nils-herrmann/clean_scirepeval huggingface dataset:
Making Image More Energy Efficient for OLED Smart Devices
Venue: Mob. Inf. Syst.

Now, more and more mobile smart devices are emerging massively; energy consumption of these devices has become an important consideration due to the limitation of battery capacity. Displays are the dominant energy consuming component of battery operated devices, giving rise to organic light emitting diode (OLED) as a new promising display technology, which consumes different power when displaying different content due to their emissive nature. Based on this property, we propose an approach to improve image energy efficiency on OLED displays by perceiving image content. The key idea of our approach is to eliminate undesired details while preserving the region of interest of the image by leveraging the color and spatial information. First, we use edge detection algorithm to extract region of interest (ROI) of an image. Next, we gradually change luminance and saturation of region of noninterest (NON ROI) of the image. Then we perform detailed experiment and case study to validate our approach; experiment results show that our approach can save 22.5% energy on average while preserving high quality of the image.
A curious interplay in the films of N heterocyclic carbene PtII complexes upon deposition of alkali metals
Venue: Scientific reports

The recently synthesized series of PtII complexes containing cyclometallating (phenylpyridine or benzoquinoline) and N heterocyclic carbene ligands possess intriguing structures, topologies, and light emitting properties. Here, we report curious physicochemical interactions between in situ PVD grown films of a typical representative of the aforementioned PtII complex compounds and Li, Na, K and Cs atoms. Based on a combination of detailed core level photoelectron spectroscopy and quantum chemical calculations at the density functional theory level, we found that the deposition of alkali atoms onto the molecular film leads to unusual redistribution of electron density: essential modification of nitrogen sites, reduction of the coordination PtII centre to Pt0 and decrease of electron density on the bromine atoms. A possible explanation for this is formation of a supramolecular system "Pt complex alkali metal ion" the latter is supported by restoration of the system to the initial state upon subsequent oxygen treatment. The discovered properties highlight a considerable potential of the PtII complexes for a variety of biomedical, sensing, chemical, and electronic applications.
Mechanistic Investigation into Olefin Epoxidation with H2O2 Catalyzed by Aqua Coordinated Sandwich Type Polyoxometalates: Role of the Noble Metal and Active Oxygen Position
Venue: ChemistryOpen

Aqua coordinated sandwich type polyoxometalates (POMs) [WZnTM2(H2O)2](ZnW9O34)2}n (TM=RhIII, PdII, and PtII) catalyze olefin epoxidation with hydrogen peroxide and have been well established, and they present an advance toward the utilization of olefins. To elucidate the epoxidation mechanism, we systematically performed density functional calculations. The reaction proceeds through a two step mechanism: activation of H2O2 and oxygen transfer. The aqua coordinated complexes show two distinct H2O2 activation pathways: "two step" and "concerted" The concerted processes are more facile and proceed with similar and rate determining energy barriers at the Rh Pd and Pt containing transition states, which agrees well with the experimental results. Next, the resulting TM OH (m OOH) intermediate transfers an O atom to olefin to form an epoxide. The higher reactivity of the Rh containing POM is attributed to more interactions between the Rh and hydroperoxo unit. We also calculated all active oxygen positions to locate the most favorable pathway. The higher reactivity of the two metal bonded oxygen position is predominantly ascribed to its lower stereoscopic hindrance. Furthermore, the presence of one and two explicit water solvent molecules significantly reduces the energy barriers, making these sandwich POMs very efficient for the olefin epoxidation with H2O2.
Correlation Review of Classification Algorithm Using Data Mining Tool: WEKA, Rapidminer, Tanagra, Orange and Knime

This paper conducts a correlation review of classification algorithm using some free available data mining and knowledge discovery tools such as WEKA, Rapid miner, Tanagra, Orange and Knime. The accuracy of classification algorithm like Decision tree, Decision Stump, K Nearest Neighbor and Naive Bayes algorithm have been compared using all five tools. Indian Liver Patient DataSet is used for testing the Classification algorithm in order to classify the people with and without Liver disorder.
Diagnosis of diabetic retinopathy using machine learning classification algorithm
Venue: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT)

Diabetic Retinopathy is human eye disease which causes damage to retina of eye and it may eventually lead to complete blindness. Detection of diabetic retinopathy in early stage is essential to avoid complete blindness. Many physical tests like visual acuity test, pupil dilation, optical coherence tomography can be used to detect diabetic retinopathy but are time consuming and affects patients as well. This review paper focuses on decision about the presence of disease by applying ensemble of machine learning classifying algorithms on features extracted from output of different retinal image processing algorithms, like diameter of optic disk, lesion specific (microaneurysms, exudates) image level (pre screening, AM/FM, quality assessment) Decision making for predicting the presence of diabetic retinopathy was performed using alternating decision tree, adaBoost, Naive Bayes, Random Forest and SVM.
LA UNIVERSIDAD, LOS PROBLEMAS SOCIALES DE LA CIENCIA Y LA TECNOLOGIA FRENTE AL RETO DEL DESARROLLO SUSTENTABLE.

