Miracle-dz/UniFAR-FTUs huggingface dataset:
The effectiveness of lower limit of normal criteria and cutoff rate of forced expiratory volume in 1 s/forced expiratory volume in 6 s using in the diagnosis of airway obstruction in chronic obstructive pulmonary disease patients diagnosed with fixed ratio

AIMS: The definition of limited airflow as forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio for chronic obstructive pulmonary disease (COPD) is still controversial. The objective of this study was to compare the prevalence of COPD using fixed ratio and lower limit of normal (LLN) of FEV1/FVC for the presence of airflow limitation and evaluate the performance of FEV1/forced expiratory volume in 6 s (FEV6) in patients diagnosed with COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) fixed ratio. SUBJECTS AND METHODS: Patients who presented to the pulmonary disease clinic between May 2008 and December 2017 and who were diagnosed with COPD were evaluated retrospectively. The distribution of FEV1/FVC-LLN according to the GOLD fixed ratio was evaluated after the patients were divided based on age groups. A receiver operating characteristics curve was used to determine the best FEV1/FEV6 cutoff value which fit into the FEV1/FVC <LLN, and sensitivity and specificity were calculated. RESULTS: Of the patients, 165 (85.9%) were male and 27 were female, with a mean age of 64.40 ± 10.66 years (range, 40–85). Among the patients, 11.5% of 61 patients aged 40–60 years, 28.4% of 67 patients aged 61–70 years, and 26.6% of 64 patients aged over 70 years were out of the diagnosis of obstruction according to the fixed ratio of FEV1/FVC >LLN. The cutoff value of FEV1/FEV6 for airway obstruction according to FEV1/FVC <LLN was 0.735 with a sensitivity of 99.3%, a specificity of 95.3%, and area under the curve of 0.900 (95% confidence interval: 0.856–0.944, P < 0.001). CONCLUSIONS: In this study, older patients, particularly, were diagnosed less often with an FEV1/FVC <LLN value calculated according to the Global lung function initiative (GLI) norms, compared to the fixed ratio. In addition, ratio of FEV1/FEV6 of < 0.735 may be useful in the diagnosis of airway obstruction in the early period and may also be used in office-type spirometers, since it is not dependent on a time constant.
Analysis of primary rhinoplasty in elderly patients

Background: The upper limit of rhinoplasty age increases as the concept of old age shifts forward. Rhinoplasty operations of this era have their own characteristics. The aim of this study is to point out these characteristic features and propose solutions to the problems observed in our series. Materials and Methods: Between 2014 and 2018, 32 primary rhinoplasties were performed on patients over 55 years of age. A comprehensive preoperative evaluation was done in all cases. Wider skin undermining was a prerequisite to overcome skin redundancy. Proper elevation of the droopy nose was the primary goal. Placement of a wide and homogeneously crushed cartilage graft over the lower and upper lateral cartilages was applied in all patients for structural support in addition to standard measures. In patients with extreme droopiness, a deep temporal or rectus fascial sling was applied between the columella and the nasal dorsum to support tip elevation. Results: The follow-up period was 6 months–4 years. All of the patients were satisfied with the postoperative results except for one case having dorsal irregularities and one case with postoperative relapse of the droopiness. Revisional operations of these patients were performed with the utilization of deep temporal fascia. A pulmonary embolus was encountered in one patient in 56 years of age at the 39th-postoperative day. She was hospitalized, and an anticoagulant treatment was applied. Conclusion: The most important problem in this age group is the droopiness of the tip. The use of a wide and homogeneously crushed septal cartilage graft over both the upper and lower lateral cartilages and autologous fascial sling rising and stabilizing the tip may help to prevent its relapse.
The efficacy of positron emission tomography-CT in simultaneously detected nodules in patients with lung cancer

