nils-herrmann/clean_scirepeval huggingface dataset:
The neocortical microcircuit as a tabula rasa.

The neocortex has a high capacity for plasticity. To understand the full scope of this capacity, it is essential to know how neurons choose particular partners to form synaptic connections. By using multineuron whole-cell recordings and confocal microscopy we found that axons of layer V neocortical pyramidal neurons do not preferentially project toward the dendrites of particular neighboring pyramidal neurons; instead, axons promiscuously touch all neighboring dendrites without any bias. Functional synaptic coupling of a small fraction of these neurons is, however, correlated with the existence of synaptic boutons at existing touch sites. These data provide the first direct experimental evidence for a tabula rasa-like structural matrix between neocortical pyramidal neurons and suggests that pre- and postsynaptic interactions shape the conversion between touches and synapses to form specific functional microcircuits. These data also indicate that the local neocortical microcircuit has the potential to be differently rewired without the need for remodeling axonal or dendritic arbors.
Frequency tunable antenna for LTE (4G) handsets operating in the 2.3–2.7GHz global roaming band

A frequency tunable antenna for 4G global roaming devices operating in the 2.3-2.7GHz band is presented. Both the design and manufacturing methods are described and measured data are provided. Antenna is a half-patch with a reconfigurable aperture realized by a collection of shorting pins that are controlled by DC signals. The design follows the teachings of the patented self-structuring antenna technology and can be operated either in open or closed loop fashion. The frequency tunable feature of the antenna also makes it immune to detuning when used in a closed loop control system. Though the design is compatible with a multitude of manufacturing and embedding methods, the particular prototype was built by wire-bonding bare-die SPST switches onto the antenna board.
Efficient fuzzy full-text type-ahead search

Traditional information systems return answers after a user submits a complete query. Users often feel “left in the dark” when they have limited knowledge about the underlying data and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step toward solving this problem. In this paper, we study a new information-access paradigm, called “type-ahead search” in which the system searches the underlying data “on the fly” as the user types in query keywords. It extends autocomplete interfaces by allowing keywords to appear at different places in the underlying data. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incremental-search algorithms for both single-keyword queries and multi-keyword queries, using previously computed and cached results in order to achieve a high interactive speed. We develop novel techniques to support fuzzy search by allowing mismatches between query keywords and answers. We have deployed several real prototypes using these techniques. One of them has been deployed to support type-ahead search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency.
Display-relative calibration for optical see-through head-mounted displays

Optical see-through head-mounted displays (OSTHMDs) have many advantages in augmented reality application, but their utility in practical applications has been limited by the complexity of calibration. Because the human subject is an inseparable part of the eye-display system, previous methods for OSTHMD calibration have required extensive manual data collection using either instrumentation or manual point correspondences and are highly dependent on operator skill. This paper describes display-relative calibration (DRC) for OSTHMDs, a new two phase calibration method that minimizes the human element in the calibration process and ensures reliable calibration. Phase I of the calibration captures the parameters of the display system relative to a normalized reference frame and is performed in a jig with no human factors issues. The second phase optimizes the display for a specific user and the placement of the display on the head. Several phase II alternatives provide flexibility in a variety of applications including applications involving untrained users.
A ] 1 0 Ju l 2 00 1 Nonlinear Sequence Transformations : Computational Tools for the Acceleration of Convergence and the Summation of Divergent Series

Convergence problems occur abundantly in all branches of mathematics or in the mathematical treatment of the sciences. Sequence transformations are principal tools to overcome convergence problems of the kind. They accomplish this by converting a slowly converging or diverging input sequence {sn} ∞ n=0 into another sequence {s ′ n }∞ n=0 with hopefully better numerical properties. Padé approximants, which convert the partial sums of a power series to a doubly indexed sequence of rational functions, are the best known sequence transformations, but the emphasis of the review will be on alternative sequence transformations which for some problems provide better results than Padé approximants.
EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial

Neurofeedback is attracting renewed interest as a method to self-regulate one's own brain activity to directly alter the underlying neural mechanisms of cognition and behavior. It not only promises new avenues as a method for cognitive enhancement in healthy subjects, but also as a therapeutic tool. In the current article, we present a review tutorial discussing key aspects relevant to the development of electroencephalography (EEG) neurofeedback studies. In addition, the putative mechanisms underlying neurofeedback learning are considered. We highlight both aspects relevant for the practical application of neurofeedback as well as rather theoretical considerations related to the development of new generation protocols. Important characteristics regarding the set-up of a neurofeedback protocol are outlined in a step-by-step way. All these practical and theoretical considerations are illustrated based on a protocol and results of a frontal-midline theta up-regulation training for the improvement of executive functions. Not least, assessment criteria for the validation of neurofeedback studies as well as general guidelines for the evaluation of training efficacy are discussed.
Stability of small-scale UAV helicopters and quadrotors with added payload mass under PID control

The application of rotorcraft to autonomous load carrying and transport is a new frontier for Unmanned Aerial Vehicles (UAVs). This task requires that hovering vehicles remain stable and balanced in flight as payload mass is added to the vehicle. If payload is not loaded centered or the vehicle properly trimmed for offset loads, the robot will experience bias forces that must be rejected. In this paper, we explore the effect of dynamic load disturbances introduced by instantaneously increased payload mass and how those affect helicopters and quadrotors under ProportionalIntegral-Derivative flight control. We determine stability bounds within which the changing mass-inertia parameters of the system due to the acquired object will not destabilize these aircraft with this standard flight controller. Additionally, we demonstrate experimentally the stability behavior of a helicopter undergoing a range of instantaneous step pay-
Aspirations and realizations: The smart city of Seattle

Smart city initiatives have been launched on every continent. That notwithstanding the concept of “smart city” has remained ambiguous. We systematically interviewed officials of an acclaimed Smart City (Seattle) and explicitly asked the officials for their own definitions of “smart city,” which we then compared to the respective projects run by that City. While the definitions given by the practitioners were found different from those in the literature, the smart city projects lived up and matched the practitioner definitions to a high degree. We document the projects and their expected and realized benefits, which illustrate where a leading City government is headed in terms of smart government. However, “Smart City” initiatives in local government might be only a steppingstone in making the greater urban space a “smart city,” which appears to be a more challenging undertaking.
User profile model: A user dimension based classification

As the amount of information offered by information systems is increasing exponentially, the need of personalized approaches for information access increases. This work discusses user profiles designed for providing personalized information access. We first present a general classification of research directions on adaptive systems, followed by a state-of-the-art study about user profiling. We propose then a new classification approach of user profile model. This classification is based on the user dimensions considered to build the user profile.
Cross-platform model-driven development of mobile applications with md2

Mobile applications usually need to be provided for more than one operating system. Developing native apps separately for each platform is a laborious and expensive undertaking. Hence, cross-platform approaches have emerged, most of them based on Web technologies. While these enable developers to use a single code base for all platforms, resulting apps lack a native look & feel. This, however, is often desired by users and businesses. Furthermore, they have a low abstraction level. We propose MD2, an approach for model-driven cross-platform development of apps. With MD2, developers specify an app in a high-level (domain-specific) language designed for describing business apps succinctly. From this model, purely native apps for Android and iOS are automatically generated. MD2 was developed in close cooperation with industry partners and provides means to develop data-driven apps with a native look and feel. Apps can access the device hardware and interact with remote servers.
Robust Algorithm for Heart Rate ( HR ) Detection and Heart Rate Variability ( HRV ) Estimation

We present algorithm for Heart Rate detection based on Short-Term Autocorrelation Center Clipping method. This algorithm is dedicated for biological signal detection, electrocardiogram, in noisy environment with lot of artifacts. Using this algorithm is also possible detect the R pointers in the PQRST complex of the ECG signal. In this paper the new implementation of the heart rate variability estimation is also presented. HRV module is based on parametric and non-parametric methods of the power spectral density computation.
Social Network Analysis and Mining for Business Applications

Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored.
 In this article we use a business process classification framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.
Manipulation Planning with Probabilistic Roadmaps

This paper deals with motion planning for robots manipulating movable objects among obstacles. We propose a general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches. The proposed algorithm relies on a topological property that characterizes the existence of solutions in the subspace of configurations where the robot grasps the object placed at a stable position. It allows us to devise a manipulation planner that captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. Experiments conducted with the planner in simulated environments demonstrate its efficacy to solve complex manipulation problems. manipulation task planning, path planning, probabilistic roadmaps
Control of Dynamics in Brain Networks

The ability to effectively control brain dynamics holds great promise for the enhancement of cognitive function in humans, and the betterment of their quality of life. Yet, successfully controlling dynamics in neural systems is challenging, in part due to the immense complexity of the brain and the large set of interactions that can drive any single change. While we have gained some understanding of the control of single neurons, the control of large-scale neural systems—networks of multiply interacting components— remains poorly understood. Efforts to address this gap include the construction of tools for the control of brain networks, mostly adapted from control and dynamical systems theory. Informed by current opportunities for practical intervention, these theoretical contributions provide models that draw from a wide array of mathematical approaches. We present intriguing recent developments for effective strategies of control in dynamic brain networks, and we also describe potential mechanisms that underlie such processes. We review efforts in the control of general neurophysiological processes with implications for brain development and cognitive function, as well as the control of altered neurophysiological processes in medical contexts such as anesthesia administration, seizure suppression, and deep-brain stimulation for Parkinson’s disease. We conclude with a forward-looking discussion regarding how emerging results from network control—especially approaches that deal with nonlinear dynamics or more realistic trajectories for control transitions—could be used to directly address pressing questions in neuroscience.
Exploration and practice of inter-bank application based on blockchain

Blockchain is considered as the important technological innovation behind Bitcoin system. It facilitates the transaction payment process by creating a decentralized, general ledger to improve regulatory capacity and remove unnecessary intermediaries. At present, the blockchain technology has been employed in the financial industry for a wide range of experimental application and exploration. In this paper, we firstly analyze the principle architecture and the technical characteristics of blockchain. Then its current research achievements and application scenarios are introduced. Finally, the application of blockchain in China Foreign Exchange Trade System is designed and explored. Combining with the credit matching trading system X-Swap, an inter-bank application based on the blockchain technology is implemented.
ANTIPODAL BIPOLAR FUZZY GRAPHS

The concept of an antipodal bipolar fuzzy graph of a given bipolar fuzzy graph is introduced. Characterizations of antipodal bipolar fuzzy graphs are presented when the bipolar fuzzy graph is complete or strong. Some isomorphic properties of antipodal bipolar fuzzy graph are discussed. The notion of self median bipolar fuzzy graphs of a given bipolar fuzzy graph is also introduced.
MIXED_FIT: A SAS ® Macro to Assess Model Fit and Adequacy for Two-Level

As multilevel models (MLMs) are useful in understanding relationships existent in hierarchical data structures, these models have started to be used more frequently in research developed in social and health sciences. In order to draw meaningful conclusions from MLMs, researchers need to make sure that the model fits the data. Model fit, and thus, ultimately model selection can be assessed by examining changes in several fit indices across nested and/or nonnested models [e.g., -2 log likelihood (-2LL), Akaike Information Criterion (AIC), and Schwarz’s Bayesian Information Criterion (BIC)]. In addition, the difference in pseudo-R 2 is often used to examine the practical significance between two nested models. Considering the importance of using all of these measures when determining model selection, researchers who use analyze multilevel models would benefit from being able to easily assess model fit across estimated models. Whereas SAS PROC MIXED produces the -2LL, AIC, and BIC, it does not provide the actual change in these fit indices or the change in pseudo-R 2 between different nested and non-nested models. In order to make this information more attainable, Bardenheier (2009) developed a macro that allowed researchers using PROC MIXED to obtain the test statistic for the difference in -2LL along with the p-value of the Likelihood Ratio Test (LRT). As an extension of Bardenheier’s work, this paper provides a comprehensive SAS macro that incorporates changes in model fit statistics (-2LL, AIC and BIC) as well as change in pseudo-R 2 . By utilizing data from PROC MIXED ODS tables, the macro produces a comprehensive table of changes in model fit measures. Thus, this expanded macro allows SAS users to examine model fit in both nested and non-nested models and both in terms of statistical and practical significance. This paper provides a review of the different methods used to assess model fit in multilevel analysis, the macro programming language, an executed example of the macro, and a copy of the complete macro.
Intrinsic and extrinsic effects on image memorability

