nils-herrmann/clean_scirepeval huggingface dataset:
Cortical Maturation and the Development of Visual Attention in Early Infancy

Bronson (1974) reviewed evidence in support of the claim that the development of visually guided behavior in the human infant over the first few months of life represents a shift from subcortical to cortical visual processing. Recently, this view has been brought into question for two reasons; first, evidence revealing apparently sophisticated perceptual abilities in the newborn, and second, increasing evidence for multiple cortica streams of visual processing. The present paper presents a reanalysis of the relation between the maturation of cortical pathways and the development of visually guided behavior, focusing in particular on how the maturational state of the primary visual cortex may constrain the functioning of neural pathways subserving oculomotor control.
Integrating Pinyin to Improve Spelling Errors Detection for Chinese Language

Most Chinese texts are inputted with keyboard via two input methods: Pinyin and Wubi, especially by Pinyin input method. In this paper, this users' habitation is used to find the spelling errors automatically. We first train a Chinese character form n-gram language model on a large scale Chinese corpus in the traditional way. In order to improve this character based model, we transform the whole corpus into Pinyin to obtain Pinyin based language model. Fatherly, the tone is considered to get the third model. Integrating these three models, we improve the performance of checking spelling error system. Experimental results demonstrate the effeteness of our model.
Toward Scalable Neural Dialogue State Tracking Model

The latency in the current neural based dialogue state tracking models prohibits them from being used efficiently for deployment in production systems, albeit their highly accurate performance. This paper proposes a new scalable and accurate neural dialogue state tracking model, based on the recently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et al. (2018) which uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. By using only one recurrent networks with global conditioning, compared to (1 + # slots) recurrent networks with global and local conditioning used in the GLAD model, our proposed model reduces the latency in training and inference times by 35% on average, while preserving performance of belief state tracking, by 97.38% on turn request and 88.51% on joint goal and accuracy. Evaluation on Multi-domain dataset (Multi-WoZ) also demonstrates that our model outperforms GLAD on turn inform and joint goal accuracy.
Morphological Tagging Approach in Document Analysis of Invoices

In this paper a morphological tagging approach for document image invoice analysis is described. Tokens close by their morphology and confirmed in their location within different similar contexts make apparent some parts of speech representative of the structure elements. This bottom up approach avoids the use of an priori knowledge provided that there are redundant and frequent contexts in the text. The approach is applied on the invoice body text roughly recognized by OCR and automatically segmented. The method makes possible the detection of the invoice articles and their different fields. The regularity of the article composition and its redundancy in the invoice is a good help for its structure. The recognition rate of 276 invoices and 1704 articles, is over than 91.02% for articles and 92.56% for fields.
3D Shape Segmentation with Projective Convolutional Networks

This paper introduces a deep architecture for segmenting 3D objects into their labeled semantic parts. Our architecture combines image-based Fully Convolutional Networks (FCNs) and surface-based Conditional Random Fields (CRFs) to yield coherent segmentations of 3D shapes. The image-based FCNs are used for efficient view-based reasoning about 3D object parts. Through a special projection layer, FCN outputs are effectively aggregated across multiple views and scales, then are projected onto the 3D object surfaces. Finally, a surface-based CRF combines the projected outputs with geometric consistency cues to yield coherent segmentations. The whole architecture (multi-view FCNs and CRF) is trained end-to-end. Our approach significantly outperforms the existing state-of-the-art methods in the currently largest segmentation benchmark (ShapeNet). Finally, we demonstrate promising segmentation results on noisy 3D shapes acquired from consumer-grade depth cameras.
A Mostly Data-Driven Approach to Inverse Text Normalization

For an automatic speech recognition system to produce sensibly formatted, readable output, the spoken-form token sequence produced by the core speech recognizer must be converted to a written-form string. This process is known as inverse text normalization (ITN). Here we present a mostly data-driven ITN system that leverages a set of simple rules and a few handcrafted grammars to cast ITN as a labeling problem. To this labeling problem, we apply a compact bi-directional LSTM. We show that the approach performs well using practical amounts of training data.
A Comparative Study of Average, Linked Mastoid, and REST References for ERP Components Acquired during fMRI

In simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) studies, average reference (AR), and digitally linked mastoid (LM) are popular re-referencing techniques in event-related potential (ERP) analyses. However, they may introduce their own physiological signals and alter the EEG/ERP outcome. A reference electrode standardization technique (REST) that calculated a reference point at infinity was proposed to solve this problem. To confirm the advantage of REST in ERP analyses of synchronous EEG-fMRI studies, we compared the reference effect of AR, LM, and REST on task-related ERP results of a working memory task during an fMRI scan. As we hypothesized, we found that the adopted reference did not change the topography map of ERP components (N1 and P300 in the present study), but it did alter the task-related effect on ERP components. LM decreased or eliminated the visual working memory (VWM) load effect on P300, and the AR distorted the distribution of VWM location-related effect at left posterior electrodes as shown in the statistical parametric scalp mapping (SPSM) of N1. ERP cortical source estimates, which are independent of the EEG reference choice, were used as the golden standard to infer the relative utility of different references on the ERP task-related effect. By comparison, REST reference provided a more integrated and reasonable result. These results were further confirmed by the results of fMRI activations and a corresponding EEG-only study. Thus, we recommend the REST, especially with a realistic head model, as the optimal reference method for ERP data analysis in simultaneous EEG-fMRI studies.
Parental refusal of a liver transplant for a child with biliary atresia.

Pediatricians are required by law to notify child protection agencies if parents are neglecting their child’smedical needs. Sometimes, however, it is difficult to determine when a particular parental choice ought to be reported. If the treatment is standard therapy and the consequences of nontreatment are dire, than parental refusal is usually considered neglectful. Organ transplants, however, represent a unique situation for 2 reasons. First, because there is a dire scarcity of organs, there are more people who need organs than there are organs to transplant. In this situation, it seems odd to force a transplant on a child whose parents do not want it, knowing that other parents are eager to have their child undergo a transplant. Second, transplantation is risky and requires lifelong follow-up and treatment. Thus, parental cooperation is essential. We present a case of a child who needs a liver transplant and whose parents refuse, and ask 2 surgeons, a gastroenterologist, and a child abuse specialist to comment on the case. David C. Cronin is director of the liver transplant program at the Medical College of Wisconsin. Robert Squires is a gastroenterologist, George Mazariegos is a transplant surgeon, and Janet Squires is chief of the child advocacy center, all at the University of Pittsburgh.
Python and Java: The Best of Both Worlds

This paper describes a new working implementation of the Python language; built on top of the Java language and run-time environment. This is in contrast to the existing implementation of Python, which has been built on top of the C language and run-time environment. Implementing Python in Java has a number of limitations when compared to the current implementation of Python in C. These include about 1.7X slower performance, portability limited by Java VM availability, and lack of compatibility with existing C extension modules. The advantages of Java over C as an implementation language include portability of binary executables, object-orientation in the implementation language to match object-orientation in Python, true garbage collection, run-time exceptions instead of segmentation faults, and the ability to automatically generate wrapper code for arbitrary Java libraries.
A broadband SiGe power amplifier in an efficient polar transmitter using envelope-tracking for mobile WiMAX

This paper presents a broadband cascode SiGe power amplifier (PA) in the polar transmitter (TX) system using the envelope-tacking (ET) technique. The cascode PA achieves the power-added efficiency (PAE) of >30% across the frequency range of 0.6∼2.4 GHz in continuous wave (CW) mode. The ET-based polar TX system using this cascode PA is evaluated and compared with the conventional stand-alone cascode PA. The experimental data shows that the cascode PA is successfully linearized by the ET scheme, passing the stringent WiMAX spectral mask and the required error vector magnitude (EVM). The entire polar TX system reaches the PAE of 30%/36% at the average output power of 18/17 dBm at 2.3/0.7 GHz for WiMAX 16QAM 3.5 MHz signals. These measurement results suggest that our saturated cascode SiGe PA can be attractive for dual-mode WiMAX applications.
BitCube: A Bottom-Up Cubing Engineering

Enhancing on line analytical processing through efficient cube computation plays a key role in Data Warehouse management. Hashing, grouping and mining techniques are commonly used to improve cube pre-computation. BitCube, a fast cubing method which uses bitmaps as inverted indexes for grouping, is presented. It horizontally partitions data according to the values of one dimension and for each resulting fragment it performs grouping following bottom-up criteria. BitCube allows also partial materialization based on iceberg conditions to treat large datasets for which a full cube pre-computation is too expensive. Space requirement of bitmaps is optimized by applying an adaption of the WAH compression technique. Experimental analysis, on both synthetic and real datasets, shows that BitCube outperforms previous algorithms for full cube computation and results comparable on iceberg cubing.
Loneliness and health risk behaviours among Russian and U.S. adolescents: a cross-sectional study

For some adolescents feeling lonely can be a protracted and painful experience. It has been suggested that engaging in health risk behaviours such as substance use and sexual behaviour may be a way of coping with the distress arising from loneliness during adolescence. However, the association between loneliness and health risk behaviour has been little studied to date. To address this research gap, the current study examined this relation among Russian and U.S. adolescents.