BACKGROUND: The aim of this study is to evaluate the efficacy of positron emission tomography-computed tomography (PET-CT) in the diagnosis of ≤1 cm nodules detected during lung cancer diagnosis. MATERIALS AND METHODS: Patients with pulmonary parenchymal nodules ≤1 cm during the diagnosis of lung cancer between January 2014 and December 2016 were included in the study. The radiologic (size, location, shape, and contour properties) and radiometabolic (presence of fluoro 2-deoxyglucose [FDG] uptake in the nodule, presence and number of PET-CT mediastinal lymphadenopathy [LAP] uptake, mediastinal LAP maximum standard uptake value [SUVmax], presence and number of PET-CT extrapulmonary metastasis) features of the nodules were recorded. Nodules that were followed for at least 6 months and unchanged in size were considered benign, and those that increased or decreased in size or completely regressed were considered malignant. RESULTS: Of a total of 167 patients with lung cancer, 116 (69.4%) had no nodules and 51 (30.5%) had nodules. Of the 51 patients with nodules, 27 (53%) had benign and 24 (47%) had malignant nodules. Compared with patients with benign nodules, the FDG uptake rate, SUVmax values, mediastinal LAP uptake in PET-CT, SUVmax value of the mediastinal LAP with uptake, the number of mediastinal LAPs with uptake, and reported the presence and number of extrapulmonary distant organ metastases in PET-CT were statistically significantly higher in malignant nodules (P < 0.05). Moreover, FDG uptake of the nodule in PET-CT and the presence of mediastinal LAP uptake in PET-CT were independent predictors of malignancy of the nodules (P < 0.05). CONCLUSION: PET-CT parameters other than SUVmax can be used to interpret accompanying nodules smaller than 1 cm in patients with lung cancer.
Role of red cell distribution width in assessing response to treatment and prognosis in community-acquired pneumonia: A prospective study

BACKGROUND AND AIM: Red blood cell distribution width (RDW) elevation occurs in various inflammatory/infectious conditions. RDW levels during monitoring have not been investigated prospectively in community-acquired pneumonia (CAP). In CAP, the relationship between antibiotherapy response and RDW levels is unclear. MATERIALS AND METHODS: This prospective cohort study included a total of 65 CAP patients. Total complete blood cell (including RDW) count, blood biochemistry analysis, and arterial blood gas tests were performed on admission. RDW was also performed on the 7th day. RESULTS: Total mean RDW level was 16.2% ± 1.9% in the study population. The level was 17.7% ± 2.1% in nonsurviving CAP patients and 15.9% ± 1.8% in surviving CAP patients (P = 0.01). Pretreatment RDW levels were 16.2% ± 1.9% as compared to 15.3% ± 2.2% on the 7th day (P = 0.002). An RDW cutoff >16.5% predicted 30-day mortality with 78% sensitivity and 70% specificity. The risk of 30-day mortality was 9-fold higher in patients with elevated RDW as assessed by multivariate logistic regression analysis for CAP. CONCLUSION: RDW can be used as an important parameter in assessing response to antibiotherapy. We also speculate that high RDW is a poor prognostic marker for CAP.
Reconstruction of axillary hidradenitis suppurativa defects with thoracodorsal artery perforator flaps: Our clinical experience

Background: Axillary hidradenitis suppurativa (HS) is a chronic, debilitating condition. Sinus tracts, abscess foci, and relapsing chronic inflammation are the hallmarks of the disease, which affects about 1% of the population. Various treatment options have been used to counter the underlying inflammation, but their efficacy profile yields an unsatisfactory result. Aims and Objectives: Subpar treatment results guided us to a different treatment approach, putting surgical modalities as the hallmark of our approach. Surgical debridement of affected tissues often results in a defect, requiring a safe,effective reconstructive method. Results: Thoracodorsal artery perforator (TDAP) flap is a versatile flap which can be applied to reconstruct thoracic and axillary defects, whereas a free fasciocutaneous flap can be used as a reliable method to cover extensive defects located elsewhere in the body. In axillary HS, TDAP flap is a reliable method for reconstruction, providing adequate soft-tissue coverage, three-dimensional regular contours, and less adverse effects in the long term compared to skin grafts. Conclusion: In our article, we shared our experience with the usage of TDAP flap. Our results indicate that TDAP should be considered as a primary option in reconstruction of axillary HS defects.
Modeling Political Framing Across Policy Issues and Contexts

