Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Faithfulness Measurements for Model Interpretations

Recent years have witnessed the emergence of a variety of post-hoc interpretations that aim to uncover how natural language processing (NLP) models make predictions. Despite the surge of new interpretations, it remains an open problem how to deﬁne and quantitatively measure the faithfulness of interpretations, i.e., to what extent they conform to the reasoning process behind the model. To tackle these issues, we start with three criteria: the removal-based criterion, the sensitivity of interpretations, and the stability of interpretations, that quantify different notions of faithfulness, and propose novel paradigms to systematically evaluate interpretations in NLP. Our results show that the performance of interpretations under different criteria of faithfulness could vary substantially. Motivated by the desideratum of these faithfulness notions, we introduce a new class of interpretation meth-ods that adopt techniques from the adversarial robustness domain. Empirical results show that our proposed methods achieve top performance under all three criteria. Along with experiments and analysis on both the text classi-ﬁcation and the dependency parsing tasks, we come to a more comprehensive understanding of the diverse set of interpretations.
Sentence Alignment with Parallel Documents Helps Biomedical Machine Translation

The existing neural machine translation system has achieved near human-level performance in general domain in some languages, but the lack of parallel corpora poses a key problem in specific domains. In biomedical domain, the parallel corpus is less accessible. This work presents a new unsupervised sentence alignment method and explores features in training biomedical neural machine translation (NMT) systems. We use a simple but effective way to build bilingual word embeddings (BWEs) to evaluate bilingual word similarity and transferred the sentence alignment problem into an extended earth mover’s distance (EMD) problem. The proposed method achieved high accuracy in both 1-to-1 and many-to-many cases. Pre-training in general domain, the larger in-domain dataset and n-to-m sentence pairs benefit the NMT model. Fine-tuning in domain corpus helps the translation model learns more terminology and fits the in-domain style of text.
Random Reshuffling with Variance Reduction: New Analysis and Better Rates

Virtually all state-of-the-art methods for training supervised machine learning models are variants of SGD enhanced with a number of additional tricks, such as minibatching, momentum, and adaptive stepsizes. One of the tricks that works so well in practice that it is used as default in virtually all widely used machine learning software is {\em random reshuffling (RR)}. However, the practical benefits of RR have until very recently been eluding attempts at being satisfactorily explained using theory. Motivated by recent development due to Mishchenko, Khaled and Richt\'{a}rik (2020), in this work we provide the first analysis of SVRG under Random Reshuffling (RR-SVRG) for general finite-sum problems. First, we show that RR-SVRG converges linearly with the rate $\mathcal{O}(\kappa^{3/2})$ in the strongly-convex case, and can be improved further to $\mathcal{O}(\kappa)$ in the big data regime (when $n>\mathcal{O}(\kappa)$), where $\kappa$ is the condition number. This improves upon the previous best rate $\mathcal{O}(\kappa^2)$ known for a variance reduced RR method in the strongly-convex case due to Ying, Yuan and Sayed (2020). Second, we obtain the first sublinear rate for general convex problems. Third, we establish similar fast rates for Cyclic-SVRG and Shuffle-Once-SVRG. Finally, we develop and analyze a more general variance reduction scheme for RR, which allows for less frequent updates of the control variate. We corroborate our theoretical results with suitably chosen experiments on synthetic and real datasets.
Towards Efficient Convolutional Network Models with Filter Distribution Templates

Increasing number of filters in deeper layers when feature maps are decreased is a widely adopted pattern in convolutional network design. It can be found in classical CNN architectures and in automatic discovered models. Even CNS methods commonly explore a selection of multipliers derived from this pyramidal pattern. We defy this practice by introducing a small set of templates consisting of easy to implement, intuitive and aggressive variations of the original pyramidal distribution of filters in VGG and ResNet architectures. Experiments on CIFAR, CINIC10 and TinyImagenet datasets show that models produced by our templates, are more efficient in terms of fewer parameters and memory needs.
Optimal Counterfactual Explanations for Scorecard modelling

