nils-herrmann/clean_scirepeval huggingface dataset:
Motivation and incentive preferences of community health officers in Ghana: an economic behavioral experiment approach

Health worker shortage in rural areas is one of the biggest problems of the health sector in Ghana and many developing countries. This may be due to fewer incentives and support systems available to attract and retain health workers at the rural level. This study explored the willingness of community health officers (CHOs) to accept and hold rural and community job postings in Ghana.



A discrete choice experiment was used to estimate the motivation and incentive preferences of CHOs in Ghana. All CHOs working in three Health and Demographic Surveillance System sites in Ghana, 200 in total, were interviewed between December 2012 and January 2013. Respondents were asked to choose from choice sets of job preferences. Four mixed logit models were used for the estimation. The first model considered (a) only the main effect. The other models included interaction terms for (b) gender, (c) number of children under 5 in the household, and (d) years worked at the same community. Moreover, a choice probability simulation was performed.



Mixed logit analyses of the data project a shorter time frame before study leave as the most important motivation for most CHOs (β 2.03; 95 % CI 1.69 to 2.36). This is also confirmed by the largest simulated choice probability (29.1 %). The interaction effect of the number of children was significant for education allowance for children (β 0.58; 95 % CI 0.24 to 0.93), salary increase (β 0.35; 95 % CI 0.03 to 0.67), and housing provision (β 0.16; 95 % CI -0.02 to 0.60). Male CHOs had a high affinity for early opportunity to go on study leave (β 0.78; 95 % CI -0.06 to 1.62). CHOs who had worked at the same place for a long time greatly valued salary increase (β 0.28; 95 % CI 0.09 to 0.47).



To reduce health worker shortage in rural settings, policymakers could provide "needs-specific" motivational packages. They should include career development opportunities such as shorter period of work before study leave and financial policy in the form of salary increase to recruit and retain them.
Many roads to maturity: microRNA biogenesis pathways and their regulation

MicroRNAs are important regulators of gene expression that control both physiological and pathological processes such as development and cancer. Although their mode of action has attracted great attention, the principles governing their expression and activity are only beginning to emerge. Recent studies have introduced a paradigm shift in our understanding of the microRNA biogenesis pathway, which was previously believed to be universal to all microRNAs. Maturation steps specific to individual microRNAs have been uncovered, and these offer a plethora of regulatory options after transcription with multiple proteins affecting microRNA processing efficiency. Here we review the recent advances in knowledge of the microRNA biosynthesis pathways and discuss their impact on post-transcriptional microRNA regulation during tumour development.
AIR-EDUTECH: Augmented immersive reality (AIR) technology for high school Chemistry education

As technology is exponentially regenerated and developed; new educational methods are needed to assure better educational outcomes. Recently schools have started integrating recent technology into the educational process in order to attract students and increase their engagement in the process. This paper will demonstrate how an augmented immersive reality mobile application can work to enrich the learning environment and attract the students to learn and explore subjects such as sciences, more specifically Chemistry. Augmented immersive reality allows students to learn in their own way and pace while encouraging them to explore and be creative as their `trial-and-error' mistakes will not impose any major consequences in the virtual world. A field study was conducted on grade 11 students to verify the benefits of using the developed application in education.
A constraint-based approach to solving games on infinite graphs

We present a constraint-based approach to computing winning strategies in two-player graph games over the state space of infinite-state programs. Such games have numerous applications in program verification and synthesis, including the synthesis of infinite-state reactive programs and branching-time verification of infinite-state programs. Our method handles games with winning conditions given by safety, reachability, and general Linear Temporal Logic (LTL) properties. For each property class, we give a deductive proof rule that --- provided a symbolic representation of the game players --- describes a winning strategy for a particular player. Our rules are sound and relatively complete. We show that these rules can be automated by using an off-the-shelf Horn constraint solver that supports existential quantification in clause heads. The practical promise of the rules is demonstrated through several case studies, including a challenging "Cinderella-Stepmother game" that allows infinite alternation of discrete and continuous choices by two players, as well as examples derived from prior work on program repair and synthesis.
Cell-Free Massive MIMO Versus Small Cells

