nils-herrmann/clean_scirepeval huggingface dataset:
Resource Allocation in D 2 D Communication – A Game Theoretic Approach

D2D communication has been proposed as a transmission approach to improve the resource efficiency and lighten the heavy load of the base station(BS) in LTE-Advanced system. In D2D communication, resource efficiency is seriously influenced by the resource allocation scheme. Additionally, the selfish behaviors of user equipment(UE), such as Unknown Channel Quality(UCQ) Problem potentially harm the system efficiency. For instance, we observed that UEs may report their experienced D2D quality untruthfully in order to gain unfair advantages in resource allocation. The so-called UCQ issue imposes a negative impact on the resource utilization efficiency. In this paper, we propose to use Game Theory to analyze this issue. First, we studied the resource allocating scheme concerning the benefit of the BS, and then analyze the system efficiency and the equilibrium. Second, we discussed the UCQ problem in D2D communication. We proposed a contract-based mechanism to resolve the UCQ problem by eliminating the incentive of UEs to report untruthfully with designed service contracts. The simulation results represents the feasibility and the effectiveness of our approach.
Design and Modeling of Microstrip Line to Substrate Integrated Waveguide Transitions 225 Design and Modeling of Microstrip Line to Substrate Integrated Waveguide Transitions

Microstrip lines (MSL) are widely used in microwave systems because of its low cost, light weight, and easy integration with other components. Substrate integrated waveguides (SIW), which inherit the advantages from traditional rectangular waveguides without their bulky configuration, aroused recently in low loss and high power planar applications. This chapter proposed the design and modeling of transitions between these two common structures. Research motives will be described firstly in the next subsection, followed by a literature survey on the proposed MSL to SIW transition structures. Outlines of the following sections in this chapter will also be given in the end of this section.
Tocilizumab for induction and maintenance of remission in giant cell arteritis: a phase 2, randomised, double-blind, placebo-controlled trial

Giant cell arteritis is an immune-mediated disease of medium and large-sized arteries that affects mostly people older than 50 years of age. Treatment with glucocorticoids is the gold-standard and prevents severe vascular complications but is associated with substantial morbidity and mortality. Tocilizumab, a humanised monoclonal antibody against the interleukin-6 receptor, has been associated with rapid induction and maintenance of remission in patients with giant cell arteritis. We therefore aimed to study the efficacy and safety of tocilizumab in the first randomised clinical trial in patients with newly diagnosed or recurrent giant cell arteritis.



In this single centre, phase 2, randomised, double-blind, placebo-controlled trial, we recruited patients aged 50 years and older from University Hospital Bern, Switzerland, who met the 1990 American College of Rheumatology criteria for giant cell arteritis. Patients with new-onset or relapsing disease were randomly assigned (2:1) to receive either tocilizumab (8 mg/kg) or placebo intravenously. 13 infusions were given in 4 week intervals until week 52. Both groups received oral prednisolone, starting at 1 mg/kg per day and tapered down to 0 mg according to a standard reduction scheme defined in the study protocol. Allocation to treatment groups was done using a central computerised randomisation procedure with a permuted block design and a block size of three, and concealed using central randomisation generated by the clinical trials unit. Patients, investigators, and study personnel were masked to treatment assignment. The primary outcome was the proportion of patients who achieved complete remission of disease at a prednisolone dose of 0·1 mg/kg per day at week 12. All analyses were intention to treat. 
Between March 3, 2012, and Sept 9, 2014, 20 patients were randomly assigned to receive tocilizumab and prednisolone, and ten patients to receive placebo and glucocorticoid; 16 (80%) and seven (70%) patients, respectively, had new-onset giant cell arteritis. 17 (85%) of 20 patients given tocilizumab and four (40%) of ten patients given placebo reached complete remission by week 12 (risk difference 45%, 95% CI 11-79; p=0·0301). Relapse-free survival was achieved in 17 (85%) patients in the tocilizumab group and two (20%) in the placebo group by week 52 (risk difference 65%, 95% CI 36-94; p=0·0010). The mean survival-time difference to stop glucocorticoids was 12 weeks in favour of tocilizumab (95% CI 7-17; p<0·0001), leading to a cumulative prednisolone dose of 43 mg/kg in the tocilizumab group versus 110 mg/kg in the placebo group (p=0·0005) after 52 weeks. Seven (35%) patients in the tocilizumab group and five (50%) in the placebo group had serious adverse events.



Our findings show, for the first time in a trial setting, the efficacy of tocilizumab in the induction and maintenance of remission in patients with giant cell arteritis.



