nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Transfer Privileged Information

We introduce a learning framework called learning using privileged information (LUPI) to the computer vision field. We focus on the prototypical computer vision problem of teaching computers to recognize objects in images. We want the computers to be able to learn faster at the expense of providing extra information during training time. As additional information about the image data, we look at several scenarios that have been studied in computer vision before: attributes, bounding boxes and image tags. The information is privileged as it is available at training time but not at test time. We explore two maximum-margin techniques that are able to make use of this additional source of information, for binary and multiclass object classification. We interpret these methods as learning easiness and hardness of the objects in the privileged space and then transferring this knowledge to train a better classifier in the original space. We provide a thorough analysis and comparison of information transfer from privileged to the original data spaces for both LUPI methods. Our experiments show that incorporating privileged information can improve the classification accuracy. Finally, we conduct user studies to understand which samples are easy and which are hard for human learning, and explore how this information is related to easy and hard samples when learning a classifier.
Physiological effects of Shinrin-yoku (taking in the atmosphere of the forest)--using salivary cortisol and cerebral activity as indicators.

The purpose of this study is to examine the physiological effects of Shinrin-yoku (taking in the atmosphere of the forest). The subjects were 12 male students (22.8+/-1.4 yr). On the first day of the experiments, one group of 6 subjects was sent to a forest area, and the other group of 6 subjects was sent to a city area. On the second day, each group was sent to the opposite area for a cross check. In the forenoon, the subjects were asked to walk around their given area for 20 minutes. In the afternoon, they were asked to sit on chairs and watch the landscapes of their given area for 20 minutes. Cerebral activity in the prefrontal area and salivary cortisol were measured as physiological indices in the morning at the place of accommodation, before and after walking in the forest or city areas during the forenoon, and before and after watching the landscapes in the afternoon in the forest and city areas, and in the evening at the place of accommodation. The results indicated that cerebral activity in the prefrontal area of the forest area group was significantly lower than that of the group in the city area after walking; the concentration of salivary cortisol in the forest area group was significantly lower than that of the group in the city area before and after watching each landscape. The results of the physiological measurements show that Shinrin-yoku can effectively relax both people's body and spirit.
Synthesizing Type-Detection Logic for Rich Semantic Data Types using Open-source Code

Given a table of data, existing systems can often detect basic atomic types (e.g., strings vs. numbers) for each column. A new generation of data-analytics and data-preparation systems are starting to automatically recognize rich semantic types such as date-time, email address, etc., for such metadata can bring an array of benefits including better table understanding, improved search relevance, precise data validation, and semantic data transformation. However, existing approaches only detect a limited number of types using regular-expression-like patterns, which are often inaccurate, and cannot handle rich semantic types such as credit card and ISBN numbers that encode semantic validations (e.g., checksum).
 We developed AUTOTYPE from open-source repositories like GitHub. Users only need to provide a set of positive examples for a target data type and a search keyword, our system will automatically identify relevant code, and synthesize type-detection functions using execution traces. We compiled a benchmark with 112 semantic types, out of which the proposed system can synthesize code to detect 84 such types at a high precision. Applying the synthesized type-detection logic on web table columns have also resulted in a significant increase in data types discovered compared to alternative approaches.
Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges

http://dx.doi.org/10.1016/j.compag.2015.08.011 0168-1699/ 2015 Elsevier B.V. All rights reserved. ⇑ Corresponding author at: School of Information Technology, Indian Institute of Technology Kharagpur, India. E-mail addresses: tojha@sit.iitkgp.ernet.in (T. Ojha), smisra@sit.iitkgp.ernet.in (S. Misra), nsr@agfe.iitkgp.ernet.in (N.S. Raghuwanshi). Tamoghna Ojha a,b,⇑, Sudip Misra , Narendra Singh Raghuwanshi b
Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks

Power forecasting of renewable energy power plants is a very active research field, as reliable information about the future power generation allow for a safe operation of the power grid and helps to minimize the operational costs of these energy sources. Deep Learning algorithms have shown to be very powerful in forecasting tasks, such as economic time series or speech recognition. Up to now, Deep Learning algorithms have only been applied sparsely for forecasting renewable energy power plants. By using different Deep Learning and Artificial Neural Network algorithms, such as Deep Belief Networks, AutoEncoder, and LSTM, we introduce these powerful algorithms in the field of renewable energy power forecasting. In our experiments, we used combinations of these algorithms to show their forecast strength compared to a standard MLP and a physical forecasting model in the forecasting the energy output of 21 solar power plants. Our results using Deep Learning algorithms show a superior forecasting performance compared to Artificial Neural Networks as well as other reference models such as physical models.
Millimeter wave wireless communications: new results for rural connectivity