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs), which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max–min power control algorithms. Max–min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly fivefold improvement in 95%-likely per-user throughput over the small-cell scheme, and tenfold improvement when shadow fading is correlated.
A Flexible Pinhole Camera Model for Coherent Nonuniform Sampling

The flexible pinhole camera (FPC) allows flexible modulation of the sampling rate over the field of view. The FPC is defined by a viewpoint and a map specifying the sampling locations on the image plane. The map is constructed from known regions of interest with interactive and automatic approaches. The FPC provides inexpensive 3D projection that allows rendering complex datasets quickly, in feed-forward fashion, by projection followed by rasterization. The FPC supports many types of data, including image, height field, geometry, and volume data. The resulting image is a coherent nonuniform sampling (CoNUS) of the dataset that matches the local variation of the dataset's importance. CoNUS images have been successfully implemented for remote visualization, focus-plus-context visualization, and acceleration of expensive rendering effects such as surface geometric detail and specular reflection. A video explaining and demonstrating the FPC is at http://youtu.be/kvFe5XjOPNM.
Hybrid transformer-based tunable integrated duplexer with antenna impedance tracking loop

Electrical balance between the antenna and the balance network impedances is crucial for achieving high isolation in a hybrid transformer duplexer. In this paper an auto calibration loop for tuning a novel integrated balance network to track the antenna impedance variations is introduced. It achieves an isolation of more than 50 dB in the transmit and receive bands with an antenna VSWR within 2:1 and between 1.7 and 2.2 GHz. The duplexer along with a cascaded direct-conversion receiver achieves a noise figure of 5.3 dB a conversion gain of 45 dB and consumes 34 mA. The insertion loss in the transmit path was less than 3.8 dB. Implemented in a 65-nm CMOS process the chip occupies an active area of 2.2 mm2.
Minimax Optimal Procedures for Locally Private Estimation

Working under a model of privacy in which data remains private even from the statistician, we study the tradeoff between privacy guarantees and the risk of the resulting statistical estimators. We develop private versions of classical information-theoretic bounds, in particular those due to Le Cam, Fano, and Assouad. These inequalities allow for a precise characterization of statistical rates under local privacy constraints and the development of provably (minimax) optimal estimation procedures. We provide a treatment of several canonical families of problems: mean estimation and median estimation, multinomial probability estimation, and nonparametric density estimation. For all of these families, we provide lower and upper bounds that match up to constant factors, and exhibit new (optimal) privacy-preserving mechanisms and computationally efficient estimators that achieve the bounds. Additionally, we present a variety of experimental results for estimation problems involving sensitive data, including salaries, censored blog posts and articles, and drug abuse; these experiments demonstrate the importance of deriving optimal procedures.
Parents' and Teens' Perspectives on Privacy In a Technology-Filled World

The life of a teenager today is far different than in past decades. Through semi-structured interviews with 10 teenagers and 10 parents of teenagers, we investigate parent-teen privacy decision making in these uncharted waters. Parents and teens generally agreed that teens had a need for some degree of privacy from their parents and that respecting teens’ privacy demonstrated trust and fostered independence. We explored the boundaries of teen privacy in both the physical and digital worlds. While parents commonly felt none of their children’s possessions should ethically be exempt from parental monitoring, teens felt strongly that cell phones, particularly text messages, were private. Parents discussed struggling to keep up with new technologies and to understand teens’ technology-mediated socializing. While most parents said they thought similarly about privacy in the physical and digital worlds, half of teens said they thought about these concepts differently. We present cases where parents made privacy decisions using false analogies with the physical world or outdated assumptions. We also highlight directions for more usable digital parenting tools.
Smart Cells for Embedded Battery Management