Roche and the University of Bern.
Maximizing Influence Propagation in Networks with Community Structure

We consider the algorithmic problem of selecting a set of target nodes that cause the biggest activation cascade in a network. In case when the activation process obeys the diminishing return property, a simple hill-climbing selection mechanism has been shown to achieve a provably good performance. Here we study models of influence propagation that exhibit critical behavior and where the property of diminishing returns does not hold. We demonstrate that in such systems the structural properties of networks can play a significant role. We focus on networks with two loosely coupled communities and show that the double-critical behavior of activation spreading in such systems has significant implications for the targeting strategies. In particular, we show that simple strategies that work well for homogenous networks can be overly suboptimal and suggest simple modification for improving the performance by taking into account the community structure.
Android phone forensic: Tools and techniques

Today when there are more than 1 billion Android users all over the world, it shows that its popularity has no equal. These days mobile phones have become so intrusive in our daily lives that when they needed can give huge amount of information to forensic examiners. Till the date of writing this paper there are many papers citing the need of mobile device forensic and ways of getting the vital artifacts through mobile devices for different purposes. With vast options of popular and less popular forensic tools and techniques available today, this papers aims to bring them together under a comparative study so that this paper could serve as a starting point for several android users, future forensic examiners and investigators. During our survey we found scarcity for papers on tools for android forensic. In this paper we have analyzed different tools and techniques used in android forensic and at the end tabulated the results and findings.
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design

An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging design problems. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques have become indispensable. Surrogate models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, prototyping, and sensitivity analysis. Consequently, in many fields there is great interest in tools and techniques that facilitate the construction of such regression models, while minimizing the computational cost and maximizing model accuracy. This paper presents a mature, flexible, and adaptive machine learning toolkit for regression modeling and active learning to tackle these issues. The toolkit brings together algorithms for data fitting, model selection, sample selection (active learning), hyperparameter optimization, and distributed computing in order to empower a domain expert to efficiently generate an accurate model for the problem or data at hand.
ChoiceNet: toward an economy plane for the internet

The Internet has been a key enabling technology for many new distributed applications and services. However, the deployment of new protocols and services in the Internet infrastructure itself has been sluggish, especially where economic incentives for network providers are unclear. In our work, we seek to develop an "economy plane" for the Internet that enables network providers to offer new network-based services (QoS, storage, etc.) for sale to customers. The explicit connection between economic relationships and network services across various time scales enables users to select among service alternatives. The resulting competition among network service providers will lead to overall better technological solutions and more competitive prices. In this paper, we present the architectural aspects of our ChoiceNet economy plane as well as some of the technological problems that need to be addressed in a practical deployment.
Smart farming—A prototype for field monitoring and automation in agriculture

The agricultural productivity of India is gradually declining due to destruction of crops by various natural calamities and the crop rotation process being affected by irregular climate patterns. Also, the interest and efforts put by farmers lessen as they grow old which forces them to sell their agricultural lands, which automatically affects the production of agricultural crops and dairy products. This paper mainly focuses on the ways by which we can protect the crops during an unavoidable natural disaster and implement technology induced smart agro-environment, which can help the farmer manage large fields with less effort. Three common issues faced during agricultural practice are shearing furrows in case of excess rain or flood, manual watering of plants and security against animal grazing. This paper provides a solution for these problems by helping farmer monitor and control various activities through his mobile via GSM and DTMF technology in which data is transmitted from various sensors placed in the agricultural field to the controller and the status of the agricultural parameters are notified to the farmer using which he can take decisions accordingly. The main advantage of this system is that it is semi-automated i.e. the decision is made by the farmer instead of fully automated decision that results in precision agriculture. It also overcomes the existing traditional practices that require high money investment, energy, labour and time.
An Efficient Recommender System using Collaborative Filtering Methods with K-separability Approach

—This paper propose an efficient recommend-er system using collaborative filtering mechanism with k-separability approach for web based marketing. We follows the collaborative recommender method in which a user rating is aggregation of various characters using matrix but dataset becomes very noisy and difficult to separate. So, the K-Separability approach extends linear separability of data clusters into k>2 segments on the discriminating hyperplane. It can be implemented by single layer or 2-layer perceptron. K-Separability is able to uncover complex statistical dependencies i.e. positive or negative. Finally, We don't need to filter the neighborhood of the target filter as other systems. All neighborhoods are considered and extremely useful in case of sparse dataset.
A Principled Framework for Evaluating Summarizers: Comparing Models of Summary Quality against Human Judgments

