Miracle-dz/UniFAR-FTUs huggingface dataset:
Keynote 057: Phase II trial of Pembrolizumab (pembro) for patients (pts) with high-risk (HR) nonmuscle invasive bladder cancer (NMIBC) unresponsive to bacillus calmette-guérin (BCG).

350 Background: The PD-1 inhibitor pembro has durable antitumor activity in pts with metastatic urothelial carcinoma. Upregulation of the PD-1 pathway has been observed in BCG-resistant NMIBC, suggesting pembro may benefit. Efficacy and safety of pembro in pts with HR, BCG-unresponsive NMIBC was evaluated in the single-arm phase 2 KEYNOTE-057 study; updated results for pts with carcinoma in situ (CIS) with or without papillary tumor (cohort A) are reported. Methods: Pts with histologically confirmed HR, BCG-unresponsive CIS with or without papillary disease, who received adequate BCG therapy and were unable/unwilling to undergo radical cystectomy received pembro 200 mg Q3W for 24 mo or until recurrence, progression, or unacceptable toxicity. Pts with HR NMIBC or progressive disease during treatment were required to discontinue. Primary end point: complete response rate (CRR); key secondary end points: duration of response and safety. Results: 103 pts (median age 73 years; CIS alone 71.8%; median number of prior BCG instillations 12) have enrolled in cohort A. 3-mo CRR rate was 38.8% (95% CI 29.4%-48.9%) by central assessment. Among 40 pts who achieved CR at 3 mo, 72.5% maintained CR at last follow-up (median 14.0 mo; range 4.0-26.3) and median CR duration has not been reached (range 0+ to 14.1+ mo). 80.2% of pts had a CR duration of ≥6 mo (Kaplan-Meier method). 10 (25.0%) experienced recurrent NMIBC after CR; at the time of analysis, none progressed to muscle invasive or metastatic disease. Treatment-related adverse events (AEs) occurred in 65 (63.1%) pts; most frequent were pruritus (10.7%), fatigue (9.7%), diarrhea (8.7%), hypothyroidism (5.8%), and maculopapular rash (5.8%). Grade 3/4 treatment-related AEs occurred in 13 (12.6%) pts; 1 death was considered treatment-related (colitis in patient inadequately treated with steroids). Immune-mediated AEs occurred in 19 (18.4%) pts. Conclusions: Pembro had encouraging activity in pts with HR, BCG-unresponsive CIS with or without papillary tumors and a safety profile consistent with that of previous experience. Updated data using additional follow-up will be presented. Clinical trial. Clinical trial information: NCT02625961.
Intake of dietary fibre, red and processed meat and risk of late-onset Chronic Inflammatory Diseases: A prospective Danish study on the “diet, cancer and health” cohort

Background: Human and animal studies support the involvement of diet in the development of CID -chronic inflammatory diseases such as inflammatory bowel disease, psoriasis, rheumatoid arthritis, psoriatic arthritis, and multiple sclerosis. Objective: This cohort study aimed to investigate the association between intake of fibre, red and processed meat, and occurrence of late-onset CID (50+ years of age) in the DCH: Danish Diet, Cancer and Health cohort. We hypothesised that risk of late-onset CID would be lower among those with high intake of fibre and/or low intake of meat compared to individuals with low fibre and/or high meat intake. Methods: The DCH recruited 56,468 individuals, aged 50-64 years, between 1993 and 1997. At recruitment, diet intake was registered using food frequency questionnaires as well as lifestyle factors in 56,075 persons. Exposure variables were generated as sex-adjusted tertiles of fibre and meat (g/day). Development of CIDs was identified in national registries. Hazard ratios (HR) of late-onset CIDs (adjusted for age, sex, energy intake, alcohol, smoking, education, comorbidity, and civil status) were estimated for all three exposure variables. Results: During follow-up of 1,123,754 years (median (Interquartile range) = 22.2 (20.1-23.1)), 1,758 (3.1%) participants developed at least one CID. The adjusted HRs for developing CID (low fibre 1.04 [0.89-1.22] and medium fibre 1.04 [0.91-1.18] (high fibre as reference), and medium meat 0.96 [0.86-1.09] and high meat 0.94 [0.82-1.07] (low meat as reference)) or the individual diseases were not statistically significant. Conclusion: This large study did not support that a high intake of fibre and/or a low intake of meat had a high impact on the risk of late-onset CID.
Solubility Enhancement of Aceclofenac by Solid Dispersion