Previous studies have identified that images carry the attribute of memorability, a predictive value of whether a novel image will be later remembered or forgotten. Here we investigate the interplay between intrinsic and extrinsic factors that affect image memorability. First, we find that intrinsic differences in memorability exist at a finer-grained scale than previously documented. Second, we test two extrinsic factors: image context and observer behavior. Building on prior findings that images that are distinct with respect to their context are better remembered, we propose an information-theoretic model of image distinctiveness. Our model can automatically predict how changes in context change the memorability of natural images. In addition to context, we study a second extrinsic factor: where an observer looks while memorizing an image. It turns out that eye movements provide additional information that can predict whether or not an image will be remembered, on a trial-by-trial basis. Together, by considering both intrinsic and extrinsic effects on memorability, we arrive at a more complete and fine-grained model of image memorability than previously available.
Light-Weight and Robust Security-Aware D2D-Assist Data Transmission Protocol for Mobile-Health Systems

With the rapid advancement of technology, healthcare systems have been quickly transformed into a pervasive environment, where both challenges and opportunities abound. On the one hand, the proliferation of smart phones and advances in medical sensors and devices have driven the emergence of wireless body area networks for remote patient monitoring, also known as mobile-health (M-health), thereby providing a reliable and cost effective way to improving efficiency and quality of health care. On the other hand, the advances of M-health systems also generate extensive medical data, which could crowd today’s cellular networks. Device-to-device (D2D) communications have been proposed to address this challenge, but unfortunately, security threats are also emerging because of the open nature of D2D communications between medical sensors and highly privacy-sensitive nature of medical data. Even, more disconcerting is healthcare systems that have many characteristics that make them more vulnerable to privacy attacks than in other applications. In this paper, we propose a light-weight and robust security-aware D2D-assist data transmission protocol for M-health systems by using a certificateless generalized signcryption (CLGSC) technique. Specifically, we first propose a new efficient CLGSC scheme, which can adaptively work as one of the three cryptographic primitives: signcryption, signature, or encryption, but within one single algorithm. The scheme is proved to be secure, simultaneously achieving confidentiality and unforgeability. Based on the proposed CLGSC algorithm, we further design a D2D-assist data transmission protocol for M-health systems with security properties, including data confidentiality and integrity, mutual authentication, contextual privacy, anonymity, unlinkability, and forward security. Performance analysis demonstrates that the proposed protocol can achieve the design objectives and outperform existing schemes in terms of computational and communication overhead.
Efficient rectenna with wide dynamic input power range for 900 MHz wireless power transfer applications

This work demonstrates the design of a rectenna to operate over wide dynamic input power range. It utilizes an adaptive reconfigurable rectifier to overcome the issue of early breakdown voltage in conventional rectifiers. A depletion-mode field-effect transistor has been introduced to operate as a switch and compensate at low and high input power levels for the rectifier. In addition, a meandered monopole antenna has been exploited to collect RF energy. The rectifier design achieves 40% of RF-DC power conversion efficiency over a wide dynamic input power range from −17 dBm to 27 dBm and the antenna exhibits a directivity of 1.92 dBi as well as a return loss of −33 dB. The rectenna is designed to operate in the 900 MHz ISM band and suitable for Wireless Power Transfer (WPT) applications.
Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex

Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships but require efficient methods for cell capture and mRNA sequencing. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths, the limitations of shallow sequencing have not been investigated directly. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (∼50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In the developing cortex, we identify diverse cell types, including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.