nils-herrmann/clean_scirepeval huggingface dataset:
The Effect of Flaxseed in Breast Cancer: A Literature Review

Breast cancer is one of the most common cancers and the second most responsible for cancer mortality worldwide. In 2014, in Portugal approximately 27,200 people died of cancer, of which 1,791 were women with breast cancer. Flaxseed has been one of the most studied foods, regarding possible relations to breast cancer, though mainly in experimental studies in animals, yet in few clinical trials. It is rich in omega-3 fatty acids, α-linolenic acid, lignan, and fibers. One of the main components of flaxseed is the lignans, of which 95% are made of the predominant secoisolariciresinol diglucoside (SDG). SDG is converted into enterolactone and enterodiol, both with antiestrogen activity and structurally similar to estrogen; they can bind to cell receptors, decreasing cell growth. Some studies have shown that the intake of omega-3 fatty acids is related to the reduction of breast cancer risk. In animal studies, α-linolenic acids have been shown to be able to suppress growth, size, and proliferation of cancer cells and also to promote breast cancer cell death. Other animal studies found that the intake of flaxseed combined with tamoxifen can reduce tumor size to a greater extent than taking tamoxifen alone. Additionally, some clinical trials showed that flaxseed can have an important role in decreasing breast cancer risk, mainly in postmenopausal women. Further studies are needed, specifically clinical trials that may demonstrate the potential benefits of flaxseed in breast cancer.
Tumors of the thyroid gland: histologic and cytologic features--part 1.

Every year, 18,000 new thyroid cancer cases are reported in the United States, representing 1.2% of the total number of new cancer cases of all sites. Thyroid cancer accounts for 1,200 deaths, or 0.2% of the total number of deaths related to  Over the last two decades, there has been important progress in our understanding of tumors of the thyroid gland, from both histopathologic and etiopathogenetic points of view. In the first part of this review, we summarize the histologic and cytologic features of benign and malignant tumors of the thyroid gland that derive from follicular cells.
Reducing the progression of myopia with atropine: a long term cohort study of Olmsted County students.

Myopia is an important public health problem because it is common and is associated with increased risk for chorioretinal degeneration, retinal detachment, and other vision- threatening abnormalities. In animals, ocular elongation and myopia progression can be lessened with atropine treatment. This study provides information about progression of myopia and atropine therapy for myopia in humans.



A total of 214 residents of Olmsted County, Minnesota (118 girls and 96 boys, median age, 11 years; range 6 to 15 years) received atropine for myopia from 1967 through 1974. Control subjects were matched by age, sex, refractive error, and date of baseline examination to 194 of those receiving atropine. Duration of treatment with atropine ranged from 18 weeks to 11.5 years (median 3.5 years).



Median followup from initial to last refraction in the atropine group (11.7 years) was similar to that in the control group (12.4 years). Photophobia and blurred vision were frequently reported, but no serious adverse effects were associated with atropine therapy. Mean myopia progression during atropine treatment adjusted for age and refractive error (0.05 diopters per year) was significantly less than that among control subjects (0.36 diopters per year)(P<.001). Final refractions standardized to the age of 20 years showed a greater mean level of myopia in the control group (3.78 diopters) than in the atropine group (2.79 diopters) (P<.001).



The data support the view that atropine therapy is associated with decreased progression of myopia and that beneficial effects remain after treatment has been discontinued.
Bridge risk investigation diagnostic grouped exploratory (BRIDGE) bot

BRIDGE bot is a 158 g, 10.7 × 8.9 × 6.5 cm3, magnetic-wheeled robot designed to traverse and inspect steel bridges. Utilizing custom magnetic wheels, the robot is able to securely adhere to the bridge in any orientation. The body platform features flexible, multi-material legs that enable a variety of plane transitions as well as robot shape manipulation. The robot is equipped with a Cortex-M0 processor, inertial sensors, and a modular wireless radio. A camera is included to provide images for detection and evaluation of identified problems. The robot has been demonstrated moving through plane transitions from 45° to 340° as well as over obstacles up to 9.5 mm in height. Preliminary use of sensor feedback to improve plane transitions has also been demonstrated.
Direct-Drive Electromechanical Linear Actuator for Shift-by-Wire Control of an Automated Transmission

Since they are potentially more efficient and simpler in construction, as well as being easier to integrate, electromechanical actuation systems are being considered as an alternative to hydraulic systems for controlling clutches and gearshifts in vehicle transmissions. A high-force, direct-drive linear electromechanical actuator has been developed which acts directly on the shift rails of either an automated manual transmission (AMT) or a dual clutch transmission (DCT) to facilitate gear selection and provide shift-by-wire functionality. It offers a number of advantages over electromechanical systems based on electric motors and gearboxes in that it reduces mechanical hysteresis, backlash and compliance, has fewer components, is more robust, and exhibits a better dynamic response
Manifold differential evolution (MDE): a global optimization method for geodesic centroidal voronoi tessellations on meshes

