Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning for Surgical Risk Assessment Decision Systems

Surgical risk assessments are central to the decision-making process utilized by medical professionals in the planning and execution of surgical procedures. In this regard, several tools are currently relied upon by surgeons to evaluate potential outcomes and associated risk. A popular and widely used assessment tool is the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) Universal Surgical Risk Calculator (ACS-SRC). One important limitation of this approach stands in its inability to exploit complex relationships among data variables, which may limit its classification accuracy. This paper tries to fill this gap by proposing a viable, low-resourced, and generalized decision support model using machine learning techniques that emulates the same general risk assessment functionality as the ACS-SRC, but utilizes more complex models such as gradient-boosted trees and deep neural networks. The experimental results show that our approach performs competitively with respect to state-of-the-art tools.
Dual-granularity Interactive Semantic Learning Based Vulnerability Detection Approach for Cross-platform Binary*

Semantic learning based vulnerability detection methods for binary are extensively utilized in the field of information security, which enables vulnerability discovery via the analysis of semantics. Despite they address the challenge of high time overhead from the conventional dynamic and static detection approaches to some extent, previous studies merely analyze the semantics in terms of function granularity and fail to consider the possibility of interactive semantics to reduce information loss. In this paper, we present DGIS, a vulnerability detection tool based on dual-granularity interactive semantics for binary, specifically, given the binary to be searched, DGIS first performs pre-processing to convert its functions into LSFGs (label semantic flow graphs) firstly, and then two parallel modules are employed to extract different types of semantic information in LSFGs, these two types of semantic information are performed interactive calculation, and fused for similarity calculation; in the end, the whole model outputs a detection report based on the similarity value. Sufficient experimental results demonstrate that our approach is superior to Genius, Gemini, α-Diff, VulSeeker, and Taurus
Si3N4 Manifold Microchannels Cooling System for High Heat Flux Electronic Applications

The increasing energy dissipation in more compact and powerful electronic systems has led to extreme high heat fluxes which call for more effective thermal management solutions. [1] One innovative cooling strategy to meet the dissipation demand is two-phase cooling utilizing manifold-microchannels, it can achieve high heat transfer while maintaining low pressure drops in comparison to conventional two-phase microchannels. For electronics cooling in extreme environment, Si3N4-based ceramic is an ideal substrate due to its high thermal conductivity, low elastic modulus, and sufficient strength and toughness. In this study, a reduced order thermal-fluidic analysis of Si3N4 manifold-microchannel for two-phase boiling flows is conducted for dissipating up to 1 kW of heat from a 1 cm2 heated area on a compact Si3N4 sample. The effect of manifold-microchannel geometry parameters on pressure drop and heat resistance are investigated. The performances of different type of working fluids are also compared, including R134a, R1234yf, R1234ze(E), R245fa, and R1233zd, FC72, and HFE7100. The proposed manifold-microchannels with R134a as the working fluid can achieve low pressure drop ~20 kPa and low thermal resistance ~0.06 K/W with a mass flow rate requirement of 9~ 13 g/s for a heat flux of 1 kW/cm2.
Pulmonary Nodule Classification with Multi-View Convolutional Vision Transformer

Pulmonary nodule classification from computerized tomography(CT) Scans is a vital task for the early screening of Lung cancers. The algorithm is aiming at distinguishing malignant pulmonary nodules, benign nodules and the ones with their subtypes. In this paper, we defined a detailed pulmonary nodule classification task considering 5 semantic labels. We are facing with a series of non-trival problems dealing with such a task. First, the available medical image data for training is quite limited. We enlarged the training dataset by cropping out three-dimension(3D) volume of each pulmonary nodule and generating 15 planes with different orientations from these volumes. Secondly, the global modeling ability of the existing convolutional neural network(CNN) based architectures can not meet the need of medical image analysis well. To learn discriminative abstract information, we down-sample feature maps between successive stages and adopt the BotNet-50 backbone which is a combination of ResNet backbone and self-attention modules. Such an architecture can extract local and non-local information in low-level and high-level layers, respectively. Last but not the least, the data distribution of training data and testing data don't share similar distribution in real-world multi-center medical image classification scenes. We assigned the samples with modified wights while calculating the loss value for optimization. The proposed method can eliminate the spurious correlation between features and labels. Experiments demonstrate the effectiveness of each component.
Photo-z quality cuts and their effect on the measured galaxy clustering

