nils-herrmann/clean_scirepeval huggingface dataset:
A comparison of flipped and traditional classroom learning: a case study in mechanical engineering
Year: 2018

The flipped classroom has been introduced to promote collaborative learning and higher order learning objectives. Incontrast to the traditional classroom, the flipped classroom hasstudents watch prerecorded lecture videos before coming toclass and then 'class becomes the place to work through problems, advance concepts, and engage in collaborativelearning' In this paper, the active flipped learning was applied in engineering mechanics class to combine flippedclassroom with active learning in order to establish an active flipped learning (AFL) model, aiming to promote activelearning. Eighty sophomores engineering students, most of whom are African American students, participated the activeflipped learning. To compare the effect of AFL, the traditional teaching was applied in the first half semester and pre andpost tests were used to evaluate their learning performance. After the mid term exams, five flipped models were applied tofive topics. All of the students attended these flipped models. Pre and post tests were conducted for the AFL. The surveyresults were analyzed to compare students' learning self efficacy and satisfaction between the traditional and the activeflipped classroom. Compared to the traditional classroom, students' learning motivation is obviously enhanced in the AFLclassroom, with students' interest, curiosity and learning initiatives in curriculum promoted, intrinsic goal orientationfurther strengthened, in which students tend to have less extrinsic goal orientation and lower test anxiety than the studentsin the traditional classroom, but with improved control beliefs and self efficacy for learning. In the traditional classroom,students use more rehearsal and elaboration strategies, while in the flipped classroom, students use more organizationstrategies and prefer to use critical thinking strategies to raise relative questions about teaching content and videomaterials. Students in the AFL model use more strategies of resource management, such as time and study environment,effort regulation, peer learning, and help seeking than in the traditional model. In comparison of different student groups,the AFL model effectively improves students' motivation in both long and short learning time groups, but the effects arenot obvious on the low GPA students. From the perspective of gender, the AFL model helps to increase the male students'learning motivation, while female students use more learning strategies in task value, control belief and self efficacy intraditional model. Therefore, the instructors should pay more attention in improving the learning motivation andeffectiveness of low GPA group and female students in the flipped classroom. In summary, the AFL model, by takingadvantage of advanced technology, is a convenient and professional avenue for engineering students to strengthen theiracademic confidence and self efficacy in Engineering Mechanics by actively participating in learning and fostering theirdeep understanding of engineering statics and dynamics.
Aggressive basal cell carcinoma: dermatoscopy vascular features as clues to the diagnosis
Venue: Dermatology practical conceptual
Year: 2012

Basal cell carcinoma (BCC) can present with indolent or aggressive subtypes. These subtypes usually display vascular features, which are often readily identified using dermatoscopy.  Dermatoscopy vascular features of aggressive BCC were compared to superficial, superficial and nodular, and nodular BCC for diagnostic discrimination.  Dermatoscopy vascular features were recorded live direct from the patient for 1,098 consecutive BCC. Cases with potential confounding influences were excluded. These tumor vascular features included branching (arborizing) serpentine, dot, coil (glomerular) loop (hairpin) and linear vessels. The proportion of pink within the tumor, central versus peripheral tumor vessel distribution and the presence of large vessels within the tumor boundary were also recorded.  Different subtypes of BCC have distinctive vascular features. Aggressive BCC (n=213) displays a tumor area with no pink (12.2% or less than half the area pink (27.2% and absent vessels in the central tumor area (22.1% CI 17.0% 28.1% P<0.001) compared to other subtypes. Superficial BCC (n=284) have more than half the tumor area pink (84.9% and absent large vessels (92.6% CI 89.0% 95.1% Nodular BCC (n=230) is characterized by larger vessels (45.7% CI 39.3% 52.1% P<0.001) as compared to other subtypes, as well as less dot, coil and loop vessels. Kappa values for all recorded features ranged from 0.48 to 1.0.  Aggressive BCCs within the combined aggressive group were not assessed separately.  Diagnostic discrimination between different subtypes of BCC is facilitated by vascular feature assessment. Compared to other subtypes, aggressive BCC displays less or no pink and less or absent central tumor vessels.
The impact of COVID 19 control measures on social contacts and transmission in Kenyan informal settlements
Venue: medRxiv
Year: 2020