Computing centroidal Voronoi tessellations (CVT) has many applications in computer graphics. The existing methods, such as the Lloyd algorithm and the quasi-Newton solver, are efficient and easy to implement; however, they compute only the local optimal solutions due to the highly non-linear nature of the CVT energy. This paper presents a novel method, called manifold differential evolution (MDE), for computing globally optimal geodesic CVT energy on triangle meshes. Formulating the mutation operator using discrete geodesics, MDE naturally extends the powerful differential evolution framework from Euclidean spaces to manifold domains. Under mild assumptions, we show that MDE has a provable probabilistic convergence to the global optimum. Experiments on a wide range of 3D models show that MDE consistently out-performs the existing methods by producing results with lower energy. Thanks to its intrinsic and global nature, MDE is insensitive to initialization and mesh tessellation. Moreover, it is able to handle multiply-connected Voronoi cells, which are challenging to the existing geodesic CVT methods.
Comment Volume Prediction using Regression

The latest decade lead to a unconstrained advancement of the importance of online networking. Due to the gigantic measures of records appearing in web organizing, there is a colossal necessity for the programmed examination of such records. Online networking customer's comments expect a basic part in building or changing the one's acknowledgments concerning some specific indicate or making it standard. This paper demonstrates a preliminary work to exhibit the sufficiency of machine learning prescient calculations on the remarks of most well known long range informal communication site, Facebook. We showed the customer remark patters, over the posts on Facebook Pages and expected that what number of remarks a post is depended upon to get in next H hrs. To automate the technique, we developed an item display containing the crawler, information processor and data disclosure module. For prediction, we used the Linear Regression model (Simple Linear model, Linear relapse model and Pace relapse model) and Non-Linear Regression model(Decision tree, MLP) on different data set varieties and evaluated them under the appraisal estimations Hits@10, AUC@10, Processing Time and Mean Absolute Error.
Radio frequency identification (RFID)

First conceived in 1948, Radio Frequency Identification (RFID) has taken many years for the technology to mature to the point where it is sufficiently affordable and reliable for widespread use. From Electronic Article Surveillance (EAS) for article (mainly clothing) security to more sophisticated uses, RFID is seen by some as the inevitable replacement for bar codes. With increasing use comes increasing concern on privacy and security. Clearly there is considerable work to be undertaken before RFID becomes as pervasive as bar codes although the tempo of change is increasing rapidly. a 2006
Smart phone based wheelchair navigation and home automation for disabled

Mobility-assistive device such as powered wheelchair is very useful for disabled people, to gain some physical independence. The three main functions of the proposed system are, 1) wheelchair navigation using multiple input, 2) obstacle detection using IR sensors, 3) home automation for disable person. Wheelchair can be navigated through i)voice command or ii) moving head or hand in four fixed position which is captured using accelerometer sensor built in android phone. Using 4 IR sensors we can avoid the risk of collision and injury and can maintain some safer distance from the objects. Disable person cannot stand up and switch on-off the light or fan every time. So to give them more relaxation this system offers home automation by giving voice command to the android phone or by manually swipe the button on the screen. The system can be available at very low cost so that more number of disable persons can get benefits.
Design of Continuously Variable Transmission ( CVT ) with Metal Pushing Belt and Variable Pulleys

Nowadays, automakers have invested in new technologies in order to improve the efficiency of their products. Giant automakers have taken an important step toward achieving this objective by designing continuously variable transmission systems (CVT) to continuously adapt the power of the engine with the external load according to the optimum efficiency curve of engine and reducing fuel consumption; beside, making smooth start up and removing the shock caused by changing the gear ratio and making more pleasurable driving. Considering the specifications of one of Iranian automaker products (the Saipa Pride 131), a CVT with a metal pushing belt and variable pulleys have been designed to replace its current manual transmission system. The necessary parts and components for the CVT have been determined and considering the necessary constraints, its mechanism and components have been designed.
BERT: BEhavioral Regression Testing