Counterfactual explanations is one of the post-hoc methods used to provide explainability to machine learning models that have been attracting attention in recent years. Most examples in the literature, address the problem of generating post-hoc explanations for black-box machine learning models after the rejection of a loan application. In contrast, in this work, we investigate mathematical programming formulations for scorecard models, a type of interpretable model predominant within the banking industry for lending. The proposed mixed-integer programming formulations combine objective functions to ensure close, realistic and sparse counterfactuals using multi-objective optimization techniques for a binary, probability or continuous outcome. Moreover, we extend these formulations to generate multiple optimal counterfactuals simultaneously while guaranteeing diversity. Experiments on two real-world datasets confirm that the presented approach can generate optimal diverse counterfactuals addressing desired properties with assumable CPU times for practice use.
Mixtures of Gaussian Processes for regression under multiple prior distributions

When constructing a Bayesian Machine Learning model, we might be faced with multiple different prior distributions and thus are required to properly consider them in a sensible manner in our model. While this situation is reasonably well explored for classical Bayesian Statistics, it appears useful to develop a corresponding method for complex Machine Learning problems. Given their underlying Bayesian framework and their widespread popularity, Gaussian Processes are a good candidate to tackle this task. We therefore extend the idea of Mixture models for Gaussian Process regression in order to work with multiple prior beliefs at once both a analytical regression formula and a Sparse Variational approach are considered. In addition, we consider the usage of our approach to additionally account for the problem of prior misspecification in functional regression problems.
Sentiment Classification in Swahili Language Using Multilingual BERT

The evolution of the Internet has increased the amount of information that is expressed by people on different platforms. This information can be product reviews, discussions on forums, or social media platforms. Accessibility of these opinions and peoples feelings open the door to opinion mining and sentiment analysis. As language and speech technologies become more advanced, many languages have been used and the best models have been obtained. However, due to linguistic diversity and lack of datasets, African languages have been left behind. In this study, by using the current state-of-the-art model, multilingual BERT, we perform sentiment classification on Swahili datasets. The data was created by extracting and annotating 8.2k reviews and comments on different social media platforms and the ISEAR emotion dataset. The data were classified as either positive or negative. The model was fine-tuned and achieve the best accuracy of 87.59%.
Exact matrix elements of the field operator in the thermodynamic limit of the Lieb–Liniger model

We study a matrix element of the field operator in the Lieb–Liniger model using the Bethe ansatz technique coupled with a functional approach to compute Slavnov determinants. We obtain the matrix element exactly in the thermodynamic limit for any coupling constant c, and compare our results to known semiclassics at the limit c → 0
Evaluation of the Accuracy of a Video and AI Solution to Measure Lower Leg and Foot Volume

Background and Objectives Peripheral edema, change in foot volume, is a marker of congestion which is regularly assessed in routine clinical care. A novel video and Artificial Intelligence (AI)-based solution used to measure anatomical parameters, including volume and foot length, Heartfelt HF-1, is compared to the laboratory gold standard (water displacement) and a medical disposable measuring tape. Design, Setting, Participants, and Measurements 58 healthy volunteers were measured with the Heartfelt device; 22 were also measured with the water displacement method and 19 with the medical tape. Bland–Altman analysis was performed for both volumes and foot lengths. Left/right foot differences provided covariance-corrected standard error of measurement (ccSEM) and minimum detectable difference (MDD) for each measurement method. Results Heartfelt device measured volumes displayed excellent correlation to the gold standard (water displacement), with Bland–Altman bias of +32mL ±81mL (1 std.dev). Clinically important change in foot volume is approximately 13%. Water displacement yielded ccSEM of ± 32.1mL and MDD of 90mL (6.7% of average foot volume), while the Heartfelt device measurements yielded ccSEM of ±12.6mL and MDD of 35.3mL (2.6% of average foot volume). The majority of differences were attributable to manual positioning of the patient foot in the waterbath. Conclusion This study finds that in clinical and non-clinical settings, the Heartfelt device measures foot volume and length more precisely than either the water displacement technique or manual foot length measurements using a medical disposable tape, while having an excellent agreement with these methods.
Electrochemical Determination of Hydrogen Peroxide Using a Prussian Blue-Copper Modified Platinum Microelectrode