Nos movemos en un mar de incertidumbres entre lo ambiental, lo humano, la educacion, y los problemas sociales de la ciencia y la tecnologia frente al reto del desarrollo sustentable. En este contexto, la universidad tiene la responsabilidad de ofrecer respuestas a la sociedad, fundamentadas estas, en procesos de investigacion, que desde lo social, aporten modelos educativos que respondan a la formacion integral de un ser humano, que comprenda el contexto que habita, y lo transforme para el desarrollo cientifico y tecnologico, respetando lo cultural, y sin sacrificar el medio ambiente.
DeepIris: Learning pairwise filter bank for heterogeneous iris verification
Venue: Pattern Recognit. Lett.

Heterogeneous iris recognition (HIR) is in great demand for a large scale identity management system. Iris images acquired in heterogeneous environment have large intra class variations, such as different resolutions or different sensor optics, etc. Therefore, it is challenging to manually design a robust encoding filter to face the complex intra class variations of heterogeneous iris images. This paper proposes a deep learning based framework for heterogeneous iris verification, namely DeepIris, which learns relational features to measure the similarity between pairs of iris images based on convolutional neural networks. DeepIris is a novel solution to iris recognition in two main aspects. (1) DeepIris learns a pairwise filter bank to establish the relationship between heterogeneous iris images, where pairs of filters are learned from two heterogeneous sources. (2) Different from two separate steps in terms of handcrafted feature extraction and feature matching in conventional solutions, DeepIris directly learns a nonlinear mapping function between pairs of iris images and their identity supervision with a pairwise filter bank (PFB) from different sources. Thus, the learned pairwise filters can adapt to new sources when given new training data. Extensive experimental results on the Q FIRE and the CASIA cross sensor datasets demonstrate that EER (Equal Error Rate) of heterogeneous iris verification is reduced by 90% using DeepIris compared to traditional methods.
Human Resource Management A General Managers Perspective

Follow up what we will offer in this article about human resource management a general managers perspective. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.
High thermally conductive and electrically insulating 2D boron nitride nanosheet for efficient heat dissipation of high power transistors

High power transistors suffer greatly from inefficient heat dissipation of the hotspots, which elevate the local temperature and significantly degrade the performance and reliability of the high power devices. Although various thermal management methods at package level have been demonstrated, the heat dissipation from non uniform hotspots at micro/nanoscale still persist in the high power transistors. Here, we develop a method for local thermal management using thermally conductive and electrical insulating few layer hexagonal boron nitride (h BN) as heat spreaders and thick counterpart as heat sinks. The electrically insulating characteristic of h BN nanosheet allows it to be intimately contacted with the hotspot region that is located at the gate electrode edge near the drain side of a high electron mobility transistor (HEMT) The high thermal conductivity of h BN nanosheet, which is quantitatively measured by Raman thermography, reduces the temperature of the hotspot by introducing an additional heat transporting pathway. Our DC and radio frequency characterizations of the HEMT show the improvement of saturation current, cut off frequency and maximum oscillation frequency. The finite element simulations show a temperature decrease of ~32 degC at the hotspot with the use of h BN nanosheet. This method can be further extended for the micro/nanoscale thermal management of other high power devices.
Evolutionary optimization of long short term memory neural network language model

Recurrent neural network language models (RNN LMs) are recently proven to produce better performance than conventional N gram based language models in various speech recognition tasks. Especially, long short term memory recurrent neural network language models (LSTM LMs) give superior performance for its ability to better modeling word history information. However, LSTM LMs have complex network structure and training configurations, which are meta parameters that need to be well tuned to achieve the state of the art performance. The tuning is usually performed manually by humans, but it is not easy because it requires expert knowledge and intensive effort with many trials. In this study, we apply covariance matrix adaptation evolution strategy (CMA ES) and automate the tuning process. CMA ES is one of the most efficient global optimization techniques that has demonstrated superior performance in various benchmark tasks. In the experiments, the meta parameters subject to the tuning included unit types at e.
Imaging Flash Lidar for Autonomous Safe Landing and Spacecraft Proximity Operation