During maintenance, it is common to run the new version of a program against its existing test suite to check whether the modifications in the program introduced unforeseen side effects. Although this kind of regression testing can be effective in identifying some change-related faults, it is limited by the quality of the existing test suite. Because generating tests for real programs is expensive, developers build test suites by finding acceptable tradeoffs between cost and thoroughness of the tests. Such test suites necessarily target only a small subset of the program's functionality and may miss many regression faults. To address this issue, we introduce the concept of behavioral regression testing, whose goal is to identify behavioral differences between two versions of a program through dynamic analysis. Intuitively, given a set of changes in the code, behavioral regression testing works by (1) generating a large number of test cases that focus on the changed parts of the code, (2) running the generated test cases on the old and new versions of the code and identifying differences in the tests' outcome, and (3) analyzing the identified differences and presenting them to the developers. By focusing on a subset of the code and leveraging differential behavior, our approach can provide developers with more (and more focused) information than traditional regression testing techniques. This paper presents our approach and performs a preliminary assessment of its feasibility.
Deep Defects Seen on Visual Fields Spatially Correspond Well to Loss of Retinal Nerve Fiber Layer Seen on Circumpapillary OCT Scans

To examine the structure-function relationship in glaucoma between deep defects on visual fields (VF) and deep losses in the circumpapillary retinal nerve fiber layer (cpRNFL) on optical coherence tomography (OCT) circle scans.



Thirty two glaucomatous eyes with deep VF defects, as defined by at least one test location worse than ≤ -15 dB on the 10-2 and/or 24-2 VF pattern deviation (PD) plots, were included from 87 eyes with "early" glaucoma (i.e., 24-2 mean deviation better than -6 dB). Using the location of the deep VF points and a schematic model, the location of local damage on an OCT circle scan was predicted. The thinnest location of cpRNFL (i.e., deepest loss) was also determined.



In 19 of 32 eyes, a region of complete or near complete cpRNFL loss was observed. All 19 of these had deep VF defects on the 24-2 and/or 10-2. All of the 32 eyes with deep VF defects had abnormal cpRNFL regions (red, 1%) and all but 2 had a region of cpRNFL thickness <21 μm. The midpoint of the VF defect and the location of deepest cpRNFL had a 95% limit of agreement within approximately two-thirds of a clock-hour (or 30°) sector (between -22.1° to 25.2°). Individual fovea-to-disc angle (FtoDa) adjustment improved agreement in one eye with an extreme FtoDa.



Although studies relating local structural (OCT) and functional (VF) measures typically show poor to moderate correlations, there is good qualitative agreement between the location of deep cpRNFL loss and deep defects on VFs.
R-CNN for Small Object Detection

Existing object detection literature focuses on detecting a big object covering a large part of an image. The problem of detecting a small object covering a small part of an image is largely ignored. As a result, the state-of-the-art object detection algorithm renders unsatisfactory performance as applied to detect small objects in images. In this paper, we dedicate an effort to bridge the gap. We first compose a benchmark dataset tailored for the small object detection problem to better evaluate the small object detection performance. We then augment the state-of-the-art R-CNN algorithm with a context model and a small region proposal generator to improve the small object detection performance. We conduct extensive experimental validations for studying various design choices. Experiment results show that the augmented R-CNN algorithm improves the mean average precision by 29.8% over the original R-CNN algorithm on detecting small objects.  This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.
FlashStore: High Throughput Persistent Key-Value Store

We present FlashStore, a high throughput persistent keyvalue store, that uses flash memory as a non-volatile cache between RAM and hard disk. FlashStore is designed to store the working set of key-value pairs on flash and use one flash read per key lookup. As the working set changes over time, space is made for the current working set by destaging recently unused key-value pairs to hard disk and recycling pages in the flash store. FlashStore organizes key-value pairs in a log-structure on flash to exploit faster sequential write performance. It uses an in-memory hash table to index them, with hash collisions resolved by a variant of cuckoo hashing. The in-memory hash table stores compact key signatures instead of full keys so as to strike tradeoffs between RAM usage and false flash read operations. FlashStore can be used as a high throughput persistent key-value storage layer for a broad range of server class applications. We compare FlashStore with BerkeleyDB, an embedded key-value store application, running on hard disk and flash separately, so as to bring out the performance gain of FlashStore in not only using flash as a cache above hard disk but also in its use of flash aware algorithms. We use real-world data traces from two data center applications, namely, Xbox LIVE Primetime online multi-player game and inline storage deduplication, to drive and evaluate the design of FlashStore on traditional and low power server platforms. FlashStore outperforms BerkeleyDB by up to 60x on throughput (ops/sec), up to 50x on energy efficiency (ops/Joule), and up to 85x on cost efficiency (ops/sec/dollar) on the evaluated datasets.
Learning to Score Olympic Events

