nils-herrmann/clean_scirepeval huggingface dataset:
Enhanced spatial multiplexing for rate-2 MIMO of DVB-NGH system

The demand for high bit-rate service transmission is increasing for digital terrestrial broadcasting just like other transmission technologies. Multiple-input multiple-output (MIMO) transmission is one of the most promising techniques to fulfill this demand for high transmission rates. This paper introduces enhanced spatial multiplexing (eSM) scheme adopted as technical baseline for the DVB next generation handheld (DVB-NGH) system. Most technical challenges for MIMO transmission in broadcasting comes from the rate-2 spatial multiplexing performance degradation in high correlation channel condition, which frequently happens in broadcasting owing to the line-of-sight (LOS) channel conditions. The proposed eSM exploits a pre-coding matrix optimized to overcome this condition. When combined with phase hopping to avoid specific harmful condition, eSM minimizes the performance loss under high correlation channels whilst keeping maximum multiplexing gain over rich scattering channels.
Solving the Rubik's Cube Without Human Knowledge

A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision. Recently, deep reinforcement learning algorithms combined with self-play have achieved superhuman proficiency in Go, Chess, and Shogi without human data or domain knowledge. In these environments, a reward is always received at the end of the game; however, for many combinatorial optimization environments, rewards are sparse and episodes are not guaranteed to terminate. We introduce Autodidactic Iteration: a novel reinforcement learning algorithm that is able to teach itself how to solve the Rubik’s Cube with no human assistance. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves — less than or equal to solvers that employ human domain knowledge.
IEEE 802.11ax uplink scheduler to minimize, delay: A classic problem with new constraints

In order to meet the continuously increasing demands for high throughput in wireless networks, IEEE 802 LAN/MAN Standard Committee is developing IEEE 802.11ax: a new amendment for the Wi-Fi standard. This amendment provides various ways to improve the efficiency of Wi-Fi. The most revolutionary one is OFDMA. Apart from obvious advantages, such as decreasing overhead for short packet transmission at high rates and improving robustness to frequency selective interference, being used for uplink transmission, OFDMA can increase power spectral density and, consequently, user data rates. However, the gain of OFDMA mainly depends on the resource scheduling between users. The peculiarities of OFDMA implementation in Wi-Fi completely change properties of classic schedulers used in other OFDMA systems, e.g. LTE. In the paper, we consider the usage of OFDMA in Wi-Fi for uplink transmission. We study peculiarities of OFDMA in Wi-Fi, adapt classic schedulers to Wi-Fi, explaining why they do not perform well. Finally we develop a novel scheduler, MUTAX, and evaluate its performance with simulation.
Beating heart motion prediction for robust visual tracking

In the context of minimally invasive cardiac surgery, robotic assistance has significantly helped surgeons to overcome difficulties related to the minimally invasive procedure. Recently, techniques have been proposed for active canceling the beating heart motion for improving the accuracy of the surgical gestures. In this scenario, computer vision techniques can be applied for estimating the heart motion based solely on natural structures on the heart surface. However, visual tracking is complicated by the particular lighting conditions and clutter (smoke, liquids, etc) during surgery. Another challenging problem are the occasional occlusions by surgical instruments. In order to overcome these problems, we exploit the quasi-periodicity of the beating heart motion for increasing the robustness of the visual tracking task. In this paper, a novel time-varying dual Fourier series for modeling the quasi-periodic beating heart motion is proposed. For estimating the series parameters, an Extended Kalman Filter (EKF) is used. The proposed method is applied in a visual tracking task for bridging tracking disturbances and automatically reestablish tracking in cases of occlusions. The efficiency of the prediction method and the sensible improvements in the visual tracking task are demonstrated through in vivo experiments.
Light - fields of Circular Camera Arrays

The ray structure and sampling properties of different light-field representations inherently determine their use-cases. Currently prevalent linear data structures do not allow for joint processing of light-fields captured from multiple sides of a scene. In this paper, we review and highlight the differences in capturing and reconstruction between light-fields captured with linear and circular camera arrays. We also examine and improve the processing of light-fields captured with circular camera arrays with a focus on their use in reconstructing dense light-fields, by proposing a new resampling technique for circular light-fields. The proposed circular epipolar light-field structure creates a simple sinusoidal relation between the objects of the scene and their curves in the epipolar image, opening the way of efficient reconstruction of circular light-fields.
High yield exfoliation of two-dimensional chalcogenides using sodium naphthalenide.