3 D Imaging flash lidar is recognized as a primary candidate sensor for safe precision landing on solar system bodies (Moon, Mars, Jupiter and Saturn moons, etc. and autonomous rendezvous proximity operations and docking/capture necessary for asteroid sample return and redirect missions, spacecraft docking, satellite servicing, and space debris removal. During the final stages of landing, from about 1 km to 500 m above the ground, the flash lidar can generate 3 Dimensional images of the terrain to identify hazardous features such as craters, rocks, and steep slopes. The onboard fli1ght computer can then use the 3 D map of terrain to guide the vehicle to a safe location. As an automated rendezvous and docking sensor, the flash lidar can provide relative range, velocity, and bearing from an approaching spacecraft to another spacecraft or a space station from several kilometers distance. NASA Langley Research Center has developed and demonstrated a flash lidar sensor system capable of generating 16k pixels range images with 7 cm precision, at a 20 Hz frame rate, from a maximum slant range of 1800 m from the target area. This paper describes the lidar instrument design and capabilities as demonstrated by the closed loop flight tests onboard a rocket propelled free flyer vehicle (Morpheus) Then a plan for continued advancement of the flash lidar technology will be explained. This proposed plan is aimed at the development of a common sensor that with a modest design adjustment can meet the needs of both landing and proximity operation and docking applications.
Effect of karst rocky desertification on soil fungal communities in Southwest China.
Venue: Genetics and molecular research GMR

Karst mountainous ecosystems are associated with karst rocky desertification (KRD) which can greatly impact soil structure and function. Despite the importance of soil microbes as a major factor maintaining ecosystem stability, we know little about the effect on soil fungal communities of KRD in karst regions. We investigated this relationship across a gradient of KRD soils from Guizhou, China by polymerase chain reaction and denaturing gradient gel electrophoresis (PCR DGGE) Fungal diversity indices (Shannon Wiener, richness, and evenness) significantly differed (P 0.05) based on KRD severity, being lowest in moderately affected areas. Cluster analysis showed that the five sites examined clustered into two main groups according to KRD grade (high and low) Moreover, a homology search using sequences recovered from PCR DGGE bands showed that the dominant fungi in each community varied remarkably, and included Aspergillus, Aphanoascus, Blastomyces, Fusarium, Glomus, Geomyces, Gibberella, Mortierella, Tetracladium, and Tumularia species, and an unclassified group. In conclusion, these findings demonstrate that KRD has a significant impact on soil fungal communities.
Multi objective optimization for long tail recommendation
Venue: Knowl. Based Syst.

Recommender systems are tools to suggest items to target users. Accuracy focused recommender systems tend to recommend popular items, while suggesting items with few ratings (long tail items) is also of great importance in practice. Recommending long tail items may cause an accuracy loss of recommendation results. Thus, it is necessary to have a recommendation framework that recommends unpopular items meanwhile minimizing the accuracy loss. In this paper, we formulate a multi objective framework for long tail items recommendation. Under this framework, two contradictory objective functions are designed to describe the abilities of recommender system to recommend accurate and unpopular items, respectively. To optimize these two objective functions, a novel multi objective evolutionary algorithm is proposed. This multi objective evolutionary algorithm aims to find a set of tradeoff solutions by optimizing two objective functions simultaneously. Experiments show that the proposed framework is effective to suggest accurate and novel items. The proposed recommendation algorithm could suggest many high quality recommendation lists for the target user based on the concept of Pareto dominance in one run.
Body diode reliability investigation of SiC power MOSFETs
Venue: Microelectron. Reliab.

A special feature of vertical power MOSFETs, in general, is the inbuilt body diode which could eliminate the need of having to use additional anti parallel diodes for current freewheeling in industrial inverter applications: this, clearly, subject to their demonstration of an acceptable level of reliability. Recent improvements in Silicon Carbide (SiC) power MOSFET device manufacturing technology has resulted in their wider commercial availability with different voltage and current ratings and from various manufacturers. Hence, it is essential to perform characterisation of its intrinsic body diode. This paper presents the reliability assessment of body diodes of latest generation discrete SiC power MOSFETs within a 3 phase 2 level DC to AC inverter representing realistic operating conditions for power electronic applications.
Crowdsourcing innovation and product development: Gamification as a motivational driver

Crowdsourcing outsourcing a job to external contributors through an open call has become an important part of innovation process and product development. However, many crowdsourcing initiatives fail due to low engagement and participation. In the current paper, we aim to explore how companies can employ gamification game elements and design techniques to motivate contributions to a crowdsourcing project. Based on an exploratory case study of Threadless, a web based apparel store, we propose implications that can assist companies in leveraging global capabilities for new product development. Threadless employs game thinking through a wide array of game elements and game mechanics including challenges, achievements, countdowns, discovery, points, reward schedules, and status. Our analysis shows that consumers are fun seekers. They will carry out activities without expecting anything in return, if they perceive it as being fun. Moreover, companies should openly acknowledge consumers' contributions and give them recognition by using flexible reward system, statuses, and challenges. Despite the importance of extrinsic rewards, it is also essential to realize that intrinsic factors are crucial for maintaining consumers' continued engagement and therefore we strongly recommend companies to build a strong virtual brand community around their crowdsourcing projects.
Hierarchical Reinforcement Learning using Spatio Temporal Abstractions and Deep Neural Networks
Venue: ArXiv