Politically-contested issues are often discussed with different emphases by different people. This emphasis is called a frame. In this paper, we examine the performance of classifiers trained using the media frames Corpus (MFC) (Card et al., 2015); a collection of US news labelled with fifteen different frame categories. Specifically, we compare pre-trained language models (XLNet, Bert, and Roberta), fine-tuned using MFC, against results from the literature and simpler models in their ability to predict frames from text. We also test these models on a new corpus that we have derived from Australian parliamentary speeches. Our experimental results first show that the fine-tuned models significantly outperform the current best methods on MFC. We also show that the model fine-tuned on US news articles can be convincingly applied to predict policy frames in Australian parliamentary speeches, though the accuracy is significantly reduced, suggesting potential discrepancy in framing strategies and/or text usage between US News and Australian Parliamentary Speeches.
A multi-constraint structured hinge loss for named-entity recognition

The negative log-likelihood or cross entropy is the usual training objective of NLP models owing to its versatility and empirical performance. However, training objectives which directly target the performance measure used to evaluate the task have the potential to lead to higher empirical accuracy. For this reason, in this short paper we propose using a multi-constraint structured hinge loss as the training objective of a contemporary named-entity recognition (NER) model. Experimental results over the challenging OntoNotes 5.0 dataset have shown that the proposed objective has been able to achieve an improvement of 0 . 62 CoNLL score points at a complete parity of testing set-up.
Copy-on-Write with Adaptive Differential Logging for Persistent Memory

File systems based on persistent memory deploy Copyon-Write (COW) or logging to guarantee data consistency. However, COW has a write amplification problem and logging has a double write problem. Both COW and logging increase write traffic on persistent memory. In this work, we present adaptive differential logging and zero-copy logging for persistent memory. Adaptive differential logging applies COW or logging selectively to each block. If the updated size of a block is smaller than or equal to half of the block size, we apply logging to the block. If the updated size of a block is larger than half of the block size, we apply COW to the block. Zero-copy logging treats an user buffer on persistent memory as a redo log. Zero-copy logging does not incur any additional data copy. We implement adaptive differential logging and zero-copy logging on both NOVA and PMFS file systems. Our measurement on real workloads shows that adaptive differential logging and zero-copy logging get 150.6% and 149.2% performance improvement over COW, respectively. key words: file system, persistent memory, consistency, COW, logging
Cryptography from Information Loss

Reductions between problems, the mainstay of theoretical computer science, efficiently map an instance of one problem to an instance of another in such a way that solving the latter allows solving the former. The subject of this work is "lossy" reductions, where the reduction loses some information about the input instance. We show that such reductions, when they exist, have interesting and powerful consequences for lifting hardness into "useful" hardness, namely cryptography. Our first, conceptual, contribution is a definition of lossy reductions in the language of mutual information. Roughly speaking, our definition says that a reduction C is t-lossy if, for any distribution X over its inputs, the mutual information I(X;C(X)) ≤ t. Our treatment generalizes a variety of seemingly related but distinct notions such as worst-case to average-case reductions, randomized encodings (Ishai and Kushilevitz, FOCS 2000), homomorphic computations (Gentry, STOC 2009), and instance compression (Harnik and Naor, FOCS 2006). We then proceed to show several consequences of lossy reductions: 1. We say that a language L has an f-reduction to a language L' for a Boolean function f if there is a (randomized) polynomial-time algorithm C that takes an m-tuple of strings X = (x_1,…,x_m), with each x_i ∈ {0,1}^n, and outputs a string z such that with high probability, L'(z) = f(L(x_1),L(x_2),…,L(x_m)). Suppose a language L has an f-reduction C to L' that is t-lossy. Our first result is that one-way functions exist if L is worst-case hard and one of the following conditions holds: - f is the OR function, t ≤ m/100, and L' is the same as L - f is the Majority function, and t ≤ m/100 - f is the OR function, t ≤ O(m log n), and the reduction has no error This improves on the implications that follow from combining (Drucker, FOCS 2012) with (Ostrovsky and Wigderson, ISTCS 1993) that result in auxiliary-input one-way functions. 2. Our second result is about the stronger notion of t-compressing f-reductions - reductions that only output t bits. We show that if there is an average-case hard language L that has a t-compressing Majority reduction to some language for t=m/100, then there exist collision-resistant hash functions. This improves on the result of (Harnik and Naor, STOC 2006), whose starting point is a cryptographic primitive (namely, one-way functions) rather than average-case hardness, and whose assumption is a compressing OR-reduction of SAT (which is now known to be false unless the polynomial hierarchy collapses). Along the way, we define a non-standard one-sided notion of average-case hardness, which is the notion of hardness used in the second result above, that may be of independent interest.
Measuring emotions during learning: lack of coherence between automated facial emotion recognition and emotional experience