Many low and middle income countries have implemented control measures against coronavirus disease 2019 (COVID 19) However, it is not clear to what extent these measures explain the low numbers of recorded COVID 19 cases and deaths in Africa. One of the main aims of control measures is to reduce respiratory pathogen transmission through direct contact with others. In this study we collect contact data from residents of informal settlements around Nairobi, Kenya to assess if control measures have changed contact patterns, and estimate the impact of changes on the basic reproduction number (R0) Methods We conducted a social contact survey with 213 residents of five informal settlements around Nairobi in early May 2020, four weeks after the Kenyan government introduced enhanced physical distancing measures and a curfew between 7pm and 5am. Respondents were asked to report all direct physical and non physical contacts made the previous day, alongside a questionnaire asking about the social and economic impact of COVID 19 and control measures. We examined contact patterns by demographic factors, including socioeconomic status. We described the impact of COVID 19 and control measures on income and food security. We compared contact patterns during control measures to patterns from non pandemic periods to estimate the change in R0.  We estimate that control measures reduced physical and non physical contacts, reducing the R0 from around 2.6 to between 0.5 and 0.7, depending on the pre COVID 19 comparison matrix used. Masks were worn by at least one person in 92% of contacts. Respondents in the poorest socioeconomic quintile reported 1.5 times more contacts than those in the richest. 86% of respondents reported a total or partial loss of income due to COVID 19, and 74% reported eating less or skipping meals due to having too little money for food.  COVID 19 control measures have had a large impact on direct contacts and therefore transmission, but have also caused considerable economic and food insecurity. Reductions in R0 are consistent with the linear epidemic growth in Kenya and other sub Saharan African countries that implemented similar, early control measures. However, negative and inequitable impacts on economic and food security may mean control measures are not sustainable in the longer term.
Robust Scale Adaptive Kernel Correlation Filter Tracker With Hierarchical Convolutional Features
Venue: IEEE Signal Processing Letters
Year: 2016

Visual object tracking is a challenging task due to object appearance changes caused by shape deformation, heavy occlusion, background clutters, illumination variation, and camera motion. In this letter, we propose a novel robust algorithm which decomposes the task of tracking into translation and scale estimation. We estimate the translation by using five correlation filters with hierarchical convolutional features which produced multilevel correlation response maps to collaboratively infer the target location. We also calculate the scale variation by another correlation filter with histogram of oriented gradient features at the same time. Extensive experimental results on a large scale 50 challenging benchmark dataset show that the proposed algorithm achieved outstanding performance against state of the art methods.
Concept Discovery and Automatic Semantic Annotation for Language Understanding in an Information Query Dialogue System Using Latent Dirichlet Allocation and Segmental Methods
Venue: IC3K
Year: 2011

Efficient statistical approaches have been recently proposed for natural language understanding in the context of dialogue systems. However, these approaches are trained on data semantically annotated at the segmental level, which increases the production cost of these resources. This kind of semantic annotation implies both to determine the concepts in a sentence and to link them to their corresponding word segments. In this paper, we propose a two step automatic method for semantic annotation. The first step is an implementation of the latent Dirichlet allocation aiming at discovering concepts in a dialogue corpus. Then this knowledge is used as a bootstrap to infer automatically a segmentation of a word sequence into concepts using either integer linear optimisation or stochastic word alignment models (IBM models) The relation between automatically derived and manually defined task dependent concepts is evaluated on a spoken dialogue task with a reference annotation.
NAD(P)H:quinone oxidoreductase 1 NQO1*2 genotype (P187S) is a strong prognostic and predictive factor in breast cancer
Venue: Nature Genetics
Year: 2008

