Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Additivity Based Data Augmentation for Semi-supervised NER

Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. In this work, to alleviate the dependence on labeled data, we propose a Local Additivity based Data Augmentation (LADA) method for semi-supervised NER, in which we create virtual samples by interpolating sequences close to each other. Our approach has two variations: Intra-LADA and Inter-LADA, where Intra-LADA performs interpolations among tokens within one sentence, and Inter-LADA samples different sentences to interpolate. Through linear additions between sampled training data, LADA creates an infinite amount of labeled data and improves both entity and context learning. We further extend LADA to the semi-supervised setting by designing a novel consistency loss for unlabeled data. Experiments conducted on two NER benchmarks demonstrate the effectiveness of our methods over several strong baselines. We have publicly released our code at this https URL.
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data

The Large Scale Visual Recognition Challenge based on the well-known Imagenet dataset catalyzed an intense flurry of progress in computer vision. Benchmark tasks have propelled other sub-fields of machine learning forward at an equally impressive pace, but in healthcare it has primarily been image processing tasks, such as in dermatology and radiology, that have experienced similar benchmark-driven progress. In the present study, we performed a comprehensive review of benchmarks in medical machine learning for structured data, identifying one based on the Medical Information Mart for Intensive Care (MIMIC-III) that allows the first direct comparison of predictive performance and thus the evaluation of progress on four clinical prediction tasks: mortality, length of stay, phenotyping, and patient decompensation. We find that little meaningful progress has been made over a 3 year period on these tasks, despite significant community engagement. Through our meta-analysis, we find that the performance of deep recurrent models is only superior to logistic regression on certain tasks. We conclude with a synthesis of these results, possible explanations, and a list of desirable qualities for future benchmarks in medical machine learning.
MetaPhys: Unsupervised Few-Shot Adaptation for Non-Contact Physiological Measurement

There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging. Existing machine learning systems struggle to generalize well to unseen subjects or contexts, especially in video-based physiological measurement. Although fine-tuning for a user might address this issue, it is difficult to collect large sets of training data for specific individuals because supervised algorithms require medical-grade sensors for generating the training target. Therefore, learning personalized or customized models from a small number of unlabeled samples is very attractive as it would allow fast calibrations. In this paper, we present a novel unsupervised meta-learning approach called MetaPhys for learning personalized cardiac signals from 18-seconds of unlabeled video data. We evaluate our proposed approach on two benchmark datasets and demonstrate superior performance in cross-dataset evaluation with substantial reductions (42% to 44%) in errors compared with state-of-the-art approaches. Visualization of attention maps and ablation experiments reveal how the model adapts to each subject and why our proposed approach leads to these improvements. We have also demonstrated our proposed method significantly helps reduce the bias in skin type.
An Efficient Algorithm for Cooperative Semi-Bandits

We consider the problem of asynchronous online combinatorial optimization on a network of communicating agents. At each time step, some of the agents are stochastically activated, requested to make a prediction, and the system pays the corresponding loss. Then, neighbors of active agents receive semi-bandit feedback and exchange some succinct local information. The goal is to minimize the network regret, defined as the difference between the cumulative loss of the predictions of active agents and that of the best action in hindsight, selected from a combinatorial decision set. The main challenge in such a context is to control the computational complexity of the resulting algorithm while retaining minimax optimal regret guarantees. We introduce Coop-FTPL, a cooperative version of the well-known Follow The Perturbed Leader algorithm, that implements a new loss estimation procedure generalizing the Geometric Resampling of Neu and Bartok [2013] to our setting. Assuming that the elements of the decision set are $k$-dimensional binary vectors with at most $m$ non-zero entries and $\alpha_1$ is the independence number of the network, we show that the expected regret of our algorithm after $T$ time steps is of order $Q\sqrt{mkT\log(k) (k\alpha_1/Q+m)}$, where $Q$ is the total activation probability mass. Furthermore, we prove that this is only $\sqrt{k\log k}$-away from the best achievable rate and that \coopftpl{} has a state-of-the-art $T^{3/2}$ worst-case computational complexity.
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning

Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships. We extend existing works to consider a generalized form of multi-hop rules, where each rule is a set of relation chains. To learn such generalized rules efficiently, we propose a two-step approach that first selects a small set of relation chains as a rule and then evaluates the confidence of the target relationship by jointly scoring the selected chains. A game-theoretical framework is proposed to this end to simultaneously optimize the rule selection and prediction steps. Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches, justifying both our formulation of generalized rules and the effectiveness of the proposed learning framework.
Sharpness-Aware Minimization for Efficiently Improving Generalization

