nils-herrmann/clean_scirepeval huggingface dataset:
Circulating tumor DNA as a liquid biopsy for cancer.

Targeted therapies have markedly changed the treatment of cancer over the past 10 years. However, almost all tumors acquire resistance to systemic treatment as a result of tumor heterogeneity, clonal evolution, and selection. Although genotyping is the most currently used method for categorizing tumors for clinical decisions, tumor tissues provide only a snapshot, or are often difficult to obtain. To overcome these issues, methods are needed for a rapid, cost-effective, and noninvasive identification of biomarkers at various time points during the course of disease. Because cell-free circulating tumor DNA (ctDNA) is a potential surrogate for the entire tumor genome, the use of ctDNA as a liquid biopsy may help to obtain the genetic follow-up data that are urgently needed.



This review includes recent studies exploring the diagnostic, prognostic, and predictive potential of ctDNA as a liquid biopsy in cancer. In addition, it covers biological and technical aspects, including recent advances in the analytical sensitivity and accuracy of DNA analysis as well as hurdles that have to be overcome before implementation into clinical routine.



Although the analysis of ctDNA is a promising area, and despite all efforts to develop suitable tools for a comprehensive analysis of tumor genomes from plasma DNA, the liquid biopsy is not yet routinely used as a clinical application. Harmonization of preanalytical and analytical procedures is needed to provide clinical standards to validate the liquid biopsy as a clinical biomarker in well-designed and sufficiently powered multicenter studies.
Signal processing and machine learning concepts using the reflections echolocation app

This paper describes the use of a space usage determination algorithm for teaching signal processing and machine learning concepts to undergraduate electrical engineering and computer science students. An Android device transmits a high-frequency signal in an unknown space. The device determines the reflective properties of this unknown space by analyzing the received signal. Based on the features extracted from this signal, the app measures distances and determines how the space can be utilized for various application such as libraries, conference rooms, or laboratories. The application and related algorithms use concepts such cross-correlation, feature extraction, learning/training algorithms, and discrimination/decision making. These concepts are typically covered in undergraduate classes such as Digital Signal Processing, Control Systems, and Probability and Statistics; and graduate-level classes such as Pattern Recognition and Detection and Estimation Theory. The app is used to create compelling demonstrations and immersive exercises to teach basic concepts related to signal processing and machine learning. Undergraduate student hands-on workshops and outreach activities are planned to evaluate the effectiveness of this approach. Assessment results will be presented at the conference.
3D human pose estimation in video with temporal convolutions and semi-supervised training

In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully convolutional model based on dilated temporal convolutions over 2D keypoints. We also introduce back-projection, a simple and effective semi-supervised training method that leverages unlabeled video data. We start with predicted 2D keypoints for unlabeled video, then estimate 3D poses and finally back-project to the input 2D keypoints. In the supervised setting, our fully-convolutional model outperforms the previous best result from the literature by 6 mm mean per-joint position error on Human3.6M, corresponding to an error reduction of 11%, and the model also shows significant improvements on HumanEva-I. Moreover, experiments with back-projection show that it comfortably outperforms previous state-of-the-art results in semisupervised settings where labeled data is scarce. Code and models are available at https://github.com/ facebookresearch/VideoPose3D
Global Relation Embedding for Relation Extraction

We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the cooccurrence statistics of textual and knowledge base relations collected from the entire corpus. This approach turns out to be more robust to the training noise introduced by distant supervision. On a popular relation extraction dataset, we show that the learned textual relation embedding can be used to augment existing relation extraction models and significantly improve their performance. Most remarkably, for the top 1,000 relational facts discovered by the best existing model, the precision can be improved from 83.9% to 89.3%.
Sleep timing is more important than sleep length or quality for medical school performance.

Overwhelming evidence supports the importance of sleep for memory consolidation. Medical students are often deprived of sufficient sleep due to large amounts of clinical duties and university load, we therefore investigated how study and sleep habits influence university performance. We performed a questionnaire-based study with 31 medical students of the University of Munich (second and third clinical semesters; surgery and internal medicine). The students kept a diary (in 30-min bins) on their daily schedules (times when they studied by themselves, attended classes, slept, worked on their thesis, or worked to earn money). The project design involved three 2-wk periods (A: during the semester; B: directly before the exam period--pre-exam; C: during the subsequent semester break). Besides the diaries, students completed once questionnaires about their sleep quality (Pittsburgh Sleep Quality Index [PSQI]), their chronotype (Munich Chronotype Questionnaire [MCTQ]), and their academic history (previous grades, including the previously achieved preclinical board exam [PBE]). Analysis revealed significant correlations between the actual sleep behavior during the semester (MS(diary); mid-sleep point averaged from the sleep diaries) during the pre-exam period and the achieved grade (p = 0.002) as well as between the grades of the currently taken exam and the PBE (p = 0.002). A regression analysis with MS(diary) pre-exam and PBE as predictors in a model explained 42.7% of the variance of the exam grade (effect size 0.745). Interestingly, MS(diary)--especially during the pre-exam period-was the strongest predictor for the currently achieved grade, along with the preclinical board exam as a covariate, whereas the chronotype did not significantly influence the exam grade.
The Use of Control Charts in Health-Care and Public-Health Surveillance

