nils-herrmann/clean_scirepeval huggingface dataset:
Papilledema: clinical clues and differential diagnosis.

The term "papilledema" describes optic disc swelling resulting from increased intracranial pressure. A complete history and direct funduscopic examination of the optic nerve head and adjacent vessels are necessary to differentiate papilledema from optic disc swelling due to other conditions. Signs of optic disc swelling include elevation and blurring of the disc and its margins, venous congestion, and retinal hard exudates, splinter hemorrhages and infarcts. Patients with papilledema usually present with signs or symptoms of elevated intracranial pressure, such as headache, nausea, vomiting, diplopia, ataxia or altered consciousness. Causes of papilledema include intracranial tumors, idiopathic intracranial hypertension (pseudotumor cerebri), subarachnoid hemorrhage, subdural hematoma and intracranial inflammation. Optic disc edema may also occur from many conditions other than papilledema, including central retinal artery or vein occlusion, congenital structural anomalies and optic neuritis.
Theta and alpha oscillations during working-memory maintenance predict successful long-term memory encoding

To date, much is known about the neural mechanisms underlying working-memory (WM) maintenance and long-term-memory (LTM) encoding. However, these topics have typically been examined in isolation, and little is known about how these processes might interact. Here, we investigated whether EEG oscillations arising specifically during the delay of a delayed matching-to-sample task reflect successful LTM encoding. Given previous findings of increased alpha and theta power with increasing WM load, together with the assumption that successful memory encoding involves processes that are similar to those that are invoked by increasing WM load, alpha and theta power should be higher for subsequently remembered stimuli. Consistent with this assumption, we found stronger alpha power for subsequently remembered stimuli over occipital-to-parietal scalp sites. Furthermore, stronger theta power was found for subsequently remembered stimuli over parietal-to-central electrodes. These results support the idea that alpha and theta oscillations modulate successful LTM encoding.
Chaos triggered image encryption - a reconfigurable security solution

Recently, diverse types of chaotic image encryption algorithms have been explored to meet the high demands in realizing secured real time image sharing applications. In this context, to achieve high sensitivity and superior key space, a multiple chaotic map based image encryption algorithm has been proposed. The proposed algorithm employs three-stage permutation and diffusion to withstand several attacks and the same is modelled in reconfigurable platform namely Field Programmable Gate Array (FPGA). The comprehensive analysis is done with various parameters to exhibit the robustness of the proposed algorithm and its ability to withstand brute-force, differential and statistical attacks. The synthesized result demonstrates that the reconfigurable hardware architecture takes approximately 0.098 ms for encrypting an image of size 256 × 256. Further the resource utilization and timing analyzer results are reported.
Who killed my battery?: analyzing mobile browser energy consumption

Despite the growing popularity of mobile web browsing, the energy consumed by a phone browser while surfing the web is poorly understood. We present an infrastructure for measuring the precise energy used by a mobile browser to render web pages. We then measure the energy needed to render financial, e-commerce, email, blogging, news and social networking sites. Our tools are sufficiently precise to measure the energy needed to render individual web elements, such as cascade style sheets (CSS), Javascript, images, and plug-in objects. Our results show that for popular sites, downloading and parsing cascade style sheets and Javascript consumes a significant fraction of the total energy needed to render the page. Using the data we collected we make concrete recommendations on how to design web pages so as to minimize the energy needed to render the page. As an example, by modifying scripts on the Wikipedia mobile site we reduced by 30% the energy needed to download and render Wikipedia pages with no change to the user experience. We conclude by estimating the point at which offloading browser computations to a remote proxy can save energy on the phone.
Flap advancement: practical techniques to attain tension-free primary closure.

Primary and tension-free closure of a flap is often required after particular surgical procedures (e.g., guided bone regeneration). Other times, flap advancement may be desired for situations such as root coverage.



The literature was searched for articles that addressed techniques, limitations, and complications associated with flap advancement. These articles were used as background information. In addition, reference information regarding anatomy was cited as necessary to help describe surgical procedures.



