nils-herrmann/clean_scirepeval huggingface dataset:
Offline Handwriting Recognition on Devanagari Using a New Benchmark Dataset

Handwriting recognition (HWR) in Indic scripts, like Devanagari is very challenging due to the subtleties in the scripts, variations in rendering and the cursive nature of the handwriting. Lack of public handwriting datasets in Indic scripts has long stymied the development of offline handwritten word recognizers and made comparison across different methods a tedious task in the field. In this paper, we release a new handwritten word dataset for Devanagari, IIIT-HW-Dev to alleviate some of these issues. We benchmark the IIIT-HW-Dev dataset using a CNN-RNN hybrid architecture. Furthermore, using this architecture, we empirically show that usage of synthetic data and cross lingual transfer learning helps alleviate the issue of lack of training data. We use this proposed pipeline on a public dataset, RoyDB and achieve state of the art results.
3D face authentication and recognition based on bilateral symmetry analysis

We present a novel and computationally fast method for automatic human face authentication. Taking a 3D triangular facial mesh as input, the approach first automatically extracts the bilateral symmetry plane of the facial surface. The intersection between the symmetry plane and the facial surface, namely the symmetry profile, is then computed. Using both the mean curvature plot of the facial surface and the curvature plot of the symmetry profile curve, three essential points of the nose on the symmetry profile are automatically extracted. The three essential points uniquely determine a Face Intrinsic Coordinate System (FICS). Different faces are aligned based on the FICS. The symmetry profile, together with two transverse profiles, composes a compact representation, called the SFC representation, of a 3D face surface. The face authentication and recognition steps are finally performed by comparing the SFC representations of the faces. The proposed method was tested on 382 face surfaces, which come from 166 individuals and cover a wide ethnic and age variety. The equal error rate (EER) of face authentication on scans with variable facial expressions is 10.8%. For scans with normal expression, the ERR is 0.8%.
Teachers' Forum: Spreadsheet Modeling and Simulation Improves Understanding of Queues

Process-driven spreadsheet queuing simulation is a better vehicle for understanding queue behavior than queuing theory or dedicated simulation software. Spreadsheet queuing simulation has many pedagogical benefits in a business school end-user modeling course, including developing students' intuition , giving them experience with active modeling skills, and providing access to tools. Spreadsheet queuing simulations are surprisingly easy to program, even for queues with balking and reneging. The ease of prototyping in spreadsheets invites thoughtless design, so careful spreadsheet programming practice is important. Spreadsheet queuing simulation is inferior to dedicated simulation software for analyzing queues but is more likely to be available to managers and students. Q ueuing theory has always been a staple in survey courses on management science. Although it is a powerful tool for computing certain steady-state performance measures, queuing theory is a poor vehicle for teaching students about what transpires in queues. Process-driven spreadsheet queuing simulation is a much better vehicle. Although Evans and Olson [1998, p. 170] state that " a serious limitation of spreadsheets for waiting-line models is that it is not possible to include behavior such as balking " and Liberatore and Ny-dick [forthcoming] indicate that a limitation of spreadsheet simulation is the in
Validation of a new coma scale: The FOUR score.

The Glasgow Coma Scale (GCS) has been widely adopted. Failure to assess the verbal score in intubated patients and the inability to test brainstem reflexes are shortcomings. We devised a new coma score, the FOUR (Full Outline of UnResponsiveness) score. It consists of four components (eye, motor, brainstem, and respiration), and each component has a maximal score of 4. We prospectively studied the FOUR score in 120 intensive care unit patients and compared it with the GCS score using neuroscience nurses, neurology residents, and neurointensivists. We found that the interrater reliability was excellent with the FOUR score (kappa(w) = 0.82) and good to excellent for physician rater pairs. The agreement among raters was similar with the GCS (kappa(w) = 0.82). Patients with the lowest GCS score could be further distinguished using the FOUR score. We conclude that the agreement among raters was good to excellent. The FOUR score provides greater neurological detail than the GCS, recognizes a locked-in syndrome, and is superior to the GCS due to the availability of brainstem reflexes, breathing patterns, and the ability to recognize different stages of herniation. The probability of in-hospital mortality was higher for the lowest total FOUR score when compared with the lowest total GCS score.
The Power of Revealed Preference Tests : Ex-Post Evaluation of Experimental Design ∗

