nils-herrmann/clean_scirepeval huggingface dataset:
IDK Cascades: Fast Deep Learning by Learning not to Overthink

Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions. We conjecture that for a majority of real-world inputs, the recent advances in deep learning have created models that effectively “over-think” on simple inputs. In this paper we revisit the classic question of building model cascades that primarily leverage class asymmetry to reduce cost. We introduce the “I Don’t Know” (IDK) prediction cascades framework, a general framework to systematically compose a set of pre-trained models to accelerate inference without a loss in prediction accuracy. We propose two search based methods for constructing cascades as well as a new cost-aware objective within this framework. The proposed IDK cascade framework can be easily adopted in the existing model serving systems without additional model retraining. We evaluate the proposed techniques on a range of benchmarks to demonstrate the effectiveness of the proposed framework.
A 1 . 2-ns 16 × 16-Bit Binary Multiplier Using High Speed Compressors

For higher order multiplications, a huge number of adders or compressors are to be used to perform the partial product addition. We have reduced the number of adders by introducing special kind of adders that are capable to add five/six/seven bits per decade. These adders are called compressors. Binary counter property has been merged with the compressor property to develop high order compressors. Uses of these compressors permit the reduction of the vertical critical paths. A 16×16 bit multiplier has been developed using these compressors. These compressors make the multipliers faster as compared to the conventional design that have been used 4-2 compressors and 3-2 compressors. Binary multiplier, Compressors, Counter, Column adder, Low power.
A WordNet-based semantic similarity measurement combining edge-counting and information content theory

Semantic similarity measuring between words can be applied to many applications, such as Artificial Intelligence, Information Processing, Medical Care and Linguistics. In this paper, we present a new approach for semantic similarity measuring which is based on edge-counting and information content theory. Specifically, the proposed measure nonlinearly transforms the weighted shortest path length between the compared concepts to achieve the semantic similarity results, and the relation between parameters and the correlation value is discussed in detail. Experimental results show that the proposed approach not only achieves high correlation value against human ratings but also has better distribution characteristics of the correlation coefficient compared with several related works in the literature. In addition, the proposed method is computationally efficient due to the simplified ways of weighting the shortest path length between the concept pairs. & 2014 Elsevier Ltd. All rights reserved.
Parameter Estimation of Multiterminal Transmission Lines Using Joint PMU and SCADA Data

This paper focuses on the parameter estimation of transmission lines which is an essential prerequisite for system studies and relay settings. Synchrophasor measurements have shown promising potentials for the transmission line parameter estimation. Majority of existing techniques entail existence of phasor measurement units (PMUs) at both ends of a given transmission line; however, this assumption rarely holds true in nowadays power networks with few installed PMUs. In this paper, a practical technique is proposed for the estimation of transmission line parameters while the required data are phasor measurements at one end of a given line and conventional magnitude measurements at the other end. The proposed method is thus on the basis of joint PMU and supervisory control and data acquisition (SCADA) measurements. A non-linear weighted least-square error (NWLSE) algorithm is employed for the maximum-likelihood estimation of parameters. The approach is initially devised for simple transmission lines with two terminals; then, it is extended for three-terminal lines. Numerical studies encompassing two- and three-terminal lines are conducted through software and hardware simulations. The results demonstrate the effectiveness of new technique and verify its applicability in present power networks.
Teachers' education, classroom quality, and young children's academic skills: results from seven studies of preschool programs.

In an effort to provide high-quality preschool education, policymakers are increasingly requiring public preschool teachers to have at least a Bachelor's degree, preferably in early childhood education. Seven major studies of early care and education were used to predict classroom quality and children's academic outcomes from the educational attainment and major of teachers of 4-year-olds. The findings indicate largely null or contradictory associations, indicating that policies focused solely on increasing teachers' education will not suffice for improving classroom quality or maximizing children's academic gains. Instead, raising the effectiveness of early childhood education likely will require a broad range of professional development activities and supports targeted toward teachers' interactions with children.
Marfan's syndrome

Marfan's syndrome is a systemic disorder of connective tissue caused by mutations in the extracellular matrix protein fibrillin 1. Cardinal manifestations include proximal aortic aneurysm, dislocation of the ocular lens, and long-bone overgrowth. Important advances have been made in the diagnosis and medical and surgical care of affected individuals, yet substantial morbidity and premature mortality remain associated with this disorder. Progress has been made with genetically defined mouse models to elucidate the pathogenetic sequence that is initiated by fibrillin-1 deficiency. The new understanding is that many aspects of the disease are caused by altered regulation of transforming growth factor beta (TGFbeta), a family of cytokines that affect cellular performance, highlighting the potential therapeutic application of TGFbeta antagonists. Insights derived from studying this mendelian disorder are anticipated to have relevance for more common and non-syndromic presentations of selected aspects of the Marfan phenotype.
A survey on algorithmic approaches for solving tourist trip design problems

