nils-herrmann/clean_scirepeval huggingface dataset:
Lipid Composition of the Human Eye: Are Red Blood Cells a Good Mirror of Retinal and Optic Nerve Fatty Acids?

The assessment of blood lipids is very frequent in clinical research as it is assumed to reflect the lipid composition of peripheral tissues. Even well accepted such relationships have never been clearly established. This is particularly true in ophthalmology where the use of blood lipids has become very common following recent data linking lipid intake to ocular health and disease. In the present study, we wanted to determine in humans whether a lipidomic approach based on red blood cells could reveal associations between circulating and tissue lipid profiles. To check if the analytical sensitivity may be of importance in such analyses, we have used a double approach for lipidomics.



Red blood cells, retinas and optic nerves were collected from 9 human donors. The lipidomic analyses on tissues consisted in gas chromatography and liquid chromatography coupled to an electrospray ionization source-mass spectrometer (LC-ESI-MS). Gas chromatography did not reveal any relevant association between circulating and ocular fatty acids except for arachidonic acid whose circulating amounts were positively associated with its levels in the retina and in the optic nerve. In contrast, several significant associations emerged from LC-ESI-MS analyses. Particularly, lipid entities in red blood cells were positively or negatively associated with representative pools of retinal docosahexaenoic acid (DHA), retinal very-long chain polyunsaturated fatty acids (VLC-PUFA) or optic nerve plasmalogens.



LC-ESI-MS is more appropriate than gas chromatography for lipidomics on red blood cells, and further extrapolation to ocular lipids. The several individual lipid species we have identified are good candidates to represent circulating biomarkers of ocular lipids. However, further investigation is needed before considering them as indexes of disease risk and before using them in clinical studies on optic nerve neuropathies or retinal diseases displaying photoreceptors degeneration.
Aging and verbal memory span: a meta-analysis.

Using Brinley plots, this meta-analysis provides a quantitative examination of age differences in eight verbal span tasks. The main conclusions are these: (a) there are age differences in all verbal span tasks; (b) the data support the conclusion that working memory span is more age sensitive than short-term memory span; and (c) there is a linear relationship between span of younger adults and span of older adults. A linear model indicates the presence of three distinct functions, in increasing order of size of age effects: simple storage span; backward digit span; and working memory span.
Exploring the Effects of Geographical Origin on the Chemical Composition and Quality Grading of Vitis vinifera L. cv. Chardonnay Grapes

The relationship between berry chemical composition, region of origin and quality grade was investigated for Chardonnay grapes sourced from vineyards located in seven South Australian Geographical Indications (GI). Measurements of basic chemical parameters, amino acids, elements, and free and bound volatiles were conducted for grapes collected during 2015 and 2016. Multiple factor analysis (MFA) was used to determine the sets of data that best discriminated each GI and quality grade. Important components for the discrimination of grapes based on GI were 2-phenylethanol, benzyl alcohol and C6 compounds, as well as Cu, Zn, and Mg, titratable acidity (TA), total soluble solids (TSS), and pH. Discriminant analysis (DA) based on MFA results correctly classified 100% of the samples into GI in 2015 and 2016. Classification according to grade was achieved based on the results for elements such as Cu, Na, Fe, volatiles including C6 and aryl alcohols, hydrolytically-released volatiles such as (Z)-linalool oxide and vitispirane, pH, TSS, alanine and proline. Correct classification through DA according to grade was 100% for both vintages. Significant correlations were observed between climate, GI, grade, and berry composition. Climate influenced the synthesis of free and bound volatiles as well as amino acids, sugars, and acids, as a result of higher temperatures and precipitation.
Casticin Induced Apoptosis in A375.S2 Human Melanoma Cells through the Inhibition of NF-[Formula: see text]B and Mitochondria-Dependent Pathways In Vitro and Inhibited Human Melanoma Xenografts in a Mouse Model In Vivo.