This paper shows the remarkable distances that can be achieved using millimeter wave communications, and presents a new rural macrocell (RMa) path loss model for millimeter wave frequencies, based on measurements at 73 GHz in rural Virginia. Path loss models are needed to estimate signal coverage and interference for wireless network design, yet little is known about rural propagation at millimeter waves. This work identifies problems with the RMa model used by the 3rd Generation Partnership Project (3GPP) TR 38.900 Release 14, and offers a close-in (CI) reference distance model that has improved accuracy, fewer parameters, and better stability as compared with the existing 3GPP RMa path loss model. The measurements and models presented here are the first to validate rural millimeter wave path loss models.
Beam steerable quartz integrated lens antenna for 60 GHz frequency band

This paper presents simulation and experimental investigation results of steerable integrated lens antennas (ILAs) operating in the 60 GHz frequency band. The feed array of the ILAs is comprised by four switched aperture coupled microstrip antenna (ACMA) elements that allows steering between four different antenna main beam directions in one plane. The dielectric lenses of the designed ILAs are extended hemispherical quartz (ε = 3.8) lenses with the radiuses of 7.5 and 12.5 mm. The extension lengths of the lenses are selected through the electromagnetic optimization in order to achieve the maximum ILAs directivities and also the minimum directivity degradations of the outer antenna elements in the feed array (± 3 mm displacement) relatively to the inner ones (± 1 mm displacement). Simulated maximum directivities of the boresight beam of the designed ILAs are 19.8 dBi and 23.8 dBi that are sufficient for the steerable antennas for the millimeter-wave WLAN/WPAN communication systems. The feed ACMA array together with the waveguide to microstrip transition dedicated for experimental investigations is fabricated on high frequency and low cost Rogers 4003C substrate. Single Pole Double Through (SPDT) switches from Hittite are used in order to steer the ILA prototypes main beam directions. The experimental results of the fabricated electronically steerable quartz ILA prototypes prove the simulation results and show ±35° and ±22° angle sector coverage for the lenses with the 7.5 and 12.5 mm radiuses respectively.
A low-cost adaptive multi-mode digital control solution maximizing AC/DC power supply efficiency

This paper presents a new low-cost digital control solution that maximizes the AC/DC flyback power supply efficiency. This intelligent digital approach achieves the combined benefits of high performance, low cost and high reliability in a single controller. It introduces unique multiple PWM and PFM operational modes adaptively based on the power supply load changes. While the multi-mode PWM/PFM control significantly improves the light-load efficiency and thus the overall average efficiency, it does not bring compromise to other system performance, such as audible noise, voltage ripples or regulations. It also seamlessly integrated an improved quasi-resonant switching scheme that enables valley-mode turn on in every switching cycle without causing modification to the main PWM/PFM control schemes. A digital integrated circuit (IC) that implements this solution, namely iW1696, has been fabricated and introduced to the industry recently. In addition to outlining the approach, this paper provides experimental results obtained on a 3-W (5V/550mA) cell phone charger that is built with the iW1696.
Supervised Attentions for Neural Machine Translation

In this paper, we improve the attention or alignment accuracy of neural machine translation by utilizing the alignments of training sentence pairs. We simply compute the distance between the machine attentions and the “true” alignments, and minimize this cost in the training procedure. Our experiments on large-scale Chinese-to-English task show that our model improves both translation and alignment qualities significantly over the large-vocabulary neural machine translation system, and even beats a state-of-the-art traditional syntax-based system.
A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders

Zero shot learning in Image Classification refers to the setting where images from some novel classes are absent in the training data but other information such as natural language descriptions or attribute vectors of the classes are available. This setting is important in the real world since one may not be able to obtain images of all the possible classes at training. While previous approaches have tried to model the relationship between the class attribute space and the image space via some kind of a transfer function in order to model the image space correspondingly to an unseen class, we take a different approach and try to generate the samples from the given attributes, using a conditional variational autoencoder, and use the generated samples for classification of the unseen classes. By extensive testing on four benchmark datasets, we show that our model outperforms the state of the art, particularly in the more realistic generalized setting, where the training classes can also appear at the test time along with the novel classes.
Batched QR and SVD algorithms on GPUs with applications in hierarchical matrix compression

We present high performance implementations of the QR and the singular value decomposition of a batch of small matrices hosted on the GPU with applications in the compression of hierarchical matrices. The one-sided Jacobi algorithm is used for its simplicity and inherent parallelism as a building block for the SVD of low rank blocks using randomized methods. We implement multiple kernels based on the level of the GPU memory hierarchy in which the matrices can reside and show substantial speedups against streamed cuSOLVER SVDs. The resulting batched routine is a key component of hierarchical matrix compression, opening up opportunities to perform H-matrix arithmetic efficiently on GPUs.
Anxiety and depression among medical students: a cross-sectional study.