The tourist trip design problem (TTDP) refers to a route-planning problem for tourists interested in visiting multiple points of interest (POIs). TTDP solvers derive daily tourist tours, i.e., ordered visits to POIs, which respect tourist constraints and POIs attributes. The main objective of the problem discussed is to select POIs that match tourist preferences, thereby maximizing tourist satisfaction, while taking into account a multitude of parameters and constraints (e.g., distances among POIs, visiting time required for each POI, POIs visiting days/hours, entrance fees, weather conditions) and respecting the time available for sightseeing on a daily basis. The aim of this work is to survey models, algorithmic approaches and methodologies concerning tourist trip design problems. Recent approaches are examined, focusing on problem models that best capture a multitude of realistic POIs attributes and user constraints; further, several interesting TTDP variants are investigated. Open issues and promising prospects in tourist trip planning research are also discussed.
Respiratory muscle strength training and neuromuscular electrical stimulation in subacute dysphagic stroke patients: a randomized controlled trial.

To evaluate the effectiveness of inspiratory/expiratory muscle training (IEMT) and neuromuscular electrical stimulation (NMES) to improve dysphagia in stroke.



Prospective, single-blind, randomized-controlled trial.



Tertiary public hospital.



Sixty-two patients with dysphagia were randomly assigned to standard swallow therapy (SST) (Group I, controls, n=21), SST+ IEMT (Group II, n=21) or SST+ sham IEMT+ NMES (Group III, n=20).



All patients followed a 3-week standard multidisciplinary rehabilitation program of SST and speech therapy. The SST+IEMT group's muscle training consisted of 5 sets/10 repetitions, twice-daily, 5 days/week. Group III's sham IEMT required no effort; NMES consisted of 40-minute sessions, 5 days/week, at 80Hz.



Dysphagia severity, assessed by Penetration-Aspiration Scale, and respiratory muscle strength (maximal inspiratory and expiratory pressures) at the end of intervention and 3-month follow-up.



Maximal respiratory pressures were most improved in Group II: treatment effect was 12.9 (95% confidence interval 4.5-21.2) and 19.3 (95% confidence interval 8.5-30.3) for maximal inspiratory and expiratory pressures, respectively. Swallowing security signs were improved in Groups II and III at the end of intervention. No differences in Penetration-Aspiration Scale or respiratory complications were detected between the 3 groups at 3-month follow-up.



Adding IEMT to SST was an effective, feasible, and safe approach that improved respiratory muscle strength. Both IEMT and NMES were associated with improvement in pharyngeal swallowing security signs at the end of the intervention, but the effect did not persist at 3-month follow-up and no differences in respiratory complications were detected between treatment groups and controls.
Efficient hardware architecture of softmax layer in deep neural network

Deep neural network (DNN) has emerged as a very important machine learning and pattern recognition technique in the big data era. Targeting to different types of training and inference tasks, the structure of DNN varies with flexible choices of different component layers, such as fully connection layer, convolutional layer, pooling layer and softmax layer. Deviated from other layers that only require simple operations like addition or multiplication, the softmax layer contains expensive exponentiation and division, thereby causing the hardware design of softmax layer suffering from high complexity, long critical path delay and overflow problems. This paper, for the first time, presents efficient hardware architecture of softmax layer in DNN. By utilizing the domain transformation technique and down-scaling approach, the proposed hardware architecture avoids the aforementioned problems. Analysis shows that the proposed hardware architecture achieves reduced hardware complexity and critical path delay.
Design and RFID signal analysis of a meander line UHF RFID tag antenna

A meander line UHF RFID tag antenna with an efficient matching network has been designed. Wideband matching and good radiation properties were achieved in parallel with small antenna size by increasing the structurepsilas electrical length by meander structure and thus lowering its self-resonant frequency. Antenna was modelled with a FEM-simulator and it showed good performance: matching for global RFID UHF frequency and simulated gain was higher than for an ideal half-wavelength dipole. Matching and backscattering properties were verified by measurements and the results were compared against two commercial Gen2 short dipole -type tags. Both, matching and backscattering properties of the designed tag were found to be competent in this respect.
WEIGHTED TEMPORAL PATTERN MINING WITH DIMENSIONALITY REDUCTION USING MODIFIED AFCM TECHNIQUE