Casticin, a polymethoxyflavone occurring in natural plants, has been shown to have anticancer activities. In the present study, we aims to investigate the anti-skin cancer activity of casticin on melanoma cells in vitro and the antitumor effect of casticin on human melanoma xenografts in nu/nu mice in vivo. A flow cytometric assay was performed to detect expression of viable cells, cell cycles, reactive oxygen species production, levels of  see text] and caspase activity. A Western blotting assay and confocal laser microscope examination were performed to detect expression of protein levels. In the in vitro studies, we found that casticin induced morphological cell changes and DNA condensation and damage, decreased the total viable cells, and induced G2/M phase arrest. Casticin promoted reactive oxygen species (ROS) production, decreased the level of  see text], and promoted caspase-3 activities in A375.S2 cells. The induced G2/M phase arrest indicated by the Western blotting assay showed that casticin promoted the expression of p53, p21 and CHK-1 proteins and inhibited the protein levels of Cdc25c, CDK-1, Cyclin A and B. The casticin-induced apoptosis indicated that casticin promoted pro-apoptotic proteins but inhibited anti-apoptotic proteins. These findings also were confirmed by the fact that casticin promoted the release of AIF and Endo G from mitochondria to cytosol. An electrophoretic mobility shift assay (EMSA) assay showed that casticin inhibited the NF-[Formula: see text]B binding DNA and that these effects were time-dependent. In the in vivo studies, results from immuno-deficient nu/nu mice bearing the A375.S2 tumor xenograft indicated that casticin significantly suppressed tumor growth based on tumor size and weight decreases. Early G2/M arrest and mitochondria-dependent signaling contributed to the apoptotic A375.S2 cell demise induced by casticin. In in vivo experiments, A375.S2 also efficaciously suppressed tumor volume in a xenotransplantation model. Therefore, casticin might be a potential therapeutic agent for the treatment of skin cancer in the future.
Unsupervised object class discovery via saliency-guided multiple class learning

Discovering object classes from images in a fully unsupervised way is an intrinsically ambiguous task; saliency detection approaches however ease the burden on unsupervised learning. We develop an algorithm for simultaneously localizing objects and discovering object classes via bottom-up (saliency-guided) multiple class learning (bMCL), and make the following contributions: (1) saliency detection is adopted to convert unsupervised learning into multiple instance learning, formulated as bottom-up multiple class learning (bMCL); (2) we utilize the Discriminative EM (DiscEM) to solve our bMCL problem and show DiscEM's connection to the MIL-Boost method[34]; (3) localizing objects, discovering object classes, and training object detectors are performed simultaneously in an integrated framework; (4) significant improvements over the existing methods for multi-class object discovery are observed. In addition, we show single class localization as a special case in our bMCL framework and we also demonstrate the advantage of bMCL over purely data-driven saliency methods.
Human Pose Estimation Using Deep Structure Guided Learning

In this paper, we propose a novel approach to incorporate structure knowledge into Convolutional Neural Networks (CNNs) for articulated human pose estimation from a single still image. Recent research on pose estimation adopt CNNs as base blocks to combine with other graphical models. Different from existing methods using features from CNNs to model the tree structure, we directly use the structure pose prior to guide the learning of CNN. First, we introduce a deep CNN with effective receptive fields which capture the holistic context of the whole image. Second, limb loss is used as intermediate supervision of CNN to learn the correlations of joints. Both parts and joints features are extracted in the middle of neural network and then are used to guide the following network learning. The proposed framework can exploit an implicit structure model of human body. Only using one stage and without any complex post processing, our method achieves state-of-art results on both FLIC and LSP benchmarks.
ARMORED: CPU-Bound Encryption for Android-Driven ARM Devices

As recently shown by attacks against Android-driven smart phones, ARM devices are vulnerable to cold boot attacks. At the end of 2012, the data recovery tool FROST was released which exploits the remanence effect of RAM to recover user data from a smart phone, at worst its disk encryption key. Disk encryption is supported in Android since version 4.0 and is today available on many smart phones. With ARMORED, we demonstrate that Android's disk encryption feature can be improved to withstand cold boot attacks by performing AES entirely without RAM. ARMORED stores necessary keys and intermediate values of AES inside registers of the ARM microprocessor architecture without involving main memory. As a consequence, cold boot attacks on encryption keys in RAM appear to be futile. We developed our implementation on a Panda Board and tested it successfully on real phones. We also present a security and a performance analysis for ARMORED.
Tagging Accuracy Analysis on Part-of-Speech Taggers

Part of Speech (POS) Tagging can be applied by several tools and several programming languages. This work focuses on the Natural Language Toolkit (NLTK) library in the Python environment and the gold standard corpora installable. The corpora and tagging methods are analyzed and compared by using the Python language. Different taggers are analyzed according to their tagging accuracies with data from three different corpora. In this study, we have analyzed Brown, Penn Treebank and NPS Chat corpuses. The taggers we have used for the analysis are; default tagger, regex tagger, n-gram taggers. We have applied all taggers to these three corpuses, resultantly we have shown that whereas Unigram tagger does the best tagging in all corpora, the combination of taggers does better if it is correctly ordered. Additionally, we have seen that NPS Chat Corpus gives different accuracy results than the other two corpuses.
Helium: a transparent inter-kernel optimizer for OpenCL

