Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Interpretations for Explainable Natural Language Processing: A Survey

As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models. This work investigates various methods to improve the interpretability of deep neural networks for Natural Language Processing (NLP) tasks, including machine translation and sentiment analysis. We provide a comprehensive discussion on the definition of the term interpretability and its various aspects at the beginning of this work. The methods collected and summarised in this survey are only associated with local interpretation and are specifically divided into three categories: 1) interpreting the model’s predictions through related input features; 2) interpreting through natural language explanation; 3) probing the hidden states of models and word representations.
Recognizing Predictive Substructures With Subgraph Information Bottleneck

The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning. However, two disturbing factors, noise and redundancy in graph data, and lack of interpretation for prediction results, impede further development of GCN. One solution is to recognize a predictive yet compressed subgraph to get rid of the noise and redundancy and obtain the interpretable part of the graph. This setting of subgraph is similar to the information bottleneck (IB) principle, which is less studied on graph-structured data and GCN. Inspired by the IB principle, we propose a novel subgraph information bottleneck (SIB) framework to recognize such subgraphs, named IB-subgraph. However, the intractability of mutual information and the discrete nature of graph data makes the objective of SIB notoriously hard to optimize. To this end, we introduce a bilevel optimization scheme coupled with a mutual information estimator for irregular graphs. Moreover, we propose a continuous relaxation for subgraph selection with a connectivity loss for stabilization. We further theoretically prove the error bound of our estimation scheme for mutual information and the noise-invariant nature of IB-subgraph. Extensive experiments on graph learning and large-scale point cloud tasks demonstrate the superior property of IB-subgraph.
Meta-learning Based Beamforming Design for MISO Downlink

Downlink beamforming is an essential technology for wireless cellular networks; however, the design of beamforming vectors that maximize the weighted sum rate (WSR) is an NP-hard problem and iterative algorithms are typically applied to solve it. The weighted minimum mean square error (WMMSE) algorithm is the most widely used one, which iteratively minimizes the WSR and converges to a local optimal. Motivated by the recent developments in meta-learning techniques to solve non-convex optimization problems, we propose a meta-learning based iterative algorithm for WSR maximization in a MISO downlink channel. A long-short-term-memory (LSTM) network based meta-learning model is built to learn a dynamic optimization strategy to update the variables iteratively. The learned strategy aims to optimize each variable in a less greedy manner compared to WMMSE, which updates variables by computing their first order stationary points at each iteration step. The proposed algorithm outperforms WMMSE significantly in the high signal to noise ratio (SNR) regime and achieves comparable performance when the SNR is low.
Probabilistic Multi-View Fusion of Active Stereo Depth Maps for Robotic Bin-Picking

The reliable fusion of depth maps from multiple viewpoints has become an important problem in many 3D reconstruction pipelines. In this work, we investigate its impact on robotic bin-picking tasks such as 6D object pose estimation. The performance of object pose estimation relies heavily on the quality of depth data. However, due to the prevalence of shiny surfaces and cluttered scenes, industrial grade depth cameras often fail to sense depth or generate unreliable measurements from a single viewpoint. To this end, we propose a novel probabilistic framework for scene reconstruction in robotic bin-picking. Based on active stereo camera data, we first explicitly estimate the uncertainty of depth measurements for mitigating the adverse effects of both noise and outliers. The uncertainty estimates are then incorporated into a probabilistic model for incrementally updating the scene. To extensively evaluate the traditional fusion approach alongside our own approach, we will release a novel representative dataset with multiple views for each bin and curated parts. Over the entire dataset, we demonstrate that our framework outperforms a traditional fusion approach by a 12.8% reduction in reconstruction error, and 6.1% improvement in detection rate. The dataset will be available at https://www.trailab. utias.utoronto. ca/robi.
Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval

Current state-of-the-art approaches to cross- modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. While offering unmatched retrieval performance, such models: 1) are typically pretrained from scratch and thus less scalable, 2) suffer from huge retrieval latency and inefficiency issues, which makes them impractical in realistic applications. To address these crucial gaps towards both improved and efficient cross- modal retrieval, we propose a novel fine-tuning framework that turns any pretrained text-image multi-modal model into an efficient retrieval model. The framework is based on a cooperative retrieve-and-rerank approach that combines: 1) twin networks (i.e., a bi-encoder) to separately encode all items of a corpus, enabling efficient initial retrieval, and 2) a cross-encoder component for a more nuanced (i.e., smarter) ranking of the retrieved small set of items. We also propose to jointly fine- tune the two components with shared weights, yielding a more parameter-efficient model. Our experiments on a series of standard cross-modal retrieval benchmarks in monolingual, multilingual, and zero-shot setups, demonstrate improved accuracy and huge efficiency benefits over the state-of-the-art cross- encoders.1
OnionNet-2: A Convolutional Neural Network Model for Predicting Protein-Ligand Binding Affinity Based on Residue-Atom Contacting Shells

One key task in virtual screening is to accurately predict the binding affinity (△G) of protein-ligand complexes. Recently, deep learning (DL) has significantly increased the predicting accuracy of scoring functions due to the extraordinary ability of DL to extract useful features from raw data. Nevertheless, more efforts still need to be paid in many aspects, for the aim of increasing prediction accuracy and decreasing computational cost. In this study, we proposed a simple scoring function (called OnionNet-2) based on convolutional neural network to predict △G. The protein-ligand interactions are characterized by the number of contacts between protein residues and ligand atoms in multiple distance shells. Compared to published models, the efficacy of OnionNet-2 is demonstrated to be the best for two widely used datasets CASF-2016 and CASF-2013 benchmarks. The OnionNet-2 model was further verified by non-experimental decoy structures from docking program and the CSAR NRC-HiQ data set (a high-quality data set provided by CSAR), which showed great success. Thus, our study provides a simple but efficient scoring function for predicting protein-ligand binding free energy.
Bridging the gap between supervised classification and unsupervised topic modelling for social-media assisted crisis management

Social media such as Twitter provide valuable information to crisis managers and affected people during natural disasters. Machine learning can help structure and extract information from the large volume of messages shared during a crisis; however, the constantly evolving nature of crises makes effective domain adaptation essential. Supervised classification is limited by unchangeable class labels that may not be relevant to new events, and unsupervised topic modelling by insufficient prior knowledge. In this paper, we bridge the gap between the two and show that BERT embeddings finetuned on crisis-related tweet classification can effectively be used to adapt to a new crisis, discovering novel topics while preserving relevant classes from supervised training, and leveraging bidirectional self-attention to extract topic keywords. We create a dataset of tweets from a snowstorm to evaluate our method’s transferability to new crises, and find that it outperforms traditional topic models in both automatic, and human evaluations grounded in the needs of crisis managers. More broadly, our method can be used for textual domain adaptation where the latent classes are unknown but overlap with known classes from other domains.
Logarithmic wave equations in non-cylindrical domains

This paper is devoted to studying a type of logarithmic wave equation in non-cylindrical domains. Firstly, by the penalty method, we prove the existence of weak solutions to such kind of equations. Secondly, different from the dissipative wave equation, the energy defined in this problem is not always positive. Thus, some suitable initial data are selected to let the energy be positive. Finally, by a difference inequality, we derive a exponential decay estimate for the positive energy.
Tuberculosis and Human Immunodeficiency Virus Co-Infection and Associated Factors at Debre Markos Comprehensive Specialized Hospital, Northwest Ethiopia: A Four-Year Retrospective Study