In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability. Indeed, optimizing only the training loss value, as is commonly done, can easily lead to suboptimal model quality. Motivated by the connection between geometry of the loss landscape and generalization---including a generalization bound that we prove here---we introduce a novel, effective procedure for instead simultaneously minimizing loss value and loss sharpness. In particular, our procedure, Sharpness-Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently. We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (e.g., CIFAR-{10, 100}, ImageNet, finetuning tasks) and models, yielding novel state-of-the-art performance for several. Additionally, we find that SAM natively provides robustness to label noise on par with that provided by state-of-the-art procedures that specifically target learning with noisy labels.
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts

Natural data are often long-tail distributed over semantic classes. Existing recognition methods tend to focus on tail performance gain, often at the expense of head performance loss from increased classifier variance. The low tail performance manifests itself in large inter-class confusion and high classifier variance. We aim to reduce both the bias and the variance of a long-tailed classifier by RoutIng Diverse Experts (RIDE). It has three components: 1) a shared architecture for multiple classifiers (experts); 2) a distribution-aware diversity loss that encourages more diverse decisions for classes with fewer training instances; and 3) an expert routing module that dynamically assigns more ambiguous instances to additional experts. With on-par computational complexity, RIDE significantly outperforms the state-of-the-art methods by 5% to 7% on all the benchmarks including CIFAR100-LT, ImageNet-LT and iNaturalist. RIDE is also a universal framework that can be applied to different backbone networks and integrated into various long-tailed algorithms and training mechanisms for consistent performance gains.
TabEAno: Table to Knowledge Graph Entity Annotation

In the Open Data era, a large number of table resources have been made available on the Web and data portals. However, it is difficult to directly utilize such data due to the ambiguity of entities, name variations, heterogeneous schema, missing, or incomplete metadata. To address these issues, we propose a novel approach, namely TabEAno, to semantically annotate table rows toward knowledge graph entities. Specifically, we introduce a "two-cells" lookup strategy bases on the assumption that there is an existing logical relation occurring in the knowledge graph between the two closed cells in the same row of the table. Despite the simplicity of the approach, TabEAno outperforms the state of the art approaches in the two standard datasets e.g, T2D, Limaye with, and in the large-scale Wikipedia tables dataset.
Integrating Categorical Semantics into Unsupervised Domain Translation

While unsupervised domain translation (UDT) has seen a lot of success recently, we argue that allowing its translation to be mediated via categorical semantic features could enable wider applicability. In particular, we argue that categorical semantics are important when translating between domains with multiple object categories possessing distinctive styles, or even between domains that are simply too different but still share high-level semantics. We propose a method to learn, in an unsupervised manner, categorical semantic features (such as object labels) that are invariant of the source and target domains. We show that conditioning the style of a unsupervised domain translation methods on the learned categorical semantics leads to a considerably better high-level features preservation on tasks such as MNIST$\leftrightarrow$SVHN and to a more realistic stylization on Sketches$\to$Reals.
Distributed Optimization Over Dependent Random Networks

We study the averaging-based distributed optimization solvers over random networks. We show a general result on the convergence of such schemes using weight matrices that are row-stochastic almost surely and column-stochastic in expectation for a broad class of dependent weight-matrix sequences. In addition to implying many of the previously known results on this domain, our work shows the robustness of distributed optimization results to link failure. Also, it provides a new tool for synthesizing distributed optimization algorithms. To prove our main theorem, we establish new results on the rate of convergence analysis of averaging dynamics over (dependent) random networks. These secondary results, along with the required martingale-type results to establish them, might be of interest to broader research endeavors in distributed computation over random networks.
The Fourier signatures of memristive hysteresis

While resistors with memory, sometimes called memristive elements (such as ReRAM cells), are often studied under conditions of periodic driving, little attention has been paid to the Fourier features of their memory response (hysteresis). Here we demonstrate experimentally that the hysteresis of memristive systems can be unambiguously distinguished from the linear or non-linear response of systems without hysteresis by the values of certain Fourier series coefficients. We also show that the Fourier series convergence depends on driving conditions, and introduce a measure of hysteresis. These results may be used to quantify the memory content of resistive memories, and tune their Fourier spectrum according to the excitation signal.
Perspectives on paralytic ileus