Frequent itemset mining from a time series database is a difficult task. Various techniques have been proposed to mine the frequent associations among the data from the temporal database, but the huge size of the database and frequent time based updates to the database lead to inefficient frequent itemsets. Hence we proposed a dimensionality reduction method which reduces the quantity of data considered for mining. In the proposed system, initially the time based data are converted into fuzzy data. These fuzzy data are provided as input to the proposed Modified Adaptive Fuzzy C Means (MoAFCM) algorithm which is a combination of FCM clustering algorithm and Cuckoo search optimization algorithm. FCM performs dimensionality reduction on the fuzzy data and clustering is performed by the combination of both FCM and cuckoo search optimization algorithm leading to optimized clusters. The resulting clusters contain reference points instead of the original data. Optimization by cuckoo search algorithm leads to better quality clusters. Weighted temporal pattern mining is performed on these clusters to identify the effective temporal patterns which consider knowledge about the patterns having low frequency but high weight in a database which undergoes time based updates. Implementation of the proposed technique is carried out using MATLAB platform and its performance is evaluated using weather forecast dataset. Time series database, Dimensionality reduction, Modified Adaptive Fuzzy C Means (MoAFCM), Cuckoo search optimization, Weighted temporal pattern mining.
HetPathMine: A Novel Transductive Classification Algorithm on Heterogeneous Information Networks

Transductive classification (TC) using a small labeled data to help classifying all the unlabeled data in information networks. It is an important data mining task on information networks. Various classification methods have been proposed for this task. However, most of these methods are proposed for homogeneous networks but not for heterogeneous ones, which include multi-typed objects and relations and may contain more useful semantic information. In this paper, we firstly use the concept of meta path to represent the different relation paths in heterogeneous networks and propose a novel meta path selection model. Then we extend the transductive classification problem to heterogeneous information networks and propose a novel algorithm, named HetPathMine. The experimental results show that: (1) HetPathMine can get higher accuracy than the existing transductive classification methods and (2) the weight obtained by HetPathMine for each meta path is consistent with human intuition or real-world situations.
A solid-phase microextraction method for the detection of ignitable liquids in fire debris.

A solid-phase microextraction (SPME) procedure involving direct contact between the SPME fibers and the solid matrix and subsequent gas chromatography/mass spectrometric analysis for the detection of accelerants in fire debris is described. The extraction performances of six fibers (100 mum polydimethylsiloxane, 65 mum polydimethylsiloxane-divinylbenzene, 85 mum polyacrylate, 85 mum carboxen-polydimethylsiloxane, 70 mum Carbowax-divinylbenzene, and 50/30 mum divinylbenzene-Carboxen-polydimethylsiloxane) were investigated by directly immersing the fibers into gasoline, kerosene, and diesel fuel. For simulated fire debris, in the direct contact extraction method, the SPME fiber was kept in contact with the fire debris matrix during extraction by penetrating plastic bags wrapping the sample. This method gave comparable results to the headspace SPME method in the extraction of gasoline and kerosene, and gave an improved recovery of low-volatile components in the extraction of diesel fuel from fire debris. The results demonstrate that this procedure is suitable as a simple and rapid screening method for detecting ignitable liquids in fire debris packed in plastic bags.
Submission to the NIPS 2016 Workshop on Machine Learning for Education Estimating student proficiency : Deep learning is not the panacea

In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to estimating student proficiency, termed deep knowledge tracing or DKT [17], has demonstrated a stunning performance advantage over the mainstay of the field, Bayesian knowledge tracing or BKT .
TOPSIS Method for Supplier Selection Problem

Supplier selection, in real situation, is affected by several qualitative and quantitative factors and is one of the most important activities of purchasing department. Since at the time of evaluating suppliers against the criteria or factors, decision makers (DMS) do not have precise, exact and complete information, supplier selection becomes more difficult. In this case, Grey theory helps us to deal with this problem of uncertainty. Here, we apply Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to evaluate and select the best supplier by using interval fuzzy numbers. Through this article, we compare TOPSIS with some other approaches and afterward demonstrate that the concept of TOPSIS is very important for ranking and selecting right supplier. TOPSIS, fuzzy number, MADM, Supplier selection
Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms

Cloud research to date has lacked data on the characteristics of the production virtual machine (VM) workloads of large cloud providers. A thorough understanding of these characteristics can inform the providers' resource management systems, e.g. VM scheduler, power manager, server health manager. In this paper, we first introduce an extensive characterization of Microsoft Azure's VM workload, including distributions of the VMs' lifetime, deployment size, and resource consumption. We then show that certain VM behaviors are fairly consistent over multiple lifetimes, i.e. history is an accurate predictor of future behavior. Based on this observation, we next introduce Resource Central (RC), a system that collects VM telemetry, learns these behaviors offline, and provides predictions online to various resource managers via a general client-side library. As an example of RC's online use, we modify Azure's VM scheduler to leverage predictions in oversubscribing servers (with oversubscribable VM types), while retaining high VM performance. Using real VM traces, we then show that the prediction-informed schedules increase utilization and prevent physical resource exhaustion. We conclude that providers can exploit their workloads' characteristics and machine learning to improve resource management substantially.
Molluscum contagiosum in Greek children: a case series.