Background Human immunodeficiency virus (HIV) and tuberculosis (TB) are the principal global causes of death among patients with communicable diseases. Because of shared immune defense mechanisms, they are the primary cause of morbidity worldwide. However, little information was found regarding the magnitude of TB/HIV co-infection in the study area, in Northwest Ethiopia. Objective The main aim of this study was to assess the prevalence of TB and HIV co-infection and associated factors among TB patients. Methods All TB patients at Debre Markos Comprehensive Specialized Hospital were included from September 11, 2012 to September 10, 2016. Data were analyzed using SPSS version 22. Logistic regression was used to determine the associations between independent and outcome variables. Results A total of 180 TB patients were enrolled. Pulmonary tuberculosis (PTB) accounted for 97/180 (53.9%), followed by extrapulmonary tuberculosis (EPTB) in 83/180 (46.1%). There were 164/180 (91.1%) new TB cases and 16/180 (8.9%) treatment failures, but no relapsing or defaulting cases were observed. Forty percent (72/180) of patients were co-infected with TB and HIV. The likelihoods of having TB/HIV co-infection were 3.2 and 2.1 times higher in PTB smear-positive and PTB smear-negative patients (AOR=3.2, 95% CI 1.4–8.1, p=0.006; and AOR=2.1, 95% CI 1.0–4.3, respectively, p=0.05), in comparison to EPTB. The rate of TB/HIV co-infection was 28/66 (42.4%) in 2013, 18/38 (47.4%) in 2014, 13/32 (40.6%) in 2015, and 13/44 (29.5%) in 2016. Conclusion TB/HIV co-infection showed a decreasing trend in the past 2 years in the study area. TB/HIV co-infection is one of the most serious community health concerns in the study area. Therefore, TB/HIV collaborative activities should be implemented to reduce co-infection and its impact on the community.
The Actinide-Lanthanide Separation Concept

The Actinide-Lanthanide SEParation (ALSEP) concept is described. This concept is based on using an extractant phase consisting of either N,N,N’,N’-tetraoctyldiglycolamide (TODGA) or N,N,N’,N’-tetra(2-ethylhexyl)diglycolamide (T2EHDGA) combined with 2-ethylhexylphosphonic acid mono-2-ethylhexyl ester (HEH[EHP]) to separate Am and Cm from lanthanide and other fission and activation products in a single solvent extraction cycle. The neutral TODGA or T2EHDGA serves to co-extract the trivalent actinide and lanthanide ions from nitric acid media. The distribution ratios for Am and the lanthanides increase with increasing nitric acid concentration. TODGA extracts these elements more strongly than T2EHDGA from nitric acid, but the weaker extracting ability of T2EHDGA could allow separation of Am from the light lanthanides during the extraction step. Switching the aqueous phase chemistry to a citrate-buffered diethylenetriaminepentaacetic acid (DTPA) solution at pH 2.5 to 4 results in selective transfer of the actinides to the aqueous phase, thus resulting in separation of these two groups of elements. Separation factors on the order of 20 to 40 can easily be achieved in the ALSEP systems.
Chronic pain as a brain imbalance between pain input and pain suppression

Abstract Chronic pain is pain that persists beyond the expected period of healing. The subjective experience of chronic pain results from pathological brain network interactions, rather than from persisting physiological sensory input of nociceptors. We hypothesize that pain is an imbalance between pain evoking dorsal anterior cingulate cortex and somatosensory cortex and pain suppression (i.e. pregenual anterior cingulate cortex). This imbalance can be measured objectively by current density ratios between pain input and pain inhibition. A balance between areas involved in pain input and pain suppression requires communication, which can be objectively identified by connectivity measures, both functional and effective connectivity. In patients with chronic neuropathic pain, electroencephalography is performed with source localization demonstrating that pain is reflected by an abnormal ratio between the dorsal anterior cingulate cortex, somatosensory cortex and pregenual anterior cingulate cortex. Functional connectivity demonstrates decreased communication between these areas, and effective connectivity puts the culprit at the dorsal anterior cingulate cortex, suggesting that the problem is related to abnormal behavioral relevance attached to the pain. In conclusion, chronic pain can be considered as an imbalance between pain input and pain suppression.
Association of High Density Cholesterol With Hyperemic Epicardial Flow and Frame Count Reserve in Patients With Moderate Coronary Lesions and Slow Coronary Flow