State of the art automatic optimization of OpenCL applications focuses on improving the performance of individual compute kernels. Programmers address opportunities for inter-kernel optimization in specific applications by ad-hoc hand tuning: manually fusing kernels together. However, the complexity of interactions between host and kernel code makes this approach weak or even unviable for applications involving more than a small number of kernel invocations or a highly dynamic control flow, leaving substantial potential opportunities unexplored. It also leads to an over complex, hard to maintain code base. We present Helium, a transparent OpenCL overlay which discovers, manipulates and exploits opportunities for inter-and intra-kernel optimization. Helium is implemented as preloaded library and uses a delay-optimize-replay mechanism in which kernel calls are intercepted, collectively optimized, and then executed according to an improved execution plan. This allows us to benefit from composite optimizations, on large, dynamically complex applications, with no impact on the code base. Our results show that Helium obtains at least the same, and frequently even better performance, than carefully handtuned code. Helium outperforms hand-optimized code where the exact dynamic composition of compute kernel cannot be known statically. In these cases, we demonstrate speedups of up to 3x over unoptimized code and an average speedup of 1.4x over hand optimized code.
Security Analysis of Voice-over-IP Protocols

The transmission of voice communications as datagram packets over IP networks, commonly known as voice-over-IP (VoIP) telephony, is rapidly gaining wide acceptance. With private phone conversations being conducted on insecure public networks, security of VoIP communications is increasingly important. We present a structured security analysis of the VoIP protocol stack, which consists of signaling (SIP), session description (SDP), key establishment (SDES, MIKEY, and ZRTP) and secure media transport (SRTP) protocols. Using a combination of manual and tool-supported formal analysis, we uncover several design flaws and attacks, most of which are caused by subtle inconsistencies between the assumptions that protocols at different layers of the VoIP stack make about each other. The most serious attack is a replay attack on SDES, which causes SRTP to repeat the keystream used for media encryption, thus completely breaking transport-layer security. We also demonstrate a man-in-the-middle attack on ZRTP, which allows the attacker to convince the communicating parties that they have lost their shared secret. If they are using VoIP devices without displays and thus cannot execute the "human authentication" procedure, they are forced to communicate insecurely, or not communicate at all, i.e., this becomes a denial of service attack. Finally, we show that the key derivation process used in MIKEY cannot be used to prove security of the derived key in the standard cryptographic model for secure key exchange.
A Variable Window Approach to Early Vision

Early vision relies heavily on rectangular windows for tasks such as smoothing and computing correspondence. While rectangular windows are efficient, they yield poor results near object boundaries. We describe an efficient method for choosing an arbitrarily shaped connected window, in a manner which varies at each pixel. Our approach can be applied to many problems, including image restoration and visual correspondence. It runs in linear time, and takes a few seconds on traditional benchmark images. Performance on both synthetic and real imagery with ground truth appears promising.
Mobile application development with augmented reality for promoting tourism objects in Southwest Sumba

Southwest Sumba is a regency located on the island of Sumba and falls under the jurisdiction area of the province of East Nusa Tenggara. This regency is well-known for its wide variety of and interesting tourism objects, such as its nature, culture, and marine tourism. Nevertheless, one of the things that proved to be obstacles in promoting tourism objects to potential visitors is the lack of information about their very own existence. Therefore, any form of location-based information services is particularly needed to be developed so that this so-called information can be accessed in real time, direct routes to tourism objects, and one that can quickly provide information regarding tourism objects. In this research, location-based information services are developed in a form of a mobile app that combines Location Based Service (LBS) with Augmented Reality (AR). This application use markerless method planted in AR to track object of Point of Interest (POIs) for tourism objects. Furthermore, each POIs of tourism objects is tracked based on three different coordinate points for each location and can make it easier for tourists to obtain information regarding tourism objects.
The ratio of diatom frustules to chrysophycean statospores: A useful paleolimnological index

Stratigraphic analyses of the postglacial sediments of Little Round Lake and the recent sediments of two additional southern Ontario lakes were used to compare the relative abundance of fossil diatom frustules to chrysophycean statospores. Relatively few statospores were recorded during eutrophic phases in the lakes' histories such as during periods of anthropogenic activity in the lakes' catch me nt, when diatom frustules were abundant. The increased relative abundance of statospores during the early postglacial in Little Round Lake was attributed to both oligotrophy and a short growing season, conditions that currently exist in many high arctic lakes. From these data it is seen that the ratio of fossil diatom frustules to chrysophycean statospores may provide a useful paleolimnological index of trophic status in temperate lakes. The advantages of this index include its simplicity, the fact that frustules and statospores can be enumerated from the same microscope slides, and that the ratio is independent of sedimentation rates.
A social-spam detection framework

Social networks such as Facebook, MySpace, and Twitter have become increasingly important for reaching millions of users. Consequently, spammers are increasing using such networks for propagating spam. Existing filtering techniques such as collaborative filters and behavioral analysis filters are able to significantly reduce spam, each social network needs to build its own independent spam filter and support a spam team to keep spam prevention techniques current. We propose a framework for spam detection which can be used across all social network sites. There are numerous benefits of the framework including: 1) new spam detected on one social network, can quickly be identified across social networks; 2) accuracy of spam detection will improve with a large amount of data from across social networks; 3) other techniques (such as blacklists and message shingling) can be integrated and centralized; 4) new social networks can plug into the system easily, preventing spam at an early stage. We provide an experimental study of real datasets from social networks to demonstrate the flexibility and feasibility of our framework.
Did You Really Just Have a Heart Attack?: Towards Robust Detection of Personal Health Mentions in Social Media