Transition-metal dichalcogenides like molybdenum disulphide have attracted great interest as two-dimensional materials beyond graphene due to their unique electronic and optical properties. Solution-phase processes can be a viable method for producing printable single-layer chalcogenides. Molybdenum disulphide can be exfoliated into monolayer flakes using organolithium reduction chemistry; unfortunately, the method is hampered by low yield, submicron flake size and long lithiation time. Here we report a high-yield exfoliation process using lithium, potassium and sodium naphthalenide where an intermediate ternary Li(x)MX(n) crystalline phase (X=selenium, sulphur, and so on) is produced. Using a two-step expansion and intercalation method, we produce high-quality single-layer molybdenum disulphide sheets with unprecedentedly large flake size, that is up to 400 μm(2). Single-layer dichalcogenide inks prepared by this method may be directly inkjet-printed on a wide range of substrates.
A Top-Down Approach to Articulated Human Pose Estimation and Tracking

Both the tasks of multi-person human pose estimation and pose tracking in videos are quite challenging. Existing methods can be categorized into two groups: top-down and bottom-up approaches. In this paper, following the top-down approach, we aim to build a strong baseline system with three modules: human candidate detector, singleperson pose estimator and human pose tracker. Firstly, we choose a generic object detector among state-of-the-art methods to detect human candidates. Then, cascaded pyramid network is used to estimate the corresponding human pose. Finally, we use a flow-based pose tracker to render keypoint-association across frames, i.e., assigning each human candidate a unique and temporally-consistent id, for the multi-target pose tracking purpose. We conduct extensive ablative experiments to validate various choices of models and configurations. We take part in two ECCV’18 PoseTrack challenges: pose estimation and pose tracking.
Ambulatory screening tool for sleep apnea: analyzing a single-lead electrocardiogram signal (ECG)

The goal was to determine the utility and accuracy of automated analysis of single-lead electrocardiogram (ECG) data using two algorithms, cardiopulmonary coupling (CPC), and cyclic variation of heart rate (CVHR) to identify sleep apnea (SA). The CPC-CVHR algorithms were applied to identify SA by analyzing ECG from diagnostic polysomnography (PSG) from 47 subjects. The studies were rescored according to updated AASM scoring rules, both manually by a certified technologist and using an FDA-approved automated scoring software, Somnolyzer (Philips Inc., Monroeville, PA). The CPC+CVHR output of Sleep Quality Index (SQI), Sleep Apnea Indicator (SAI), elevated low frequency coupling broadband (eLFCBB) and elevated low frequency coupling narrow-band (eLFCNB) were compared to the manual and automated scoring of apnea hypopnea index (AHI). A high degree of agreement was noted between the CPC-CVHR against both the manually rescored AHI and the computerized scored AHI to identify patients with moderate and severe sleep apnea (AHI > 15). The combined CPC+CVHR algorithms, when compared to the manually scored PSG output presents sensitivity 89%, specificity 79%, agreement 85%, PPV (positive predictive value) 0.86 and NPV (negative predictive value) 0.83, and substantial Kappa 0.70. Comparing the output of the automated scoring software to the manual scoring demonstrated sensitivity 93%, specificity 79%, agreement 87%, PPV 0.87, NPV 0.88, and substantial Kappa 0.74. The CPC+CVHR technology performed as accurately as the automated scoring software to identify patients with moderate to severe SA, demonstrating a clinically powerful tool that can be implemented in various clinical settings to identify patients at risk for SA. NCT01234077.
Perceptual Assimilation and L2 Learning: Evidence from the Perception of Southern British English Vowels by Native Speakers of Greek and Japanese

