nils-herrmann/clean_scirepeval huggingface dataset:
Long ties accelerate noisy threshold-based contagions

Changes to network structure can substantially affect when and how widely new ideas, products, and conventions are adopted. In models of biological contagion, interventions that randomly rewire edges (generally making them “longer”) accelerate spread. However, there are other models relevant to social contagion, such as those motivated by myopic best-response in games with strategic complements, in which an individual’s behavior is described by a threshold number of adopting neighbors above which adoption occurs (i.e., complex contagions). Recent work has argued that highly clustered, rather than random, networks facilitate spread of these complex contagions. This conclusion is based primarily on theoretical and simulation results assuming adoption never occurs with only one adopting neighbor. Here we show that minor modifications to this model, which make it more realistic, reverse this result. The modification is that we allow very rare below-threshold adoption, i.e., very rarely adoption occurs when there is only one adopting neighbor. To model the trade-off between long and short edges we consider networks that are the union of cycle-power-k graphs and random graphs on n nodes. We study how the time to global spread changes as we replace the cycle edges with (random) long ties. Allowing adoptions below threshold to occur with order 1/ √ n probability along some cycle edges is enough to ensure that random rewiring accelerates spread. Simulations illustrate the robustness of these results to other commonly-posited models for noisy best-response behavior. We then examine empirical social networks, where we find that hypothetical interventions that (a) randomly rewire existing edges, or (b) add random edges, reduce time to spread compared with the original network or addition of “short”, triad-closing edges, respectively. This substantially revises conclusions about how interventions change the spread of behavior, suggesting that those wanting to increase spread should induce formation of long ties, rather than triad-closing ties. More generally, this highlights the importance of noise in game-theoretic analyses of behavior.
Open-Ended Visual Question-Answering

This thesis studies methods to solve Visual Question-Answering (VQA) tasks with a Deep Learning framework. As a preliminary step, we explore Long Short-Term Memory (LSTM) networks used in Natural Language Processing (NLP) to tackle Question-Answering (text based). We then modify the previous model to accept an image as an input in addition to the question. For this purpose, we explore the VGG-16 and K-CNN convolutional neural networks to extract visual features from the image. These are merged with the word embedding or with a sentence embedding of the question to predict the answer. This work was successfully submitted to the Visual Question Answering Challenge 2016, where it achieved a 53,62% of accuracy in the test dataset. The developed software has followed the best programming practices and Python code style, providing a consistent baseline in Keras for different configurations. The source code and models are publicly available at https://github.com/imatge-upc/vqa-2016-cvprw.
Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes

In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit admissions for clinically deteriorating patients.  The risk scoring system is based on the idea of sequential hypothesis testing under an uncertain time horizon. The system learns a set of latent patient subtypes from the offline electronic health record data, and trains a mixture of Gaussian Process experts, where each expert models the physiological data streams associated with a specific patient subtype. Transfer learning techniques are used to learn the relationship between a patient's latent subtype and her static admission information (e.g., age, gender, transfer status, ICD-9 codes, etc).  Experiments conducted on data from a heterogeneous cohort of 6321 patients admitted to Ronald Reagan UCLA medical center show that our score significantly outperforms the currently deployed risk scores, such as the Rothman index, MEWS, APACHE, and SOFA scores, in terms of timeliness, true positive rate, and positive predictive value.  Our results reflect the importance of adopting the concepts of personalized medicine in critical care settings; significant accuracy and timeliness gains can be achieved by accounting for the patients’ heterogeneity.   The proposed risk scoring methodology can confer huge clinical and social benefits on a massive number of critically ill inpatients who exhibit adverse outcomes including, but not limited to, cardiac arrests, respiratory arrests, and septic shocks.
VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery

The ecosystem of open source software (OSS) has been growing considerably in size. In addition, code clones - code fragments that are copied and pasted within or between software systems - are also proliferating. Although code cloning may expedite the process of software development, it often critically affects the security of software because vulnerabilities and bugs can easily be propagated through code clones. These vulnerable code clones are increasing in conjunction with the growth of OSS, potentially contaminating many systems. Although researchers have attempted to detect code clones for decades, most of these attempts fail to scale to the size of the ever-growing OSS code base. The lack of scalability prevents software developers from readily managing code clones and associated vulnerabilities. Moreover, most existing clone detection techniques focus overly on merely detecting clones and this impairs their ability to accurately find "vulnerable" clones. In this paper, we propose VUDDY, an approach for the scalable detection of vulnerable code clones, which is capable of detecting security vulnerabilities in large software programs efficiently and accurately. Its extreme scalability is achieved by leveraging function-level granularity and a length-filtering technique that reduces the number of signature comparisons. This efficient design enables VUDDY to preprocess a billion lines of code in 14 hour and 17 minutes, after which it requires a few seconds to identify code clones. In addition, we designed a security-aware abstraction technique that renders VUDDY resilient to common modifications in cloned code, while preserving the vulnerable conditions even after the abstraction is applied. This extends the scope of VUDDY to identifying variants of known vulnerabilities, with high accuracy. In this study, we describe its principles and evaluate its efficacy and effectiveness by comparing it with existing mechanisms and presenting the vulnerabilities it detected. VUDDY outperformed four state-of-the-art code clone detection techniques in terms of both scalability and accuracy, and proved its effectiveness by detecting zero-day vulnerabilities in widely used software systems, such as Apache HTTPD and Ubuntu OS Distribution.
A Lightweight and High Performance Monolingual Word Aligner