The aim of the study was to improve the solubility of aceclofenac, which is poorly water soluble drug belongs to BCS class-II. Aceclofenac appears to be particularly well tolerated among the NSAIDs, with a lower incidence of gastrointestinal adverse effects. For poorly soluble orally administered drugs, the rate of absorption is often controlled by the rate of dissolution. To improve the solubility of drug by solid dispersions were prepared with different methods like physical mixture, kneading method and solvent evaporation method with various carriers like poloxamer188, poloxamer407 and PEG 6000 in different ratios from 1:1 to 1:5. The prepared formulations were evaluated for physicochemical characteristics, characterized by differential scanning calorimetry (DSC), X-ray diffraction (XRD), in-vitro dissolution studies and saturation solubility. Based on the evaluation parameters poloxamer407 in ratio of 1:5 through solvent evaporation method was optimized and formulated into tablets by direct compression method. These tablets showed a higher in-vitro dissolution drug release which is 99.62% in 30 minutes when compared with pure drug which showed 26.62% in 60 minutes, whereas marketed tablet (Hifenac) shows 99.64±0.10% in 40 minutes. Hence it was concluded that solid dispersion of aceclofenac drug by using polaxamer 407 with solvent evaporation method enhances solubility, absorption rate and increase bioavailability of the aceclofenac drug.
EXPLORING ECONOMIC OPPORTUNITIES IN SOME SELECTED SPICES FARMING ENTERPRISE IN ONDO STATE, NIGERIA

This study explored the current economic issues and isolated constraints involved in the spice business in Ondo State, Nigeria. A quantitative research approach was used to elicit information from 60 randomly selected respondents through an individual interview schedule with the aid of a well-structured questionnaire. Data collected was explored using descriptive statistics, and budgetary and multiple regression analyses. The results showed that most (57%) respondents were male, and 87% were married with a mean household size of 7. Most (77%) had at least a primary education, and all of them were members of an association with the majority (78%) engaged in cooperative marketing of spices. Also, of the four selected spices, Aframomum melegueta, Piper guineese, Monodora myristica, Sylopia acthipea, only Aframomum melegueta was cultivated, others were gathered from the wild. The prominent constraints identified were the inability to propagate Piper guineese, Monodora myristica, and Sylopia acthipea; the lack of technology to harvest and dry Sylopia acthipea to meet the standard of the buyers; and the lack of access to high-value markets. The rate of return on investment was 0.98, and the factors that determine the profitability of the spice business included the level of education, household size, labor and value addition. Research efforts should be focused on the propagation of P. guineese, M. myristica, S. acthipea, the technology for drying and harvesting of S. acthipea, and capacity building on value addition to increase business profitability.
Effect of planting methods and irrigation intervals on productivity of some bread wheat cultivars

A field experiment was carried out at Experimental Farm, in Al- kalahyn, Al- Hajer village, Qeft, Qena governorate, Egypt during 2017/2018 and 2018/2019 seasons, to evaluate of three planting methods (Drilling, broadcasting and furrows) and three irrigation intervals (21 , 28 and 35 days) on productivity of three wheat cultivars ( Sids 12 –Misr 2 and Giza 168 ). The results showed that exposing wheat plants to drought stress by increasing irrigation intervals significantly decrease (1000-grain weight, grains spike weight, number of spikes/m2, grain yield (ardab/fed) (argab = 150 Kg, fed= feddan= 42002 m² = 0.420 hectares = 1.037 acres), straw yield (ton/fed) and protein %). Irrigation intervals showed highly significant difference in for all characters studies. The highest value resulted from application irrigation intervals at 21 day compare with the other irrigation intervals. Planting methods significantly affected by all a previous characters. The highest value for all studies characters resulted from using drilling method. There were highly significant differences among wheat cultivars for all characters under study. The wheat cultivar sids-12 gave of the highest values of all traits compared to the other wheat cultivars used in both seasons.
AN ANALYSIS OF STUDENTS’ ABILITY AND PROBLEM IN WRITING ANALYTICAL EXPOSITION TEXT

Abstract This research aims to analyze the ability and problem of students in writing analytical exposition text. The participants of this research were the students at XI IPA 2 SMAN I Ulakan Tapakis. The type of this research was descriptive research. The data of the research was collected by administering writing task. The data collected were analyzed by using quantitative and qualitative analysis. The finding shows that the ability of students in writing thesis arguments, reiteration, language features (Simple present, passive voice and conjunction), vocabulary and mechanics was unsatisfactory. Furthermore they had some problems in writing thesis arguments, reiteration, language features (Simple present, passive voice and conjunction), vocabulary and mechanics. From the data gotten, it can be concluded that the students had unsatisfactory performance in writing analytical exposition text. Key words: students’ability, students’ problems, analytical exposition text.
Regional Differences in External Cephalic Version and Management of Vaginal Breech Delivery: A Survey in the Netherlands