Paralytic ileus is the condition where the motor activity of the bowel is impaired, usually not associated with a mechanical cause. Although the condition may be self‐limiting, it is serious and if prolonged and untreated will result in death in much the same way as in acute mechanical obstruction. Management of paralytic ileus depends on the knowledge of the most likely cause and the perceived chance of resolution without operation. Postoperative ileus is the single largest factor influencing length of hospital stay after bowel resection, and has great implications for patients and resource utilization. Early diagnosis and correct management is essential in reducing complications. This article briefly outlined the plausible pathophysiological mechanisms and clinical implications of paralytic ileus.
Long-Term, All-Day Exposure to Circadian-Effective Light Improves Sleep, Mood, and Behavior in Persons with Dementia

Background: Persons with Alzheimer’s disease and related dementias (ADRD) frequently experience sleep–wake (circadian) cycle disturbances that lead them to remain awake at night, causing stress and fatigue for families and caregivers. Light therapy shows promise as a nonpharmacological treatment for regulating sleep in this population. Objective: We investigated the long-term impact of a circadian-effective lighting intervention on sleep, mood, and behavior problems in persons with ADRD. Methods: This 25-week clinical trial administered an all-day lighting intervention to 47 patients with ADRD in 9 senior-care facilities, employing wrist-worn actigraphy measures and standardized measures of sleep quality, mood, and behavior. Results: The intervention significantly improved Pittsburgh Sleep Quality Index scores, from an estimated mean±SEM of 11.89±0.53 at baseline to 5.36±0.63 at the end of the intervention. Additional improvements were noted for sleep efficiency data from actigraph measurements. The intervention significantly reduced Cornell Scale for Depression in Dementia scores (mean±SEM of 11.36±0.74 at baseline and 4.18±0.88 at the end of the intervention) and Cohen-Mansfield Agitation Inventory scores (mean±SEM of 47.10±1.98 at baseline and 35.33±2.23 at the end of the intervention). Conclusion: A regular circadian-effective daytime lighting intervention can improve sleep at night and reduce depression and agitation in patients with dementia living in controlled environments. More importantly, the positive effects of the tailored lighting intervention on these outcomes appear to be cumulative over time.
Evaluation of Oxygenation in 129 Proning Sessions in 34 Mechanically Ventilated COVID-19 Patients

A minority of patients with Severe Acutre Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) disease-2019 (Covid-19) develop pulmonary features consistent with the Acute Respiratory Distress Syndrome (ARDS). Prone positioning (PP) is an intervention with proven survival benefits in moderate-to-severe and severe ARDS. It is advocated in international guidelines as an intervention in mechanically ventilated Covid-19 patients, despite very few published trials investigating its efficacy in Covid-19. There is an ongoing debate regarding the prevalence of reported mismatches between the severity of hypoxaemia and the preservation of pulmonary compliance in some patients, in the early stages of SARS-CoV-2 infection. This has led some to question its utility within this context. 129 proning sessions were identified in 34 consecutively prone patients admitted to the intensive care unit at a single center in the United Kingdom. Baseline characteristics of patients were consistent with previously published national and international reports and patients were ventilated in general concordance with the ARDSnet ventilation protocol. Paired analysis of the partial pressure of arterial oxygen(PaO2): fraction of inspired oxygen(FiO2) ratio (PF ratio) (n = 89) and FiO2 (n = 129) was recorded within 3 hours of both the initiation and termination of PP and differences were assessed with the paired Student’s t-test and Wilcoxon Signed-Rank test. Proning improved the PF ratio by 43.5 ± 54.9 from 99.8 ± 37.5 to 151.9 ± 58.9 (43.6% increase) [p < 0.0001] and reduced FiO2 by 0.17 ± 0.2 from 0.68 ± 0.2 to 0.51 ± 0.2 (25% decrease) [p < 0.0001]. 82% of proning maneouveres resulted in an improvement in the PF ratio. In summary, PP improved arterial oxygenation and reduced oxygen requirements in most Covid-19 patients in this single- center, retrospective analysis.
Enhancing Knowledge in Informal Settlements: Assessing Health Beliefs and Behaviors in Nigeria

