Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of alum and PAC neutral sizing systems

The paper industry is moving towards neutral papermaking but there is a major concern : how to achieve the desired sizing degree. Historically, soap rosin size with alum is used for sizing, a system which has to be operated in the acidic region (pH 4-5) because of aluminium hydrolysis. Research on sizing without alum led to development of reactive sizes, the most successful of which are alkyl ketene dimers (AKD) and alkenyl succinic anhydrides (ASA). Homever, for several reasons, many mills want to move into neutral pH without precluding the use of rosin-based size. This can now be achieved by using dispersed rosin acid size with either polyaluminium chloride (PAC) or alum. Both neutral rosin/alum and rosin/PAC sizing systems were evaluated in a UK board-mill. The evaluation, which involves laboratory work and machine trials, is detailed in this paper.
Plasma Functionalization of Multi-Walled Carbon Nanotubes for Ammonia Gas Sensors

The role of plasma functionalization of multi-walled carbon nanotubes (MWCNTs) for room-temperature ammonia gas sensors was investigated. Plasma functionalization of MWCNTs with maleic anhydride was carried out at various durations. The active material of the gas sensor was investigated by scanning electron microscopy, transmission electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy. It was shown that the formation of functional groups on the surface of carbon nanotubes led to an increase in the ammonia sensor response by two to four times. The increase in functionalization duration induced the rise of O/C from 0.28 to 0.335, an increase in sensor resistance, and the distortion of the shape of the I-V curves.
Innovation Compression for Communication-Efficient Distributed Optimization With Linear Convergence

Information compression is essential to reduce communication cost in distributed optimization over peer-to-peer networks. This article proposes a communication-efficient linearly convergent distributed (COLD) algorithm to solve strongly convex optimization problems. By compressing innovation vectors, which are the differences between decision vectors and their estimates, COLD achieves linear convergence for a class of $\delta$-contracted compressors, and we explicitly quantify how the compression affects the convergence rate. Interestingly, our results strictly improve existing results for the quantized consensus problem. Numerical experiments demonstrate the advantages of COLD under different compressors.
Multi-Resolution Data Fusion for Super Resolution Imaging

Applications in materials and biological imaging are limited by the ability to collect high-resolution data over large areas in practical amounts of time. One solution to this problem is to collect low-resolution data and interpolate to produce a high-resolution image. However, most existing super-resolution algorithms are designed for natural images, often require aligned pairing of high and low-resolution training data, and may not directly incorporate a model of the imaging sensor. In this paper, we present a Multi-resolution Data Fusion (MDF) algorithm for accurate interpolation of low-resolution electron microscope data at multiple resolutions up to 8x. Our approach uses small quantities of unpaired high-resolution data to train a neural network prior model denoiser and then uses the Multi-Agent Consensus Equilibrium (MACE) problem formulation to balance this denoiser with a forward model agent that promotes fidelity to measured data. A key theoretical novelty is the analysis of mismatched back-projectors, which modify typical forward model updates for computational efficiency or improved image quality. We use MACE to prove that using a mismatched back-projector is equivalent to using a standard back-projector and an appropriately modified prior model. We present electron microscopy results at 4x and 8x interpolation factors that exhibit reduced artifacts relative to existing methods while maintaining fidelity to acquired data and accurately resolving sub-pixel-scale features.
Information-theoretic Evolution of Model Agnostic Global Explanations

Explaining the behavior of black box machine learning models through human interpretable rules is an important research area. Recent work has focused on explaining model behavior locally i.e. for specific predictions as well as globally across the fields of vision, natural language, reinforcement learning and data science. We present a novel model-agnostic approach that derives rules to globally explain the behavior of classification models trained on numerical and/or categorical data. Our approach builds on top of existing local model explanation methods to extract conditions important for explaining model behavior for specific instances followed by an evolutionary algorithm that optimizes an information theory based fitness function to construct rules that explain global model behavior. We show how our approach outperforms existing approaches on a variety of datasets. Further, we introduce a parameter to evaluate the quality of interpretation under the scenario of distributional shift. This parameter evaluates how well the interpretation can predict model behavior for previously unseen data distributions. We show how existing approaches for interpreting models globally lack distributional robustness. Finally, we show how the quality of the interpretation can be improved under the scenario of distributional shift by adding out of distribution samples to the dataset used to learn the interpretation and thereby, increase robustness. All of the datasets used in our paper are open and publicly available. Our approach has been deployed in a leading digital marketing suite of products.
Efficacy of percutaneous release of trigger finger