Standard control charts are often recommended for use in the monitoring and improvement of hospital performance. For example, one might monitor infection rates, rates of patient falls, or waiting times of various sorts. See, for example, Benneyan (1998a,b), Lee and McGreevey (2002a), or Benneyan et al. (2003). There are several books on this topic, including Carey (2003), reviewed by Woodall (2004), Hart and Hart (2002), and Morton (2005). The more standard uses of control charts in hospital applications are not reviewed here even though improvements are widely needed, as discussed by Millenson (1999), the Institute of Medicine (2000), and others. We also do not discuss the monitoring of health-related variables for individual patients, as recommended, for example, by Alemi and Neuhauser (2004).
Cs229 Problem Set #4 Cs 229, Autumn 2016 Problem Set #4: Unsupervised Learning & Rl

Notes: (1) These questions require thought, but do not require long answers. Please be as concise as possible. (2) If you have a question about this homework, we encourage you to post your question on our Piazza forum, at https://piazza.com/stanford/autumn2016/cs229. (3) If you missed the first lecture or are unfamiliar with the collaboration or honor code policy, please read the policy on Handout #1 (available from the course website) before starting work. (4) For problems that require programming, please include in your submission a printout of your code (with comments) and any figures that you are asked to plot. If you are scanning your document by cellphone, please check the Piazza forum for recommended cellphone scanning apps and best practices.
Table-to-Text: Describing Table Region With Natural Language

In this paper, we present a generative model to generate a natural language sentence describing a table region, e.g., a row. The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table. To deal with rare words appearing in a table, we develop a flexible copying mechanism that selectively replicates contents from the table in the output sequence. Extensive experiments demonstrate the accuracy of the model and the power of the copying mechanism. On two synthetic datasets, WIKIBIO and SIMPLEQUESTIONS, our model improves the current state-of-the-art BLEU-4 score from 34.70 to 40.26 and from 33.32 to 39.12, respectively. Furthermore, we introduce an open-domain dataset WIKITABLETEXT including 13,318 explanatory sentences for 4,962 tables. Our model achieves a BLEU-4 score of 38.23, which outperforms template based and language model based approaches.
Analyzing Locally Coordinated Cyber-Physical Attacks for Undetectable Line Outages

This paper offers the pertinent studies on the locally coordinated cyber-physical attacks that would use the incomplete network information and could cause undetectable transmission line outages. First, power line reactances in the targeted local areas are estimated and the remaining power system is modeled equivalently. Next, a mathematical model in the mixed-integer linear programming form is presented for analyzing the attacks, which would cause undetectable line outages while maximizing the system risk resulting from the power flow redistribution. An efficient greedy search-based heuristic method is developed in order to offer satisfactory solutions for real-time applications. Last, the proposed model for the locally coordinated cyber-physical attacks and the corresponding solution strategies are verified by the case studies based on a six-bus system and the two-area IEEE RTS-96 system.
Global Pose Estimation with an Attention-Based Recurrent Network

The ability for an agent to localize itself within an environment is crucial for many real-world applications. For unknown environments, Simultaneous Localization and Mapping (SLAM) enables incremental and concurrent building of and localizing within a map. We present a new, differentiable architecture, Neural Graph Optimizer, progressing towards a complete neural network solution for SLAM by designing a system composed of a local pose estimation model, a novel pose selection module, and a novel graph optimization process. The entire architecture is trained in an end-to-end fashion, enabling the network to automatically learn domain-specific features relevant to the visual odometry and avoid the involved process of feature engineering. We demonstrate the effectiveness of our system on a simulated 2D maze and the 3D ViZ-Doom environment.
Inverse Cooking: Recipe Generation from Food Images

People enjoy food photography because they appreciate food. Behind each meal there is a story described in a complex recipe and, unfortunately, by simply looking at a food image we do not have access to its preparation process. Therefore, in this paper we introduce an inverse cooking system that recreates cooking recipes given food images. Our system predicts ingredients as sets by means of a novel architecture, modeling their dependencies without imposing any order, and then generates cooking instructions by attending to both image and its inferred ingredients simultaneously. We extensively evaluate the whole system on the large-scale Recipe1M dataset and show that (1) we improve performance w.r.t. previous baselines for ingredient prediction; (2) we are able to obtain high quality recipes by leveraging both image and ingredients; (3) our system is able to produce more compelling recipes than retrieval-based approaches according to human judgment.
Semantic Visualization for Short Texts with Word Embeddings

