nils-herrmann/clean_scirepeval huggingface dataset:
Recognition judgments under risk: Low confidence when certainty is low

Prior research has shown that misleading information about memory probes impairs accuracy in recognition memory tests. Such misleading information also impairs confidence in recognition responses, but for correct rejections only, not for hits. It is unknown whether such effects are preserved when participants face different outcomes according to performance. In the current study, after studying a series of words, participants performed a recognition memory task in which each memory probe could be preceded by a cue forecasting probabilistically the probe's study status "Likely old" or "Likely new" Seventy five percent of the cues forecasted accurately the study status of the probes (i.e. valid cues) and 25% inaccurately (i.e. invalid cues) In addition, participants gained or lost points according to whether they made correct or incorrect memory judgments, and received a comestible reward after accumulating 10 of such points. This accumulation scheme was administered in half the study test blocks. The accuracy/confidence dissociation demonstrated in prior experiments is replicated here. Furthermore, we demonstrate that the decrease in confidence for novel items preceded by invalid cues is accentuated when response accuracy can yield gains or losses. We interpret this confidence decrease for invalidly cued/rewarded correct rejections as reflecting a combination of loss aversion, as postulated by Prospect theory, and absence of recollection, as postulated by dual process models of recognition.
Visibility Is Not Equivalent to Confidence in a Low Contrast Orientation Discrimination Task
Venue: Front. Psychol.

In several visual tasks, participants report that they feel confident about discrimination responses at a level of stimulation at which they would report not seeing the stimulus. How general and reliable is this effect? We compared subjective reports of discrimination confidence and subjective reports of visibility in an orientation discrimination task with varying stimulus contrast. Participants applied more liberal criteria for subjective reports of discrimination confidence than for visibility. While reports of discrimination confidence were more efficient in predicting trial accuracy than reports of visibility, only reports of visibility but not confidence were associated with stimulus contrast in incorrect trials. It is argued that the distinction between discrimination confidence and visibility can be reconciled with both the partial awareness hypothesis and higher order thought theory. We suggest that consciousness research would benefit from differentiating between subjective reports of visibility and confidence.
INCREASING THE QUALITY OF TIER 1 READING INSTRUCTION: USING PERFORMANCE FEEDBACK TO INCREASE OPPORTUNITIES TO RESPOND DURING IMPLEMENTATION OF A CORE READING PROGRAM

Recent data on reading achievement indicates that a majority of young students are reading below proficiency. However, current research has shown that providing students with quality, research based reading instruction can help prevent many reading difficulties. Through the use of core reading programs, teachers have tools available to be able to provide students with evidence based instruction in the Tier 1 setting. However, classroom teachers need support in instructing core reading programs with the highest quality to increase student reading outcomes. This study provides evidence that providing teachers with performance feedback can improve quality of instruction by increasing the numbers of OTRs delivered during Tier 1 reading instruction. Teachers were observed during core reading program instruction, number of opportunities to respond were counted at the end of each observed lesson. Baseline data trends were used to determine what type of feedback was given during the intervention phase (i.e. teachers were encouraged to increase individual, group, or both OTRs during instruction) During intervention phase, teachers were provided graphic and oral feedback on implementation of the core reading program; suggestions were provided for increasing opportunities to respond during instruction.
Online Stochastic Linear Optimization under One bit Feedback
Venue: ICML

In this paper, we study a special bandit setting of online stochastic linear optimization, where only one bit of information is revealed to the learner at each round. This problem has found many applications including online advertisement and online recommendation. We assume the binary feedback is a random variable generated from the logit model, and aim to minimize the regret defined by the unknown linear function. Although the existing method for generalized linear bandit can be applied to our problem, the high computational cost makes it impractical for real world applications. To address this challenge, we develop an efficient online learning algorithm by exploiting particular structures of the observation model. Specifically, we adopt online Newton step to estimate the unknown parameter and derive a tight confidence region based on the exponential concavity of the logistic loss. Our analysis shows that the proposed algorithm achieves a regret bound of O(dT) which matches the optimal result of stochastic linear bandits.
Implementing Semantics Based Cross domain Collaboration Recommendation in Biomedicine with a Graph Database
Venue: AMIA

We describe a novel approach for cross domain recommendation for research collaboration. We first constructed a large Neo4j graph database representing authors, their expertise, current collaborations, and general biomedical knowledge. This information comes from MEDLINE and from semantic relations extracted with SemRep. Then, by using an extended literature based discovery paradigm, implemented with the Cypher graph query language, we recommend novel collaborations, which include author pairs, along with novel topics for collaboration and motivation for that collaboration. KeywordsResearch collaboration; Recommendation system; Literature based discovery; Semantic MEDLINE; Graph
Effect of ambient humidity on the light absorption amplification of black carbon in Beijing during January 2013

