Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequentist uncertainty estimates for deep learning

We provide frequentist estimates of aleatoric and epistemic uncertainty for deep neural networks. To estimate aleatoric uncertainty we propose simultaneous quantile regression, a loss function to learn all the conditional quantiles of a given target variable. These quantiles can be used to compute well-calibrated prediction intervals. To estimate epistemic uncertainty we propose orthonormal certificates, a collection of diverse non-constant functions that map all training samples to zero. These certificates map out-of-distribution examples to non-zero values, signaling high epistemic uncertainty. Our uncertainty estimators are computationally attractive, since they do not require training an ensemble of deep models. Throughout a variety of real-world datasets and tasks, we show the state-of-the-art performance of our uncertainty estimators.
ChemBoost: A Chemical Language Based Approach for Protein – Ligand Binding Affinity Prediction

Identification of high affinity drug‐target interactions is a major research question in drug discovery. Proteins are generally represented by their structures or sequences. However, structures are available only for a small subset of biomolecules and sequence similarity is not always correlated with functional similarity. We propose ChemBoost, a chemical language based approach for affinity prediction using SMILES syntax. We hypothesize that SMILES is a codified language and ligands are documents composed of chemical words. These documents can be used to learn chemical word vectors that represent words in similar contexts with similar vectors. In ChemBoost, the ligands are represented via chemical word embeddings, while the proteins are represented through sequence‐based features and/or chemical words of their ligands. Our aim is to process the patterns in SMILES as a language to predict protein‐ligand affinity, even when we cannot infer the function from the sequence. We used eXtreme Gradient Boosting to predict protein‐ligand affinities in KIBA and BindingDB data sets. ChemBoost was able to predict drug‐target binding affinity as well as or better than state‐of‐the‐art machine learning systems. When powered with ligand‐centric representations, ChemBoost was more robust to the changes in protein sequence similarity and successfully captured the interactions between a protein and a ligand, even if the protein has low sequence similarity to the known targets of the ligand.
Metagenome Analysis as a Tool to Study Bacterial Infection Associated with Acute Surgical Abdomen

Background: The purpose of this study was to profile the bacterium in the ascites and blood of patients with acute surgical abdomen by metagenome analysis. Methods: A total of 97 patients with acute surgical abdomen were included in this study. Accompanied with the standard culture procedures, ascites and blood samples were collected for metagenome analysis to measure the relative abundance of bacteria among groups of patients and between blood and ascites. Results: Metagenomic analysis identified 107 bacterial taxa from the ascites of patients. A principal component analysis (PCA) could separate the bacteria of ascites into roughly three groups: peptic ulcer, perforated or non-perforated appendicitis, and a group which included cholecystitis, small bowel lesion, and colon perforation. Significant correlation between the bacteria of blood and ascites was found in nine bacterial taxa both in blood and ascites with more than 500 sequence reads. However, the PCA failed to separate the variation in the bacteria of blood into different groups of patients, and the bacteria of metagenomic analysis is only partly in accordance with those isolated from a conventional culture method. Conclusion: This study indicated that the metagenome analysis can provide limited information regarding the bacteria in the ascites and blood of patients with acute surgical abdomen.
Internal Medicine Resident Experiences With a 5-Month Ambulatory Panel Management Curriculum.

Background Panel management is emphasized as a subcompetency in internal medicine graduate medical education. Despite its importance, there are few published curricula on population medicine in internal medicine residency programs. Objective We explored resident experiences and clinical outcomes of a 5-month diabetes and obesity ambulatory panel management curriculum. Methods From August through December 2016, internal medicine residents at the University of Vermont Medical Center reviewed registries of their patients with diabetes, prediabetes, and obesity; completed learning modules; coordinated patient outreach; and updated gaps in care. Resident worksheets, surveys, and reflections were analyzed using descriptive and thematic analyses. Before and after mean hemoglobin A1c results were obtained for patients in the diabetic group. Results Most residents completed the worksheet, survey, and reflection (93%-98%, N = 42). The worksheets showed 70% of participants in the diabetic group had appointments scheduled after outreach, 42% were offered referrals to the Community Health Team, and 69% had overdue laboratory tests ordered. Residents reported they worked well with staff (95%), were successful in coordinating outreach (67%), and increased their sense of patient care ownership (66%). In reflections, identified successes were improved patient care, teamwork, and relationship with patients, while barriers included difficulty ensuring follow-up, competing patient priorities, and difficulty with patient engagement. Precurricular mean hemoglobin A1c was 7.7%, and postcurricular was 7.6% (P = .41). Conclusions The curriculum offered a feasible, longitudinal model to introduce residents to population health skills and interdisciplinary care coordination. Although mean hemoglobin A1c did not change, residents reported improved patient care. Identified barriers present opportunities for resident education in patient engagement.
A New Mixed-Valence Mn(II)Mn(III) Compound With Catalase and Superoxide Dismutase Activities