Background: This study aims to gain insight into external cephalic version (ECV) and regional differences in management of vaginal breech delivery in the Netherlands. Methods: A nationwide online survey was sent to the obstetric department of each hospital (n = 80). Results: The response rate was 81% (65 of 80 hospitals). All centers performed ECV and 98% offered ECV to more than 90% of the eligible women. ECV was mostly performed by gynecologists (89%). A special ECV outpatient clinic existed in 35% of the centers, ECV was conducted at the regular outpatient clinic in 15% and in 44% ECV was conducted in the clinic. For uterine relaxation atosiban (46%) was the most given, followed by ritodrine (32%), fenoterol (18%) and nifedipine (4%). All centers are experienced in vaginal breech delivery. Induction for obstetrical reasons was allowed in 72% and labor augmentation, if needed, was administered in 83% of the responders. Pain relief for vaginal breech delivery was offered in all centers when requested. A selected team of gynecologists assisting vaginal breech delivery existed in 28% of the centers. Preterm vaginal breech delivery was possible in 92% of the centers. Conclusions: In the Netherlands, ECV is well implemented, and large variations in practice concerning ECV and the use of several tocolytic agents for ECV exist. Vaginal breech delivery at term is possible in all centers; however, intrapartum management of vaginal breech delivery differs among centers. J Clin Gynecol Obstet. 2020;9(1-2):3-11 doi: https://doi.org/10.14740/jcgo624
Evaluating Self-Supervised Pretraining Without Using Labels

A common practice in unsupervised representation learning is to use labeled data to evaluate the learned representations - oftentimes using the labels from the "unlabeled" training dataset. This supervised evaluation is then used to guide the training process, e.g. to select augmentation policies. However, supervised evaluations may not be possible when labeled data is difficult to obtain (such as medical imaging) or ambiguous to label (such as fashion categorization). This raises the question: is it possible to evaluate unsupervised models without using labeled data? Furthermore, is it possible to use this evaluation to make decisions about the training process, such as which augmentation policies to use? In this work, we show that the simple self-supervised evaluation task of image rotation prediction is highly correlated with the supervised performance of standard visual recognition tasks and datasets (rank correlation > 0.94). We establish this correlation across hundreds of augmentation policies and training schedules and show how this evaluation criteria can be used to automatically select augmentation policies without using labels. Despite not using any labeled data, these policies perform comparably with policies that were determined using supervised downstream tasks. Importantly, this work explores the idea of using unsupervised evaluation criteria to help both researchers and practitioners make decisions when training without labeled data.
Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text

OBJECTIVE We sought to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DSs) in clinical text. MATERIALS AND METHODS Two tasks were performed in this study. For the named entity recognition (NER) task, Bi-LSTM-CRF (bidirectional long short-term memory conditional random field) and BERT (bidirectional encoder representations from transformers) models were trained and compared with CRF model as a baseline to recognize the named entities of DSs and events from clinical notes. In the relation extraction (RE) task, 2 deep learning models, including attention-based Bi-LSTM and convolutional neural network as well as a random forest model were trained to extract the relations between DSs and events, which were categorized into 3 classes: positive (ie, indication), negative (ie, adverse events), and not related. The best performed NER and RE models were further applied on clinical notes mentioning 88 DSs for discovering DSs adverse events and indications, which were compared with a DS knowledge base. RESULTS For the NER task, deep learning models achieved a better performance than CRF, with F1 scores above 0.860. The attention-based Bi-LSTM model performed the best in the RE task, with an F1 score of 0.893. When comparing DS event pairs generated by the deep learning models with the knowledge base for DSs and event, we found both known and unknown pairs. CONCLUSIONS Deep learning models can detect adverse events and indication of DSs in clinical notes, which hold great potential for monitoring the safety of DS use.
An Information Theoretic View on Selecting Linguistic Probes

There is increasing interest in assessing the linguistic knowledge encoded in neural representations. A popular approach is to attach a diagnostic classifier -- or "probe" -- to perform supervised classification from internal representations. However, how to select a good probe is in debate. Hewitt and Liang (2019) showed that a high performance on diagnostic classification itself is insufficient, because it can be attributed to either "the representation being rich in knowledge", or "the probe learning the task", which Pimentel et al. (2020) challenged. We show this dichotomy is valid information-theoretically. In addition, we find that the methods to construct and select good probes proposed by the two papers, *control task* (Hewitt and Liang, 2019) and *control function* (Pimentel et al., 2020), are equivalent -- the errors of their approaches are identical (modulo irrelevant terms). Empirically, these two selection criteria lead to results that highly agree with each other.
Surface Brightness Evolution of Galaxies in the CANDELS GOODS Fields up to z ∼ 6: High-z Galaxies Are Unique or Remain Undetected