NQO1 guards against oxidative stress and carcinogenesis and stabilizes p53. We find that a homozygous common missense variant (NQO1*2, rs1800566(T) NM_000903.2:c.558C>T) that disables NQO1 strongly predicts poor survival among two independent series of women with breast cancer (P 0.002, N 1,005; P 0.005, N 1,162) an effect particularly evident after anthracycline based adjuvant chemotherapy with epirubicin (P 7.52 x 10 6) and in p53 aberrant tumors (P 6.15 x 10 5) Survival after metastasis was reduced among NQO1*2 homozygotes, further implicating NQO1 deficiency in cancer progression and treatment resistance. Consistently, response to epirubicin was impaired in NQO1*2 homozygous breast carcinoma cells in vitro, reflecting both p53 linked and p53 independent roles of NQO1. We propose a model of defective anthracycline response in NQO1 deficient breast tumors, along with increased genomic instability promoted by elevated reactive oxygen species (ROS) and suggest that the NQO1 genotype is a prognostic and predictive marker for breast cancer.
Hodge Deligne polynomials of abelian character varieties
Year: 2017

Let F be a finite group and X be a complex quasi projective F variety. For r in N, we consider the mixed Hodge Deligne polynomials of quotients X^r/F, where F acts diagonally, and compute them for certain classes of varieties X with simple mixed Hodge structures. A particularly interesting case is when X is the maximal torus of an affine reductive group G, and F is its Weyl group. As an application, we obtain explicit formulae for the Hodge Deligne and E polynomials of (the distinguished component of) G character varieties of free abelian groups. In the cases G=GL(n,C) and SL(n,C) we get even more concrete expressions for these polynomials, using the combinatorics of partitions.
Blue luminescent graphene quantum dots and graphene oxide prepared by tuning the carbonization degree of citric acid
Year: 2012

An easy bottom up method for the preparation of photoluminescent (PL) graphene quantum dots (GQDs) and graphene oxide (GO) has been developed by tuning the carbonization degree of citric acid and dispersing the carbonized products into alkaline solutions. The GQDs are nanosheets ~15 nm in width, and 0.5 2.0 nm in thickness. They show a relatively strong (9.0% PL quantum yield and an excitation independent PL emission activity. In contrast, the GO nanostructures consist of sheets that are hundreds of nanometers in width and ~1 nm in height. They exhibit a relatively weak (2.2% PL quantum yield and an excitation dependent PL emission activity.
Association of Medical and Adult Use Marijuana Laws With Opioid Prescribing for Medicaid Enrollees
Venue: JAMA internal medicine
Year: 2018

Importance Overprescribing of opioids is considered a major driving force behind the opioid epidemic in the United States. Marijuana is one of the potential nonopioid alternatives that can relieve pain at a relatively lower risk of addiction and virtually no risk of overdose. Marijuana liberalization, including medical and adult use marijuana laws, has made marijuana available to more Americans.  To examine the association of state implementation of medical and adult use marijuana laws with opioid prescribing rates and spending among Medicaid enrollees.  This cross sectional study used a quasi experimental difference in differences design comparing opioid prescribing trends between states that started to implement medical and adult use marijuana laws between 2011 and 2016 and the remaining states. This population based study across the United States included all Medicaid fee for service and managed care enrollees, a high risk population for chronic pain, opioid use disorder, and opioid overdose.  State implementation of medical and adult use marijuana laws from 2011 to 2016.  Opioid prescribing rate, measured as the number of opioid prescriptions covered by Medicaid on a quarterly, per 1000 Medicaid enrollee basis.  State implementation of medical marijuana laws was associated with a 5.88% lower rate of opioid prescribing (95% CI, 11.55% to approximately 0.21% Moreover, the implementation of adult use marijuana laws, which all occurred in states with existing medical marijuana laws, was associated with a 6.38% lower rate of opioid prescribing (95% CI, 12.20% to approximately 0.56% Conclusions and Relevance The potential of marijuana liberalization to reduce the use and consequences of prescription opioids among Medicaid enrollees deserves consideration during the policy discussions about marijuana reform and the opioid epidemic.
A 58 65 GHz Neutralized CMOS Power Amplifier With PAE Above 10% at 1 V Supply
Venue: IEEE Journal of Solid State Circuits
Year: 2010