This study examined the extent to which previous experience with duration in first language (L1) vowel distinctions affects the use of duration when perceiving vowels in a second language (L2). Native speakers of Greek (where duration is not used to differentiate vowels) and Japanese (where vowels are distinguished by duration) first identified and rated the eleven English monophthongs, embedded in /bVb/ and /bVp/ contexts, in terms of their L1 categories and then carried out discrimination tests on those English vowels. The results demonstrated that both L2 groups were sensitive to durational cues when perceiving the English vowels. However, listeners were found to temporally assimilate L2 vowels to L1 category/categories. Temporal information was available in discrimination only when the listeners' L1 duration category/categories did not interfere with the target duration categories and hence the use of duration in such cases cannot be attributed to its perceptual salience as has been proposed.
Accuracy of Three-Dimensional Planning in Surgery-First Orthognathic Surgery: Planning Versus Outcome

The benefit of computer-assisted planning in orthognathic surgery (OGS) has been extensively documented over the last decade. This study aimed to evaluate the accuracy of three-dimensional (3D) virtual planning in surgery-first OGS.



Fifteen patients with skeletal class III malocclusion who underwent bimaxillary OGS with surgery-first approach were included. A composite skull model was reconstructed using data from cone-beam computed tomography and stereolithography from a scanned dental cast. Surgical procedures were simulated using Simplant O&O software, and the virtual plan was transferred to the operation room using 3D-printed splints. Differences of the 3D measurements between the virtual plan and postoperative results were evaluated, and the accuracy was reported using root mean square deviation (RMSD) and the Bland-Altman method.



The virtual planning was successfully transferred to surgery. The overall mean linear difference was 0.88 mm (0.79 mm for the maxilla and 1 mm for the mandible), and the overall mean angular difference was 1.16°. The RMSD ranged from 0.86 to 1.46 mm and 1.27° to 1.45°, within the acceptable clinical criteria.



In this study, virtual surgical planning and 3D-printed surgical splints facilitated the diagnosis and treatment planning, and offered an accurate outcome in surgery-first OGS.
OPTIMAL POWER FLOW USING PARTICLE SWARM OPTIMIZATION

The Optimal Power Flow (OPF) is an important criterion in today’s power system operation and control due to scarcity of energy resources, increasing power generation cost and ever growing demand for electric energy. As the size of the power system increases, load may be varying. The generators should share the total demand plus losses among themselves. The sharing should be based on the fuel cost of the total generation with respect to some security constraints. Conventional optimization methods that make use of derivatives and gradients are, in general, not able to locate or identify the global optimum. Heuristic algorithms such as genetic algorithms (GA) and evolutionary programming have been recently proposed for solving the OPF problem. Unfortunately, recent research has identified some deficiencies in GA performance. Recently, a new evolutionary computation technique, called Particle Swarm Optimization (PSO), has been proposed and introduced. This technique combines social psychology principles in socio-cognition human agents and evolutionary computations. In this paper, a novel PSO based approach is presented to solve Optimal Power Flow problem.
Dermoscopic rainbow pattern in atypical fibroxanthoma.

why the pattern is found in other types of skin lesions with active vascularization, such as our patient’s scars. When first described in actinic keratosis, rosettes were characterized as ‘‘4 white points arranged as a 4-leaf clover.’’2 The sign has since been reported in other skin lesions such as squamous cell carcinoma, basal cell carcinoma, melanoma, and lichenoid keratosis.3--7 Rosettes are believed to be the result of an optical effect caused by interaction between polarized light and follicular openings.6 The rainbow pattern and rosettes are not considered to be specific dermoscopic features of the lesion. Since it appears that they are secondary effects of the interaction between different skin structures and polarized light, they will likely be observed in various types of skin lesions.
Dynamic pricing when consumers are strategic: Analysis of posted and contingent pricing schemes

