nils-herrmann/clean_scirepeval huggingface dataset:
Pythia v0.1: the Winning Entry to the VQA Challenge 2018

This document describes Pythia v0.1, the winning entry from Facebook AI Research (FAIR)’s A-STAR team to the VQA Challenge 20181. Our starting point is a modular re-implementation of the bottom-up top-down (up-down) model [1, 14]. We demonstrate that by making subtle but important changes to the model architecture and the learning rate schedule, finetuning image features, and adding data augmentation, we can significantly improve the performance of the up-down model on VQA v2.0 dataset [6] – from 65.67% to 70.22%. Furthermore, by using a diverse ensemble of models trained with different features and on different datasets, we are able to significantly improve over the ‘standard’ way of ensembling (i.e. same model with different random seeds) by 1.31%. Overall, we achieve 72.27% on the teststd split of the VQA v2.0 dataset. Our code in its entirety (training, evaluation, data-augmentation, ensembling) and pre-trained models are publicly available at: https:// github.com/facebookresearch/pythia.
Color attributes for object detection

State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification, leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-of-the-art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.
Comparison of the intrusion effects on the maxillary incisors between implant anchorage and J-hook headgear.

Recently, miniscrews have been used to provide anchorage during orthodontic treatment, especially for incisor intrusion. Miniscrews during incisor intrusion are commonly used in implant orthodontics. Traditionally, effective incisor intrusion has been accomplished with J-hook headgear. In this study, we compared the effect of incisor intrusion, force vector, and amount of root resorption between implant orthodontics and J-hook headgear.



Lateral cephalometric radiographs from 8 patients in the implant group and 10 patients in the J-hook headgear group were analyzed for incisor retraction. The estimated force vector was analyzed in the horizontal and vertical directions in both groups. Root resorption was also measured on periapical radiographs.



In the implant group, significant reductions in overjet, overbite, maxillary incisor to palatal plane, and maxillary incisor to upper lip were observed after intrusion of the incisors. In the J-hook headgear group, significant reductions in overjet, overbite, maxillary incisor to upper lip, and maxillary incisor to SN plane were observed after intrusion of the incisors. There were significantly greater reductions in overbite, maxillary incisor to palatal plane, and maxillary incisor to upper lip in the implant group than in the J-hook headgear group. Estimated force analysis resulted in significantly more force in the vertical direction and less in the horizontal direction in the implant group. Furthermore, significantly less root resorption was observed in the implant group compared with the J-hook headgear group.



The maxillary incisors were effectively intruded by using miniscrews as orthodontic anchorage without patient cooperation. The amount of root resorption was not affected by activating the ligature wire from the miniscrew during incisor intrusion.
TICSync: Knowing when things happened

Modern robotic systems are composed of many distributed processes sharing a common communications infrastructure. High bandwidth sensor data is often collected on one computer and served to many consumers. It is vital that every device on the network agrees on how time is measured. If not, sensor data may be at best inconsistent and at worst useless. Typical clocks in consumer grade PCs are highly inaccurate and temperature sensitive. We argue that traditional approaches to clock synchronization, such as the use of NTP are inappropriate in the robotics context. We present an extremely efficient algorithm for learning the mapping between distributed clocks, which typically achieves better than millisecond accuracy within just a few seconds. We also give a probabilistic analysis providing an upper-bound error estimate.
On computing task-oriented grasps

This paper addresses the problem of optimal grasping of an object with a multi-fingered robotic hand for accomplishing a given task. The task is first demonstrated by a human operator and its force/torque requirements are captured through the usage of a sensorized tool. The grasp quality is computed through a task compatibility criterion. Grasp synthesis is then formulated as a single constrained optimization problem, generating grasps that are feasible for the hand’s kinematics by maximizing the corresponding task-oriented quality criterion and ensuring grasp stability. The method was validated on a human hand model and is shown to be easily adapted to different hand kinematic models.
Sagittal Spinopelvic Parameters in Scheuermann's Kyphosis: A Preliminary Study.

Retrospective, controlled, clinical study.



To define the average values for sagittal spinopelvic parameters including pelvic incidence (PI), pelvic tilt (PT), and sacral slope (SS) in Scheuermann's kyphosis (SK); evaluate the differences in spinopelvic parameters among patients with SK and unaffected normal controls; and evaluate the correlation of various sagittal spinopelvic parameters to each other in SK and normal controls.