Photometric galaxy surveys are an essential tool to further our understanding of the large-scale structure of the universe, its matter and energy content and its evolution. These surveys necessitate the determination of the galaxy redshifts using photometric techniques (photo-z). Oftentimes, it is advantageous to remove from the galaxy sample those for which one suspects that the photo-z estimation might be unreliable. In this paper, we show that applying these photo-z quality cuts blindly can grossly bias the measured galaxy correlations within and across photometric redshift bins. We then extend the work of Ho et al. (2012) and Ross et al. (2011) to develop a simple and effective method to correct for this using the data themselves. Finally, we apply the method to the Mega-Z catalog, containing about a million luminous red galaxies in the redshift range 0.45 < z < 0.65. After splitting the sample into four \Delta z = 0.05 photo-z bins using the BPZ algorithm, we see how our corrections bring the measured galaxy auto- and cross-correlations into agreement with expectations. We then look for the BAO feature in the four bins, with and without applying the photo-z quality cuts, and find a broad agreement between the BAO scales extracted in both cases. Intriguingly, we observe a correlation between galaxy density and photo-z quality even before any photo-z quality cuts are applied. This may be due to uncorrected observational effects that result in correlated gradients across the sky of the galaxy density and the galaxy photo-z precision. Our correction procedure could also help to mitigate some of these systematic effects.
Fast and Accurate Pose Estimation in Videos based on Knowledge Distillation and Pose Propagation

Existing video-based human pose estimation methods typically adopt large networks to perform body joints localization on all frames. Despite of impressive accuracy performance, the relatively high memory and computation requirements significantly burden their applicability on resource-constraint systems (e.g., embedded devices). To solve this issue, this paper proposes a novel yet effective lightweight framework, called FVPE, for fast and accurate human pose estimation in videos. Specifically, FVPE adopts the knowledge distillation (KD) strategy to train a small pose estimator network, which is capable of executing rapidly with low computational cost. To increase the overall efficiency, FVPE exploits the temporal coherence between successive video frames and explicitly propagates body joints from previous frames rather than naively extracting them using a pose estimator. Furthermore, FVPE introduces an online key-frame selection scheme to decide whether the current pose should be calculated by the pose estimator or be propagated from the previous key-frame, being able to flexibly deal with video sequences with different length, frame rate, pose complexity, etc. Experiments on Penn Action and Sub-JHMDB datasets demonstrate that the proposed method achieves comparative accuracy, but with substantial speed-up.
Automatic Segmentation of Hemorrhagic Stroke on Brain CT Images Using Convolutional Neural Networks Through Fine-Tuning

Strokes are among the top three global causes of death. The diagnosis of stroke is commonly made based on the symptoms displayed and, specifically, on the the results of the neuroimaging tests. Currently, computed tomography (CT) is the fastest, most accessible, and most financially viable neuroimaging method. Thus, Computer-Aided Diagnosis (CAD) systems that can analyze the CT images are essential for obtaining diagnostic information. In order to aid medical diagnosis, this paper proposes a new automatic method of segmenting the areas affected by a hemorrhagic stroke in the CT images based on deep learning, using Mask R-CNN combined with Windowing of Parzen, Clustering and Region Growing methods through the techniques of fine-tuning. Our best model achieved an accuracy of 99.72% and the segmentation time of 6.49 s. Thus, we surpass methods already consolidated in the literature, with both manual and automatic initialization, and even Deep Learning techniques using fine-tuning. We validate the proposed model by comparing it to existing methods that segment images from the same dataset. We overcoming the state of art among the analyzed models, which proves the efficiency of our method for systems based on CAD.
A Stepwise Haemostasis Intraoperative Protocol Driven Reduction of Haematoma Rate Following Symptomatic Carotidendarterectomy