Millions of users share their experiences on social media sites, such as Twitter, which in turn generate valuable data for public health monitoring, digital epidemiology, and other analyses of population health at global scale. The first, critical, task for these applications is classifying whether a personal health event was mentioned, which we call the (PHM ) problem. This task is challenging for many reasons, including typically short length of social media posts, inventive spelling and lexicons, and figurative language, including hyperbole using diseases like “heart attack” or “cancer” for emphasis, and not as a health self-report. This problem is even more challenging for rarely reported, or frequent but ambiguously expressed conditions, such as “stroke”. To address this problem, we propose a general, robust method for detecting PHMs in social media, which we call WESPAD , that combines lexical, syntactic, word embedding-based, and context-based features. WESPAD is able to generalize from few examples by automatically distorting the word embedding space to most effectively detect the true health mentions. Unlike previously proposed state-of-the-art supervised and deep-learning techniques, WESPAD requires relatively little training data, which makes it possible to adapt, with minimal effort, to each new disease and condition. We evaluateWESPAD on both an established publicly available Flu detection benchmark, and on a new dataset that we have constructed with mentions of multiple health conditions. The experiments show thatWESPAD outperforms the baselines and state-of-the-art methods, especially in cases when the number and proportion of true health mentions in the training data is small.
How Social Ties Influence Consumer: Evidence from Event-Related Potentials

A considerable amount of marketing research has reported that consumers are more saliently influenced by friends (strong social ties) than by acquaintances and strangers (weak social ties). To shed light on the neural and psychological processes underlying such phenomenon, in this study we designed an amended S1-S2 paradigm () that is based on realistic consumer purchase experiences. After incoming all given information (product, reviewer, review), participants were required to state their purchase intentions. The neurocognitive and emotional processes related to friend and stranger stimuli were delineated to suggest how social ties influence consumers during their shopping processes. Larger P2 (fronto-central scalp areas) and P3 (central and posterior-parietal scalp areas) components under stranger condition were elicited successfully. These findings demonstrate that the cognitive and emotional processing of friend and stranger stimuli occurs at stages of neural activity, and can be indicated by the P2 and P3 components. Electrophysiological data also support the hypothesis that different neural and emotional processing magnitude and strength underlie friend and stranger effect in the context of consumer purchase. During this process, the perception of stimuli evoked P2, subsequently emotional processing and attention modulation were activated and indicated by P2 and P3. The friend dominated phenomenon can be interpreted as the result of distinctive neurocognitive and emotional processing magnitude, which suggests that psychological and emotional factors can guide consumer decision making. This study consolidates that event related potential (ERP) methodology is likely to be a more sensitive method for investigating consumer behaviors. From the perspectives of management and marketing, our findings show that the P2 and P3 components can be employed as an indicator to probe the influential factors of consumer purchase intentions.
Towards Optimal Cardinality Estimation of Unions and Intersections with Sketches

Estimating the cardinality of unions and intersections of sets is a problem of interest in OLAP. Large data applications often require the use of approximate methods based on small sketches of the data. We give new estimators for the cardinality of unions and intersection and show they approximate an optimal estimation procedure. These estimators enable the improved accuracy of the streaming MinCount sketch to be exploited in distributed settings. Both theoretical and empirical results demonstrate substantial improvements over existing methods.
Reorder buffer: an energy-efficient multithreading architecture for hardware MIMD ray traversal

In this paper, we present an energy- and area-efficient multithreading architecture for Multiple Instruction, Multiple Data (MIMD) ray tracing hardware targeted at low-power devices. Recent ray tracing hardware has predominantly adopted an MIMD approach for efficient parallel traversal of incoherent rays, and supports a multithreading scheme to hide latency and to resolve memory divergence. However, the conventional multithreading scheme has problems such as increased memory cost for thread storage and consumption of additional energy for bypassing threads to the pipeline. Consequently, we propose a new multithreading architecture called Reorder Buffer. Reorder Buffer solves these problems by constituting a dynamic reordering of the rays in the input buffer according to the results of cache accesses. Unlike conventional schemes, Reorder Buffer is cost-effective and energy-efficient because it does not need additional thread memory nor does it consume more energy because it makes use of existing resources. Simulation results show that our architecture is a potentially versatile solution for future ray tracing hardware in low-energy devices because it provides as much as 11.7% better cache utilization and is up to 4.7 times more energy-efficient than the conventional architecture.