Data were used from the Social and Health Assessment (SAHA), a school-based survey conducted in 2003. A total of 1995 Russian and 2050 U.S. students aged 13-15 years old were included in the analysis. Logistic regression was used to examine the association between loneliness and substance use, sexual risk behaviour, and violence.



After adjusting for demographic characteristics and depressive symptoms, loneliness was associated with a significantly increased risk of adolescent substance use in both Russia and the United States. Lonely Russian girls were significantly more likely to have used marijuana (odds ratio [OR]: 2.28; confidence interval [CI]: 1.17-4.45), while lonely Russian boys had higher odds for past 30-day smoking (OR, 1.87; CI, 1.08-3.24). In the U.S. loneliness was associated with the lifetime use of illicit drugs (excepting marijuana) among boys (OR, 3.09; CI, 1.41-6.77) and with lifetime marijuana use (OR, 1.79; CI, 1.26-2.55), past 30-day alcohol consumption (OR, 1.80; CI, 1.18-2.75) and past 30-day binge drinking (OR, 2.40; CI, 1.56-3.70) among girls. The only relation between loneliness and sexual risk behaviour was among Russian girls, where loneliness was associated with significantly higher odds for ever having been pregnant (OR, 1.69; CI: 1.12-2.54). Loneliness was not associated with violent behaviour among boys or girls in either country.



Loneliness is associated with adolescent health risk behaviour among boys and girls in both Russia and the United States. Further research is now needed in both settings using quantitative and qualitative methods to better understand the association between loneliness and health risk behaviours so that effective interventions can be designed and implemented to mitigate loneliness and its effects on adolescent well-being.
Scalable Gaussian Process Classification via Expectation Propagation

Variational methods have been recently considered for scaling the training process of Gaussian process classifiers to large datasets. As an alternative, we describe here how to train these classifiers efficiently using expectation propagation (EP). The proposed EP method allows to train Gaussian process classifiers on very large datasets, with millions of instances, that were out of the reach of previous implementations of EP. More precisely, it can be used for (i) training in a distributed fashion where the data instances are sent to different nodes in which the required computations are carried out, and for (ii) maximizing an estimate of the marginal likelihood using a stochastic approximation of the gradient. Several experiments involving large datasets show that the method described is competitive with the variational approach.
Loss of Consciousness Is Associated with Stabilization of Cortical Activity.

What aspects of neuronal activity distinguish the conscious from the unconscious brain? This has been a subject of intense interest and debate since the early days of neurophysiology. However, as any practicing anesthesiologist can attest, it is currently not possible to reliably distinguish a conscious state from an unconscious one on the basis of brain activity. Here we approach this problem from the perspective of dynamical systems theory. We argue that the brain, as a dynamical system, is self-regulated at the boundary between stable and unstable regimes, allowing it in particular to maintain high susceptibility to stimuli. To test this hypothesis, we performed stability analysis of high-density electrocorticography recordings covering an entire cerebral hemisphere in monkeys during reversible loss of consciousness. We show that, during loss of consciousness, the number of eigenmodes at the edge of instability decreases smoothly, independently of the type of anesthetic and specific features of brain activity. The eigenmodes drift back toward the unstable line during recovery of consciousness. Furthermore, we show that stability is an emergent phenomenon dependent on the correlations among activity in different cortical regions rather than signals taken in isolation. These findings support the conclusion that dynamics at the edge of instability are essential for maintaining consciousness and provide a novel and principled measure that distinguishes between the conscious and the unconscious brain.