Trigger finger is one of the most common cause of pain and disability of the hand. This condition results in painful catching or popping of the involved flexor tendon as the patient flexes and extends the digit. This study aimed to test the efficiency of percutaneous release in the treatment of Trigger Finger in 100 patients. Percutaneous Release of A1 pulley of the affected digit was performed using 18G needle under Local Anaesthesia in the Out Patient Department. Results were graded using Quinnel’s criteria as excellent if there was no pain after the procedure, good if pain occurred with heavy use or poor if there was no reduction in pain. Excellent result was achieved in 83 patients while good results achieved in 13 patients who were satisfied and were able to return to their work the next day of the procedure. In 4 patients the procedure failed to relieve the symptoms and there was some degree of residual triggering and pain left. At 1 week follow up only 9 patients had some degree of pain and stiffness which had completely resolved on taking medication and there were no complaints at 3 months follow up. Percutaneous release is a safe, effective and cheap procedure, providing immediate relief from symptoms in the treatment of trigger finger which is well tolerated and has a low rate of complications when done by an experienced Orthopaedic surgeon.
Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer

Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content. We show that fine-tuning pre-trained language (GPT-2) and sequence-to-sequence (BART) models boosts content preservation, and that this is possible even with limited amounts of parallel data. Augmenting these models with rewards that target style and content –the two core aspects of the task– we achieve a new state-of-the-art.
Physical Artificial Intelligence: The Concept Expansion of Next-Generation Artificial Intelligence

Artificial Intelligence has been a growth catalyst to our society and is cosidered across all idustries as a fundamental technology. However, its development has been limited to the signal processing domain that relies on the generated and collected data from other sensors. In recent research, concepts of Digital Artificial Intelligence and Physicial Artifical Intelligence have emerged and this can be considered a big step in the theoretical development of Artifical Intelligence. In this paper we explore the concept of Physicial Artifical Intelligence and propose two subdomains: Integrated Physicial Artifical Intelligence and Distributed Physicial Artifical Intelligence. The paper will also examine the trend and governance of Physicial Artifical Intelligence.
Predicting Surface Reflectance Properties of Outdoor Scenes Under Unknown Natural Illumination

Estimating and modeling the appearance of an object under outdoor illumination conditions is a complex process. This article addresses this problem and proposes a complete framework to predict the surface reflectance properties of outdoor scenes under unknown natural illumination. Uniquely, we recast the problem into its two constituent components involving the bidirectional reflectance distribution function incoming light and outgoing view directions: first, surface points’ radiance captured in the images, and outgoing view directions are aggregated and encoded into reflectance maps, and second, a neural network trained on reflectance maps infers a low-parameter reflection model. Our model is based on phenomenological and physics-based scattering models. Experiments show that rendering with the predicted reflectance properties results in a visually similar appearance to using textures that cannot otherwise be disentangled from the reflectance properties.
Plot and Rework: Modeling Storylines for Visual Storytelling

Writing a coherent and engaging story is not easy. Creative writers use their knowledge and worldview to put disjointed elements together to form a coherent storyline, and work and rework iteratively toward perfection. Automated visual storytelling (VIST) models, however, make poor use of external knowledge and iterative generation when attempting to create stories. This paper introduces PR-VIST, a framework that represents the input image sequence as a story graph in which it finds the best path to form a storyline. PR-VIST then takes this path and learns to generate the final story via an iterative training process. This framework produces stories that are superior in terms of diversity, coherence, and humanness, per both automatic and human evaluations. An ablation study shows that both plotting and reworking contribute to the model's superiority.
Prevalence and Trends of Metabolic Syndrome in Mexican Adults: Data of the National Health and Nutrition Surveys 2006 and 2016

Background: The metabolic syndrome (MetSx) is a group of risk factors interconnected with cardiovascular diseases and type 2 diabetes, major morbidity and mortality causes globally and in Mexico. More than a thousand million individuals in the world were found with MetSx in 2018, and the last national investigation in Mexico on this condition carried out in 2006 registered a prevalence of 49.8% in adults. This study aimed to describe the prevalence of MetSx in Mexican adults, the degree of association with risk factors, and its trends in the period 2006 to 2016. Methods: We gathered and analyzed sociodemographic, clinical, dietary, and physical activity data from 8,626 adults aged ≥20 years who participated in the Mexican National Health and Nutrition Survey Mid-way 2016 (ENSANUT MC-2016). To define MetSx, we used the harmonized diagnosis criteria as the main instrument. Other classification systems were also used to complement our analysis. To identify the prevalence trends of MetSx, we compared information of ENSANUT-2006 with that of ENSANUT-2016.Results: The prevalence of MetSx in Mexican adults was of 60.5% (57.9% in men and 63.2% in women). The proportion of subjects with a least one MetSx component was of 95.3% and with at least two components was of 21.3%. The most frequent combination of three MetSx components was the cluster of abdominal obesity, low HDL-cholesterol, and hypertriglyceridemia (16%). In comparison with the MetSx prevalence reported in ENSANUT-2006, the prevalence of this condition in ENSANUT-2016 increased 15.3%. Conclusions: A high prevalence of MetSx was registered in Mexico in 2016, and an increased trend of this condition during the period 2006 to 2016 was observed. This study shows the necessity to improve prevention and diagnosis programs for MetSx and its components, as well as the need to adopt a healthy lifestyle in the highest risk population in order to achieve corrections and reductions in associated susceptible risk factors.
In Situ Insulated Gate Bipolar Transistor Junction Temperature Estimation Method via a Bond Wire Degradation Independent Parameter Turn-OFF Vce Overshoot