We study dynamic pricing policies for a monopolist selling perishable products over a finite time horizon to strategic buyers. Buyers are strategic in the sense that they anticipate the firm’s price policies. It is expensive and administratively difficult for most brick and mortar retailers to change prices, placing limits on the number of price changes and the types of pricing policies they can adopt. The simplest policy is to commit to a set of price changes. A more complex alternative is to let the price depend on sales history. We investigate two pricing schemes that we call posted and contingent pricing. Using the posted pricing scheme, the firm announces a set of prices at the beginning of the horizon. In the contingent pricing scheme, price evolution depends upon demand realization. Our focus is on the posted pricing scheme because of its ease of implementation. Counter to intuition, we find that neither a posted pricing scheme nor a contingent pricing scheme is dominant and the difference in expected revenues of these two schemes is small. Limiting the number of price changes will result in a decrease in expected revenues. We show that a multi-unit auction with a reservation price provides an upper bound for expected revenues for both pricing schemes. Numerical examples suggest that a posted pricing scheme with two or three price changes is enough to achieve revenues that are close to the upper bound. Dynamic pricing is only useful when strategic buyers perceive scarcity. We study the impact of scarcity and derive the optimal stocking levels for large markets. Finally, we investigate whether or not it is optimal for the seller to conceal inventory or sales information from buyers. A firm benefits if it does not reveal the number of units it has available for sale at the beginning of the season, or subsequently withholds information about the number of units sold.
Adversarial Detection with Model Interpretation

Machine learning (ML) systems have been increasingly applied in web security applications such as spammer detection, malware detection and fraud detection. These applications have an intrinsic adversarial nature where intelligent attackers can adaptively change their behaviors to avoid being detected by the deployed detectors. Existing efforts against adversaries are usually limited by the type of applied ML models or the specific applications such as image classification. Additionally, the working mechanisms of ML models usually cannot be well understood by users, which in turn impede them from understanding the vulnerabilities of models nor improving their robustness. To bridge the gap, in this paper, we propose to investigate whether model interpretation could potentially help adversarial detection. Specifically, we develop a novel adversary-resistant detection framework by utilizing the interpretation of ML models. The interpretation process explains the mechanism of how the target ML model makes prediction for a given instance, thus providing more insights for crafting adversarial samples. The robustness of detectors is then improved through adversarial training with the adversarial samples. A data-driven method is also developed to empirically estimate costs of adversaries in feature manipulation. Our approach is model-agnostic and can be applied to various types of classification models. Our experimental results on two real-world datasets demonstrate the effectiveness of interpretation-based attacks and how estimated feature manipulation cost would affect the behavior of adversaries.
Dynamic and temporal user profiling for personalized recommenders using heterogeneous data sources

In modern Web applications, the process of user-profiling provides a way to capture user-specific information, which then serves as a source for designing personalized user experiences. Currently, such information about a particular user is available from multiple online sources/services, like social media applications, professional/social networking sites, location based service providers or even from simple Web-pages. The nature of this data being truly heterogeneous, high in volume and also highly dynamic over time, the problem of collecting these data artifacts from disparate sources, to enable complete user-profiling can be challenging. In this paper, we present an approach to dynamically build a structured user profile, that emphasizes the temporal nature to capture dynamic user behavior. The user profile is compiled from multiple, heterogeneous data sources which capture dynamic user actions over time, to capture changing preferences accurately. Natural language processing techniques, machine learning and concepts of the semantic Web were used for capturing relevant user data and implement the proposed “3D User Profile”. Our technique also supports the representation of the generated user profiles as structured data so that other personalized recommendation systems and Semantic Web/Linked Open Data applications can consume them for providing intelligent, personalized services.
A Parallel Algorithm for POMDP Solution