Abstract Measuring emotions non-intrusively via affective computing provides a promising source of information for adaptive learning and intelligent tutoring systems. Using non-intrusive, simultaneous measures of emotions, such systems could steadily adapt to students emotional states. One drawback, however, is the lack of evidence on how such modern measures of emotions relate to traditional self-reports. The aim of this study was to compare a prominent area of affective computing, facial emotion recognition, to students’ self-reports of interest, boredom, and valence. We analyzed different types of aggregation of the simultaneous facial emotion recognition estimates and compared them to self-reports after reading a text. Analyses of 103 students revealed no relationship between the aggregated facial emotion recognition estimates of the software FaceReader and self-reports. Irrespective of different types of aggregation of the facial emotion recognition estimates, neither the epistemic emotions (i.e., boredom and interest), nor the estimates of valence predicted the respective self-report measure. We conclude that assumptions on the subjective experience of emotions cannot necessarily be transferred to other emotional components, such as estimated by affective computing. We advise to wait for more comprehensive evidence on the predictive validity of facial emotion recognition for learning before relying on it in educational practice.
Dither NN: Hardware/Algorithm Co-Design for Accurate Quantized Neural Networks

SUMMARY Deep neural network (NN) has been widely accepted for enabling various AI applications, however, the limitation of computational and memory resources is a major problem on mobile devices. Quantized NN with a reduced bit precision is an e ﬀ ective solution, which relaxes the resource requirements, but the accuracy degradation due to its numerical approximation is another problem. We propose a novel quantized NN model employing the “dithering” technique to improve the accuracy with the minimal additional hardware requirement at the view point of the hardware-algorithm co-designing. Dithering distributes the quantization error occurring at each pixel (neuron) spatially so that the total information loss of the plane would be minimized. The experiment we conducted using the software-based accuracy evaluation and FPGA-based hardware resource estimation proved the e ﬀ ectiveness and e ﬃ ciency of the concept of an NN model with dithering.
Egyptian Social Insurance Big Data Mining Using Supervised Learning Algorithms

Received: 8 July 2019 Accepted: 13 September 2019 Social insurance is an important way to defend individuals from the dangers of retirement, sickness, and financial despondency. However, the social insurance data of Egypt are extremely massive, diverse and complex, due to the intricate provisions in Egyptian laws on social insurance. This calls for an effective method to distinguish between the insured and the uninsured based on the big data. Therefore, this paper fully demonstrates the capability of supervised learning algorithms in predicting the insured and the uninsured based on some of the data on social insurance. The big dataset of Egyptian social insurance was preprocessed in details, including replacing the missing values with mean values, normalization, and removal of outliers and extreme values. Next, three supervised learning algorithms, namely, Naive Bayes, Decision Tree Algorithm, and CN2 Rule Induction, were applied to classify the Egyptians based on the preprocessed data. The results show that all three algorithms achieved highly accuracy classification results. The research provides a new way to mine useful information from the big data of social insurance.
Design and Application of a Text Clustering Algorithm Based on Parallelized K-Means Clustering

