proposed improvements show a consistent increase in lane detection accuracy results across different datasets and deep learning models, as well as a decrease in computational complexity observed via an up to two-fold decrease in runtime, which enhances its suitability for real-time use on autonomous vehicles and lane departure warning systems.
A new approach for load flow calculation in AC/DC distribution networks considering the control strategies of different converters

Summary Utilization of AC/DC distribution networks has become more reasonable because of the recent developments in power electronic devices and increasing employment of DC electronic loads. In this paper, load flow calculation problem in AC/DC distribution networks is addressed. Because a large number of load buses in both AC and DC parts and high R/X ratios in the AC part of these networks can lead to the divergence of recent Jacobian matrix based methods, in this paper, a new direct approach based on backward forward sweep algorithm is presented. Comprehensive investigation considering weakly meshed and different types of converters and their control strategies is also carried out. The presented algorithm is applied to the test systems equipped with different types of converters. The results demonstrate the validity and capability of the proposed method.
Orientation-Discriminative Feature Representation for Decentralized Pedestrian Tracking

This paper focuses on the problem of decentralized pedestrian tracking using a sensor network. Traditional works on pedestrian tracking usually use a centralized framework, which becomes less practical for robotic applications due to limited communication bandwidth. Our paper proposes a communication-efficient, orientation-discriminative feature representation to characterize pedestrian appearance information, that can be shared among sensors. Building upon that representation, our work develops a cross-sensor track association approach to achieve decentralized tracking. Extensive evaluations are conducted on publicly available datasets and results show that our proposed approach leads to improved performance in multi-sensor tracking.
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. However, it operates on sensible data collected from the users' cars; thus, protecting the clients' privacy becomes a primary concern. For similar reasons, Federated Learning has been recently introduced as a new machine learning paradigm aiming to learn a global model while preserving privacy and leveraging data on millions of remote devices. Despite several efforts on this topic, no work has explicitly addressed the challenges of federated learning in semantic segmentation for driving so far. To fill this gap, we propose FedDrive, a new benchmark consisting of three settings and two datasets, incorporating the real-world challenges of statistical heterogeneity and domain generalization. We benchmark state-of-the-art algorithms from the federated learning literature through an in-depth analysis, combining them with style transfer methods to improve their generalization ability. We demonstrate that correctly handling normalization statistics is crucial to deal with the aforementioned challenges. Furthermore, style transfer improves performance when dealing with significant appearance shifts. Official website: https://feddrive.github. io.
Automated Creation and Human-assisted Curation of Computable Scientific Models from Code and Text

Scientific models hold the key to better understanding and predicting the behavior of complex systems. The most comprehensive manifestation of a scientific model, including crucial assumptions and parameters that underpin its usability, is usually embedded in associated source code and documentation, which may employ a variety of (potentially outdated) programming practices and languages. Domain experts cannot gain a complete understanding of the implementation of a scientific model if they are not familiar with the code. Furthermore, rapid research and development iterations make it challenging to keep up with constantly evolving scientific model codebases. To address these challenges, we develop a system for the automated creation and human-assisted curation of a knowledge graph of computable scientific models that analyzes a model’s code in the context of any associated inline comments and external documentation. Our system uses knowledge-driven as well as datadriven approaches to identify and extract relevant concepts from code and equations from textual documents to semantically annotate models using domain terminology. These models are converted into executable Python functions and then can further be composed into complex workflows to answer different forms of domain-driven questions. We present experimental results obtained using a dataset of code and associated text derived from NASA’s Hypersonic Aerodynamics website.
An isogeometric finite element formulation for boundary and shell viscoelasticity based on a multiplicative surface deformation split

This work presents a numerical formulation to model isotropic viscoelastic material behavior for membranes and thin shells. The surface and the shell theory are formulated within a curvilinear coordinate system, which allows the representation of general surfaces and deformations. The kinematics follow from Kirchhoff–Love theory and the discretization makes use of isogeometric shape functions. A multiplicative split of the surface deformation gradient is employed, such that an intermediate surface configuration is introduced. The surface metric and curvature of this intermediate configuration follow from the solution of nonlinear evolution laws—ordinary differential equations—that stem from a generalized viscoelastic solid model. The evolution laws are integrated numerically with the implicit Euler scheme and linearized within the Newton–Raphson scheme of the nonlinear finite element framework. The implementation of membrane and bending viscosity is verified with the help of analytical solutions and shows ideal convergence behavior. The chosen numerical examples capture large deformations and typical viscoelasticity behavior, such as creep, relaxation, and strain rate dependence. It is also shown that the proposed formulation can be straightforwardly applied to model boundary viscoelasticity of 3D bodies.
Supervising Remote Sensing Change Detection Models With 3d Surface Semantics

Remote sensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications. Recent advances in multimodal self-supervised pretraining have resulted in state-of-the-art methods which surpass vision models trained solely on optical imagery. In the remote sensing field, there is a wealth of overlapping 2D and 3D modalities which can be exploited to supervise representation learning in vision models. In this paper we propose Contrastive Surface-Image Pretraining (CSIP) for joint learning using optical RGB and above ground level (AGL) map pairs. We then evaluate these pretrained models on several building segmentation and change detection datasets to show that our method does, in fact, extract features relevant to downstream applications where natural and artificial surface information is relevant.1
'Tis but Thy Name: Semantic Question Answering Evaluation with 11M Names for 1M Entities

Classic lexical-matching-based QA metrics are slowly being phased out because they punish succinct or informative outputs just because those answers were not provided as ground truth. Recently proposed neural metrics can evaluate semantic similarity but were trained on small textual similarity datasets grafted from foreign domains. We introduce the Wiki Entity Similarity (WES) dataset, an 11M example, domain targeted, semantic entity similarity dataset that is generated from link texts in Wikipedia. WES is tailored to QA evaluation: the examples are entities and phrases and grouped into semantic clusters to simulate multiple ground-truth labels. Human annotators consistently agree with WES labels, and a basic cross encoder metric is better than four classic metrics at predicting human judgments of correctness.
Unsupervised machine learning correlations in EoS of neutron stars

Neutron stars are compact objects of large interest in the nuclear astrophysics community. The extreme conditions present in such systems impose big challenges to our current microscopic models of nuclear structure. Equation of states (EoS) are frequently derived from sophisticated quantum mechanical models, such as: relativistic, non-relativistic and many mean-field approaches. Every single model, in general, contains many parameters such as the NN interaction strength, particle compositions, etc. These are particular features of each model and can be represented by numbers and categories in a machine learning context. Different choices of features will affect EoS properties leading to different macroscopic properties of the star. In this work we analyze a selection of EoS containing a variety of different physics models. One of our objectives is to develop tools that enable a better understanding of the correlations among the different model features and the outcome produced by them when employed to model neutron stars.