Introduction Carotid endarterectomy (CEA) for symptomatic stenosis reduces further stroke risk. Post-CEA haematoma increases the risk of complications including stroke. There are few studies considering protocols aimed at reducing post-CEA haematoma rates. Presented are the outcomes of a protocol developed to reduce this surgical complication. Method The protocol was implemented in 112 consecutive CEA. It involves stepwise additional measures to ensure haemostasis before wound closure. Attention to bleeding points is followed by light compression for 10 min. Protamine is then given if haemostasis has not been achieved. If after 20 min the problem persists Tranexamic acid is given. Following a further 20 min if haemostasis is not yet achieved a platelet transfusion is undertaken. Haematoma rates, return to theatre for post-operative haematoma and other complications were compared with 100 consecutive pre-protocol introduction CEA cases. Results Of 112 CEA patients, 19 received protamine, 8 protamine and tranexamic acid. One case required platelet transfusion. Neck haematoma rate fell from 10 to 3 cases (P = .02, OR: 0.25 [95% CI .07-.94]), of which returned to theatre for haematoma evacuation fell from 6 to 1 case (P = .03, OR: 0.14 [95% CI .02-1.19]). 30 day stroke and death rate reduced from 5% to 1.8% (P = .11, OR: 0.35 [95% CI .07-1.82]). Conclusion The stepwise haemostasis intraoperative protocol can reduce post-CEA haematoma rates.
Analysis of the Influence of Waste Seashell as Modified Materials on Asphalt Pavement Performance

An increasing amount of waste seashells in China has caused serious environmental pollution and resource waste. This paper aims to solve these problems by using waste seashells as modified materials to prepare high-performance modified asphalt. In this study, seashell powder (SP) and stratum corneum-exfoliated seashell powder (SCESP) were adopted to prepare 10%, 20% and 30% of seashell powder-modified asphalt (SPMA) and stratum corneum-exfoliated seashell powder-modified asphalt (SCESPMA) by the high-speed shear apparatus, respectively. The appearance and composition of two kinds of SPs were observed and determined by the scanning electron microscope (SEM). The types of functional groups, temperature frequency characteristics, low temperature performance and adhesion of SPMA were tested by the Fourier-transform infrared (FTIR) spectrometer, dynamic shear rheometer (DSR), bending beam rheometer (BBR) and contact angle meter. The results show that the SP and SCESP are rough and porous, and their main component is CaCO3, which is physically miscible to asphalt. When the loading frequency ranges from 0.1 Hz to 10 Hz, the complex shear modulus (G*) and phase angle (δ) of SPMA and SCESPMA increase and decrease, respectively. At the same load frequency, SCESPMA has a larger G* and a smaller δ than SPMA. At the same temperature, SCESPMA has a larger rutting factor (G*/sin δ) and better high-temperature deformation resistance than SPMA. SP and SCESP reduce the low-temperature cracking resistance of asphalt, of which SCESP has a more adverse effect on the low-temperature performance of asphalt than SP. When SP and SCESP are mixed with asphalt, the cohesion work (Waa), adhesion work (Was) and comprehensive evaluation parameters of water stability (ER1, ER2 and ER3) of asphalt are improved. It is shown that both SP and SCESP have good water damage resistance, of which SCESP has better water damage resistance than SP. These research results have important reference value for the application of waste biological materials in asphalt pavement.
Edge-level privacy in Graph Neural Networks