Black carbon (BC) and its mixing state were measured with a ground based single particle soot photometer in urban Beijing during the extremely polluted winter of 2013. Up to 70 14% of the BC containing particles were thickly coated during periods of haze, compared to 37 9% on non hazy days. The thickly coated number fraction (NF BC thick increased with increasing BC, reaching a plateau at ~80 90% when BC concentrations were =15 mg m 3 and visibility was =2 km. Regional inflows brought more aged, highly thickly coated BC to Beijing during haze. The absorption coefficient showed a distinct linear correlation with BC concentration; the mass absorption efficiency (MAE) of BC was acquired, with an overall mean of 4.2 0.01 m 2 g 1 at 870 nm. The MAE of BC amplified with increasing ambient relative humidity. This was largely explained by the increase in NF BC thick which was likely due to the enhanced production of secondary aerosol under humid conditions.
Limitation of the Use of the Absorption Angstrom Exponent for Source Apportionment of Equivalent Black Carbon: a Case Study from the North West Indo Gangetic Plain.
Venue: Environmental science technology

Angstrom exponent measurements of equivalent black carbon (BCeq) have recently been introduced as a novel tool to apportion the contribution of biomass burning sources to the BCeq mass. The BCeq is the mass of ideal BC with defined optical properties that, upon deposition on the aethalometer filter tape, would cause equal optical attenuation of light to the actual PM2.5 aerosol deposited. The BCeq mass hence is identical to the mass of the total light absorbing carbon deposited on the filter tape. Here, we use simultaneously collected data from a seven wavelength aethalometer and a high sensitivity proton transfer reaction mass spectrometer installed at a suburban site in Mohali (Punjab) India, to identify a number of biomass combustion plumes. The identified types of biomass combustion include paddy and wheat residue burning, leaf litter, and garbage burning. Traffic plumes were selected for comparison. We find that the combustion efficiency, rather than the fuel used, determines aabs, and consequently, the aabs can be ~1 for flaming biomass combustion and >1 for older vehicles that operate with poorly optimized engines. Thus, the absorption angstrom exponent is not representative of the fuel used and, therefore, cannot be used as a generic tracer to constrain source contributions.
'Who would have thought of that! A Hierarchical Topic Model for Extraction of Sarcasm prevalent Topics and Sarcasm Detection
Venue: ArXiv

Topic Models have been reported to be beneficial for aspect based sentiment analysis. This paper reports a simple topic model for sarcasm detection, a first, to the best of our knowledge. Designed on the basis of the intuition that sarcastic tweets are likely to have a mixture of words of both sentiments as against tweets with literal sentiment (either positive or negative) our hierarchical topic model discovers sarcasm prevalent topics and topic level sentiment. Using a dataset of tweets labeled using hashtags, the model estimates topic level, and sentiment level distributions. Our evaluation shows that topics such as `work' `gun laws' `weather' are sarcasm prevalent topics. Our model is also able to discover the mixture of sentiment bearing words that exist in a text of a given sentiment related label. Finally, we apply our model to predict sarcasm in tweets. We outperform two prior work based on statistical classifiers with specific features, by around 25\
Political Issue Extraction Model: A Novel Hierarchical Topic Model That Uses Tweets By Political And Non Political Authors
Venue: WASSA@NAACL HLT

People often use social media to discuss opinions, including political ones. We refer to relevant topics in these discussions as political issues, and the alternate stands towards these topics as political positions. We present a Political Issue Extraction (PIE) model that is capable of discovering political issues and positions from an unlabeled dataset of tweets. A strength of this model is that it uses twitter timelines of political and non political authors, and affiliation information of only political authors. The model estimates word specific distributions (that denote political issues and positions) and hierarchical author/group specific distributions (that show how these issues divide people) Our experiments using a dataset of 2.4 million tweets from the US show that this model effectively captures the desired properties (with respect to words and groups) of political discussions. We also evaluate the two components of the model by experimenting with: (a) Use to alternate strategies to classify words, and (b) Value addition due to incorporation of group membership information. Estimated distributions are then used to predict political affiliation with 68% accuracy.
Yellow fever outbreaks, vaccine shortages and the Hajj and Olympics: call for global vigilance
Venue: The Lancet

www.thelancet.com Vol 388 September 17, 2016 1155 Submissions should be made via our electronic submission system at http:/ees.elsevier.com/ thelancet/ Atlanta, GA, USA (HE) Elizabeth Glaser Pediatric AIDS Foundation, Kinshasa, Democratic Republic of the Congo (JD) Guangxi Centers for Disease Control and Prevention, Guangxi, China (JZ) Instituto Nacional de Salud, Lima, Peru (EG) Universidad Peruana Cayetano Heredia, Lima, Peru (EG) Ministry of Health, Brasilia, Brazil (PM) MES Academy of Medical Sciences, Kerala, India (MR) Center for Vaccine Development, Vaccine Research Institute, Bamako, Mali (SS) Consultant General Pediatric, King Saud bin Abdulaziz University for Health Sciences, Pediatric Department, King Abdullah Specialist Children's Hospital, Riyadh, Saudi Arabia (RFK) Ministry of Health, Riyadh, Saudi Arabia (ZAM) and College of Medicine, Alfaisal University, PO Box 54146 Riyadh 11514, Saudi Arabia (ZAM)
Impact of the Use of Regional Poison Control Centers in an Urban EMS Dispatch System
Venue: Journal of Medical Toxicology