We investigate the rest-frame ultraviolet (UV, λ ∼ 2000 Å) surface brightness (SB) evolution of galaxies up to z ∼ 6 using a variety of deep Hubble Space Telescope (HST) imaging. UV SB is a measure of the density of emission from mostly young stars and correlates with an unknown combination of star formation rate, initial mass function, cold gas mass density, dust attenuation, and the size evolution of galaxies. In addition to physical effects, the SB is, unlike magnitude, a more direct way in which a galaxy’s detectability is determined. We find a very strong evolution in the intrinsic SB distribution that declines as , decreasing by 4–5 mag arcsec−2 between z = 6 and z = 1. This change is much larger than expected in terms of the evolution in UV luminosity, sizes, or dust extinction, and we demonstrate that this evolution is “unnatural” and due to selection biases. We also find no strong correlation between mass and UV SB. Thus, deep HST imaging is unable to discover all of the most massive galaxies in the distant universe. Through simulations we show that only ∼15% of galaxies that we can detect at z = 2 would be detected at high z. We furthermore explore possible origins of high-SB galaxies at high z by investigating the relationship between intrinsic SB and star formation rates. We conclude that ultra–high-SB galaxies are produced by very gas-rich dense galaxies that are in a unique phase of evolution, possibly produced by mergers. Analogs of such galaxies do not exist in the relatively nearby universe.
Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles

This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 ”Detection of Propaganda Techniques in News Articles”. We participated in Task ”Technique Classification” (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependent-minority-class classifiers. A special classifier, which employs dynamically adapted Least Common Sub-sequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.
PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features

Leveraging line features to improve localization accuracy of point-based visual-inertial SLAM (VINS) is gaining interest as they provide additional constraints on scene structure. However, real-time performance when incorporating line features in VINS has not been addressed. This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line features, developed based on the state-of-the-art point-based VINS-Mono \cite{vins}. We observe that current works use the LSD \cite{lsd} algorithm to extract line features; however, LSD is designed for scene shape representation instead of the pose estimation problem, which becomes the bottleneck for the real-time performance due to its high computational cost. In this paper, a modified LSD algorithm is presented by studying a hidden parameter tuning and length rejection strategy. The modified LSD can run at least three times as fast as LSD. Further, by representing space lines with the Plucker coordinates, the residual error in line estimation is modeled in terms of the point-to-line distance, which is then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Plucker coordinates. Experiments in a public benchmark dataset show that the localization error of our method is 12-16\% less than that of VINS-Mono at the same pose update frequency. %For the benefit of the community, The source code of our method is available at: this https URL.
Dual Semantic Fusion Network for Video Object Detection

Video object detection is a tough task due to the deteriorated quality of video sequences captured under complex environments. Currently, this area is dominated by a series of feature enhancement based methods, which distill beneficial semantic information from multiple frames and generate enhanced features through fusing the distilled information. However, the distillation and fusion operations are usually performed at either frame level or instance level with external guidance using additional information, such as optical flow and feature memory. In this work, we propose a dual semantic fusion network (abbreviated as DSFNet) to fully exploit both frame-level and instance-level semantics in a unified fusion framework without external guidance. Moreover, we introduce a geometric similarity measure into the fusion process to alleviate the influence of information distortion caused by noise. As a result, the proposed DSFNet can generate more robust features through the multi-granularity fusion and avoid being affected by the instability of external guidance. To evaluate the proposed DSFNet, we conduct extensive experiments on the ImageNet VID dataset. Notably, the proposed dual semantic fusion network achieves, to the best of our knowledge, the best performance of 84.1% mAP among the current state-of-the-art video object detectors with ResNet-101 and 85.4% mAP with ResNeXt-101 without using any post-processing steps.
Text Generation by Learning from Demonstrations

Current approaches to text generation largely rely on autoregressive models and maximum likelihood estimation. This paradigm leads to (i) diverse but low-quality samples due to mismatched learning objective and evaluation metric (likelihood vs. quality) and (ii) exposure bias due to mismatched history distributions (gold vs. model-generated). To alleviate these problems, we frame text generation as an offline reinforcement learning (RL) problem with expert demonstrations (i.e., the reference), where the goal is to maximize quality given model-generated histories. We propose GOLD (generation by off-policy learning from demonstrations): an easy-to-optimize algorithm that learns from the demonstrations by importance weighting. Intuitively, GOLD upweights confident tokens and downweights unconfident ones in the reference during training, avoiding optimization issues faced by prior RL approaches that rely on online data collection. According to both automatic and human evaluation, models trained by GOLD outperform those trained by MLE and policy gradient on summarization, question generation, and machine translation. Further, our models are less sensitive to decoding algorithms and alleviate exposure bias.