Fast and accurate online monitoring of junction temperature of insulated gate bipolar transistor (IGBT) chips is of great significance for overtemperature protection and thermal stress optimization of IGBTs. However, existing IGBT junction temperature estimation methods have drawbacks of dependence on bond wire degradation, low resolution, nonlinearity, and difficulty in online implementation. In this article, a novel in situ IGBT junction temperature estimation method via turn-off collector voltage overshoot is proposed, which is independent on bond wire degradation. Based on theoretical analysis, the turn-off collector voltage overshoot is approximately linear to the junction temperature. The peak value of the turn-off collector voltage can be extracted during operation. Experiments are conducted to verify the technique proposed. A linear relationship between collector voltage overshoot and IGBT junction temperature is revealed, which illustrates the validity of the proposed method. Besides, the operation parameters (bus voltage, load current, and temperature) are considered, and the calibration on operation parameter variations is discussed. At last, the proposed method is compared with a traditional IGBT junction temperature estimation method based on the on-state voltage drop. The advantages of the proposed method lie in its independence on bond wire degradation and high resolution for online temperature estimation.
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs

Quantized neural networks (NN) are the common standard to efficiently deploy deep learning models on tiny hardware platforms. However, we notice that quantized NNs are as vulnerable to adversarial attacks as the full-precision models. With the proliferation of neural networks on small devices that we carry or surround us, there is a need for efficient models without sacrificing trust in the prediction in presence of malign perturbations. Current mitigation approaches often need adversarial training or are bypassed when the strength of adversarial examples is increased. In this work, we investigate how a probabilistic framework would assist in overcoming the aforementioned limitations for quantized deep learning models. We explore Stochastic-Shield: a flexible defense mechanism that leverages an input filtering layer and a probabilistic deep learning approach materialized via Monte Carlo dropout. We show that it is possible to jointly achieve efficiency and robustness by accurately enabling each module without the burden of re-retraining or ad hoc fine-tuning.
Physical Investigation of Spin-Coated MoS2 Films

Among emerging Transition Metal Dichalcogenides (TMDCs), molybdenum disulfide (MoS2) has attracted a remarkable interest due to its many possible applications. In particular, MoS2 has potentialities not yet fully realized in solution-based applications. The morphological and the structural properties of MoS2 films deposited by spin-coating onto Si/SiO2 substrates were investigated by Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM) and Micro-Raman Spectroscopy. High resolution AFM imaging highlights the presence of a layered structure. The thickness of each layer is estimated to be around 13 nm. Micro-Raman measurements reveal that there is a coexistence between 2H-MoS2 and 1T-MoS2 phases, which could be useful for electrical applications. Moreover, the band at 290 cm−1 is assigned to the amorphous phase of MoS2. The detectability of the mode E1g in back scattering geometry is ascribed to the disorder of the amorphous phase.
SORGHUM GENOTYPES RESPONSE TO INOCULATION WITH PHOSPHATE SOLUBILIZING BACTERIA

Sorghum bicolor adapts to phosphorus (P) deficient soils through mechanisms that contribute to its absorption and solubilization, including the association with microorganisms. The direct application of rock phosphate (RP) and the inoculation with phosphate solubilizing bacteria (PSB) is a sustainable alternative for P supply to the crops. The aim of this study was to evaluate the effect of PSB inoculation of two sorghum genotypes with different P responses (BR007 - efficient and responsive and SC283 - efficient and non-responsive), cultivated in soil fertilized with RP and triple superphosphate (TSP), in greenhouse and field experiments. The sorghum genotypes were inoculated separately with the Bacillus strains that are efficient in P solubilization, B116 and B70, and cultivated under different P fertilization sources (TSP, RP, ½TSP + ½RP). The results suggest that the inoculation response was dependent on sorghum genotype, P source and microbial strain. Inoculation of the genotype BR007 significantly increased root biomass and grain P content under greenhouse conditions, as well as yield and grain P content in field experiments, but no effect was observed on genotype SC283. The use of PSB as bioinoculants, in combination with RP, is a promising alternative to reduce the use of synthetic fertilizers, contributing to the sustainable sorghum production.
Intelligent phototriggered nanoparticles induce a domino effect for multimodal tumor therapy