Fast alignment is essential for many natural language tasks. But in the setting of monolingual alignment, previous work has not been able to align more than one sentence pair per second. We describe a discriminatively trained monolingual word aligner that uses a Conditional Random Field to globally decode the best alignment with features drawn from source and target sentences. Using just part-of-speech tags and WordNet as external resources, our aligner gives state-of-the-art result, while being an order-of-magnitude faster than the previous best performing system.
Corpus Specific Stop Words to Improve the Textual Analysis in Scientometrics

With the availability of vast collection of research articles on internet, textual analysis is an increasingly important technique in scientometric analysis. While the context in which it is used and the specific algorithms implemented may vary, typically any textual analysis exercise involves intensive pre-processing of input text which includes removing topically uninteresting terms (stop words). In this paper we argue that corpus specific stop words, which take into account the specificities of a collection of texts, improve textual analysis in scientometrics. We describe two relatively simple techniques to generate corpus-specific stop words; stop words lists following a Poisson distribution and keyword adjacency stop words lists. In a case study to extract keywords from scientific abstracts of research project funded by the European Research Council in the domain of Life sciences, we show that a combination of those techniques gives better recall values than standard stop words or any of the two techniques alone. The method we propose can be implemented to obtain stop words lists in an automatic way by using author provided keywords for a set of abstracts. The stop words lists generated can be updated easily by adding new texts to the training corpus. Conference Topic Methods and techniques
Literature Survey : Study of Neural Machine Translation

We build Neural Machine Translation (NMT) systems for EnglishHindi,Bengali-Hindi and Gujarati-Hindi with two different units of translation i.e. word and subword and present a comparative study of subword NMT and word level NMT systems, along with strong results and case studies. We train attention-based encoder-decoder model for word level and use Byte Pair Encoding (BPE) in subword NMT for word segmentation. We conduct case studies to study the effects of BPE. Since the NMT approach is a data driven approach, it suffers a lot by resource scarcity. This report also covers the Multitask learning which is an approach of transfer learning or inductive transfer. MultiTask Learning helps the learner to improve generalization performance by adding extra related tasks to the backpropagation net. The nub behind adding extra related tasks is domain specific information contained in the training signals of other tasks helps to learn shared feature of the main task better. We explained Multiway multilingual model which is based on the MTL approach which learns the translation of several Indian language pairs in parallel. We also covers the performance gained by Multi-way multilingual neural machine translation in contrast with single pair neural machine translation.
A consequent pole, dual rotor, axial flux vernier permanent magnet machine

A consequent pole, dual rotor, axial flux vernier permanent magnet (VPM) machine is developed to reduce magnet usage and increase torque density. Its end winding length is much shorter than that of regular VPM machines due to its toroidal winding configuration. The configurations and features of the proposed machine are discussed. Benefited from its vernier and consequent pole structure, this new machine exhibits much higher back-EMF and torque density than that of a regular dual rotor axial flux machine, while the magnet usage is halved. The influence of main design parameters, such as slot opening, ratio of inner to outer stator diameter, magnet thickness etc., on torque performance is analyzed based on the quasi-3-dimensional (quasi-3D) finite element analysis (FEA). The analyzing results are validated by real 3D FEA.
Cross-trial prediction of treatment outcome in depression: a machine learning approach.

Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram.



We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes [COMED]; ClinicalTrials.gov, number NCT00590863).



We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64·6% [SD 3·2]; p<0·0001). The model was externally validated in the escitalopram treatment group (N=151) of COMED (accuracy 59·6%, p=0.043). The model also performed significantly above chance in a combined escitalopram-buproprion treatment group in COMED (n=134; accuracy 59·7%, p=0·023), but not in a combined venlafaxine-mirtazapine group (n=140; accuracy 51·4%, p=0·53), suggesting specificity of the model to underlying mechanisms.



Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific antidepressant.