Revealed preference tests are elegant nonparametric tools that ask whether choice data conforms to optimizing behavior. These tests present a vexing tension between goodness-of-fit and power. If the test finds violations, is there an acceptable tolerance for goodness-of-fit? If no violations are found, was the test demanding enough to be powerful? This paper complements the many on goodness-of-fit by presenting several new indices of power. By focusing on the underlying probability model induced by sampling, we attempt to unify the two approaches. We illustrate applications of the indices, and provide a field guide to applying them to experimental data. ∗We are grateful to Oleg Balashov, David Bjerk, Khai Chiong, Ian Crawford, Federico Echenique, Joseph Guse, Shachar Kariv, Grigory Kosenok, Justin McCrary, Matt Shum, and Hal Varian, as well as seminar participants at the California Econometrics Conference, California Institute of Technology, Stanford University, and the University of California Berkeley for helpful comments. We owe special thanks to Gautam Tripathi for important insights at the early stages of this project. We also acknowledge the financial support of the National Science Foundation.
Chapter 4 . 17 Evaluation of a Migration to Open Source Software

The chapter discusses the adoption and assimilation process of open source software as a new form of information technology. Specifically, the case reports a general positive attitude towards the widely used technology, the OpenOffice. org suite for office automation. Nevertheless, it shows the difficulties of the first early adopters to lead the innovation process and push other users. Different usage patterns, interoperability issues, and, in general, the reduction in personal productivity typical of the early phases of adoption are also remarked. The aim of this chapter is to give the reader an overview of the adoption process by means of the analysis of quantitative and qualitative data gathered during real world experimentation, and to shed some light on how empirical data can corroborate or challenge the existing literature about open source software and technology adoption.
Electromagnetic signals are produced by aqueous nanostructures derived from bacterial DNA sequences

A novel property of DNA is described: the capacity of some bacterial DNA sequences to induce electromagnetic waves at high aqueous dilutions. It appears to be a resonance phenomenon triggered by the ambient electromagnetic background of very low frequency waves. The genomic DNA of most pathogenic bacteria contains sequences which are able to generate such signals. This opens the way to the development of highly sensitive detection system for chronic bacterial infections in human and animal diseases.
Contrasting information theoretic decompositions of modulatory and arithmetic interactions in neural information processing systems

Biological and artificial neural systems are composed of many local processors, and their capabilities depend upon the transfer function that relates each local processor’s outputs to its inputs. This paper uses a recent advance in the foundations of information theory to study the properties of local processors that use contextual input to amplify or attenuate transmission of information about their driving inputs. This advance enables the information transmitted by processors with two distinct inputs to be decomposed into those components unique to each input, that shared between the two inputs, and that which depends on both though it is in neither, i.e. synergy. The decompositions that we report here show that contextual modulation has information processing properties that contrast with those of all four simple arithmetic operators, that it can take various forms, and that the form used in our previous studies of artificial nets composed of local processors with both driving and contextual inputs is particularly well-suited to provide the distinctive capabilities of contextual modulation under a wide range of conditions. We argue that the decompositions reported here could be compared with those obtained from empirical neurobiological and psychophysical data under conditions thought to reflect contextual modulation. That would then shed new light on the underlying processes involved. Finally, we suggest that such decompositions could aid the design of context-sensitive machine learning algorithms.
Summarization using Neural Networks and Rhetorical Structure Theory

A new technique for summarization is presented here for summarizing articles known as text summarization using neural network and rhetorical structure theory. A neural network is trained to learn the relevant characteristics of sentences by using back propagation technique to train the neural network which will be used in the summary of the article. After training neural network is then modified to feature fusion and pruning the relevant characteristics apparent in summary sentences. Finally, the modified neural network is used to summarize articles and combining it with the rhetorical structure theory to form final summary of an article.
Interactive Data Exploration with Smart Drill-Down

We present <italic>smart drill-down</italic>, an operator for interactively exploring a relational table to discover and summarize “interesting” groups of tuples. Each group of tuples is described by a <italic>rule</italic> . For instance, the rule <inline-formula><tex-math notation="LaTeX">$(a, b, \star, 1000)$</tex-math><alternatives> <inline-graphic xlink:href="joglekar-ieq1-2685998.gif"/></alternatives></inline-formula> tells us that there are 1,000 tuples with value <inline-formula><tex-math notation="LaTeX">$a$</tex-math><alternatives> <inline-graphic xlink:href="joglekar-ieq2-2685998.gif"/></alternatives></inline-formula> in the first column and <inline-formula><tex-math notation="LaTeX">$b$</tex-math><alternatives> <inline-graphic xlink:href="joglekar-ieq3-2685998.gif"/></alternatives></inline-formula> in the second column (and any value in the third column). Smart drill-down presents an analyst with a list of rules that together describe interesting aspects of the table. The analyst can tailor the definition of interesting, and can interactively apply smart drill-down on an existing rule to explore that part of the table. We demonstrate that the underlying optimization problems are <sc>NP-Hard</sc>, and describe an algorithm for finding the approximately optimal list of rules to display when the user uses a smart drill-down, and a dynamic sampling scheme for efficiently interacting with large tables. Finally, we perform experiments on real datasets on our experimental prototype to demonstrate the usefulness of smart drill-down and study the performance of our algorithms.
Focal fibrocartilaginous dysplasia (FFCD)