Received: 1 May 2019 Accepted: 8 August 2019 The traditional text clustering algorithms face two common problems: the high dimensionality of computing vectors and poor calculation efficiency. To solve these problems, this paper explores deep into the K-means clustering (KMC), Hadoop and Spark big data technique, and then proposes a novel text clustering algorithm based on the KMC parallelized on big data platform. The propose algorithm is denoted as the SWCK-means. First, the Word2vec was adopted to calculate the weights of word vectors, and thus reduce the dimensionality of the massive text data. Next, the Canopy algorithm was introduced to cluster the weight data, and identify the initial cluster centers for the KMC. On this basis, the KMC was employed to cluster the preprocessed data. To improve the efficiency, a parallel design for the Canopy algorithm and the KMC was developed under the Spark architecture. The proposed algorithm was verified through experiments on a massive amount of online text data. The results show that our algorithm achieved more accurate classification effects than the traditional KMC, especially in handling a huge amount of data.
P‐type non‐isolated boost DC‐DC converter with high voltage gain and extensibility for DC microgrid applications

In this paper, the concept of converter design, using the least number of elements and achieving high voltage gain at the low duty cycle, is proposed for the microgrids. One of the important issues in the microgrids is boosting the low voltage output of sources to the utility voltage level. Therefore, the step‐up DC‐DC converters are widely used in these systems to attain the utility voltage. The benchmarking of the converters mainly in terms of the voltage gain, efficiency, the number of active and passive components, stresses on semiconductors, and simplicity is considered. In this paper, a new extendable non‐isolated boost DC‐DC converter is presented. Comparing the conventional boost converter, the basic structure of the proposed converter has a high voltage gain and reduced stress on the switch. To increase the voltage gain, the basic structure of the proposed converter can be easily extended. The modulation technique employed is high‐frequency pulse‐width modulation (PWM). The detailed analysis of the proposed converter in continuous current mode (CCM) and discontinuous current mode (DCM) is presented. The relations between currents and voltages and the voltage gain in CCM and DCM are obtained. Experimental results are carried out to verify theoretical concepts by using the hardware prototype.
Parameterization Above a Multiplicative Guarantee

Parameterization above a guarantee is a successful paradigm in Parameterized Complexity. To the best of our knowledge, all ﬁxed-parameter tractable problems in this paradigm share an additive form deﬁned as follows. Given an instance ( I, k ) of some (parameterized) problem Π with a guarantee g ( I ), decide whether I admits a solution of size at least (at most) k + g ( I ). Here, g ( I ) is usually a lower bound (resp. upper bound) on the maximum (resp. minimum) size of a solution. Since its introduction in 1999 for Max SAT and Max Cut (with g ( I ) being half the number of clauses and half the number of edges, respectively, in the input), analysis of parameterization above a guarantee has become a very active and fruitful topic of research. We highlight a multiplicative form of parameterization above a guarantee: Given an instance ( I, k ) of some (parameterized) problem Π with a guarantee g ( I ), decide whether I admits a solution of size at least (resp. at most) k · g ( I ). In particular, we study the Long Cycle problem with a multiplicative parameterization above the girth g ( I ) of the input graph, and provide a parameterized algorithm for this problem. Apart from being of independent interest, this exempliﬁes how parameterization above a multiplicative guarantee can arise naturally. We also show that, for any ﬁxed constant (cid:15) > 0, multiplicative parameterization above g ( I ) 1+ (cid:15) of Long Cycle yields para-NP-hardness, thus our parameterization is tight in this sense. We complement our main result with the design (or refutation of the existence) of algorithms for other problems parameterized multiplicatively above girth.
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks

Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs). Although many decompositions are conceivable, only CP decomposition and a few others have been applied in practice, and no extensive comparisons have been made between available methods. Previous studies have not determined how many decompositions are available, nor which of them is optimal. In this study, we first characterize a decomposition class specific to CNNs by adopting a flexible graphical notation. The class includes such well-known CNN modules as depthwise separable convolution layers and bottleneck layers, but also previously unknown modules with nonlinear activations. We also experimentally compare the tradeoff between prediction accuracy and time/space complexity for modules found by enumerating all possible decompositions, or by using a neural architecture search. We find some nonlinear decompositions outperform existing ones.