The problem of privacy in graph neural networks (GNNs) is being studied recently. It is necessary in order to ensure the privacy of the system that is being modeled as graphs. Currently, the existing models primarily consider the node features and the node labels as privacy information corresponding to the graphs. We propose an edge-privacy preserving methodology called EP-GNN to incorporate the privacy of the structural information of the graphs as well in addition to the features and the label information of the graphs. In this preliminary work, we investigate the impact of the noisy neighborhood on the accuracy of the GNNs. This is in addition to the We experiment with the amount of neighborhood that we perturb and the privacy budget for the edge privacy. We propose two methods to consider the neighborhood, namely 1. λ − selector from the neighborhood, and 2. complete neighborhood in order to ensure privacy in the edge data of the graph. We continue to use the node and label privacy as they are implemented in the previous methods for privacy in GNNs.
Comparison of gemcitabine/carboplatin versus paclitaxel/cisplatin for the management of non-small cell lung cancer

Purpose: To determine the comparative efficacy and toxicity of gemcitabine/carboplatin and paclitaxel/cisplatin in patients with completely resected stage IIa - IIIa non-small cell lung cancer (NSCLC). Methods: Sixty eligible NSCLC patients treated in Funan County People's Hospital were enrolled and assigned to two groups by randomization (n = 30 each). One group (CG group) received the combination of gemcitabine and carboplatin, while the second group (CP group) received a combination of cisplatin and paclitaxel. Efficacy was assessed based on 2-year progression-free survival, while adverse reactions were recorded to assess the toxicity of the chemotherapy treatments. Results: No marked difference was found in the 2-year relapse-free survival in the two groups with similar clinical baseline characteristics after follow-up (60 % in CG group vs. 56.67 % in CP group, p = 0.826). Specifically, no significant difference was found between the two groups with regard to incidence of local metastases, distant metastases, or brain tissue metastases within 2 years, and there were no treatment-related deaths. CG group was more likely to develop leukopenia (93.33 % vs. 63.33 % for CP group, p = 0.04), but no significant difference was observed for other adverse effects such as anemia, vomiting, and nausea. Conclusion: This study shows that adjuvant treatment using carboplatin and gemcitabine produces the same therapeutic efficacy as cisplatin and paclitaxel, but exhibits higher toxicity levels than the latter.
Semantic Parsing for Planning Goals as Constrained Combinatorial Contextual Bandits

We are working towards AI planning systems 001 with natural language interfaces. In this pa-002 per, we tackle the semantic parsing problem of 003 learning to set the logical goals of the planning 004 system based on a natural language description 005 of the task. The current state of the art in se-006 mantic parsing is to use supervised learning 007 with deep neural networks but this needs a lot 008 of labelled data made by domain experts. To 009 reduce this need, we additionally use a reward 010 signal that comes from completing the AI plan-011 ning task. We formalize this as a constrained 012 combinatorial contextual bandit problem. The 013 context is created by using a deep neural net-014 work for feature extraction and the constrained 015 combinatorial nature of the task can be used to 016 increase the efficiency of learning. We show 017 this result theoretically with our lower regret 018 bound and then experimentally in our extension 019 of the TextWorld problem. 020
Unmodiﬁed Cadmium Telluride Quantum Dots Induce Reactive Oxygen Species Formation Leading to Multiple Organelle Damage and Cell Death

Quantum dots (QDs) are luminescent nanoparticles withuniqueopticalpropertiesthathavebeenexploited forsingle-cellandwhole-animalimaging.Whencoatedwithproteinsorbiocompatiblepolymers,QDsarenot deleterious to cells and organisms. However, when QDs are retained in cells or accumulated in the body for a long period of time, their coatings may be degraded, yielding ‘‘naked’’ QDs. Here, we show that ‘‘naked’’ QDs induce damage to the plasma membrane, mitochondrion, and nucleus, leading to cell death. Reactive oxygen species (ROS) are important players in mediating QD-induced cellular damage. QD-induced cytotoxicity can be reduced or even eliminated without covalent binding of protective agents to the QD surface. Results from these studies suggest the critical role of several subcellular compartments in QD-induced cytotoxicity and point toward multiple molecular targets in nonclassical apoptosis.
EiCi: A New Method of Dynamic Embedding Incorporating Contextual Information in Chinese NER