Rationale: Integration of several monotherapies into a single nanosystem can produce remarkable synergistic antitumor effects compared with separate delivery of combination therapies. We developed near-infrared (NIR) light-triggered nanoparticles that induce a domino effect for multimodal tumor therapy. Methods: The designed intelligent phototriggered nanoparticles (IPNs) were composed of a copper sulfide-loaded upconversion nanoparticle core, a thermosensitive and photosensitive enaminitrile molecule (EM) organogel shell loaded with anticancer drugs, and a cancer cell membrane coating. Irradiation with an NIR laser activated a domino effect beginning with photothermal generation by copper sulfide for photothermal therapy that also resulted in phase transformation of the EM gel to release the anticancer drug. Meanwhile, the NIR light energy was converted to ultraviolet light by the upconversion core to excite the EM, which generated reactive oxygen species for photodynamic therapy. Results: IPNs achieved excellent antitumor effects in vitro and in vivo with little systemic toxicity, indicating that IPNs could serve as a safe and high-performance instrument for synergetic antitumor therapy. Conclusion: This intelligent drug delivery system induced a chain reaction generating multiple antitumor therapies after a single stimulus.
A Re-Evaluation of the Effect of Trauma Center Verification Level on the Early Risk of Death in Hemodynamically Unstable Patients

Background Studies show increased early and overall mortality at level II compared to level I trauma centers in hemodynamically unstable patients. We hypothesize there is no mortality difference between level I and level II centers applying more contemporary data. Study design Utilizing the 2017 Trauma Quality Program Participant Use File (TQP-PUF), we identified adult patients (age >14 years) who presented to an American College of Surgeons (ACS) verified level I or II center with hypotension (systolic blood pressure [SBP] < 90 mmHg). Logistic regression was performed to identify adjusted associations with mortality. Results A total of 7,264 patients met the inclusion criteria, of whom most were males (4,924 [67.8%]) with blunt trauma (5,924 [81.6%]) being predominated. Mean admission SBP was 73.2 (±13.0) mmHg. There were 1,097 (15.1%) deaths. Level I admissions (4,931 (67.9%]) were more likely male (3,389 [68.7%] vs. 1,535 [65.8]; p=0.012), non-white (3,119 [63.3%] vs. 1,664 [71.3%]; p<0.001), a victim of penetrating trauma (933 [18.9%] vs. 385 [16.5%]; p=0.015), and more severely injured (mean Injury Severity Score: 19.3 [±15] vs. 16.7 [±13.7]; p<0.001). Level II admissions (2,333 [32.1%]) were older (46.8 [±18.5] vs. 50.3 [±20.1] years; p<0.001) with more co-morbidities (mean Charlson Comorbidity Index: 1.43 [±2] vs. 1.77 [±2.2]; p<0.001). Adjusted mortality between level I and II admissions was similar (766 [15.5%] vs. 331 [14.2%]; p=0.918). Early hourly mortality also did not differ. Conclusion There is no overall or hourly mortality discrepancy between ACS-verified level I and II centers for patients presenting with hypotension. This potentially relates to the use of more contemporary data gathered after implementation of updated verification requirements.
Fast approximate inference for multivariate longitudinal data

Summary Collecting information on multiple longitudinal outcomes is increasingly common in many clinical settings. In many cases, it is desirable to model these outcomes jointly. However, in large data sets, with many outcomes, computational burden often prevents the simultaneous modeling of multiple outcomes within a single model. We develop a mean field variational Bayes algorithm, to jointly model multiple Gaussian, Poisson, or binary longitudinal markers within a multivariate generalized linear mixed model. Through simulation studies and clinical applications (in the fields of sight threatening diabetic retinopathy and primary biliary cirrhosis), we demonstrate substantial computational savings of our approximate approach when compared to a standard Markov Chain Monte Carlo, while maintaining good levels of accuracy of model parameters.
Uncertainty of Rules Extracted from Artificial Neural Networks

ABSTRACT Artificial neural networks evolve into deep learning recently and perform well in various fields, such as image and speech recognition and translation. However, there is a problem that it is difficult for a person to understand what exactly the trained knowledge of an artificial neural network. As one of the methods for solving the problem of the artificial neural network, rule extraction methods had been devised. In this study, rules are extracted from artificial neural networks using ordered-attribute search (OAS) algorithm, which is one of the methods of extracting rules from trained neural networks, and the rules are analyzed to improve the extracted rules. As a result, we found that when the output value of the hidden layer has an intermediate value that is not close to 0 or 1 after passing through the sigmoid function, the problem of rule uncertainty occurs and affects the accuracy of the rules. In order to solve the uncertainty problem of the rules, we applied the hidden unit clarification method and suggested that it is possible to extract the efficient rule by binarizing the hidden layer output value. In addition, we extracted CDRPs (critical data routing paths) from the trained neural networks and used CDRPs to prune the extracted rules, which showed that the uncertainty problem of rules can be improved.