This article describes techniques to advance mucoperiosteal flaps, which facilitate healing. Methods are presented for a variety of treatment scenarios, ranging from minor to major coronal tissue advancement. Anatomic landmarks are identified that need to be considered during surgery. In addition, management of complications associated with flap advancement is discussed.



Tension-free primary closure is attainable. The technique is dependent on the extent that the flap needs to be advanced.
Chaotic Homes and Children’s Disruptive Behavior

Chaotic home lives are correlated with behavior problems in children. In the study reported here, we tested whether there was a cross-lagged relation between children's experience of chaos and their disruptive behaviors (conduct problems and hyperactivity-inattention). Using genetically informative models, we then tested for the first time whether the influence of household chaos on disruptive behavior was environmentally mediated and whether genetic influences on children's disruptive behaviors accounted for the heritability of household chaos. We measured children's perceptions of household chaos and parents' ratings of children's disruptive behavior at ages 9 and 12 in a sample of 6,286 twin pairs from the Twins Early Development Study (TEDS). There was a phenotypic cross-lagged relation between children's experiences of household chaos and their disruptive behavior. In genetically informative models, we found that the effect of household chaos on subsequent disruptive behavior was environmentally mediated. However, genetic influences on disruptive behavior did not explain why household chaos was heritable.
Recent insights into the molecular mechanisms of the NLRP3
inflammasome activation

Inflammasomes are high-molecular-weight protein complexes that are formed in the cytosolic compartment in response to danger- or pathogen-associated molecular patterns. These complexes enable activation of an inflammatory protease caspase-1, leading to a cell death process called pyroptosis and to proteolytic cleavage and release of pro-inflammatory cytokines interleukin (IL)-1β and IL-18. Along with caspase-1, inflammasome components include an adaptor protein, ASC, and a sensor protein, which triggers the inflammasome assembly in response to a danger signal. The inflammasome sensor proteins are pattern recognition receptors belonging either to the NOD-like receptor (NLR) or to the AIM2-like receptor family. While the molecular agonists that induce inflammasome formation by AIM2 and by several other NLRs have been identified, it is not well understood how the NLR family member NLRP3 is activated. Given that NLRP3 activation is relevant to a range of human pathological conditions, significant attempts are being made to elucidate the molecular mechanism of this process. In this review, we summarize the current knowledge on the molecular events that lead to activation of the NLRP3 inflammasome in response to a range of K (+) efflux-inducing danger signals. We also comment on the reported involvement of cytosolic Ca (2+) fluxes on NLRP3 activation. We outline the recent advances in research on the physiological and pharmacological mechanisms of regulation of NLRP3 responses, and we point to several open questions regarding the current model of NLRP3 activation.
cooling options and challenges of high power semiconductor modules

Trends in power electronics systems and devices over the last decade have placed increasing demands on the efficiencies of the thermal management systems used for power MOSFET and IGBT modules. The pressure to decrease the size of power electronics systems and, subsequently, the module, has resulted in a 50% footprint area reduction of some IGBT modules. This has resulted in higher power dissipation densities for the IGBT die as well as the module due to denser packing of the die. Increases in switching frequencies and voltage ratings of IGBTs also result in higher power dissipation at the die level. Even though a portion of the die power losses has been offset by advances in both MOSFET and IGBT chip design, the cooling capabilities of present modules limit the device performance. Power modules used in Hybrid Electric Vehicles (HEV) provide an additional challenge in that the inlet temperature of the coolant can be over 100C, requiring low temperature differentials (20 – 30C) between the die and the coolant to keep the IGBT comfortably below the maximum operating temperature of 150C, while simultaneously dissipating high heat fluxes. In other HEV designs, less efficient liquids, namely oil or transmission fluid, may be the only coolants available for cooling the power module. Faced with the challenges of efficiently and economically cooling increasing power dissipation densities with low temperature differentials, power semiconductor module designers are turning to new heat sink technologies. In this article, several methods for reducing the thermal resistance of power modules are presented and compared in the context of a baseline 1200V isolated base IGBT power module design.
Understanding student learning trajectories using multimodal learning analytics within an embodied-interaction learning environment