Prospectively collected radiographic data from a study on SK were compared with those from previously published series of unaffected patients. Measures were made according to standard, defined measurement methods. Parameters measured included PT, PI, SS, thoracic kyphosis, lumbar lordosis, and radiographic sagittal alignment. Values were compared using independent-samples t test. Pearson correlation coefficient was used to analyze relationships between variables.



A total of 47 patients with SK and 50 control patients, mean age 16.1 and 13.5 years, respectively, were included. In SK, average PI was 42°, average PT was 7°, and average SS was 35°. These values were not different from those of normal controls (PI, 46° [p = .084]; PT, 8° [p = .476]; SS, 37° [p = .162]). Pelvic incidence directly correlated with lordosis in both groups (p < .005). T5-12 kyphosis correlated with lordosis in normal controls (p ≤ .05) but not in the SK group. Kyphosis in SK as quantified by greatest measurable Cobb angle did not correlate with PI or lordosis.



Sagittal pelvic alignment in patients with SK is not different from that in normal subjects. Furthermore, in SK thoracic kyphosis did not correlate with any distal region of the spine (lumbar or pelvic). Further understanding of the relationship between sagittal spinopelvic alignment in various conditions causing spinal deformity will lead to better treatment of these conditions.
DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images

Understanding fashion images has been advanced by benchmarks with rich annotations such as DeepFashion, whose labels include clothing categories, landmarks, and consumer-commercial image pairs. However, DeepFashion has nonnegligible issues such as single clothing-item per image, sparse landmarks (4∼8 only), and no per-pixel masks, making it had significant gap from real-world scenarios. We fill in the gap by presenting DeepFashion2 to address these issues. It is a versatile benchmark of four tasks including clothes detection, pose estimation, segmentation, and retrieval. It has 801K clothing items where each item has rich annotations such as style, scale, viewpoint, occlusion, bounding box, dense landmarks (e.g. 39 for ‘long sleeve outwear’ and 15 for ‘vest’), and masks. There are also 873K Commercial-Consumer clothes pairs. The annotations of DeepFashion2 are much larger than its counterparts such as 8× of FashionAI Global Challenge. A strong baseline is proposed, called Match RCNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner. Extensive evaluations are conducted with different criterions in DeepFashion2. DeepFashion2 Dataset will be released at : https://github.com/switchablenorms/DeepFashion2
Decision Tree Induction Based on Efficient Tree Restructuring

The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications.
Streaming Live Media over a Peer-to-Peer Network

The high bandwidth required by live streaming video greatly limits the number of clients that can be served by a source. In this work, we discuss and evaluate an architecture, calledSpreadIt, for streaming live media over a network of clients, using the resources of the clients themselves. Using SpreadIt, we can distribute bandwidth requirements over the network. The key challenge is to allow an application level multicast tree to be easily maintained over a network of transient peers, while ensuring that quality of service does not degrade. We propose a basic peering infrastructure layer for streaming applications, which uses a redirect primitive to meet the challenge successfully. Through empirical and simulation studies, we show that SpreadIt provides a good quality of service, which degrades gracefully with increasing number of clients. Perhaps more significantly, existing applications can be made to work with SpreadIt, without any change to their code base.  peer-to-peer networks, live streaming media, peering layer, redirect primitive, transient nodes
A Modified Node2vec Method for Disappearing Link Prediction

Disappearing link prediction aims to predict the possibility of the links disappearing in the future. This paper describes the disappearing link prediction problem in scientific collaboration networks based on network embedding. We propose a novel network embedding method called TDL2vec, which is an extension of node2vec algorithm. TDL2vec generates the link embdeddings considering with the time factor. In this paper, the disappearing link prediction problem is considered as a binary classification problem, and support vector machine (SVM) is used as the classifier after link embedding. To evaluate the performance in disappearing link prediction, this paper tests the proposed method and several baseline methods on a real-world network. The experimental results show that TDL2vec achieves better performance than other baselines.
Approximate dynamic programming using fluid and diffusion approximations with applications to power management

TD learning and its refinements are powerful tools for approximating the solution to dynamic programming problems. However, the techniques provide the approximate solution only within a prescribed finite-dimensional function class. Thus, the question that always arises is how should the function class be chosen? The goal of this paper is to propose an approach for TD learning based on choosing the function class using the solutions to associated fluid and diffusion approximations. In order to illustrate this new approach, the paper focuses on an application to dynamic speed scaling for power management.
IoT-based continuous glucose monitoring system: A feasibility study