BackgroundThe public commonly calls 911 for unintentional ingestions, rather than calling the local poison center. By utilizing a series of scripted questions, 911 dispatchers in Los Angeles determine if an ingestion meets "omega 1" classification. Under such circumstances, the regional poison center is contacted prior to dispatch of paramedics. If the poison center advises that the patient can remain at home, EMS is not dispatched and the patient is followed at home by the poison center. The primary objective is to determine the number of averted transports through involvement of a poison center. A secondary objective is to determine the potential costs and charges saved with the use of such a strategy.MethodsA retrospective review of all overdose calls with an "omega 1" classification to a single EMS system between 1/2008 6/2012. Each call culminating in an EMS dispatch was subsequently reviewed by two additional reviewers. The cost savings was determined by utilizing data from the Medical Expenditure Panel Survey (MEPS) from 2000 to 2010. Monetary values were adjusted to 2012 dollars.ResultsThree hundred eighteen cases received "omega 1" dispatch classification. EMS was dispatched 19 times (5.98 and 11 patients (3.46 were ultimately transported. The most common reasons for transport were ambiguity over the ingested agent or amount, and caller insistence. Using these estimates, routine consultation of a regional poison center as part of EMS dispatch averted $486,595 in charges, and $183,279 in payments.ConclusionsRoutine consultation of a poison center by emergency medical dispatchers can reduce unnecessary dispatches, ambulance transports, and ED visits with significant associated cost savings.
[GABA Receptors in Modulation of Fear Memory Extinction]

GABA is the major inhibitory neurotransmitter in the central nervous system determining the efficacy of neuronal interaction. GABA receptors play a key role in different aspects of fear memory acquisition and consolidation, retention, reconsolidation and extinction. Extinction is an important behavioural phenomenon which allows organism to adapt its behavior to a changing environment. Extinction of fear memory is a form of new inhibitory learning which interferes with expression of the initial acquired fear conditioning. Resistance to extinction is symptom of depression and posttraumatic stress disorder. The aim of the present review was to summarize own and literary data about GABAergic modulation of fear extinction and pharmacological correction of extinction impairment at influences on GABA(A) and GABA(B) receptors.
Not Just Academics: Paths of Longitudinal Effects From Parent Involvement to Substance Abuse in Emerging Adulthood.
Venue: The Journal of adolescent health official publication of the Society for Adolescent Medicine

By the 12th grade, half of American adolescents have abused an illicit drug at least once (Johnston et al. 2015) Although many substance misuse prevention programs exist, we propose an alternative mechanism for reducing substance use. There is evidence that parent involvement is related to reductions in children's behavior problems which then predict later substance abuse. We examine the Child Parent Center (CPC) program, an early childhood intervention, as a strategy to impact substance abuse.  We conducted a path analysis from CPC to parent involvement through early adolescent problem behaviors and competencies to young adult substance abuse. Participants (N 1,203; 51.5% female; 93.8% African American) were assessed from age 3 to 26 years.  CPC participation initiates a pathway to increased parent involvement and expectations, which positively impact adolescents' competencies and problem behaviors, lowering rates of substance abuse.  Through early childhood education, increasing early parental involvement and expectations can alter life course outcomes by providing children with a foundation for positive behaviors and encouraging adaptive functioning in adolescence.
Challenges to translating new media interventions in community practice: a sexual health SMS program case study.
Venue: Health promotion journal of Australia official journal of Australian Association of Health Promotion Professionals

Issue addressed Herein we discuss translational challenges for new media interventions, using the Sexual Health Youth (SHY) short message service (SMS) project to illustrate particular challenges relating to recruitment and evaluation.  Following the delivery of an SMS sexual health program, available documents (progress reports, communications with project staff, ethics submissions and reporting) were analysed thematically to elucidate the barriers to recruitment, implementation and evaluation.  Despite being framed by evidence based research, the project had little impact on the intended population. Only 119 of an expected 5100 young people (2% enrolled to receive SMS messages. Program documents highlighted the difficulty of recruiting participants for new media interventions. Key issues identified in recruitment included under resourcing, delays waiting to receive ethics approval and challenges of school based recruitment.  The minimal impact of the SHY program illustrates the need for improved research translation in the field of new media interventions. It is important that recruitment procedures align with the convenience and appeal of mobile phone based interventions. So what? New media research is not always easily translated into community settings. Large scale recruitment requires adequate resourcing and careful planning, even for low cost mobile interventions. Stronger formative research, documentation and use of partnerships are essential for successful implementation. Researchers must also consider translation in planning and disseminating their work.
Preparation of three dimensional nanoporous Si using dealloying by metallic melt and application as a lithium ion rechargeable battery negative electrode