The aim of this paper is to show how multimodal learning analytics (MMLA) can help understand how elementary students explore the concept of feedback loops while controlling an embodied simulation of a predator-prey ecosystem using hand movements as an interface with the computer simulation. We represent student motion patterns from fine-grained logs of hands and gaze data, and then map these observed motion patterns against levels of student performance to make inferences about how embodiment plays a role in the learning process. Results show five distinct motion sequences in students' embodied interactions, and these motion patterns are statistically associated with initial and post-tutorial levels of students' understanding of feedback loops. Analysis of student gaze also shows distinctive patterns as to how low- and high-performing students attended to information presented in the simulation. Using MMLA, we show how students' explanations of feedback loops look differently according to cluster membership, which provides evidence that embodiment interacts with conceptual understanding.
Improve text classification accuracy based on classifier fusion methods

Naive-Bayes and k-NN classifiers are two machine learning approaches for text classification. Rocchio is the classic method for text classification in information retrieval. Based on these three approaches and using classifier fusion methods, we propose a novel approach in text classification. Our approach is a supervised method, meaning that the list of categories should be defined and a set of training data should be provided for training the system. In this approach, documents are represented as vectors where each component is associated with a particular word. We proposed voting methods and OWA operator and decision template method for combining classifiers. Experimental results show that these methods decrese the classification error 15 percent as measured on 2000 training data from 20 newsgroups dataset.
Spectroscopic and morphological studies of polyaniline (PANI) — Carbon nanotubes (CNTs) nanocomposite synthesized via dynamic, liquid-liquid interfacial polymerization process and its application as optical pH sensor

Herein, we report the development of highly water dispersible nanocomposite of conducting polyaniline and multiwalled carbon nanotubes (PANI-MWCNTs) via novel, `dynamic' or `stirred' liquid-liquid interfacial polymerization method using sulphonic acid as a dopant. MWCNTs were functionalized prior to their use and then dispersed in water. The nanocomposite was further subjected for physico-chemical characterization using spectroscopic (UV-Vis and FT-IR), FE-SEM analysis. The UV-VIS spectrum of the PANI-MWCNTs nanocomposite shows a free carrier tail with increasing absorption at higher wavelength. This confirms the presence of conducting emeraldine salt phase of the polyaniline and is further supported by FT-IR analysis. The FE-SEM images show that the thin layer of polyaniline is coated over the functionalized MWCNTs forming a `core-shell' like structure. The synthesized nanocomposite was found to be highly dispersible in water and shows beautiful colour change from dark green to blue with change in pH of the solution from 1 to 12 (i.e. from acidic to basic pH). The change in colour of the polyaniline-MWCNTs nanocomposite is mainly due to the pH dependent chemical transformation /change of thin layer of polyaniline.
Evaluation of Algorithms Using Games: The Case of Music Tagging

Search by keyword is an extremely popular method for retrieving music. To support this, novel algorithms that automatically tag music are being developed. The conventional way to evaluate audio tagging algorithms is to compute measures of agreement between the output and the ground truth set. In this work, we introduce a new method for evaluating audio tagging algorithms on a large scale by collecting set-level judgments from players of a human computation game called TagATune. We present the design and preliminary results of an experiment comparing five algorithms using this new evaluation metric, and contrast the results with those obtained by applying several conventional agreement-based evaluation metrics.
Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low. Entropy 2014, 16 6574
Depression: a new animal model sensitive to antidepressant treatments

A MAJOR problem in the search for new antidepressant drugs is the lack of animal models which both resemble depressive illness and are selectively sensitive to clinically effective antidepressant treatments. We have been working on a new behavioural model in the rat which attempts to meet these two requirements. The method is based on the observation that a rat, when forced to swim in a situation from which there is no escape, will, after an initial period of vigorous activity, eventually cease to move altogether making only those movements necessary to keep its head above water. We think that this characteristic and readily identifiable behavioural immobility indicates a state of despair in which the rat has learned that escape is impossible and resigns itself to the experimental conditions. This hypothesis receives support from results presented below which indicate that immobility is reduced by different treatments known to be therapeutic in depression including three drugs, iprindole, mianserin and viloxazine which although clinically active1–3 show little or no ‘antidepressant’ activity in the usual animal tests4–6.
Multi-Mode and Multi-Frequency Guided Wave Imaging via Chirp Excitations