A 60 GHz band, three stage pseudo differential power amplifier (PA) is implemented with input and output baluns on chip. Each stage consists of a neutralized common source amplifier pair. Neutralization mitigates the intrinsic gate drain feedback of each transistor for increased power gain and reverse isolation. Shielded transformers couple the gain stages and allow low supply voltage operation. Fabricated in a 65 nm bulk CMOS process, the measured small signal gain of the 0.13 x 0.41 mm2 PA is 16 dB at 60 GHz with 3 dB bandwidth more than 8.5 GHz, while consuming 50 mW from a 1 V supply. Reverse isolation is better than 42 dB from 55 to 65 GHz. Maximum saturated output power is 11.5 dBm with a peak PAE of 15.2% measured at 62 GHz; from 58 to 65 GHz, the measured PAE is above 10%
EmotionO+ Physiological signals knowledge representation and emotion reasoning model for mental health monitoring
Venue: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2014

Emotion is an important indicator of depressive conditions. Emotion recognition based on physiological signals such as electroencephalogram (EEG) and functional near infrared spectroscopy (fNIRS) has gained significant attraction in healthcare domain research. Sharing of physiological signal data related to emotional response between different healthcare systems has the potential to benefit both laboratory based healthcare research and `real world' clinical practice. However, management and distribution of the data presents significant challenges; addressing these challenges requires advanced tools for data representation, mining and integration. In this paper we propose such a tool which contains an ontology model called EmotionO+ and rules set based on EEG, which is obtained by random forest algorithm to predict emotional state. It presents not only an effective method to enable semantic representation of the EEG and fNIRS data, but also an emotion knowledge mining tool. Results using EEG data in the eNTERFACE'06 dataset show an accuracy for our proposed model of 99.11% as compared to 97.8% for competing methods using the C4.5 algorithm. The experimental results demonstrate that the posited approach is potentially usable for early stage prediction and intervention for depressive disorders.
Improvements the Seccomp Sandbox Based on PBE Theory
Venue: 2013 27th International Conference on Advanced Information Networking and Applications Workshops
Year: 2013

Providing a safe computing condition to unknown user is a crucial task in the existing network computing, and usually we can use the sandbox technology to shield security issues, but the behavior of malicious occupying the resource has not been well controlled in the sandbox. In this passage, permission rate to access the computational efficiency and accuracy can be available by improving the Linux Kernel Secure Computing Mode(Seccomp) System, furthermore using the system calls judgment technology to prevent its malicious acts from user code can protect the system. During the calculations procedure, specifically, the improved Perfect Bayesian Equilibrium (PBE) Algorithm can be used to determine user behavior in system call process, utilize this algorithm to construct policy engine, and use the engine decision making engine to decide existing users' behavior as a result to maximize the profits of both the user code operating and server system capacity. Moreover agent technology that works in achieving the interrupted determination and interrupted access separate the computing and operating systems simultaneously. After all, improving sandbox technology is to achieve the relative optimization between the user service efficiency and security guarantees. Finally, the experiments show that compared with the Sandboxie and Buffer Zone technology, the proposed algorithm optimizes the consumption of the system resources in the original Seccomp Sandbox, and its access determine in rate also speeds up in the certain degree. In particular, it can effectively prevent special system call from malicious code, which can protect the system mainly in large extent. Moreover, the testing speed and the performance of several regular system calls such as file access operation, write operation also are under the progressive improvement.
Q EEGNet: an Energy Efficient 8 bit Quantized Parallel EEGNet Implementation for Edge Motor Imagery Brain Machine Interfaces
Venue: 2020 IEEE International Conference on Smart Computing (SMARTCOMP)
Year: 2020