This paper introduces a novel approach to battery management. In contrast to state-of-the-art solutions where a central Battery Management System (BMS) exists, we propose an Embedded Battery Management (EBM) that entirely decentralizes the monitoring and control of the battery pack. For this purpose, each cell of the pack is equipped with a Cell Management Unit (CMU) that monitors and controls local parameters of the respective cell, using its computational and communication resources. This combination of a battery cell and CMU forms the smart cell. Consequently, system-level functions are performed in a distributed fashion by the network of smart cells, applying concepts of self-organization to enable plug-and-play integration. This decentralized distributed architecture might offer significant advantages over centralized BMSs, resulting in higher modularity, easier integration and shorter time to market for battery packs. A development platform has been set up to design and analyze circuits, protocols and algorithms for EBM enabled by smart cells.
DEEP AUTOENCODING GAUSSIAN MIXTURE MODEL

Unsupervised anomaly detection on multior high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from decoupled model learning with inconsistent optimization goals and incapability of preserving essential information in the low-dimensional space. In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection. Our model utilizes a deep autoencoder to generate a low-dimensional representation and reconstruction error for each input data point, which is further fed into a Gaussian Mixture Model (GMM). Instead of using decoupled two-stage training and the standard Expectation-Maximization (EM) algorithm, DAGMM jointly optimizes the parameters of the deep autoencoder and the mixture model simultaneously in an end-to-end fashion, leveraging a separate estimation network to facilitate the parameter learning of the mixture model. The joint optimization, which well balances autoencoding reconstruction, density estimation of latent representation, and regularization, helps the autoencoder escape from less attractive local optima and further reduce reconstruction errors, avoiding the need of pre-training. Experimental results on several public benchmark datasets show that, DAGMM significantly outperforms state-of-the-art anomaly detection techniques, and achieves up to 14% improvement based on the standard F1 score.
Efficient Dense Stereo with Occlusions for New View-Synthesis by Four-State Dynamic Programming

A new algorithm is proposed for efficient stereo and novel view synthesis. Given the video streams acquired by two synchronized cameras the proposed algorithm synthesises images from a virtual camera in arbitrary position near the physical cameras. The new technique is based on an improved, dynamic-programming, stereo algorithm for efficient novel view generation. The two main contributions of this paper are: (i) a new four state matching graph for dense stereo dynamic programming, that supports accurate occlusion labelling; (ii) a compact geometric derivation for novel view synthesis by direct projection of the minimum cost surface. Furthermore, the paper presents an algorithm for the temporal maintenance of a background model to enhance the rendering of occlusions and reduce temporal artefacts (flicker); and a cost aggregation algorithm that acts directly in the three-dimensional matching cost space. The proposed algorithm has been designed to work with input images with large disparity range, a common practical situation. The enhanced occlusion handling capabilities of the new dynamic programming algorithm are evaluated against those of the most powerful state-of-the-art dynamic programming and graph-cut techniques. Four-state DP is also evaluated against the disparity-based Middlebury error metrics and its performance found to be amongst the best of the efficient algorithms. A number of examples demonstrate the robustness of four-state DP to artefacts in stereo video streams. This includes demonstrations of cyclopean view synthesis in extended conversational sequences, synthesis from a freely translating virtual camera and, finally, basic 3D scene editing.
Cancer Evolution: Mathematical Models and Computational Inference

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.
Efficient methods for topic model inference on streaming document collections

Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of training documents requires approximate inference techniques that are computationally expensive. With today's large-scale, constantly expanding document collections, it is useful to be able to infer topic distributions for new documents without retraining the model. In this paper, we empirically evaluate the performance of several methods for topic inference in previously unseen documents, including methods based on Gibbs sampling, variational inference, and a new method inspired by text classification. The classification-based inference method produces results similar to iterative inference methods, but requires only a single matrix multiplication. In addition to these inference methods, we present SparseLDA, an algorithm and data structure for evaluating Gibbs sampling distributions. Empirical results indicate that SparseLDA can be approximately 20 times faster than traditional LDA and provide twice the speedup of previously published fast sampling methods, while also using substantially less memory.
Investigation of forecasting methods for the hourly spot price of the day-ahead electric power markets