Estimating action quality, the process of assigning a "score" to the execution of an action, is crucial in areas such as sports and health care. Unlike action recognition, which has millions of examples to learn from, the action quality datasets that are currently available are small-typically comprised of only a few hundred samples. This work presents three frameworks for evaluating Olympic sports which utilize spatiotemporal features learned using 3D convolutional neural networks (C3D) and perform score regression with i) SVR ii) LSTM and iii) LSTM followed by SVR. An efficient training mechanism for the limited data scenarios is presented for clip-based training with LSTM. The proposed systems show significant improvement over existing quality assessment approaches on the task of predicting scores of diving, vault, figure skating. SVR-based frameworks yield better results, LSTM-based frameworks are more natural for describing an action and can be used for improvement feedback.
Evaluating Bug Finders -- Test and Measurement of Static Code Analyzers

Software static analysis is one of many options for finding bugs in software. Like compilers, static analyzers take a program as input. This paper covers tools that examine source codewithout executing itand output bug reports. Static analysis is a complex and generally undecidable problem. Most tools resort to approximation to overcome these obstacles and it sometimes leads to incorrect results. Therefore, tool effectiveness needs to be evaluated. Several characteristics of the tools should be examined. First, what types of bugs can they find? Second, what proportion of bugs do they report? Third, what percentage of findings is correct? These questions can be answered by one or more metrics. But to calculate these, we need test cases having certain characteristics: statistical significance, ground truth, and relevance. Test cases with all three attributes are out of reach, but we can use combinations of only two to calculate the metrics.
 The results in this paper were collected during Static Analysis Tool Exposition (SATE) V, where participants ran 14 static analyzers on the test sets we provided and submitted their reports to us for analysis. Tools had considerably different support for most bug classes. Some tools discovered significantly more bugs than others or generated mostly accurate warnings, while others reported wrong findings more frequently. Using the metrics, an evaluator can compare candidates and select the tool that aligns best with his or her objectives. In addition, our results confirm that the bugs most commonly found by tools are among the most common and important bugs in software. We also observed that code complexity is a major hindrance for static analyzers and detailed which code constructs tools handle well and which impede their analysis.
More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing

Pushing is a motion primitive useful to handle objects that are too large, too heavy, or too cluttered to be grasped. It is at the core of much of robotic manipulation, in particular when physical interaction is involved. It seems reasonable then to wish for robots to understand how pushed objects move. In reality, however, robots often rely on approximations which yield models that are computable, but also restricted and inaccurate. Just how close are those models? How reasonable are the assumptions they are based on? To help answer these questions, and to get a better experimental understanding of pushing, we present a comprehensive and high-fidelity dataset of planar pushing experiments. The dataset contains time-stamped poses of a circular pusher and a pushed object, as well as forces at the interaction. We vary the push interaction in 6 dimensions: surface material, shape of the pushed object, contact position, pushing direction, pushing speed, and pushing acceleration. An industrial robot automates the data capturing along precisely controlled position-velocity-acceleration trajectories of the pusher, which give dense samples of positions and forces of uniform quality. We finish the paper by characterizing the variability of friction, and evaluating the most common assumptions and simplifications made by models of frictional pushing in robotics.
On-line active learning: A new paradigm to improve practical useability of data stream modeling methods

The central purpose of this survey is to provide readers an insight into the recent advances and challenges in on-line active learning. Active learning has attracted the data mining and machine learning community since around 20 years. This is because it served for important purposes to increase practical applicability of machine learning techniques, such as (i) to reduce annotation and measurement costs for operators and measurement equipments, (ii) to reduce manual labelling effort for experts and (iii) to reduce computation time for model training. Almost all of the current techniques focus on the classical pool-based approach, which is off-line by nature as iterating over a pool of (unlabelled) reference samples a multiple times to choose the most promising ones for improving the performance of the classifiers. This is achieved by (time-intensive) re-training cycles on all labelled samples available so far. For the on-line, stream mining case, the challenge is that the sample selection strategy has to operate in a fast, ideally single-pass manner. Some first approaches have been proposed during the last decade (starting from around 2005) with the usage of machine learning (ML) oriented incremental classifiers, which are able to update their parameters based on selected samples, but not their structures. Since 2012, on-line active learning concepts have been proposed in connection with the paradigm of evolving models, which are able to expand their knowledge into feature space regions so far unexplored. This opened the possibility to address a particular type of uncertainty, namely that one which stems from a significant novelty content in streams, as, e.g., caused by drifts, new operation modes, changing system behaviors or non-stationary environments. We will provide an overview about the concepts and techniques for sample selection and active learning within these two principal major research lines (incremental ML models versus evolving systems), a comparison of their essential characteristics and properties (raising some advantages and disadvantages), and a study on possible evaluation techniques for them. We conclude with an overview of real-world application examples where various online AL approaches have been already successfully applied in order to significantly reduce user’s interaction efforts and costs for model updates.