Motor Imagery Brain Machine Interfaces (MI BMIs) promise direct and accessible communication between human brains and machines by analyzing brain activities recorded with Electroencephalography (EEG) Latency, reliability, and privacy constraints make it unsuitable to offload the computation to the cloud. Practical use cases demand a wearable, battery operated device with low average power consumption for longterm use. Recently, sophisticated algorithms, in particular deep learning models, have emerged for classifying EEG signals. While reaching outstanding accuracy, these models often exceed the limitations of edge devices due to their memory and computational requirements. In this paper, we demonstrate algorithmic and implementation optimizations for EEGNET, a compact Convolutional Neural Network (CNN) suitable for many BMI paradigms. We quantize weights and activations to 8 bit fixed point with a negligible accuracy loss of 0.4% on 4 class MI, and present an energy efficient hardware aware implementation on the Mr. Wolf parallel ultra low power (PULP) System on Chip (SoC) by utilizing its custom RISC VISA extensions and 8 core compute cluster. With our proposed optimization steps, we can obtain an overall speedup of 64 x and a reduction of up to 85% in memory footprint with respect to a single core layer wise baseline implementation. Our implementation takes only 5.82 ms and consumes 0.627 mJ per inference. With 21.0 GMAC/s/W, it is 256x more energy efficient than an EEGNET implementation on an ARM Cortex M7 (0.082 GMAC/s/W)
ConvAI3: Generating Clarifying Questions for Open Domain Dialogue Systems (ClariQ)
Venue: ArXiv
Year: 2020

This document presents a detailed description of the challenge on clarifying questions for dialogue systems  The challenge is organized as part of the Conversational AI challenge series (ConvAI3) at Search Oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In IR settings such a situation is handled mainly thought the diversification of the search result page. It is however much more challenging in dialogue settings with limited bandwidth. Therefore, in this challenge, we provide a common evaluation framework to evaluate mixed initiative conversations. Participants are asked to rank clarifying questions in an information seeking conversations. The challenge is organized in two stages where in Stage 1 we evaluate the submissions in an offline setting and single turn conversations. Top participants of Stage 1 get the chance to have their model tested by human annotators.
Topology, connectivity and percolation in fracture networks
Year: 2018

The connectivity of a fracture network depends on both its geometry and topology, which may be characterized in terms of the types of nodes and branches in the system. A number of dimensionless parameters (dimensionless intensity P 22 B 22 connections per line C L and connections per branch C B are used to characterize and assess network connectivity based on well established lattice and continuum percolation models. These models result in networks with only I and X nodes, whereas most natural fracture networks have many Y nodes. A simple scheme to transform IX systems into IYX systems is developed that allows the expected value of dimensionless branch intensity B 22C at the percolation threshold to be derived for any network topology. The method is used to assess if a network is likely to be connected or not, and demonstrated by analysing the development of a network of desiccation cracks.
The genetic aetiology of Silver Russell syndrome
Venue: Journal of Medical Genetics
Year: 2007

Silver Russell syndrome (SRS MIM180860) is a disorder characterised by intrauterine and/or postnatal growth restriction and typical facies. However, the clinical picture is extremely diverse due to numerous diagnostic features reflecting a heterogeneous genetic disorder. The mode of inheritance is variable with sporadic cases also being described. Maternal uniparental disomy (mUPD) of chromosome 7 accounts for 10% of SRS cases and many candidate imprinted genes on 7 have been investigated. Chromosome 11 has moved to the forefront as the key chromosome in the aetiology, with reports of methylation defects in the H19 imprinted domain associated with the phenotype in 35 65% of SRS patients. Methylation aberrations have been described in a number of other imprinted growth related disorders such as Beckwith Wiedmann syndrome. This review discusses these recent developments as well as the previous work on chromosome 7. Other candidate genes/chromosomal regions previously investigated are tabled.