Semantic visualization integrates topic modeling and visualization, such that every document is associated with a topic distribution as well as visualization coordinates on a low-dimensional Euclidean space. We address the problem of semantic visualization for short texts. Such documents are increasingly common, including tweets, search snippets, news headlines, or status updates. Due to their short lengths, it is difficult to model semantics as the word co-occurrences in such a corpus are very sparse. Our approach is to incorporate auxiliary information, such as word embeddings from a larger corpus, to supplement the lack of co-occurrences. This requires the development of a novel semantic visualization model that seamlessly integrates visualization coordinates, topic distributions, and word vectors. We propose a model called GaussianSV, which outperforms pipelined baselines that derive topic models and visualization coordinates as disjoint steps, as well as semantic visualization baselines that do not consider word embeddings.
Covalent Organic Frameworks: From Materials Design to Biomedical Application

Covalent organic frameworks (COFs) are newly emerged crystalline porous polymers with well-defined skeletons and nanopores mainly consisted of light-weight elements (H, B, C, N and O) linked by dynamic covalent bonds. Compared with conventional materials, COFs possess some unique and attractive features, such as large surface area, pre-designable pore geometry, excellent crystallinity, inherent adaptability and high flexibility in structural and functional design, thus exhibiting great potential for various applications. Especially, their large surface area and tunable porosity and π conjugation with unique photoelectric properties will enable COFs to serve as a promising platform for drug delivery, bioimaging, biosensing and theranostic applications. In this review, we trace the evolution of COFs in terms of linkages and highlight the important issues on synthetic method, structural design, morphological control and functionalization. And then we summarize the recent advances of COFs in the biomedical and pharmaceutical sectors and conclude with a discussion of the challenges and opportunities of COFs for biomedical purposes. Although currently still at its infancy stage, COFs as an innovative source have paved a new way to meet future challenges in human healthcare and disease theranostic.
Study on Secret Sharing Schemes (SSS) and their applications

Hiding a secret is needed in many situations. One might need to hide a password, an encryption key, a secret recipe, and etc. Information can be secured with encryption, but the need to secure the secret key used for such encryption is important too. Imagine you encrypt your important files with one secret key and if such a key is lost then all the important files will be inaccessible. Thus, secure and efficient key management mechanisms are required. One of them is secret sharing scheme (SSS) that lets you split your secret into several parts and distribute them among selected parties. The secret can be recovered once these parties collaborate in some way. This paper will study these schemes and explain the need for them and their security. Across the years, various schemes have been presented. This paper will survey some of them varying from trivial schemes to threshold based ones. Explanations on these schemes constructions are presented. The paper will also look at some applications of SSS.
Substance Use and Associated Factors among University Students in Ethiopia: A Cross-Sectional Study

Studies indicate that substance use among Ethiopian adolescents is considerably rising; in particular college and university students are the most at risk of substance use. The aim of the study was to assess substance use and associated factors among university students. A cross-sectional survey was carried out among 1040 Haramaya University students using self-administered structured questionnaire. Multistage sampling technique was used to select students. Descriptive statistics, bivariate, and multivariate analysis were done. About two-thirds (62.4%) of the participants used at least one substance. The most commonly used substance was alcohol (50.2%). Being male had strong association with substance use (AOR (95% CI), 3.11 (2.20, 4.40)). The odds of substance use behaviour is higher among third year students (AOR (95% CI), 1.48 (1.01, 2.16)). Being a follower of Muslim (AOR (95% CI), 0.62 (0.44, 0.87)) and Protestant (AOR (95% CI), 0.25 (0.17, 0.36)) religions was shown to be protective of substance use. Married (AOR (95% CI), 1.92 (1.12, 3.30)) and depressed (AOR (95% CI), 3.30 (2.31, 4.72)) students were more likely to use substances than others. The magnitude of substance use was high. This demands special attention, emergency preventive measures, and targeted information, education and communication activity.
1. Lexicalized meaning and the internal temporal structure of events

Most current studies of aspect assume the existence of the four Vendler classes: states, activities, achievements and accomplishments. Despite the fact that other classifications have been offered, (for example, those in Mourelatos 1978, Bach 1981, and Carlson 1981) none has achieved the status of the Vendler classification. Often, linguists take these classes to be a linguistic fact, and then attempt to come up with theories which explain their existence and their properties, usually by offering basic elements of meaning and modes of composition that together produce just these four aspectual classes.
Subgroups of patients with cancer with different symptom experiences and quality-of-life outcomes: a cluster analysis.