This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstract states and extended actions between abstract states. Identifying such structures present in the task provides ways to simplify and speed up reinforcement learning learning algorithms. These structures also help to generalize such algorithms over multiple tasks without relearning policies from scratch. We use ideas from dynamical systems to find metastable regions in the state space and associate them with abstract states. The spectral clustering algorithm PCCA+ is used to identify suitable abstractions aligned to the underlying structure. Skills are defined in terms of the transitions between such abstract states. The connectivity information from PCCA+ is used to generate these skills or options. The skills are independent of the learning task and can be efficiently reused across a variety of tasks defined over a common state space. Another major advantage of the approach is that it does not need a prior model of the MDP and can work well even when the MDPs are constructed from sampled trajectories. Finally, we present our attempts to extend the automated skills acquisition framework to complex tasks such as learning to play video games where we use deep learning techniques for representation learning to aid our spatio temporal abstraction framework.
Tempting foods and the affordability axiom: Food cues change beliefs about the costs of healthy eating
Venue: Appetite

Many consumers report that healthy eating is more expensive than unhealthy eating (the affordability axiom) We hypothesize that endorsement of this belief may be driven by the motivation to eat unhealthy foods. We tested this hypothesis in three studies. Study 1 revealed that the affordability axiom is associated with poorer eating habits and higher Body Mass Index (BMI) Study 2 found that the presence of a tasty food cue in the environment increased endorsement of affordability axiom. Study 3 found that these effects were moderated by one's food intake goals. Food cues led non dieters to increase endorsement of the affordability axiom, but had the opposite effect among those seeking to restrict their calorie intake. The affordability axiom might persist as a means of validating unhealthy food choices.
Pictorial instrument of food and nutrition education for promoting healthy eating

To trace the course of building a pictorial instrument that explores semiotic resources about food and nutrition education. The instrument is directed at the treatment and prevention of obesity, considering the food and nutrition problems of the Brazilian population. The criteria for photo production were: images that could cause visual impact and transmit applied nutrition information, insinuating positive and negative eating practices for promoting healthy eating, and preventing and treating obesity. Themes were created to guide photo production, and preparations, food composition tables, and food labels were used for composing the images. Focus groups were conducted with nonobese and obese women, and dietitians to evaluate image comprehension. The pictorial instrument totaled to 20 photos, with 5 photos in each of the following themes: 'Sweet life, being aware of sugar' about sugar content in sweets and drinks; 'Tasty food with little fat' about fat content in foods; 'Eating well by making the best choices' about food replacements; and 'I take care of myself by eating healthy food, about encouraging fruit and vegetable intakes. The photos contain food, meals, and semiotic resources. The pictorial instrument describes four relevant themes to approach food problems in the Brazilian population. It can easily be used for both preventing and treating obesity, and for promoting healthy eating.
The child brain computes and utilizes internalized maternal choices
Venue: Nature communications

As children grow, they gradually learn how to make decisions independently. However, decisions like choosing healthy but less tasty foods can be challenging for children whose self regulation and executive cognitive functions are still maturing. We propose a computational decision making process in which children estimate their mother's choices for them as well as their individual food preferences. By employing functional magnetic resonance imaging during real food choices, we find that the ventromedial prefrontal cortex (vmPFC) encodes children's own preferences and the left dorsolateral prefrontal cortex (dlPFC) encodes the projected mom's choices for them at the time of children's choice. Also, the left dlPFC region shows an inhibitory functional connectivity with the vmPFC at the time of children's own choice. Our study suggests that in part, children utilize their perceived caregiver's choices when making choices for themselves, which may serve as an external regulator of decision making, leading to optimal healthy decisions.
Ultrafast Surface Enhanced Raman Probing of the Role of Hot Electrons in Plasmon Driven Chemistry.
Venue: The journal of physical chemistry letters

Hot electrons generated through plasmonic excitations in metal nanostructures show great promise for efficiently driving chemical reactions with light. However, the lifetime, yield, and mechanism of action of plasmon generated hot electrons involved in a given photocatalytic process are not well understood. Here, we develop ultrafast surface enhanced Raman scattering (SERS) as a direct probe of plasmon molecule interactions in the plasmon catalyzed dimerization of 4 nitrobenzenethiol to p,p' dimercaptoazobenzene. Ultrafast SERS probing of these molecular reporters in plasmonic hot spots reveals transient Fano resonances, which we attribute to near field coupling of Stokes shifted photons to hot electron driven metal photoluminescence. Surprisingly, we find that hot spots that yield more photoluminescence are much more likely to drive the reaction, which indirectly proves that plasmon generated hot electrons induce the photochemistry. These ultrafast SERS results provide insight into the relative reactivity of different plasmonic hot spot environments and quantify the ultrafast lifetime of hot electrons involved in plasmon driven chemistry.