Health monitoring systems based on Internet-of-things (IoT) have been recently introduced to improve the quality of health care services. However, the number of advanced IoT-based continuous glucose monitoring systems is small and the existing systems have several limitations. In this paper we study feasibility of invasive and continuous glucose monitoring (CGM) system utilizing IoT based approach. We designed an IoT-based system architecture from a sensor device to a back-end system for presenting real-time glucose, body temperature and contextual data (i.e. environmental temperature) in graphical and human-readable forms to end-users such as patients and doctors. In addition, nRF communication protocol is customized for suiting to the glucose monitoring system and achieving a high level of energy efficiency. Furthermore, we investigate energy consumption of the sensor device and design energy harvesting units for the device. Finally, the work provides many advanced services at a gateway level such as a push notification service for notifying patient and doctors in case of abnormal situations (i.e. too low or too high glucose level). The results show that our system is able to achieve continuous glucose monitoring remotely in real-time. In addition, the results reveal that a high level of energy efficiency can be achieved by applying the customized nRF component, the power management unit and the energy harvesting unit altogether in the sensor device.
Capturing the mood: facebook and face-to-face encounters in the workplace

What makes people feel happy, engaged and challenged at work? We conducted an in situ study of Facebook and face-to-face interactions examining how they influence people's mood in the workplace. Thirty-two participants in an organization were each observed for five days in their natural work environment using automated data capture and experience sampling. Our results show that online and offline social interactions are associated with different moods, suggesting that they serve different purposes at work. Face-to-face interactions are associated with a positive mood throughout the day whereas Facebook use and engagement in work contribute to a positive feeling at the end of the day. Email use is associated with negative affect and along with multitasking, is associated with a feeling of engagement and challenge throughout the day. Our findings provide initial evidence of how online and offline interactions affect workplace mood, and could inform practices to improve employee morale.
Role of Broca's Area in Implicit Motor Skill Learning: Evidence from Continuous Theta-burst Magnetic Stimulation

Complex actions can be regarded as a concatenation of simple motor acts, arranged according to specific rules. Because the caudal part of the Broca's region (left Brodmann's area 44, BA 44) is involved in processing hierarchically organized behaviors, we aimed to test the hypothesis that this area may also play a role in learning structured motor sequences. To address this issue, we investigated the inhibitory effects of a continuous theta-burst TMS (cTBS) applied over left BA 44 in healthy subjects, just before they performed a serial RT task (SRTT). SRTT has been widely used to study motor skill learning and is also of interest because, for complex structured sequences, subjects spontaneously organize them into smaller subsequences, referred to as chunks. As a control, cTBS was applied over the vertex in another group, which underwent the same experiment. Control subjects showed both a general practice learning effect, evidenced by a progressive decrease in RT across blocks and a sequence-specific learning effect, demonstrated by a significant RT increase in a pseudorandom sequence. In contrast, when cTBS was applied over left BA 44, subjects lacked both the general practice and sequence-specific learning effects. However, surprisingly, their chunking pattern was preserved and remained indistinguishable from controls. The present study indicates that left BA 44 plays a role in motor sequence learning, but without being involved in elementary chunking. This dissociation between chunking and sequence learning could be explained if we postulate that left BA 44 intervenes in high hierarchical level processing, possibly to integrate elementary chunks together.
Plagiarism Detection in arXiv

We describe a large-scale application of methods for finding plagiarism in research document collections. The methods are applied to a collection of 284,834 documents collected by arXiv.org over a 14 year period, covering a few different research disciplines. The methodology efficiently detects a variety of problematic author behaviors, and heuristics are developed to reduce the number of false positives. The methods are also efficient enough to implement as a real-time submission screen for a collection many times larger.
Objective reduction using a feature selection technique

This paper introduces two new algorithms to reduce the number of objectives in a multiobjective problem by identifying the most conflicting objectives. The proposed algorithms are based on a feature selection technique proposed by Mitra et. al. . One algorithm is intended to determine the minimum subset of objectives that yields the minimum error possible, while the other finds a subset of objectives of a given size that yields the minimum error. To validate these algorithms we compare their results against those obtained by two similar algorithms recently proposed. The comparative study shows that our algorithms are very competitive with respect to the reference algorithms. Additionally, our approaches require a lower computational time. Also, in this study we propose to use the inverted generational distance to evaluate the quality of a subset of objectives.
On path planning methods for automotive collision avoidance