Forecasting hourly spot prices for real-time electricity usage is a challenging task. This paper investigates a series of forecasting methods to 90 and 180 days of load data collection acquired from the Iberian Electricity Market (MIBEL). This dataset was used to train and test multiple forecast models. The Mean Absolute Percentage Error (MAPE) for the proposed Hybrid combination of Auto Regressive Integrated Moving Average (ARIMA) and Generalized Linear Model (GLM) was compared against ARIMA, GLM, Random forest (RF) and Support Vector Machines (SVM) methods. The results indicate significant improvement in MAPE and correlation co-efficient values for the proposed hybrid ARIMA-GLM method.
Unsupervised colorization of black-and-white cartoons

We present a novel color-by-example technique which combines image segmentation, patch-based sampling and probabilistic reasoning. This method is able to automate colorization when new color information is applied on the already designed black-and-white cartoon. Our technique is especially suitable for cartoons digitized from classical celluloid films, which were originally produced by a paper or cel based method. In this case, the background is usually a static image and only the dynamic foreground needs to be colored frame-by-frame. We also assume that objects in the foreground layer consist of several well visible outlines which will emphasize the shape of homogeneous regions.
Attacks on the Keeloq Block Cipher and Authentication Systems

KeeLoq is a block cipher used in numerous widespread passive entry and remote keyless entry systems as well as in various component identification applications. The KeeLoq algorithm has a 64-bit key and operates on 32-bit blocks. It is based on an NLFSR with a nonlinear feedback function of 5 variables. In this paper new key recovery attacks on KeeLoq are proposed. The first one has a complexity of about 2 KeeLoq encryptions. The second attack finds the key in 2 encryptions and works for the whole key space. In our attacks we use the techniques of guess-and-determine, slide, and linear attacks as well as cycle structure analysis. Both attacks need 2 known plaintextciphertext pairs. We also analyze the KeeLoq key management and authentication protocols applied in rollingcode and IFF access systems widely used in real-world applications. We demonstrate several practical vulnerabilities.
Performance on tests of central auditory processing by individuals exposed to high-intensity blasts.

Thirty-six blast-exposed patients and twenty-nine non-blast-exposed control subjects were tested on a battery of behavioral and electrophysiological tests that have been shown to be sensitive to central auditory processing deficits. Abnormal performance among the blast-exposed patients was assessed with reference to normative values established as the mean performance on each test by the control subjects plus or minus two standard deviations. Blast-exposed patients performed abnormally at rates significantly above that which would occur by chance on three of the behavioral tests of central auditory processing: the Gaps-In-Noise, Masking Level Difference, and Staggered Spondaic Words tests. The proportion of blast-exposed patients performing abnormally on a speech-in-noise test  was also significantly above that expected by chance. These results suggest that, for some patients, blast exposure may lead to difficulties with hearing in complex auditory environments, even when peripheral hearing sensitivity is near normal limits.
Being cosmopolitan: evolutionary history and phylogeography of a specialized raptor, the Osprey Pandion haliaetus

The Osprey (Pandion haliaetus) is one of only six bird species with an almost world-wide distribution. We aimed at clarifying its phylogeographic structure and elucidating its taxonomic status (as it is currently separated into four subspecies). We tested six biogeographical scenarios to explain how the species’ distribution and differentiation took place in the past and how such a specialized raptor was able to colonize most of the globe. Using two mitochondrial genes (cyt b and ND2), the Osprey appeared structured into four genetic groups representing quasi non-overlapping geographical regions. The group Indo-Australasia corresponds to the cristatus ssp, as well as the group Europe-Africa to the haliaetus ssp. In the Americas, we found a single lineage for both carolinensis and ridgwayi ssp, whereas in north-east Asia (Siberia and Japan), we discovered a fourth new lineage. The four lineages are well differentiated, contrasting with the low genetic variability observed within each clade. Historical demographic reconstructions suggested that three of the four lineages experienced stable trends or slight demographic increases. Molecular dating estimates the initial split between lineages at about 1.16 Ma ago, in the Early Pleistocene. Our biogeographical inference suggests a pattern of colonization from the American continent towards the Old World. Populations of the Palearctic would represent the last outcomes of this colonization. At a global scale the Osprey complex may be composed of four different Evolutionary Significant Units, which should be treated as specific management units. Our study brought essential genetic clarifications, which have implications for conservation strategies in identifying distinct lineages across which birds should not be artificially moved through exchange/reintroduction schemes.