Background: Community Health Education (CHE) programs have been shown to be effective in relieving the burden on healthcare systems in Sub-Saharan Africa. Objective: This project aimed to determine the baseline level of health literacy, behavioral practices, and accessibility to resources in a set of 16 informal settlements located around Lagos, Nigeria in order to identify topics that should be emphasized in a new teaching curriculum directed at local Community Health Educators. Methods: In June of 2017, a unique cross-sectional survey composed of 37 questions was conducted in informal settlements around Lagos. Sites selected were areas in which future CHE trainings were planned to take place and survey participants were chosen by trained community health educators based on convenience sampling with snowball effect. Survey questions included both multiple-choice and open-ended questions and were asked in the local language. We collected demographic information and assessed health literacy, health behaviors, and community practices. Results were analyzed using descriptive statistics to assess for differences between demographic groups. Findings: Our survey collected 348 total responses. Respondents displayed a high level of knowledge regarding the benefits of hand washing (97.1%) and childhood immunizations (81.0%). Knowledge around infectious diseases and reproductive health was lower, including a large proportion of people (50%) incorrectly indicating that HIV could be spread through a mosquito bite. Malaria was reported to be the most prevalent disease affecting both adults (40.0%) and children (58.3%). Health access was limited, with most people not reporting access to a nearby health center (55.8%). Conclusions: Areas of knowledge that should be emphasized in future versions of CHE training curricula include infectious diseases, reproductive health, and reinforcement of the importance of sanitation and clean water. The curriculum should address the reality of limited health access and develop strategies to improve this.
Brain Natriuretic Peptide for Predicting Contrast-Induced Acute Kidney Injury in Patients with Acute Coronary Syndrome Undergoing Coronary Angiography: A Systematic Review and Meta-Analysis

Objective To assess the diagnostic value of B-type natriuretic peptide (BNP) or N-terminal pro-B-type natriuretic peptide (NT-proBNP) for contrast-induced acute kidney injury (CI-AKI) in patients with acute coronary syndrome (ACS) undergoing coronary angiography. Background ACS remains a major cause of death worldwide. Patients with ACS undergoing coronary angiography are more likely to develop CI-AKI, which correlates highly with poor clinical outcomes. Early diagnosis of CI-AKI remains a challenge. Many recent studies have suggested that BNP or NT-proBNP may be a useful biomarker for the early diagnosis of CI-AKI. Methods We searched databases (PubMed, EMBASE, and Cochrane Library) to identify eligible studies. Two authors independently screened the studies and extracted data. We used the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria to assess the methodological quality of the included studies and STATA to perform all statistical analyses. Results Nine studies including 2832 patients were identified. The pooled sensitivity of 0.73 (95% CI 0.65–0.79), specificity of 0.79 (95% CI 0.70–0.85), and area under the summary receiver operating characteristic curve of 0.81 (95% CI 0.77–0.84) suggested that BNP or NT-proBNP had a good diagnostic value for CI-AKI in patients with ACS undergoing coronary angiography. Conclusions Our findings suggest that BNP or NT-proBNP may be an effective predictive marker for CI-AKI. However, additional high-quality studies are required to find the optimal cutoff value and the diagnostic value of BNP or NT-proBNP in combination with other biomarkers.
Proximal Anterior-Antrum Posterior (PAAP) Overlapping Anastomosis in Minimally Invasive Pylorus-Preserving Gastrectomy for Early Gastric Cancer Located in the High Body and Posterior Wall of the Stomach

Purpose To evaluate the feasibility and safety of intracorporeal overlapping gastrogastrostomy between the proximal anterior wall and antrum posterior wall (PAAP; PAAP anastomosis) of the stomach in minimally invasive pylorus-preserving gastrectomy (PPG) for early gastric cancer (EGC). Materials and Methods From December 2016 to December 2019, 17 patients underwent minimally invasive PPG with PAAP anastomosis for EGC in the high body and posterior wall of the stomach. Intraoperative gastroscopy was performed with the rotation maneuver during proximal transection. A longer antral cuff (>4–5 cm) was created for PAAP than for conventional PPG (≤3 cm) at the point where a safe distal margin and good vascular perfusion were secured. Because the posterior wall of the proximal remnant stomach was insufficient for intracorporeal anastomosis, the anterior wall was used to create an overlapping anastomosis with the posterior wall of the remnant antrum. The surgical and oncological outcomes were analyzed, and the stomach volume was measured in patients who completed the 6-month follow-up. The results were compared to those after conventional PPG (n=11 each). Results PAAP anastomosis was successfully performed in 17 patients. The proximal and distal resection margins were 2.4±1.9 cm and 4.0±2.6 cm, respectively. No postoperative complications were observed during the 1-year follow-up esophagogastroduodenoscopy (n=10). The postoperative remnant stomach (n=11) was significantly larger with PAAP than with conventional PPG (225.6±118.3 vs. 99.1±63.2 mL; P=0.001). The stomach length from the anastomosis to the pylorus was 4.9±2.4 cm after PAAP. Conclusions PAAP anastomosis is a feasible alternative for intracorporeal anastomosis in minimally invasive PPG for highly posteriorly located EGC.