There is a strong trend for increasingly sophisticated Advanced Driver Assistance Systems (ADAS) such as Autonomous Emergency Braking (AEB) systems, Lane Keeping Aid (LKA) systems, and indeed autonomous driving. This trend generates a need for online maneuver generation, for which numerous approaches can be found in the large body of work related to path planning and obstacle avoidance. In order to ease the challenge of choosing a method, this paper reports quantitative and qualitative insights about three different path planning methods: a state lattice planner, predictive constraint-based planning, and spline-based search tree. Each method is described, implemented and compared on two specific traffic situations. The paper will not provide a final answer about which method is best. This depends on several factors such as computational constraints and the formulation of maneuver optimality that is appropriate for a given assistance or safety function. Instead, the conclusions will highlight qualitative merits and drawbacks for each method, in order to provide guidance for choosing a method for a specific application.
Breastfeeding Progression in Preterm Infants Is Influenced by Factors in Infants, Mothers and Clinical Practice: The Results of a National Cohort Study with High Breastfeeding Initiation Rates

Many preterm infants are not capable of exclusive breastfeeding from birth. To guide mothers in breastfeeding, it is important to know when preterm infants can initiate breastfeeding and progress. The aim was to analyse postmenstrual age (PMA) at breastfeeding milestones in different preterm gestational age (GA) groups, to describe rates of breastfeeding duration at pre-defined times, as well as analyse factors associated with PMA at the establishment of exclusive breastfeeding.



The study was part of a prospective survey of a national Danish cohort of preterm infants based on questionnaires and structured telephone interviews, including 1,221 mothers and their 1,488 preterm infants with GA of 24-36 weeks.



Of the preterm infants, 99% initiated breastfeeding and 68% were discharged exclusively breastfed. Breastfeeding milestones were generally reached at different PMAs for different GA groups, but preterm infants were able to initiate breastfeeding at early times, with some delay in infants less than GA 32 weeks. Very preterm infants had lowest mean PMA (35.5 weeks) at first complete breastfeed, and moderate preterm infants had lowest mean PMA at the establishment of exclusive breastfeeding (36.4 weeks). Admitting mothers to the NICU together with the infant and minimising the use of a pacifier during breastfeeding transition were associated with 1.6 (95% CI 0.4-2.8) and 1.2 days (95% CI 0.1-2.3) earlier establishment of exclusive breastfeeding respectively. Infants that were small for gestational age were associated with 5.6 days (95% CI 4.1-7.0) later establishment of exclusive breastfeeding.



Breastfeeding competence is not developed at a fixed PMA, but is influenced by multiple factors in infants, mothers and clinical practice. Admitting mothers together with their infants to the NICU and minimising the use of pacifiers may contribute to earlier establishment of exclusive breastfeeding.
The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge

This article describes the final solution 1 of team monkeytyping, who finished in second place in the YouTube-8M video understanding challenge. The dataset used in this challenge is a large-scale benchmark for multi-label video classification. We extend the work in  and propose several improvements for frame sequence modeling. We propose a network structure called Chaining that can better capture the interactions between labels. Also, we report our approaches in dealing with multi-scale information and attention pooling. In addition, We find that using the output of model ensemble as a side target in training can boost single model performance. We report our experiments in bagging, boosting, cascade, and stacking, and propose a stacking algorithm called attention weighted stacking. Our final submission is an ensemble that consists of 74 sub models, all of which are listed in the appendix.
A* Parsing: Fast Exact Viterbi Parse Selection

A PCFGparsingcandramaticallyreducethetime requiredto find theexactViterbi parseby conservatively estimatingoutsideViterbi probabilities.Wediscussvariousestimatesandgive efficientalgorithmsfor computingthem.On Penntreebanksentences, ourmostdetailedestimate reducesthetotalnumberof edgesprocessedto lessthan3%of thatrequiredby exhausti ve parsing,andevenasimplerestimatewhichcanbepre-computedin underaminutestill reduces thework by a factorof 5. ThealgorithmextendstheclassicA graphsearchprocedureto a certainhypergraphassociatedwith parsing.Unlike best-firstandfinite-beammethodsfor achieving thiskind of speed-up, theA parseris guaranteedto returnthemostlikely parse,not justanapproximation.Thealgorithmis alsocorrectfor awide rangeof parsercontrolstrategies andmaintainsaworst-casecubictime bound.