The synthesis, X-ray molecular structure, physico-chemical characterization and dual antioxidant activity (catalase and superoxide dismutase) of a new polymeric mixed valence Mn(III)Mn(II) complex, containing the ligand H2BPClNOL (N-(2-hydroxybenzyl)-N-(2-pyridylmethyl)[(3-chloro)(2-hydroxy)] propylamine) is described. The monomeric unit is composed of a dinuclear Mn(II)Mn(III) moiety, [Mn(III)(μ-HBPClNOL)(μ-BPClNOL)Mn(II)(Cl)](ClO4)·2H2O, 1, in which the Mn ions are connected by two different bridging groups provided by two molecules of the ligand H2BPClNOL, a phenoxide and an alkoxide group. In the solid state, this mixed valence dinuclear unit is connected to its neighbors through chloro bridges. Magnetic measurements indicated the presence of ferromagnetic [J = +0.076(13) cm−1] and antiferromagnetic [J = −5.224(13) cm−1] interactions. The compound promotes O2•- dismutation in aqueous solution (IC50 = 0.370 μmol dm−3, kcat = 3.6x106 M−1 s−1). EPR studies revealed that a high-valent Mn(III)-O-Mn(IV) species is involved in the superoxide dismutation catalytic cycle. Complex 1 shows catalase activity only in the presence of a base, e.g., piperazine or triethylamine. Kinetic studies were carried out in the presence of piperazine and employing two different methods, resulting in kcat values of 0.58 ± 0.03 s−1 (detection of O2 production employing a Clark electrode) and 2.59 ± 0.12 s−1 (H2O2 consuption recorded via UV-Vis). EPR and ESI-(+)-MS studies indicate that piperazine induces the oxidation of 1, resulting in the formation of the catalytically active Mn(III)-O-Mn(IV) species.
Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News

Fake news is nowadays an issue of pressing concern, given its recent rise as a potential threat to high-quality journalism and well-informed public discourse. The Fake News Challenge (FNC-1) was organized in early 2017 to encourage the development of machine-learning-based classification systems for stance detection (i.e., for identifying whether a particular news article agrees, disagrees, discusses, or is unrelated to a particular news headline), thus helping in the detection and analysis of possible instances of fake news. This article presents a novel approach to tackle this stance detection problem, based on the combination of string similarity features with a deep neural network architecture that leverages ideas previously advanced in the context of learning-efficient text representations, document classification, and natural language inference. Specifically, we use bi-directional Recurrent Neural Networks (RNNs), together with max-pooling over the temporal/sequential dimension and neural attention, for representing (i) the headline, (ii) the first two sentences of the news article, and (iii) the entire news article. These representations are then combined/compared, complemented with similarity features inspired on other FNC-1 approaches, and passed to a final layer that predicts the stance of the article toward the headline. We also explore the use of external sources of information, specifically large datasets of sentence pairs originally proposed for training and evaluating natural language inference methods to pre-train specific components of the neural network architecture (e.g., the RNNs used for encoding sentences). The obtained results attest to the effectiveness of the proposed ideas and show that our model, particularly when considering pre-training and the combination of neural representations together with similarity features, slightly outperforms the previous state of the art.
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background. To investigate question answering with prior knowledge, we present CommonsenseQA: a challenging new dataset for commonsense question answering. To capture common sense beyond associations, we extract from ConceptNet (Speer et al., 2017) multiple target concepts that have the same semantic relation to a single source concept. Crowd-workers are asked to author multiple-choice questions that mention the source concept and discriminate in turn between each of the target concepts. This encourages workers to create questions with complex semantics that often require prior knowledge. We create 12,247 questions through this procedure and demonstrate the difficulty of our task with a large number of strong baselines. Our best baseline is based on BERT-large (Devlin et al., 2018) and obtains 56% accuracy, well below human performance, which is 89%.
The Curse and Blessing of Not-All-Equal in k-Satisfiability

We study upper bounds for the running time of algorithms for NAE-$k$-SAT and MAX-NAE-$k$-SAT approximation, as functions of $k$, the number of variables $n$ and the performance ratio $\delta$. For the first time, deterministic NAE-$k$-SAT algorithm is faster than the best $k$-SAT algorithm. The analysis relies on Linear Programming. However we do not know such improvement for randomized algorithms. As for approximation algorithms, we show that a number of MAX-$k$-SAT algorithms do not apply to MAX-NAE-$k$-SAT, including the current best one. We present a better MAX-NAE-$k$-SAT approximation algorithm, which is even faster than the best MAX-$k$-SAT approximation algorithm in a wide range of $\delta$ when $k = 3$. We also provide a tighter analysis on an existing MAX-$k$-SAT approximation algorithm, and generalize it for MAX-NAE-$k$-SAT, which is currently the best for all $k \ge 4$.
A Survey on Natural Language Processing for Fake News Detection