Guided wave imaging has shown great potential for structural health monitoring applications by providing a way to visualize and characterize structural damage. For successful implementation of delay-and-sum and other elliptical imaging algorithms employing guided ultrasonic waves, some degree of mode purity is required because echoes from undesired modes cause imaging artifacts that obscure damage. But it is also desirable to utilize multiple modes because different modes may exhibit increased sensitivity to different types and orientations of defects. The well-known modetuning effect can be employed to use the same PZT transducers for generating and receiving multiple modes by exciting the transducers with narrowband tone bursts at different frequencies. However, this process is inconvenient and timeconsuming, particularly if extensive signal averaging is required to achieve a satisfactory signal-to-noise ratio. In addition, both acquisition time and data storage requirements may be prohibitive if signals from many narrowband tone burst excitations are measured. In this paper, we utilize a chirp excitation to excite PZT transducers over a broad frequency range to acquire multi-modal data with a single transmission, which can significantly reduce both the measurement time and the quantity of data. Each received signal from a chirp excitation is post-processed to obtain multiple signals corresponding to different narrowband frequency ranges. Narrowband signals with the best mode purity and echo shape are selected and then used to generate multiple images of damage in a target structure. The efficacy of the proposed technique is demonstrated experimentally using an aluminum plate instrumented with a spatially distributed array of piezoelectric sensors and with simulated damage.
Circadian Polymorphisms in Night Owls, in Bipolars, and in Non-24-Hour Sleep Cycles

People called night owls habitually have late bedtimes and late times of arising, sometimes suffering a heritable circadian disturbance called delayed sleep phase syndrome (DSPS). Those with DSPS, those with more severe progressively-late non-24-hour sleep-wake cycles, and those with bipolar disorder may share genetic tendencies for slowed or delayed circadian cycles. We searched for polymorphisms associated with DSPS in a case-control study of DSPS research participants and a separate study of Sleep Center patients undergoing polysomnography. In 45 participants, we resequenced portions of 15 circadian genes to identify unknown polymorphisms that might be associated with DSPS, non-24-hour rhythms, or bipolar comorbidities. We then genotyped single nucleotide polymorphisms (SNPs) in both larger samples, using Illumina Golden Gate assays. Associations of SNPs with the DSPS phenotype and with the morningness-eveningness parametric phenotype were computed for both samples, then combined for meta-analyses. Delayed sleep and "eveningness" were inversely associated with loci in circadian genes NFIL3 (rs2482705) and RORC (rs3828057). A group of haplotypes overlapping BHLHE40 was associated with non-24-hour sleep-wake cycles, and less robustly, with delayed sleep and bipolar disorder (e.g., rs34883305, rs34870629, rs74439275, and rs3750275 were associated with n=37, p=4.58E-09, Bonferroni p=2.95E-06). Bright light and melatonin can palliate circadian disorders, and genetics may clarify the underlying circadian photoperiodic mechanisms. After further replication and identification of the causal polymorphisms, these findings may point to future treatments for DSPS, non-24-hour rhythms, and possibly bipolar disorder or depression.
A deep learning SAR ATR system using regularization and prioritized classes

Recently an increased interest in deep learning for radar and particularly SAR ATR systems has been observed. Many authors proposed systems that outperform established classification systems on benchmark datasets like MSTAR. In this paper we present a new implementation of our recently proposed convolutional neural network classifier, which has a more flexible structure and at the same time less free parameters and thus a reduced training time. Furthermore, the training itself is improved through regularization techniques that improve the convergence properties of the network. Another feature of this new implementation is the dependency of the learning rate on the target class. With this feature the network can focus on classes that cause higher costs of misclassification.
Is momentum really momentum ?

Momentum is primarily driven by firms’ performance 12 to seven months prior to portfolio formation, not by a tendency of rising and falling stocks to keep rising and falling. Strategies based on recent past performance generate positive returns but are less profitable than those based on intermediate horizon past performance, especially among the largest, most liquid stocks. These facts are not particular to the momentum observed in the cross section of US equities. Similar results hold for momentum strategies trading international equity indices, commodities, and currencies.