What distinguishes brain activity during consciousness from that observed during unconsciousness? Answering this question has proven difficult because neither consciousness nor lack thereof have universal signatures in terms of most specific features of brain activity. For instance, different anesthetics induce different patterns of brain activity. We demonstrate that loss of consciousness is universally and reliably associated with stabilization of cortical dynamics regardless of the specific activity characteristics. To give an analogy, our analysis suggests that loss of consciousness is akin to depressing the damper pedal on the piano, which makes the sounds dissipate quicker regardless of the specific melody being played. This approach may prove useful in detecting consciousness on the basis of brain activity under anesthesia and other settings.
Minimally invasive direct restorations: a practical guide

The primary objectives of minimum intervention dentistry (MID) are to prevent or arrest active disease using non-operative management techniques. However, patients commonly present with cavitated caries lesions or failed restorations that are in need of operative intervention. Although much of clinical practice is devoted to preventing and managing the effects of caries and subsequent failure of the tooth-restoration complex, the clinical survival of restorations is often poor and becomes significantly worse as they increase in size and complexity. Minimally invasive (MI) restorative techniques present a range of well-documented advantages over more tissue-destructive traditional restorations by minimising unnecessary tooth tissue loss, insult to the dentine-pulp complex and reducing the risk of iatrogenic damage to adjacent hard and soft tissues. They also maximise the strength of the residual tooth structure by use of optimal adhesive restorative materials designed to restore function and aesthetics with durable, long-lasting restorations that are easy for the patient to maintain. In contemporary oral healthcare practice, if patients are to give valid consent for operative interventions, minimally invasive options must be offered, and may be expected to be the first choice of fully informed patients. This paper describes concepts of MID and provides an update of the latest materials, equipment and clinical techniques that are available for the minimally invasive restoration of anterior and posterior teeth with direct restorations.
Recovering 3D human pose from monocular images

We describe a learning-based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labeling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descriptor vectors extracted automatically from image silhouettes. For robustness against local silhouette segmentation errors, silhouette shape is encoded by histogram-of-shape-contexts descriptors. We evaluate several different regression methods: ridge regression, relevance vector machine (RVM) regression, and support vector machine (SVM) regression over both linear and kernel bases. The RVMs provide much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. The loss of depth and limb labeling information often makes the recovery of 3D pose from single silhouettes ambiguous. To handle this, the method is embedded in a novel regressive tracking framework, using dynamics from the previous state estimate together with a learned regression value to disambiguate the pose. We show that the resulting system tracks long sequences stably. For realism and good generalization over a wide range of viewpoints, we train the regressors on images resynthesized from real human motion capture data. The method is demonstrated for several representations of full body pose, both quantitatively on independent but similar test data and qualitatively on real image sequences. Mean angular errors of 4-6/spl deg/ are obtained for a variety of walking motions.
A theoretical comparison of job scheduling algorithms in cloud computing environment

Cloud computing is a dynamic, scalable and payper-use distributed computing model empowering designers to convey applications amid job designation and storage distribution. Cloud computing encourages to impart a pool of virtualized computer resource empowering designers to convey applications amid job designation and storage distribution. The cloud computing mainly aims to give proficient access to remote and geographically distributed resources. As cloud technology is evolving day by day and confronts numerous challenges, one of them being uncovered is scheduling. Scheduling is basically a set of constructs constructed to have a controlling hand over the order of work to be performed by a computer system. Algorithms are vital to schedule the jobs for execution. Job scheduling algorithms is one of the most challenging hypothetical problems in the cloud computing domain area. Numerous deep investigations have been carried out in the domain of job scheduling of cloud computing. This paper intends to present the performance comparison analysis of various pre-existing job scheduling algorithms considering various parameters. This paper discusses about cloud computing and its constructs in section (i). In section (ii) job scheduling concept in cloud computing has been elaborated. In section (iii) existing algorithms for job scheduling are discussed, and are compared in a tabulated form with respect to various parameters and lastly section (iv) concludes the paper giving brief summary of the work.
An Architecture for Integrated Near-Data Processors

To increase the performance of data-intensive applications, we present an extension to a CPU architecture that enables arbitrary near-data processing capabilities close to the main memory. This is realized by introducing a component attached to the CPU system-bus and a component at the memory side. Together they support hardware-managed coherence and virtual memory support to integrate the near-data processors in a shared-memory environment. We present an implementation of the components, as well as a system-simulator, providing detailed performance estimations. With a variety of synthetic workloads we demonstrate the performance of the memory accesses, the mixed fine- and coarse-grained coherence mechanisms, and the near-data processor communication mechanism. Furthermore, we quantify the inevitable start-up penalty regarding coherence and data writeback, and argue that near-data processing workloads should access data several times to offset this penalty. A case study based on the Graph500 benchmark confirms the small overhead for the proposed coherence mechanisms and shows the ability to outperform a real CPU by a factor of two.