Fake news detection is a critical yet challenging problem in Natural Language Processing (NLP). The rapid rise of social networking platforms has not only yielded a vast increase in information accessibility but has also accelerated the spread of fake news. Thus, the effect of fake news has been growing, sometimes extending to the offline world and threatening public safety. Given the massive amount of Web content, automatic fake news detection is a practical NLP problem useful to all online content providers, in order to reduce the human time and effort to detect and prevent the spread of fake news. In this paper, we describe the challenges involved in fake news detection and also describe related tasks. We systematically review and compare the task formulations, datasets and NLP solutions that have been developed for this task, and also discuss the potentials and limitations of them. Based on our insights, we outline promising research directions, including more fine-grained, detailed, fair, and practical detection models. We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.
Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk

Background Test resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone code regions. However, defect prediction tends to low precision in cross-project prediction scenarios. Aims We take an inverse view on defect prediction and aim to identify methods that can be deferred when testing because they contain hardly any faults due to their code being “trivial”. We expect that characteristics of such methods might be project-independent, so that our approach could improve cross-project predictions. Method We compute code metrics and apply association rule mining to create rules for identifying methods with low fault risk (LFR). We conduct an empirical study to assess our approach with six Java open-source projects containing precise fault data at the method level. Results Our results show that inverse defect prediction can identify approx. 32–44% of the methods of a project to have a LFR; on average, they are about six times less likely to contain a fault than other methods. In cross-project predictions with larger, more diversified training sets, identified methods are even 11 times less likely to contain a fault. Conclusions Inverse defect prediction supports the efficient allocation of test resources by identifying methods that can be treated with less priority in testing activities and is well applicable in cross-project prediction scenarios.
Scanning Laser Rangefinders for the Unobtrusive Monitoring of Gait Parameters in Unsupervised Settings

Since variations in common gait parameters (such as cadence, velocity and stride-length) of elderly people are a reliable indicator of functional and cognitive decline in aging and increased fall risks, such gait parameters have to be monitored continuously to enable preventive interventions as early as possible. With scanning laser rangefinders (SLR) having been shown to be suitable for standardised (frontal) gait assessments, this article introduces an unobtrusive gait monitoring (UGMO) system for lateral gait monitoring in homes for the elderly. The system has been evaluated in comparison to a GAITRite (as reference system) with 86 participants (ranging from 21 to 82 years) passing the 6-min walk test twice. Within the considered 56,351 steps within an overall 7877 walks and approximately 34 km distance travelled, it has been shown that the SLR Hokuyo UST10-LX is more sensitive than the cheaper URG-04LX version in regard to the correct (automatic) detection of lateral steps (98% compared to 77%) and walks (97% compared to 66%). Furthermore, it has been confirmed that the UGMO (with the SLR UST10-LX) can measure gait parameters such as gait velocity and stride length with sufficient sensitivity to determine age- and disease-related functional (and cognitive) decline.
Efficient Neural Network Robustness Certification with General Activation Functions

Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem. Nevertheless, recently it has been shown to be possible to give a non-trivial certified lower bound of minimum adversarial distortion, and some recent progress has been made towards this direction by exploiting the piece-wise linear nature of ReLU activations. However, a generic robustness certification for general activation functions still remains largely unexplored. To address this issue, in this paper we introduce CROWN, a general framework to certify robustness of neural networks with general activation functions for given input data points. The novelty in our algorithm consists of bounding a given activation function with linear and quadratic functions, hence allowing it to tackle general activation functions including but not limited to four popular choices: ReLU, tanh, sigmoid and arctan. In addition, we facilitate the search for a tighter certified lower bound by adaptively selecting appropriate surrogates for each neuron activation. Experimental results show that CROWN on ReLU networks can notably improve the certified lower bounds compared to the current state-of-the-art algorithm Fast-Lin, while having comparable computational efficiency. Furthermore, CROWN also demonstrates its effectiveness and flexibility on networks with general activation functions, including tanh, sigmoid and arctan.
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction

We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM-CNN) that exploits word embeddings and positional embeddings for cross-sentence n-ary relation extraction. The proposed model brings together the properties of both LSTMs and CNNs, to simultaneously exploit long-range sequential information and capture most informative features, essential for cross-sentence n-ary relation extraction. The LSTM-CNN model is evaluated on standard dataset on cross-sentence n-ary relation extraction, where it significantly outperforms baselines such as CNNs, LSTMs and also a combined CNN-LSTM model. The paper also shows that the LSTM-CNN model outperforms the current state-of-the-art methods on cross-sentence n-ary relation extraction.
Playing Hide-and-Seek in Beta-Globin Genes: Gene Conversion Transferring a Beneficial Mutation between Differentially Expressed Gene Duplicates