To identify subgroups of outpatients with cancer based on their experiences with the symptoms of fatigue, sleep disturbance, depression, and pain; to explore whether patients in the subgroups differed on selected demographic, disease, and treatment characteristics; and to determine whether patients in the subgroups differed on two important patient outcomes: functional status and quality of life (QOL).



Descriptive, correlational study.



Four outpatient oncology practices in northern California.


SAMPLE
191 outpatients with cancer receiving active treatment.



Patients completed a demographic questionnaire, Karnofsky Performance Status scale, Lee Fatigue Scale, General Sleep Disturbance Scale, Center for Epidemiological Studies Depression Scale, Multidimensional Quality-of-Life Scale Cancer, and a numeric rating scale of worst pain intensity. Medical records were reviewed for disease and treatment information. Cluster analysis was used to identify patient subgroups based on patients symptom experiences. Differences in demographic, disease, and treatment characteristics as well as in outcomes were evaluated using analysis of variance and chi square analysis.



Subgroup membership, fatigue, sleep disturbance, depression, pain, functional status, and QOL.



Four relatively distinct patient subgroups were identified based on patients experiences with four highly prevalent and related symptoms.



The subgroup of patients who reported low levels of all four symptoms reported the best functional status and QOL.



The findings from this study need to be replicated before definitive clinical practice recommendations can be made. Until that time, clinicians need to assess patients for the occurrence of multiple symptoms that may place them at increased risk for poorer outcomes.
Fast two-dimensional phase-unwrapping algorithm based on sorting by reliability following a noncontinuous path.

We describe what is to our knowledge a novel technique for phase unwrapping. Several algorithms based on unwrapping the most-reliable pixels first have been proposed. These were restricted to continuous paths and were subject to difficulties in defining a starting pixel. The technique described here uses a different type of reliability function and does not follow a continuous path to perform the unwrapping operation. The technique is explained in detail and illustrated with a number of examples.
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model

I. Unary terms • Body part detection candidates • Capture distribution of scores over all part classes II. Pairwise terms • Capture part relationships within/across people – proximity: same body part class (c = c) – kinematic relations: different part classes (c!= c) III. Integer Linear Program (ILP) • Substitute zdd cc = xdc xd c ydd ′ to linearize objective • NP-Hard problem solved via branch-and-cut (1% gap) • Linear constraints on 0/1 labelings: plausible poses – uniqueness
Improving Neural Abstractive Text Summarization with Prior Knowledge (Position Paper)

Abstractive text summarization is a complex task whose goal is to generate a concise version of a text without necessarily reusing the sentences from the original source, but still preserving the meaning and the key contents. In this position paper we address this issue by modeling the problem as a sequence to sequence learning and exploiting Recurrent Neural Networks (RNN). Moreover, we discuss the idea of combining RNNs and probabilistic models in a unified way in order to incorporate prior knowledge, such as linguistic features. We believe that this approach can obtain better performance than the state-of-the-art models for generating well-formed summaries.
Decreases in psychological well-being among American adolescents after 2012 and links to screen time during the rise of smartphone technology.

In nationally representative yearly surveys of United States 8th, 10th, and 12th graders 1991-2016 (N = 1.1 million), psychological well-being (measured by self-esteem, life satisfaction, and happiness) suddenly decreased after 2012. Adolescents who spent more time on electronic communication and screens (e.g., social media, the Internet, texting, gaming) and less time on nonscreen activities (e.g., in-person social interaction, sports/exercise, homework, attending religious services) had lower psychological well-being. Adolescents spending a small amount of time on electronic communication were the happiest. Psychological well-being was lower in years when adolescents spent more time on screens and higher in years when they spent more time on nonscreen activities, with changes in activities generally preceding declines in well-being. Cyclical economic indicators such as unemployment were not significantly correlated with well-being, suggesting that the Great Recession was not the cause of the decrease in psychological well-being, which may instead be at least partially due to the rapid adoption of smartphones and the subsequent shift in adolescents' time use.
Bitcoin Pricing , Adoption , and Usage : Theory and Evidence ∗

This paper develops a model of user adoption and use of virtual currency (such as Bitcoin), and focusing on the dynamics of adoption in the presence of frictions arising from exchange rate uncertainty. The theoretical model can be used to analyze how market fundamentals determine the exchange rate of fiat currency to Bitcoin. Empirical evidence from Bitcoin prices and utilization provides mixed evidence about the ability of the model to explain prices. Further analysis of the history of all individual transactions on Bitcoin’s public ledger establishes patterns of adoption and utilization across user types, transaction type, and geography. We show that as of mid-2015, active usage was not growing quickly, and that investors and infrequent users held the majority of Bitcoins. We document the extent to which the attributes of the anonymous users of Bitcoin can be inferred through their behavior, and we find that users who engage in illegal activity are more likely to try to protect their financial privacy.