Yale University.
Broadband Circularly Polarized Crossed Bowtie Dipole Antenna Loaded With Parasitic Elements

An extremely broadband circularly polarized (CP) antenna is presented. The proposed antenna consists of a crossed bowtie dipole loaded with four unequal parasitic cross slots, four parasitic bowtie patches, and four parasitic rectangular strips. The cross slots can generate a CP mode due to the sequential phases of the crossed bowtie dipole. The parasitic bowtie patches are gap-coupled with two arms of the crossed bowtie dipole, and they are sequentially rotated along diagonals to generate an additional CP mode. The parasitic strips also generate one CP mode due to the sequentially rotated configuration parallel to the crossed bowtie dipole. By properly combining with two CP modes of the crossed bowtie dipole, broadband CP operation is achieved. The experimental and simulated results are in agreement, and the measured results show that the proposed antenna exhibits a very wide impedance bandwidth of 93.1% (2.21–6.06 GHz) and an excellent axial-ratio bandwidth of 90.9% (2.2–6.4 GHz). In addition, the proposed antenna produces a right-hand circular polarization with a peak gain of 8.6 dBi.
An Introduction to Image Synthesis with Generative Adversarial Nets

There has been a drastic growth of research in Generative Adversarial Nets (GANs) in the past few years. Proposed in 2014, GAN has been applied to various applications such as computer vision and natural language processing, and achieves impressive performance. Among the many applications of GAN, image synthesis is the most well-studied one, and research in this area has already demonstrated the great potential of using GAN in image synthesis. In this paper, we provide a taxonomy of methods used in image synthesis, review different models for text-to-image synthesis and image-to-image translation, and discuss some evaluation metrics as well as possible future research directions in image synthesis with GAN.
Information Security in Big Data: Privacy and Data Mining

The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.
Annotating and Automatically Tagging Constructions of Causal Language

Automatically extracting relationships such as causality from text presents a challenge at the frontiers of natural language processing. This thesis focuses on annotating and automatically tagging causal expressions and their cause and effect arguments. One popular paradigm for such tasks is SHALLOW SEMANTIC PARSING—marking relations and their arguments in text. Efforts to date have focused on individual propositions expressed by individual words. While this approach has been fruitful, it falters on semantic relationships that can be expressed by more complex linguistic patterns than words. It also struggles when multiple meanings are entangled in the same expression. Causality exhibits both challenges: it can be expressed using a variety of words, multi-word expressions, or even complex patterns spanning multiple clauses. Additionally, causality competes for linguistic space with phenomena such as temporal relations and obligation (e.g., allow can indicate causality, permission, or both). To expand shallow semantic parsing to such challenging relations, this thesis presents approaches based on the linguistic paradigm known as CONSTRUCTION GRAMMAR (CxG). CxG places arbitrarily complex form/function pairings called CONSTRUCTIONS at the heart of both syntax and semantics. Because constructions pair meanings with arbitrary forms, CxG allows predicates to be expressed by any linguistic pattern, no matter how complex. This thesis advocates for a new “surface construction labeling” (SCL) approach to applying CxG: given a relation of interest, such as causality, we annotate just the words that consistently signal a construction expressing that relation. Then, to automatically tag such constructions and their arguments, we need not wait for automated CxG tools that can analyze all the underlying grammatical constructions. Instead, we can build on top of existing tools, approximating the underlying constructions with patterns of words and conventional linguistic categories. The contributions of this thesis include a CxG-based annotation scheme and methodology for annotating explicit causal relations in English; an annotated corpus based on this scheme; and three methods for automatically tagging causal constructions. The first two tagging methods use a pipeline architecture based on tentative pattern-matching to combine automatically induced rules with statistical classifiers. The third method is a transition-based deep neural network. The thesis demonstrates the promise of these methods, discusses the tradeoffs of each, and suggests future applications and extensions.
Hand Gesture Recognition in Real Time for Automotive Interfaces: A Multimodal Vision-Based Approach and Evaluations

In this paper, we develop a vision-based system that employs a combined RGB and depth descriptor to classify hand gestures. The method is studied for a human-machine interface application in the car. Two interconnected modules are employed: one that detects a hand in the region of interaction and performs user classification, and another that performs gesture recognition. The feasibility of the system is demonstrated using a challenging RGBD hand gesture data set collected under settings of common illumination variation and occlusion.
Answering Complicated Question Intents Expressed in Decomposed Question Sequences

Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. Existing QA systems face two major problems when evaluated on our dataset: (1) handling questions that contain coreferences to previous questions or answers, and (2) matching words or phrases in a question to corresponding entries in the associated table. We conclude by proposing strategies to handle both of these issues.
3D curve sketch: Flexible curve-based stereo reconstruction and calibration

Interest point-based multiview 3D reconstruction and calibration methods have been very successful in select applications but are not applicable when an abundance of feature points are not available. They also lead to an unorganized point cloud reconstruction where the geometry of the scene is not explicit. The multiview stereo methods on the other hand yield dense surface geometry but require a highly controlled or calibrated setting. We propose and develop a novel framework for 3D reconstruction and calibration based on image curve content, whose output is a 3D curve sketch, an unorganized set of 3D curve fragments. This approach, which is meant to augment the previous approaches, results in a reconstruction of geometric curve structure which can serve as a scaffold on which surface patches can be potentially reconstructed. It is intented for the setting where a number of images are available with coarsely calibrated cameras. The approach operates in two stages. A reliable partial 3D curve sketch is first reconstructed and this is used to refine the cameras to yield a more complete 3D curve sketch in a second stage. A key advantage of this approach is the ability to integrate information across a large number of views. The results have been evaluated on a few datasets.