Increasing evidence suggests that adaptation to diverse environments often involves selection on existing variation rather than new mutations. A previous study identified a nonsynonymous single nucleotide polymorphism (SNP) in exon 2 of two paralogous β-globin genes of the bank vole (Clethrionomys glareolus) in Britain in which the ancestral serine (Ser) and the derived cysteine (Cys) allele represent geographically partitioned functional variation affecting the erythrocyte antioxidative capacity. Here we studied the geographical pattern of the two-locus Ser/Cys polymorphism throughout Europe and tested for the geographic correlation between environmental variables and allele frequency, expected if the polymorphism was under spatially heterogeneous environment-related selection. Although bank vole population history clearly is important in shaping the dispersal of the oxidative stress protective Cys allele, analyses correcting for population structure suggest the Europe-wide pattern is affected by geographical variation in environmental conditions. The β-globin phenotype is encoded by the major paralog HBB-T1 but we found evidence of bidirectional gene conversion of exon 2 with the low-expression paralog HBB-T2. Our data support the model where gene conversion reshuffling genotypes between high- and low- expressed paralogs enables tuning of erythrocyte thiol levels, which may help maintain intracellular redox balance under fluctuating environmental conditions. Therefore, our study suggests a possible role for gene conversion between differentially expressed gene duplicates as a mechanism of physiological adaptation of populations to new or changing environments.
Importance of a Search Strategy in Neural Dialogue Modelling

Search strategies for generating a response from a neural dialogue model have received relatively little attention compared to improving network architectures and learning algorithms in recent years. In this paper, we consider a standard neural dialogue model based on recurrent networks with an attention mechanism, and focus on evaluating the impact of the search strategy. We compare four search strategies: greedy search, beam search, iterative beam search and iterative beam search followed by selection scoring. We evaluate these strategies using human evaluation of full conversations and compare them using automatic metrics including log-probabilities, scores and diversity metrics. We observe a significant gap between greedy search and the proposed iterative beam search augmented with selection scoring, demonstrating the importance of the search algorithm in neural dialogue generation.
Engaging Image Chat: Modeling Personality in Grounded Dialogue

To achieve the long-term goal of machines being able to engage humans in conversation, our models should be engaging. We focus on communication grounded in images, whereby a dialogue is conducted based on a given photo, a setup that is naturally engaging to humans (Hu et al., 2014). We collect a large dataset of grounded human-human conversations, where humans are asked to play the role of a given personality, as the use of personality in conversation has also been shown to be engaging (Shuster et al., 2018). Our dataset, Image-Chat, consists of 202k dialogues and 401k utterances over 202k images using 215 possible personality traits. We then design a set of nat-ural architectures using state-of-the-art image and text representations, considering various ways to fuse the components. Automatic metrics and human evaluations show the efﬁcacy of approach, in particular where our best performing model is preferred over human con-versationalists 47.7% of the time.
Impact of Hyperbaric Oxygen Therapy on Subsequent Neurological Sequelae Following Carbon Monoxide Poisoning

The purpose of this study was to evaluate the effects of hyperbaric oxygen therapy (HBOT) on reducing neurological sequelae (NS) in patients with carbon monoxide poisoning (COP). Using a nationwide database of insurance claims in Taiwan, we conducted a population-based cohort study to identify 24,046 patients with COP diagnosed between 1999 and 2012, including 6793 (28.2%) patients who received HBOT and 17,253 (71.8%) patients who did not. We followed the two cohorts of patients and compared the occurrence of NS. The two cohorts had similar sex ratios, but patients who received HBOT were younger (34.8 ± 14.8 vs. 36.1 ± 17.2 years, p < 0.001). Patients who received HBOT had a higher risk for NS (adjusted hazard ratio [AHR]: 1.4; 95% confidence interval [CI]: 1.4–1.5), after adjusting for age, sex, underlying comorbidities (hypertension, diabetes, chronic obstructive pulmonary disease, hyperlipidemia, malignancy, coronary artery disease, congestive heart failure, liver disease, renal disease, connective tissue disease, human immunodeficiency virus [HIV] infection, and alcoholism), monthly income, suicide, drug poisoning, and acute respiratory failure. We observed similar findings when we stratified the patients by age, sex, underlying comorbidities, and monthly income. The increased risk was most prominent in the first 2 weeks (AHR: 2.4; 95% CI: 2.1–2.7) and remained significant up to 6 months later (AHR: 1.6; 95% CI: 1.4–1.7). The risk for NS was higher in patients with COP who received HBOT than in those who did not, even after considering the possible impact of longer observation periods on survivors. Further studies that included the potential confounding factors we did not measure are needed to confirm findings in this study.