Going further At this point, the theorem is proved. While for every summarizer σ there exists at least one tuple (θ,O), in practice there exist multiple tuples, and the one proposed by the proof would not be useful to rank models of summary quality. We can formulate an algorithm which constructs θ from σ and which yields an ordering of candidate summaries. Let σD\{s1,...,sn} be the summarizer σ which still uses D as initial document collection, but which is not allowed to output sentences from {s1, . . . , sn} in the final summary. For a given summary S to score, let Rσ,S be the smallest set of sentences {s1, . . . , sn} that one has to remove fromD such that σD\R outputs S. Then the definition of θσ follows:
entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation

Knowledge Graphs have proven to be extremely valuable to recommender systems, as they enable hybrid graph-based recommendation models encompassing both collaborative and content information. Leveraging this wealth of heterogeneous information for top-N item recommendation is a challenging task, as it requires the ability of effectively encoding a diversity of semantic relations and connectivity patterns. In this work, we propose entity2rec, a novel approach to learning user-item relatedness from knowledge graphs for top-N item recommendation. We start from a knowledge graph modeling user-item and item-item relations and we learn property-specific vector representations of users and items applying neural language models on the network. These representations are used to create property-specific user-item relatedness features, which are in turn fed into learning to rank algorithms to learn a global relatedness model that optimizes top-N item recommendations. We evaluate the proposed approach in terms of ranking quality on the MovieLens 1M dataset, outperforming a number of state-of-the-art recommender systems, and we assess the importance of property-specific relatedness scores on the overall ranking quality.
FPGA implementation of an advanced encoding and decoding architecture of polar codes

Polar code, newly formulated by Erdal Arikan, has got a wide recognition from the information theory community. Polar code achieves the capacity of the class of symmetric binary memory less channels. In this paper, we propose efficient hardware architecture on a FPGA platform using Xilinx Virtex VI for implementing the advanced encoding and decoding schemes. The performance of the proposed architecture out performs the existing techniques such as: successive cancellation decoder, list successive cancellation, belief propagation etc; with respect to bit error rate and resource utilization.
Fatal asphyxial episodes in the very young: classification and diagnostic issues.

Infants and young children are exposed to a relatively limited range of circumstances that may result in accidental or inflicted asphyxial deaths. These usually involve situations that interfere with oxygen uptake by the blood, or that decrease the amount of circulating oxygen. Typically infants and toddlers asphyxiate in sleeping accidents where they smother when their external airways are covered, hang when clothing is caught on projections inside cots, or wedge when they slip between mattresses and walls. Overlaying may cause asphyxiation due to a combination of airway occlusion and mechanical asphyxia, as may inflicted asphyxia with a pillow. The diagnosis of asphyxiation in infancy is difficult as there are usually no positive findings at autopsy and so differentiating asphyxiation from sudden infant death syndrome (SIDS) based purely on the pathological features will usually not be possible. Similarly, the autopsy findings in inflicted and accidental suffocation will often be identical. Classifications of asphyxia are sometimes confusing as particular types of asphyxiating events may involve several processes and so it may not be possible to precisely compartmentalize a specific incident. For this reason asphyxial events have been classified as being due to: insufficient oxygen availability in the surrounding environment, critical reduction of oxygen transfer from the atmosphere to the blood, impairment of oxygen transport in the circulating blood, or compromise of cellular oxygen uptake. The range of possible findings at the death scene and autopsy are reviewed, and the likelihood of finding markers/indicators of asphyxia is discussed. The conclusion that asphyxiation has occurred often has to be made by integrating aspects of the history, scene, and autopsy, while recognizing that none of these are necessarily pathognomonic, and also by excluding other possibilities. However, even after full investigation a diagnosis of asphyxia may not be possible and a number of issues concerning possible lethal terminal mechanisms may remain unresolved.
Hypnosis and Mindfulness: The Twain Finally Meet.

Mindfulness meditation (or simply mindfulness) is an ancient method of attention training. Arguably, developed originally by the Buddha, it has been practiced by Buddhists over 2,500 years as part of their spiritual training. The popularity in mindfulness has soared recently following its adaptation as Mindfulness-Based Stress Management by Jon Kabat-Zinn (1995). Mindfulness is often compared to hypnosis but not all assertions are accurate. This article, as a primer, delineates similarities and dissimilarities between mindfulness and hypnosis in terms of 12 specific facets, including putative neuroscientific findings. It also provides a case example that illustrates clinical integration of the two methods.
Automated gesture tracking in head-fixed mice

The preparation consisting of a head-fixed mouse on a spherical or cylindrical treadmill offers unique advantages in a variety of experimental contexts. Head fixation provides the mechanical stability necessary for optical and electrophysiological recordings and stimulation. Additionally, it can be combined with virtual environments such as T-mazes, enabling these types of recording during diverse behaviors.