ABSTRACT Prussian blue modified with copper was electrodeposited on a platinum microelectrode for the amperometric determination of hydrogen peroxide at −50 mV. The Prussian blue-copper layer was stable for 2 h at pH 7.4 and a storage period of 2 months. The Prussian blue-copper modified microelectrodes were used to develop oxidase based microbiosensors for ethanol and lactate. Lactate and alcohol oxidases were immobilized by cross-linking with glutaraldehyde. No interferences were observed from possible interfering compounds up to a concentration of 0.5 mM. The calibration plot of the lactate microbiosensor was linear between 0.005 and 0.2 mM with a limit of detection of 0.0035 mM lactate. The linear dynamic range of the alcohol microbiosensor was between 0.05 and 2 mM and the limit of detection was 0.02 mM ethanol. The developed microbiosensors were used for the analysis of pickled cucumber juice, yogurt, and wine.
KEGG2Net: Deducing gene interaction networks and acyclic graphs from KEGG pathways

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database provides a manual curation of biological pathways that involve genes (or gene products), metabolites, chemical compounds, maps, and other entries. However, most applications and datasets involved in omics are gene or protein-centric requiring pathway representations that include direct and indirect interactions only between genes. Furthermore, special methodologies, such as Bayesian networks require acyclic representations of graphs. We developed KEGG2Net, a web resource that generates a network involving only the genes represented on a KEGG pathway with all of the direct and indirect gene-gene interactions deduced from the pathway. KEGG2Net offers four different methods to remove cycles from the resulting gene interaction network, converting them into directed acyclic graphs (DAGs). We generated synthetic gene expression data using the gene interaction networks deduced from the KEGG pathways and performed a comparative analysis of different cycle removal methods by testing the fitness of their DAGs to the data and by the number of edges they eliminate. Our results indicate that an ensemble method for cycle removal performs as the best approach to convert the gene interaction networks into DAGs. Resulting gene interaction networks and DAGs are represented in multiple user-friendly formats that can be used in other applications, and as images for quick and easy visualisation. The KEGG2Net web portal converts KEGG maps for any organism into gene-gene interaction networks and corresponding DAGS representing all of the direct and indirect interactions among the genes.
Original Article--Surgical site infections following pediatric cardiac surgery in a tertiary care hospital: Rate and risk factors

Background Surgical site infections [SSIs] are the second most common type of healthcare-associated infections and leading cause of postoperative morbidity and mortality in pediatric cardiac surgery. This study aims to determine the rate of, risk factors for, and most common pathogen associated with the development of SSIs after pediatric cardiac surgery. Methods Patients aged ≤14 years who underwent cardiac surgery at our tertiary care hospital between January 2010 and December 2015 were retrospectively reviewed. Results The SSI rate was 7.8% among the 1510 pediatric patients reviewed. Catheter-associated urinary tract infection [CAUTI] [odds ratio [OR] 5.7; 95% confidence interval [CI] 2.3–13.8; P < 0.001], ventilator-associated pneumonia [VAP] [OR 3.2; 95% CI 1.4–7.2; P = 0.005], longer postoperative stay [≥25 days] [OR 4.1; 95% CI 2.1–8.1; P < 0.001], and a risk adjustment in congenital heart surgery [RACHS-1] score of ≥2 [OR 2.4; 95% CI 1.2–5.6; P = 0.034] were identified as risk factors for SSIs. Staphylococcus aureus was the most common pathogen [32.2%]. Conclusions SSI risk factors were longer postoperative stay, CAUTI, VAP, and RACHS-1 score of ≥2. Identification and confirmation of risk factors in this study is important in order to reduce the rate of SSIs following cardiac surgery.
Quality of Life After Bilateral Risk-Reducing Mastectomy and Simultaneous Reconstruction Using Pre-Pectoral Silicone Implants