Silicon is a promising material for negative electrode in Li ion batteries because of high gravimetric capacity. A Si nanomaterial that can accommodate volume expansion accompanied by lithiation is needed for practical application in Li ion batteries. We prepare three dimensional nanoporous interconnected silicon material with controlled pore and ligament sizes by dealloying using an Mg Si precursor and Bi melt. The Mg atoms in the precursor selectively dissolve into Bi, and the remaining Si atoms self organize into a nanoporous structure with characteristic length ranging from several ten to hundred nanometer. The Li ion battery electrodes made from nanoporous silicon exhibit higher capacities, increased cycle lives, and improved rate performances compared with those made from commercial Si nanoparticles. Measurements on the electrical resistivity and electrode thickness change by lithiation/delithiation suggest that the superior performance of nanoporous Si electrode originates from the following: (1) The nanoporous Si has much lower electrical resistivity compared with that of the nanoparticle Si owing to the n type dopant incorporated during dealloying. (2) The nanoporous Si based electrode has higher porosity owing to the presence of intra particle pores, which can accommodate Si expansion up to higher levels of lithiation.
Characterization of Nanocathode Material for Rechargeable Lithium Ion Battery

Li ion batteries are one of the most commercialized solutions to store electrochemical energy, but until now their broad use is limited to small electronic devices. During the past few years, much attention was focused on cathode materials with either high voltage or high capacity coupled with high stability. In the present work, we focused on the synthesis and characterization of LiNiPO4 and lanthanum doped LiNiPO4 prepared by Pechinitype precursor method. The X ray diffraction analysis confirms the formation of compounds LiNiPO4 and lanthanumdoped LiNiPO4 with orthorhombic structure. The Fourier transform infra red analysis has been made to confirm the formation of Li1 yLayNiPO4 (y 0.05, 0.07, 0.09 mol% From AC impedance analysis, it is found that the undoped LiNiPO4 has ionic conductivity of 8.13x10 S cm and the La doped sample (Li0.93La0.07NiPO4) has maximum conductivity of 3.83x10 S cm at ambient temperature. This value is one order greater than that of the undoped LiNiPO4.  Lanthanum; Lithium Ion Battery; Nickel Phosphate; Rare Earths
Convolutional neural net bagging for online visual tracking
Venue: Comput. Vis. Image Underst.

The proposed CNN bagging method is simple yet effective.It addresses the label noise and model uncertainty problems simultaneously for CNN based trackers.The state of the art performances on 3 recent benchmarks i.e. CVPR2013, VOT2013 and TB50 illustrate the validity of the proposed algorithm. Recently, Convolutional Neural Nets (CNNs) have been successfully applied to online visual tracking. However, a major problem is that such models may be inevitably over fitted due to two main factors. The first one is the label noise because the online training of any models relies solely on the detection of the previous frames. The second one is the model uncertainty due to the randomized training strategy. In this work, we cope with noisy labels and the model uncertainty within the framework of bagging (bootstrap aggregating) resulting in efficient and effective visual tracking. Instead of using multiple models in a bag, we design a single multitask CNN for learning effective feature representations of the target object. In our model, each task has the same structure and shares the same set of convolutional features, but is trained using different random samples generated for different tasks. A significant advantage is that the bagging overhead for our model is minimal, and no extra efforts are needed to handle the outputs of different tasks as done in those multi lifespan models. Experiments demonstrate that our CNN tracker outperforms the state of the art methods on three recent benchmarks (over 80 video sequences) which illustrates the superiority of the feature representations learned by our purely online bagging framework.
Online visual tracking via correlation filter with convolutional networks
Venue: 2016 Visual Communications and Image Processing (VCIP)

Robust online visual tracking is a challenging task of the computer vision due to its violent variation within the video sequences. To approach these issues, deep networks have been applied in order to improve accuracy and correlation filter based trackers perform excellent efficiency and adaptation to scale. In this paper, we present a novel method with convolutional networks and correlation filter. A simple two layer convolutional network is constructed to learn robust representations, which encode the inner geometric layout and local structural information, and the tracking framework resorts to learning discriminative correlation filters based on them. For our method satisfies both veracity and efficiency by finding a compromise between two theories, it performs favorably against several state of the art methods with 50 public challenging videos.