Most exact algorithms for solving partially observable Markov decision processes (POMDPs) are based on a form of dynamic programming in which a piecewise-linear and convex representation of the value function is updated at every iteration to more accurately approximate the true value function. However, the process is computationally expensive, thus limiting the practical application of POMDPs in planning. To address this current limitation, we present a parallel distributed algorithm based on the Restricted Region method proposed by Cassandra, Littman and Zhang . We compare performance of the parallel algorithm against a serial implementation Restricted Region.
Methods of Conflict Resolution in African Traditional Society

This study examined the patterns or mechanism for conflict resolution in traditional African societies with particular reference to Yoruba and Igbo societies in Nigeria and Pondo tribe in South Africa. The paper notes that conflict resolution in traditional African societies provides opportunity to interact with the parties concerned, it promotes consensus-building, social bridge reconstructions and enactment of order in the society. The paper submits further that the western world placed more emphasis on the judicial system presided over by council of elders, kings’ courts, peoples (open place)
Time Provisioning Evaluation of KVM, Docker and Unikernels in a Cloud Platform

Unikernels are a promising alternative for application deployment in cloud platforms. They comprise a very small footprint, providing better deployment agility and portability among virtualization platforms. Similar to Linux containers, they are a lightweight alternative for deploying distributed applications based on microservices. However, the comparison of unikernels with other virtualization options regarding the concurrent provisioning of instances, as in the case of microservices-based applications, is still lacking. This paper provides an evaluation of KVM (Virtual Machines), Docker (Containers), and OSv (Unikernel), when provisioning multiple instances concurrently in an OpenStack cloud platform. We confirmed that OSv outperforms the other options and also identified opportunities for optimization.
The job demands-resources model of burnout.

The job demands-resources (JD-R) model proposes that working conditions can be categorized into 2 broad categories, job demands and job resources. that are differentially related to specific outcomes. A series of LISREL analyses using self-reports as well as observer ratings of the working conditions provided strong evidence for the JD-R model: Job demands are primarily related to the exhaustion component of burnout, whereas (lack of) job resources are primarily related to disengagement. Highly similar patterns were observed in each of 3 occupational groups: human services, industry, and transport (total N = 374). In addition, results confirmed the 2-factor structure (exhaustion and disengagement) of a new burnout instrument--the Oldenburg Burnout Inventory--and suggested that this structure is essentially invariant across occupational groups.
A Markov Chain Model Checker

Markov chains are widely used in the context of performance and reliability evaluation of systems of various nature. Model checking of such chains with respect to a given (branching) temporal logic formula has been proposed for both the discrete [17, 6] and the continuous time setting [4, 8]. In this paper, we describe a prototype model checker for discrete and continuous-time Markov chains, the Erlangen–Twente Markov Chain Checker (E T MC), where properties are expressed in appropriate extensions of CTL. We illustrate the general benefits of this approach and discuss the structure of the tool. Furthermore we report on first successful applications of the tool to non-trivial examples, highlighting lessons learned during development and application of E T MC.
Compressed Sensing Dynamic MRI Reconstruction Using GPU-accelerated 3D Convolutional Sparse Coding

In this paper, we introduce a fast alternating method for reconstructing highly undersampled dynamic MRI data using 3D convolutional sparse coding. The proposed solution leverages Fourier Convolution Theorem to accelerate the process of learning a set of 3D filters and iteratively refine the MRI reconstruction based on the sparse codes found subsequently. In contrast to conventional CS methods which exploit the sparsity by applying universal transforms such as wavelet and total variation, our approach extracts and adapts the temporal information directly from the MRI data using compact shift-invariant 3D filters. We provide a highly parallel algorithm with GPU support for efficient computation, and therefore, the reconstruction outperforms CPU implementation of the state-of-the art dictionary learning-based approaches by up to two orders of magnitude.