Purpose Bilateral risk-reducing mastectomy (BRRM) can reduce the risk of developing breast cancer by up to 95% in women with increased exposure. Although survival is increased, mastectomies can adversely affect a patient physically, psychologically, and psychosexually. High health-related quality of life (HRQoL) is often achieved after simultaneous breast reconstruction (BR) following BRRM; however, data on the pre- and postoperative results of HRQoL are lacking. Therefore, we investigated the quality of life, esthetic outcome, and patient well-being after BRRM and simultaneous implant-based BR. Patients and Methods Of the 35 patients who underwent skin-sparing or nipple-sparing mastectomy between May 2012 and December 2017 at a university hospital, only 22 completed the evaluation. Baseline data and data on previous operations and operation techniques were retrieved from the patient’s charts. BREAST-Q and short form-36 health survey (SF-36) questionnaires were used to evaluate patient satisfaction and HRQoL. Results SF-36 analysis showed a significantly higher score for pain (p=0.043) in our population than in the general female population. Comparing the pre- and postoperative BREAST-Q results, a significant decrease in the physical well-being of the chest (p=0.0179) and a slight improvement in breast satisfaction were observed (p=0.3266). All patients were well-satisfied with the postoperative outcome, reconstruction, and perioperative surgeon care. Conclusion Bilateral mastectomy with simultaneous BR using pre-pectoral implants is associated with an HRQoL similar to that of the healthy population. Although bilateral mastectomy may have an immense effect on the psychological, physical, and social aspects, immediate BR preserves the outer appearance and improves self-esteem.
Management Options for Shoulder Impingement Syndrome in Athletes: Insights and Future Directions

Abstract Athletes participating in overhead sports are at particularly high risk of shoulder impingement syndrome. Subcoracoid impingement is defined as impingement of the anterior soft tissues of the shoulder between the coracoid process and the lesser tuberosity. Subacromial impingement syndrome (SIS) occurs due to extrinsic compression of the rotator cuff between the humeral head and coracoacromial structures or intrinsic degeneration of the supraspinatus tendon and subsequent superior migration of the humerus. Internal impingement is a major cause of shoulder pain in overhead athletes, and it occurs due to repetitive impingement of the articular surface of the rotator cuff with the glenoid during maximum abduction and external rotation of the arm. When examining athletes with suspected impingement of the shoulder, it is important to discuss the sport-specific motion that regenerates the symptoms and perform a combination of physical examination tests to improve the diagnostic accuracy. Radiographic evaluation is recommended, and the extent of soft tissue abnormalities can be assessed on ultrasound or magnetic resonance imaging of the shoulder. Management of shoulder impingement syndrome can be conservative or operative, based on the severity and chronicity of symptoms and the associated structural abnormalities. This review provides an update on the management of SIS, subcoracoid impingement, and internal impingement in the athletic population.
A Case Report Examining a Contraindication for Mechanical Thrombectomy in the Setting of a Large Vessel Occlusion and a Concurrent Contralateral Intracranial Hemorrhage

An acute ischemic stroke occurring contralateral to a hemorrhagic stroke is an uncommon occurrence that presents unique challenges. Hemorrhages have classically been described as a contraindication for mechanical thrombectomy. However, the natural course of a large vessel occlusion with or without decompressive hemicraniectomy is associated with significant morbidity and mortality. This paper investigates the origin of the contraindication for mechanical thrombectomy, the natural history of large vessel occlusion, risks of craniectomy, and risks of mechanical thrombectomy. Given the likelihood of poor outcomes without intervention, mechanical thrombectomy could be considered in select individuals, but future studies into the natural course of contralateral ischemic and hemorrhagic strokes would better guide management.
High Methionine Diet-Induced Alzheimer's Disease like Symptoms Are Accompanied by 5-Methylcytosine Elevated Levels in the Brain