To determine the prevalence of anxiety and depression among medical students at Nishtar Medical College, Multan.



A cross-sectional study was carried out at Nishtar Medical College, Multan in 2008. The questionnaire was administered to 815 medical students who had spent more than 6 months in college and had no self reported physical illness. They were present at the time of distribution of the questionnaires and consented. Prevalence of anxiety and depression was assessed using a structured validated questionnaire, the Aga Khan University Anxiety and Depression Scale with a cut-off score of 19. Data Analysis was done using SPSS v. 14.



Out of 815 students, 482 completed the questionnaire with a response rate of 59.14%. The mean age of students was 20.66 +/- 1.8 years. A high prevalence of anxiety and depression (43.89%) was found amongst medical students. Prevalence of anxiety and depression among students of first, second, third, fourth and final years was 45.86%, 52.58%, 47.14%, 28.75% and 45.10% respectively. Female students were found to be more depressed than male students (OR = 2.05, 95% CI = 1.42-2.95, p = 0.0001). There was a significant association between the prevalence of anxiety and depression and the respective year of medical college (p = 0.0276). It was seen that age, marital status, locality and total family income did not significantly affect the prevalence of anxiety and depression.



The results showed that medical students constitute a vulnerable group that has a high prevalence of psychiatric morbidity comprising of anxiety and depression.
Energy Harvesting in Substations for Powering Autonomous Sensors

The benefits of enhanced condition monitoring in the asset management of the electricity transmission infrastructure are increasingly being exploited by the grid operators. Adding more sensors helps to track the plant health more accurately. However, the installation or operating costs of any additional sensors could outweigh the benefits they bring due to the requirement for new cabling or battery maintenance. Energy harvesting devices are therefore being proposed to power a new generation of wireless sensors. The harvesting devices could enable the sensors to be maintenance free over their lifetime and substantially reduce the cost of installing and operating a condition monitoring system.
Dimensions of peri-implant mucosa: an evaluation of maxillary anterior single implants in humans.

Attempts have been made to evaluate the biologic dimension of osseointegrated implants, however, most are histologic studies in animals, and the effect of soft tissue support from adjacent teeth on the interproximal dimension of the peri-implant mucosa for anterior single implants has not been addressed. This study clinically evaluated the dimensions of the peri-implant mucosa around 2-stage maxillary anterior single implants in humans after 1 year of function. The influence of the peri-implant biotype was also examined.



Forty-five patients (20 males and 25 females) with a mean age of 47.3 years were included in this study. A total of 45 maxillary anterior single implant crowns with a mean functional time of 32.5 months (range, 12 to 78) were evaluated. The dimensions of peri-implant mucosa were measured by bone sounding using a periodontal probe at the mesial (MI), mid-facial (F), and distal (DI) aspects of the implant restoration and the proximal aspects (MT, DT) of adjacent natural teeth. In addition, the peri-implant biotype was evaluated and categorized as thick or thin. Statistical analysis was performed using an independent t test (P<0.05).



The means and standard deviations of the dimensions of peri-implant mucosa at MT, MI, F, DI, and DT were 4.20 +/- 0.77 mm, 6.17 +/- 1.27 mm, 3.63 +/- 0.91 mm, 5.93 +/- 1.21 mm, and 4.20 +/- 0.64 mm, respectively. The dimensions of peri-implant mucosa in the thick biotype were significantly greater than the thin biotype at MT, MI, and DT (P<0.05).