With the continuous development of deep 001 learning technology, the ﬁeld of Named Entity 002 Recognition(NER) has made great achieve-003 ments in recent years. In Chinese NER, mak-004 ing full use of word information is becoming 005 the key to improve model performance. In the 006 previous related work, lexicon was applied to 007 add word information. However, the word vec-008 tors generated by that way is static. It means 009 that it cannot accurately describe some pol-010 ysemous words in a speciﬁc context, which 011 will affect the performance of the NER task. 012 This paper presents EiCi to solve this prob-013 lem. The new method is proposed that, with-014 out relying on external pre-trained word vec-015 tors, it takes the advantage of the pre-trained 016 language model BERT to extract polysemous 017 word information. In order to further uti-018 lize the word information, a sub-module for 019 type recognition is also added to assist the 020 main task of NER. Experiments on two main 021 Chinese NER datasets show EiCi has better 022 performance than the traditional NER models 023 and other NER models that use word informa-024 tion.Source codes of this paper are available 025 on Github 1 026
Filter Modiﬁed with Hydrophilic and Oleophobic Coating for Efﬁcient and Affordable Oil/Water Separation

: To mitigate the damage of oil spills, a ﬁlter modiﬁed with a hydrophilic and oleophobic coating is proposed for affordable and efﬁcient oil separation and recovery from water. The sol–gel method was chosen to produce a colloidal suspension of titanium dioxide particles for its ease of production and its versatility in application for many different substrates, including paper and cloth fabric. After immersing the substrates into a titanium-containing solution, three techniques were applied to increase the production of titanium dioxide—microwave-assisted, refrigeration, and ultra-sonication. Contact angle tests were done to investigate the change in the ﬁlter’s oleophobicity. The titanium dioxide present on the surface of the ﬁlter was amorphous, but all treatment methods showed an improvement in oleophobicity. All treated ﬁlters improved oil ﬁltration performance by up to eighty percent. The ﬁlters isolated motor oil from a mixture while allowing water to pass through. The coated ﬁlters also displayed photocatalytic activity by degrading methylene blue on its surface when exposed to sunlight, demonstrating the ﬁlter’s self-cleaning ability. For real-world applications, the ﬁlter can be supported by a stainless mesh for enhanced strength and durability. While being dragged through the water, the ﬁlter collects the surface oil, allowing water to pass through via gravity.
A Weak supervision with Syntactic Cues for Reference Resolution

In recipes, contextual understanding of instruc-001 tions depends on temporal interpretation of 002 the entities because of their spatio-temporal 003 changes. Accordingly, we propose the use of 004 reference resolution to find the origin action of 005 entities, provided that the entity is an output 006 from a previous action, instead of being a raw 007 ingredient. Here, we introduce a weak super-008 vision method that exploits syntactic features 009 for producing latent links between entities and 010 their origin actions. The results show that our 011 weak supervision outperforms the previous un-012 supervised studies with %8 F1. In particular, 013 our approach indicates %82 resolution perfor-014 mance on pronoun, and %85 on null entities. 015
Anti-Viral Photodynamic Inactivation of T4-like Bacteriophage as a Mammalian Virus Model in Blood

The laboratorial available methods applied in plasma disinfection can induce damage in other blood components. Antimicrobial photodynamic therapy (aPDT) represents a promising approach and is approved for plasma and platelet disinfection using non-porphyrinic photosensitizers (PSs), such as methylene blue (MB). In this study, the photodynamic action of three cationic porphyrins (Tri-Py(+)-Me, Tetra-Py(+)-Me and Tetra-S-Py(+)-Me) towards viruses was evaluated under white light irradiation at an irradiance of 25 and 150 mW·cm−2, and the results were compared with the efficacy of the approved MB. None of the PSs caused hemolysis at the isotonic conditions, using a T4-like phage as a model of mammalian viruses. All porphyrins were more effective than MB in the photoinactivation of the T4-like phage in plasma. Moreover, the most efficient PS promoted a moderate inactivation rate of the T4-like phage in whole blood. Nevertheless, these porphyrins, such as MB, can be considered promising and safe PSs to photoinactivate viruses in blood plasma.