Background Excessive or insufficient intake of methionine (Met) causes neuronal dysfunction, neurodegeneration, cerebrovascular dysfunction, vascular leakage, and short-term memory loss, which result in the occurrence of Alzheimer's disease- (AD-) like symptoms. Objective To determine the relationship between high methionine diets (HMD) induced AD-like symptoms and 5-methylcytosine (5-mC) level. Methods C57BL/6J mice were randomly divided into two groups: the control group (Maintain diets) and the model group (2% HMD). Mice were fed with 2% HMD for 9 weeks. Animals were weighed and food intake was recorded weekly. Open field test, nesting ability test, Y maze test, new object recognition test, and Morris water maze test were used to detect the motor, learning, and memory ability. Hematoxylin-eosin (HE) staining was used to observe the damage of cells in hippocampus and cortex. Immunofluorescence (IF) staining was used to detect the expression and distribution of amyloid-β 1-40 (Aβ1-40), amyloid-β 1-42 (Aβ1-42), and 5-methylcytosine (5-mC) in hippocampus and cortex. Western blotting (WB) was used to determine the expression of Aβ and DNA methyltransferases- (DNMTs-) related proteins in the cortex. Enzyme-linked immunosorbent assay (ELISA) was performed to detect homocysteine (Hcy) level (ELISA). Results Feeding of HMD decreased the body weight and food intake of mice. Behavioral testing revealed that HMD caused learning, memory, and motor ability impairment in the mice. HE staining results showed that HMD feeding caused damage of hippocampal and cortical neurons, along with disordered cell arrangement, and loss of neurons. Furthermore, HMD increased the contents of Aβ1-40, Aβ1-42, and 5-mC in the hippocampus and cortex. WB results showed that HMD increased the expression of Aβ production-related proteins, such as amyloid precursor protein (APP) and beta-secretase 1 (BACE1), and decreased the expression of Aβ metabolism-related protein in the cortex, including insulin-degrading enzyme (IDE) and neprilysin (NEP). Additionally, the decreased expression of DNA methyltransferase1 (DNMT1) was observed in HMD-treated mice, but there was no significant change of DNMT3a level. ELISA results showed that HMD increased the levels of Hcy in serum. Conclusion Our result suggested that the HMD can cause neurotoxicity, leading to AD-like symptoms in mice, which may be related to 5-mC elevated.
A Prospective Comparison of Three Strategies for Evaluating Blood Loss in Transurethral Resection of the Prostate

Objective The aim of the current investigation is to develop a new strategy for evaluating blood loss in the process of transurethral resection of the prostate (TURP). Methods 318 patients diagnosed with benign prostatic hyperplasia (BPH) that need TURP were enrolled in this study. Hospitalization information including age, height, weight, surgery time, prostate volume, hemoglobin (Hb) concentration, hematocrit (HCT) percentage, and red blood cell count (RBC) was evaluated for each patient. All statistical analysis drawing were conducted using R software. Results Three methods were employed for calculating blood loss in TURP. Results from a new method display 0 missing value and got higher confidence (0 of 318, Poisson distribution, P < 0.001) compared with blood loss calculated with hemoglobin concentration (20.44%) and hematocrit percentage (19.18%). Also, the new method demonstrated narrow range (0.03~270.03 ml) and approximate normal distribution compared with blood loss calculated with hemoglobin concentration and hematocrit percentage. More importantly, the new method explained positive correlation with prostate volume (R2 = 0.138, P < 0.001) and also surgery lasting time (R2 = 0.193, P < 0.001). Conclusion Methods developed for calculating blood loss in TURP in the current study displayed more accurate and reasonable evaluation of bleeding, which can guide the transfusion blood for patients.