The mean facial dimension of peri-implant mucosa of 2-stage implants is slightly greater than the average dimension of the dentogingival complex. The level of the interproximal papilla of the implant is independent of the proximal bone level next to the implant, but is related to the interproximal bone level next to the adjacent teeth. Greater peri-implant mucosal dimensions were noted in the presence of a thick peri-implant biotype as compared to a thin biotype.
Code Churn: A Measure for Estimating the Impact of Code Change

This study presents a methodology that will produce a viable fault surrogate. The focus of the effort is on the precise measurement of software development process and product outcomes. Tools and processes for the static measurement of the source code have been installed and made operational in a large embedded software system. Source code measurements have been gathered unobtrusively for each build in the software evolution process. The measurements are synthesized to obtain the fault surrogate. The complexity of sequential builds is compared and a new measure, code churn, is calculated. This paper will demonstrate the effectiveness of code complexity churn by validating it against the testing problem reports.
Exceptions That Prove the Rule — Using a Theory of Motivated Social Cognition to Account for Ideological Incongruities and Political Anomalies : Reply to Greenberg and Jonas ( 2003 )

A meta-analysis by J. T. Jost, J. Glaser, A. W. Kruglanski, and F. J. Sulloway (2003) concluded that political conservatism is partially motivated by the management of uncertainty and threat. In this reply to J. Greenberg and E. Jonas (2003), conceptual issues are clarified, numerous political anomalies are explained, and alleged counterexamples are incorporated with a dynamic model that takes into account differences between “young” and “old” movements. Studies directly pitting the rigidity-of-the-right hypothesis against the ideological extremity hypothesis demonstrate strong support for the former. Medium to large effect sizes describe relations between political conservatism and dogmatism and intolerance of ambiguity; lack of openness to experience; uncertainty avoidance; personal needs for order, structure, and closure; fear of death; and system threat.
Measuring Political Polarization: Twitter shows the two sides of Venezuela

We say that a population is perfectly polarized when divided in two groups of the same size and opposite opinions. In this paper, we propose a methodology to study and measure the emergence of polarization from social interactions. We begin by proposing a model to estimate opinions in which a minority of influential individuals propagate their opinions through a social network. The result of the model is an opinion probability density function. Next, we propose an index to quantify the extent to which the resulting distribution is polarized. Finally, we apply the proposed methodology to a Twitter conversation about the late Venezuelan president, Hugo Chávez, finding a good agreement between our results and offline data. Hence, we show that our methodology can detect different degrees of polarization, depending on the structure of the network.
ERP Implementation: Chief Information Officers' Perceptions of Critical Success Factors

This article reports the results of a survey of Chief Information Officers (CIOs) from Fortune 1000 companies on their perceptions of the critical success factors in Enterprise Resource Planning (ERP) implementation. Through a review of the literature, 11 critical success factors were identified , with underlying subfactors, for successful ERP implementation. The degree of criticality of each of these factors were assessed in a survey administered to the CIOs. The 5 most critical factors identified by the CIOs were top management support, project champion, ERP teamwork and composition, project management, and change management program and culture. The importance of each of these factors is discussed.
A dynamic trajectory control algorithm for improving the communication throughput and delay in UAV-aided networks

Recently, the UAS has been extensively exploited for data collection from remote and dangerous or inaccessible areas. While most of its existing applications have been directed toward surveillance and monitoring tasks, the UAS can play a significant role as a communication network facilitator. For example, the UAS may effectively extend communication capability to disaster-affected people (who have lost cellular and Internet communication infrastructures on the ground) by quickly constructing a communication relay system among a number of UAVs. However, the distance between the centers of trajectories of two neighboring UAVs, referred to as IUD, plays an important role in the communication delay and throughput. For instance, the communication delay increases rapidly while the throughput is degraded when the IUD increases. In order to address this issue, in this article, we propose a simple but effective dynamic trajectory control algorithm for UAVs. Our proposed algorithm considers that UAVs with queue occupancy above a threshold are experiencing congestion resulting in communication delay. To alleviate the congestion at UAVs, our proposal adjusts their center coordinates and also, if needed, the radius of their trajectory. The performance of our proposal is evaluated through computer-based simulations. In addition, we conduct several field experiments in order to verify the effectiveness of UAV-aided networks.
Network neuroscience

Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.
PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming

Suppose we wish to recover a signal x ∈ Cn from m intensity measurements of the form |〈x,zi〉|, i = 1,2, . . . ,m; that is, from data in which phase information is missing. We prove that if the vectors zi are sampled independently and uniformly at random on the unit sphere, then the signal x can be recovered exactly (up to a global phase factor) by solving a convenient semidefinite program— a trace-norm minimization problem; this holds with large probability provided that m is on the order of n logn, and without any assumption about the signal whatsoever. This novel result demonstrates that in some instances, the combinatorial phase retrieval problem can be solved by convex programming techniques. Finally, we also prove that our methodology is robust vis a vis additive noise.