Background Patients with microvascular angina and non-obstructive coronary atherosclerotic disease have an elevated risk of adverse events and all-cause mortality compared with individuals without ischaemic heart disease. The diagnosis coronary microvascular dysfunction in this setting relies on the detection of impaired coronary flow at rest or on calculation of coronary flow reserve. Previous studies demonstrate that the coronary flow reserve assessed by the corrected thrombolysis in myocardial infarction method - the frame count reserve is an objective quantitative alternative to other widely used invasive methods for microvascular status evaluation. Purpose We assessed the significance of clinical, hemodynamic, angiographic variables and therapy with reference to FCR in a small group of patients with up to moderate atherosclerotic coronary lesions and slow coronary flow. Materials and methods: Frame count reserve was evaluated in 15 patients without flow-limiting (>50%) coronary stenoses admitted with unstable angina. Frame count reserve was calculated by dividing the baseline corrected thrombolysis in myocardial infarction frame count (cTFC) by the cTFC assessed after intracoronary infusion of 100 µg of the calcium channel blocker - verapamil. Results The values of frame count reserve correlate positively with the levels of high density cholesterol (r= 0.900, p=0.001), inversely coronary flow after the application of verapamil - cTFCv (r= - 0.534, p=0.049). cTFCv was positively related with the levels of high density lipoproteins (r = - 0.645; p= 0.044) and was negatively influenced by the presence of atherosclerotic lesions at quantitative angiography (42.8±19.1 (n=8) vs 23±5.4 (n=7), p=0.029). The therapy with β-blocker and long-acting nitrate was associated with insignificantly higher frame count reserves after intracoronary verapamil compared to the continuous intake only of β-blocker or β-blocker and verapamil (2.1±0.78 vs 1.34±0.14 vs 1.70±0.70, p=NS). Conclusions Higher high-density lipoproteins relate to higher frame count reserves evaluated using verapamil. The improved blood flow after this microvascular vasodilator is consistently positively related to high-density cholesterol and the lack of atherosclerosis at conventional coronary angiography. The combined intake of micro- and macrovascular vasodilator could be associated with higher frame count reserves compared to therapy with β-blocker and one vasodilating drug.
CDK1 serves as a novel therapeutic target for endometrioid endometrial cancer

Background: Endometrial cancer (EC) is one of the most common and prevalent gynecologic malignancies worldwide. The aim of this study was to identify a novel therapeutic target for endometrioid endometrial cancer. Materials and Methods: Bioinformatic analysis was performed and CDK1 was screen out as one of the hub genes in the pathogenesis of EC. Immunohistochemistry was used to verify the expression of CDK1 in endometrial cancer tissue. Cell viability and colony formation were used to study the effects of CDK1 on the proliferation and colony formation of endometrial cancer cells in vitro. Apoptosis and cell cycle assays were used to elucidate the mechanism of CDK1 affecting cell proliferation. Tumor xenograft transplantation assay was performed to show the effects of CDK1 on the growth of endometrial cancer cells in vivo. Results: CDK1 was over expressed in endometrioid endometrial cancer, and accumulation of cytoplasmic CDK1 was associated with histological grade of EC. CDK1 promoted endometrial cancer cell growth and colony formation in vitro. The inhibition of CDK1 activity induced cell apoptosis and caused G2/M phase arrest of cell cycle in endometrial cancer cells. The inhibition of CDK1 activity also inhibited endometrial cancer growth in xenograft models. Conclusion: CDK1 was involved in the pathogenesis of endometrioid endometrial cancer and provided a novel therapeutic target for endometrioid endometrial cancer.
Association of tumor size and internal echo pattern with coagulopathy associated with hepatic hemangioma