An otherwise healthy 15-month-old girl with normal developmental milestones was imaged for bowed legs. MRI was performed 9 months later because the condition had worsened. Plain radiographs showed medial concave bowing of the left tibia . A characteristic lucent defect was present at the medial aspect of the metadiaphysis of the proximal tibia . MRI showed a linear mixed signal (intermediate to low on all sequences) in the metadiaphysis of the proximal medial tibia. The diagnosis was FFCD.
Exploring feedback and student characteristics relevant for personalizing feedback strategies

Personalized tutoring feedback is a powerful method that expert human tutors apply when helping students to optimize their learning. Thus, research on tutoring feedback strategies tailoring feedback according to important factors of the learning process has been recognized as a promising issue in the field of computer-based adaptive educational technologies. Our paper seeks to contribute to this area of research by addressing the following aspects: First, to investigate how students’ gender, prior knowledge, and motivational characteristics relate to learning outcomes (knowledge gain and changes in motivation). Second, to investigate the impact of these student characteristics on how tutoring feedback strategies varying in content (procedural vs. conceptual) and specificity (concise hints vs. elaborated explanations) of tutoring feedback messages affect students’ learning and motivation. Third, to explore the influence of the feedback parameters and student characteristics on students’ immediate postfeedback behaviour (skipping vs. trying to accomplish a task, and failing vs. succeeding in providing a correct answer). To address these issues, detailed log-file analyses of an experimental study have been conducted. In this study, 124 sixth and seventh graders have been exposed to various tutoring feedback strategies while working on multi-trial error correction tasks in the domain of fraction arithmetic. The web-based intelligent learning environment ActiveMath was used to present the fraction tasks and trace students’ progress and activities. The results reveal that gender is an important factor for feedback efficiency: Male students achieve significantly lower knowledge gains than female students under all tutoring feedback conditions (particularly, under feedback strategies starting with a conceptual hint). Moreover, perceived competence declines from preto post-test significantly more for boys than for girls. Yet, the decline in perceived competence is not accompanied by a decline in intrinsic motivation, which, instead, increases significantly from preto post-test. With regard to the post-feedback behaviour, the results indicate that students skip further attempts more frequently after conceptual than after procedural feedback messages.
The social context of severe child malnutrition: a qualitative household case study from a rural area of the Democratic Republic of Congo

The magnitude of child malnutrition including severe child malnutrition is especially high in the rural areas of the Democratic Republic of Congo (the DRC). The aim of this qualitative study is to describe the social context of malnutrition in a rural part of the DRC and explore how some households succeed in ensuring that their children are well-nourished while others do not.



This study is based on participant observation, key informant interviews, group discussions and in-depth interviews with four households with malnourished children and four with well-nourished children. We apply social field theory to link individual child nutritional outcomes to processes at local level and to the wider socio-economic environment.



We identified four social fields that have implications for food security and child nutritional outcomes: 1) household size and composition which determined vulnerability to child malnutrition, 2) inter-household cooperation in the form of 'gbisa work party' which buffered scarcity of labour in peak seasons and facilitated capital accumulation, 3) the village associated with usufruct rights to land, and 4) the local NGO providing access to agricultural support, clean drinking water and health care.



Households that participated in inter-household cooperation were able to improve food and nutrition security. Children living in households with high pressure on productive members were at danger of food insecurity and malnutrition. Nutrition interventions need to involve local institutions for inter-household cooperation and address the problem of social inequalities in service provision. They should have special focus on households with few resources in the form of land, labour and capital.
Caching-aware recommendations: Nudging user preferences towards better caching performance

Caching decisions by default seek to maximize some notion of social welfare: the content to be cached is determined so that the maximum possible aggregate demand over all users served by the cache is satisfied. Recommendation systems, on the contrary, are oriented towards user individual preferences: the recommended content should be most appealing to the user so as to elicit further content consumption. In our paper we explore how these, phenomenically conflicting, objectives can be jointly addressed. To this end, we depart radically from current practice with recommender systems, and we approach them as network traffic engineering tools that can actively shape content demand towards optimizing user- and network-centric performance objectives. We formulate the resulting joint theoretical optimization problem of deciding on the cached content and the recommendations to each user so that the cache hit ratio is maximized subject to a maximum tolerable distortion that the recommendation should undergo. We conclude on its complexity, and we propose a practical algorithm for its solution. The algorithm is essentially a form of lightweight control over the user recommendations so that the recommended content is both appealing to the end user and more friendly to the caching system and the network resources.
Strained-Si heterostructure field effect transistors