Epidemiological studies of molluscum contagiosum (MC) in children are limited.



Between 1997 and 1999, 110 children with MC (60 boys, 50 girls, mean age: 4.8 years) were studied. Treatment consisted of removal of all MC lesions in a single session by sterilized tweezers.



In children aged < or = 2 years, the MC lesions were located mainly on the face, whereas in older children they were located on the trunk. Twenty children had atopic dermatitis (18.2% vs. 5% of Greek children aged 1-6 years; P < 0.001). Four children had numerous and recurrent MC lesions without any other systemic manifestations. In these children, humoral and cellular immunities were found to be normal. Seventy-seven children (70%) were cured after one treatment session, 22 children (20%) after a second session and 11 (10%) after > or = three sessions. No patients experienced secondary bacterial infection or scarring.



In contrast to cool climates where the age of peak incidence of MC in children is 10-12 years, in a warm country such as Greece, it is at the younger age of 4.8 years. In young children aged 2 years, the MC lesions are located mainly on the face, whereas in older children they are located on the trunk. Atopic dermatitis is a predisposing factor for MC. In cases where MC lesions are numerous and/or persist but there are no other signs of systemic infections, the possibility of immunodeficiency is minimal. Removal of MC lesions by tweezers is an efficient, simple and inexpensive method of treatment without sequelae.
Trustability-Based Dynamic Active Learning for Crowdsourced Labelling of Emotional Audio Data

The process of collecting annotated data is expensive and time-consuming. Making use of crowdsourcing instead of experts in a laboratory setting is a viable alternative to reduce these costs. However, without adequate quality control the obtained labels may be less reliable. Whereas crowdsourcing reduces only the costs per annotation, another technique, active learning, aims at reducing the overall annotation costs by selecting the most important instances of the dataset and only asking for manual annotations for these selected samples. Herein, we investigate the advantages of combining crowdsourcing and different iterative active learning paradigms for audio data annotation. Further, we incorporate an annotator trustability score to further reduce the labelling effort needed and, at the same time, to achieve better classification results. In this context, we introduce a novel active learning algorithm, called Trustability-based dynamic active learning, which accumulates manual annotations in each step until a trustability-weighted agreement level of annotators is reached. Furthermore, we bring this approach into the real world and integrate it in our gamified intelligent crowdsourcing platform iHEARu-PLAY. Key experimental results on an emotion recognition task indicate that a considerable relative annotation cost reduction of up to 90.57 % can be achieved when compared with a non-intelligent annotation approach. Moreover, our proposed method reaches an unweighted average recall value of 73.71 %, while a conventional passive learning algorithm peaks at 60.03 %. Therefore, our novel approach not only efficiently reduces the manual annotation work load but also improves the classification performance.
Learning Scalar Adjective Intensity from Paraphrases

Adjectives like warm, hot, and scalding all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrasebased method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair really hot↔ scalding suggests that hot < scalding. We show that combining this paraphrase evidence with existing, complementary patternand lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to yes/no questions.
Interior point methods 25 years later

Interior point methods for optimization have been around for more than 25 years now. Their presence has shaken up the field of optimization. Interior point methods for linear and (convex) quadratic programming display several features which make them particularly attractive for very large scale optimization. Among the most impressive of them are their lowdegree polynomial worst-case complexity and an unrivalled ability to deliver optimal solutions in an almost constant number of iterations which depends very little, if at all, on the problem dimension. Interior point methods are competitive when dealing with small problems of dimensions below one million constraints and variables and are beyond competition when applied to large problems of dimensions going into millions of constraints and variables. In this survey we will discuss several issues related to interior point methods including the proof of the worst-case complexity result, the reasons for their amazingly fast practical convergence and the features responsible for their ability to solve very large problems. The ever-growing sizes of optimization problems impose new requirements on optimization methods and software. In the final part of this paper we will therefore address a redesign of interior point methods to allow them to work in a matrix-free regime and to make them well-suited to solving even larger problems.