Little is known concerning causal factors associated with the size and echogenicity of hepatic hemangiomas. The aim of the present study was to investigate the associations between tumor size and echo pattern and coagulation factors, and to elucidate the growth pattern of hemangiomas. In 214 consecutive patients with hepatic hemangiomas, ultrasonography was performed to determine total tumor number, size, echogenicity and location, and serum laboratory tests for liver function and coagulation factors were carried out. The ultrasonographic appearance of hemangiomas was homogeneous in 75.7% of cases and mixed in 24.3% of cases. A mixed echo pattern was seen in 1 out of 145 masses (0.7%) with a diameter <20 mm, in 30 out of 48 (62.5%) with a diameter of 20-40 mm, and in all of the 21 (100%) with a diameter >40 mm. Platelet counts (P<0.0001) and fibrinogen levels (P<0.01) were lower in patients with larger and mixed tumors. Levels of thrombin-antithrombin III complex (TAT), D-dimer, and fibrin and fibrinogen degradation products (FDP) were significantly elevated along with an increase in tumor size (all P<0.0001), and the number of patients with the abnormal values of TAT, D-dimer, and FDP was significantly higher in the mixed group than in the homogeneous group (all P<0.0001). Fibrinogen (P<0.01), platelet count (P<0.001), portal vein diameter (P<0.0001), splenic index (P<0.01), and levels of TAT, D-dimer and FDP (all P<0.0001) were significantly associated with tumor size. Multivariate analysis revealed TAT, D-dimer and FDP as independent predictors of tumor size. The internal echo pattern became mixed as size increased. The size and echogenicity of hemangiomas were closely associated with coagulation factors. Therefore, it was speculated that differences in size and echogenicity were caused by intratumoral thrombosis and subsequent hemorrhage.
Transition-metal catalyzed autoxidation of cis- and trans-pinane to a mixture of diastereoisomeric pinanols

Autoxidations of the pinanes, obtained after hydrogenation of naturally occurring Pinus elliottii oil, were performed with or without solvent, using the catalytic system Co(OAc)2/Mn(OAc)2/NH4Br in a 9:1:5 molar ratio, and dioxygen as the oxidant. The best selectivity for the pinanols was 71% (cis:trans ratio, 3:1) with 17% conversion. Autoxidations were also carried out in the absence of catalyst. The hydroperoxides formed with 17% conversion were decomposed with Na2SO3 and PPh3, resulting in 62% pinanols (cis:trans ratio, 5:1). The pyrolysis of the pinanols at 600 °C and a contact time of 1.15 × 10-2 s/mol yielded 54% of linalool. The side products were mainly due to an “ene” reaction, giving diastereoisomeric 1,2-dimethyl-3-isopropenylcyclopentanols. Keywords: Catalytic oxidation; pinane; pinanol; pyrolysis; linalool
The Clinical Value of Klotho and FGF23 in Cardiac Valve Calcification Among Patients with Chronic Kidney Disease

Objective This study aims to investigate the clinical value of serum Klotho and FGF23 in cardiac valve calcification in patients with chronic kidney disease (CKD). Methods In the present study, 180 patients with CKD, who were admitted to the department of nephrology of our hospital on April 1, 2016 (solstice, 2019), were selected as the main subjects. According to the CKD stage, these patients were divided into three groups: CKD2~3 group, CKD4 group, and CKD5 group. In each group, ultrasound was used to evaluate the cardiac valve calcification, and the independent risk factors for cardiac valve calcification were analyzed by Logistic regression. Results The levels of hemoglobin and blood calcium in CKD2~3 patients were higher than those in CKD4 and CKD5 patients, and the levels of hemoglobin and blood calcium in CKD5 patients were higher than those in CKD4 patients (P<0.05). Albumin was lower in CKD2~3 patients when compared to CKD5 patients while albumin was higher in CKD5 patients when compared to CKD4 patients (P<0.05). The serum levels of FGF23 was lower in CKD2~3 patients when compared to CKD4 and CKD5 patients while the serum levels of FGF23 was lower in CKD4 patients when compared to CKD5 patients (P<0.05). The serum levels of Klotho was higher in CKD2~3 patients, when compared to CKD4 and CKD5 patients, while the serum levels of Klotho was higher in CKD4 patients, when compared to CKD5 patients (P<0.05). The logistic regression analysis revealed that GFR, serum creatinine, FGF23 and Klotho were independent risk factors for cardiac valve calcification in patients with CKD. Conclusion With the decrease of GFR in CKD patients, the serum levels of FGF23 increases, while the serum levels of Klotho decreases. Furthermore, the serum levels of FGF23 and Klotho are affected by various factors, and the levels of FGF23 and Klotho in CKD patients are negatively correlated. GFR, serum creatinine, FGF23 and Klotho are independent risk factors for heart valve calcification in patients with CKD.