The purpose of this review article is to report on the recent developments and the performance level achieved in the strained-Si/SiGe material system. In the first part, the technology of the growth of a high-quality strained-Si layer on a relaxed, linear or step-graded SiGe buffer layer is reviewed. Characterization results of strained-Si films obtained with secondary ion mass spectroscopy, Rutherford backscattering spectroscopy, atomic force microscopy, spectroscopic ellipsometry and Raman spectroscopy are presented. Techniques for the determination of bandgap parameters from electrical characterization of metal–oxide–semiconductor (MOS) structures on strained-Si film are discussed. In the second part, processing issues of strained-Si films in conventional Si technology with low thermal budget are critically reviewed. Thermal and low-temperature microwave plasma oxidation and nitridation of strained-Si layers are discussed. Some recent results on contact metallization of strained-Si using Ti and Pt are presented. In the last part, device applications of strained Si with special emphasis on heterostructure metal oxide semiconductor field effect transistors and modulation-doped field effect transistors are discussed. Design aspects and simulation results of nand p-MOS devices with a strained-Si channel are presented. Possible future applications of strained-Si/SiGe in high-performance SiGe CMOS technology are indicated.
Train Model using Network Architecture and Log Records Trained Architecture N 2 . Predict Performance of Untrained Architecture

Using machine learning to analyze data often results in developer exhaust – code, logs, or metadata that do not de ne the learning algorithm but are byproducts of the data analytics pipeline. We study how the rich information present in developer exhaust can be used to approximately solve otherwise complex tasks. Speci cally, we focus on using log data associated with training deep learning models to perform model search by predicting performance metrics for untrainedmodels. Instead of designing a di erent model for each performance metric, we present two preliminary methods that rely only on information present in logs to predict these characteristics for di erent architectures. We introduce (i) a nearest neighbor approachwith a hand-crafted edit distancemetric to comparemodel architectures and (ii) a more generalizable, end-to-end approach that trains an LSTM using model architectures and associated logs to predict performancemetrics of interest.We performmodel search optimizing for best validation accuracy, degree of over tting, and best validation accuracy given a constraint on training time. Our approaches can predict validation accuracy within 1.37% error on average, while the baseline achieves 4.13% by using the performance of a trainedmodel with the closest number of layers.When choosing the best performing model given constraints on training time, our approaches select the top-3 models that overlap with the true top3 models 82% of the time, while the baseline only achieves this 54% of the time. Our preliminary experiments hold promise for how developer exhaust can help learnmodels that can approximate various complex tasks e ciently. ACM Reference Format: Jian Zhang, Max Lam, Stephanie Wang, Paroma Varma, Luigi Nardi, Kunle Olukotun, Christopher Ré. 2018. Exploring the Utility of Developer Exhaust. In DEEM’18: International Workshop on Data Management for End-to-End Machine Learning, June 15, 2018, Houston, TX, USA.
A Multi-objective Modified Particle Swarm Optimization (MMPSO) technique with an application to data clustering

Among the nature inspired heuristic or metaheuristic optimization algorithms Particle Swarm Optimization(PSO) algorithms are widely used to solve clustering problem. In this paper, a modified multi-objective PSO (MMPSO) algorithm is proposed for data clustering. In the proposed MMPSO, the intra-cluster distance and inter-cluster distance are considered as the objective functions. In-order to improve the convergence rate of the MMPSO, a Cloning concept is introduced. To validate the proposed algorithms, five standard data sets are considered. The result shows that, the proposed method gives better quality solution as compared to some existing well known algorithms. The simulation results infer that the proposed algorithms can be efficiently used for data clustering.
Insulin secretory defect in familial partial lipodystrophy Type 2 and successful long-term treatment with a glucagon-like peptide 1 receptor agonist.

Familial partial lipodystrophies are rare monogenic disorders that are often associated with diabetes. In such cases, it can be difficult to achieve glycaemic control.



We report a 34-year old woman with familial partial lipodystrophy type 2 (Dunnigan) and diabetes; her hyperglycaemia persisted despite metformin treatment. A combined intravenous glucose tolerance-euglycaemic clamp test showed severe insulin resistance, as expected, but also showed strongly diminished first-phase insulin secretion. After the latter finding, we added the glucagon-like peptide-1 receptor agonist liraglutide to the patient's treatment regimen, which rapidly normalized plasma glucose levels. HbA1c values <42 mmol/mol (6.0%) have now been maintained for over 4 years.



This case suggests that a glucagon-like peptide-1 receptor agonist may be a useful component of glucose-lowering therapy in individuals with familial partial lipodystrophy and diabetes mellitus.