NEW METHOD
In this paper we present a low-cost, easy-to-build acquisition system, along with scalable computational methods to quantitatively measure behavior (locomotion and paws, whiskers, and tail motion patterns) in head-fixed mice locomoting on cylindrical or spherical treadmills.



Several custom supervised and unsupervised methods have been developed for measuring behavior in mice. However, to date there is no low-cost, turn-key, general-purpose, and scalable system for acquiring and quantifying behavior in mice.



We benchmark our algorithms against ground truth data generated either by manual labeling or by simpler methods of feature extraction. We demonstrate that our algorithms achieve good performance, both in supervised and unsupervised settings.



We present a low-cost suite of tools for behavioral quantification, which serve as valuable complements to recording and stimulation technologies being developed for the head-fixed mouse preparation.
Metopic suture in fetuses with Apert syndrome at 22-27 weeks of gestation.

To examine the possible association of skull deformity and the development of the cranial sutures in fetuses with Apert syndrome.



Three-dimensional (3D) ultrasound was used to examine the metopic and coronal sutures in seven fetuses with Apert syndrome at 22-27 weeks of gestation. The gap between the frontal bones in the transverse plane of the head at the level of the cavum septi pellucidi was measured and compared to findings in 120 anatomically normal fetuses undergoing routine ultrasound examination at 16-32 weeks.



In the normal group, the gap between the frontal bones in the metopic suture at the level of the cavum septi pellucidi, decreased significantly with gestation from a mean of 2.2 mm (5th and 95th centiles: 1.5 mm and 2.9 mm) at 16 weeks to 0.9 mm (5th and 95th centiles: 0.3 mm and 1.6 mm) at 32 weeks. In the seven cases with Apert syndrome, two-dimensional ultrasound examination demonstrated the characteristic features of frontal bossing, depressed nasal bridge and bilateral syndactyly. On 3D examination there was complete closure of the coronal suture and a wide gap in the metopic suture (15-23 mm).



In normal fetuses, cranial bones are believed to grow in response to the centrifugal pressure from the expanding brain and proximity of the dura to the suture is critical in maintaining its patency. In Apert syndrome, the frontal bossing may be a mere consequence of a genetically predetermined premature closure of the coronal suture. Alternatively, there is a genetically predetermined deformation of the brain, which in turn, through differential stretch of the dura in the temporal and frontal regions, causes premature closure of the coronal suture and impaired closure of the metopic suture.
The relative neighbourhood graph of a finite planar set

The relative neighbourhood graph (RNG) of a set of n points on the plane is defined. The ability of the RNG to extract a perceptually meaningful structure from the set of points is briefly discussed and compared to that of two other graph structures: the minimal spanning tree (MST) and the Delaunay (Voronoi) triangulation (DT). It is shown that the RNG is a superset of the MST and a subset of the DT. Two algorithms for obtaining the RNG of n points on the plane are presented. One algorithm runs in 0(n 2) time and the other runs in 0(n 3) time but works also for the d-dimensional case. Finally, several open problems concerning the RNG in several areas such as geometric complexity, computational perception, and geometric probability, are outlined. Relative neighbourhood graph Minimal spanning tree Triangulations Delaunay triangulation Dot patterns Computational perception Pattern recognition Algorithms Geometric complexity Geometric probability CR Categories: 3.36 3.63 5.25 5.32 5.5
An Efficient Hardware Random Number Generator Based on the MT Method

Mersenne Twister (MT) algorithm is one of the most widely used long-period uniform random number generators. In this paper, we present a novel and efficient hardware architecture for MT method. Our design is implemented on a Xilinx XC6VLX240T-1 FPGA device at 450 MHz. It takes up 0.1% of the device and produces 450 million samples per second, which is 2.25 times faster than a dedicated software version running on a 2.67-GHz Intel core i5 multi-core processor. A dedicated 3R/1W RAM structure is also proposed. It is capable of providing 3 reads and 1 write concurrently in a single clock cycle and is the key component for the entire system to achieve 1 sample-per-cycle throughput. The architecture is also implemented on different FPGA devices. Experimental results show that our generator is superior to those existing architectures reported in the literatures in both performance and hardware complexity. The samples generated by our design are verified via the standard statistics testing suites of Diehard and TestU01.
Optimality of human teachers for robot learners

In this paper we address the question of how closely everyday human teachers match a theoretically optimal teacher. We present two experiments in which subjects teach a concept to our robot in a supervised fashion. In the first experiment we give subjects no instructions on teaching and observe how they teach naturally as compared to an optimal strategy. We find that people are suboptimal in several dimensions. In the second experiment we try to elicit the optimal teaching strategy. People can teach much faster using the optimal teaching strategy, however certain parts of the strategy are more intuitive than others.