nils-herrmann/clean_scirepeval huggingface dataset:
Design of an online EEG based neurofeedback game for enhancing attention and memory

Brain-Computer Interface (BCI) is an alternative communication and control channel between brain and computer which finds applications in neuroprosthetics, brain wave controlled computer games etc. This paper proposes an Electroencephalogram (EEG) based neurofeedback computer game that allows the player to control the game with the help of attention based brain signals. The proposed game protocol requires the player to memorize a set of numbers in a matrix, and to correctly fill the matrix using his attention. The attention level of the player is quantified using sample entropy features of EEG. The statistically significant performance improvement of five healthy subjects after playing a number of game sessions demonstrates the effectiveness of the proposed game in enhancing their concentration and memory skills.
Proximity of the mandibular molar root apex from the buccal bone surface: a cone-beam computed tomographic study

The purpose of this study was to evaluate the proximity of the mandibular molar apex to the buccal bone surface in order to provide anatomic information for apical surgery.



Cone-beam computed tomography (CBCT) images of 127 mandibular first molars and 153 mandibular second molars were analyzed from 160 patients' records. The distance was measured from the buccal bone surface to the root apex and the apical 3.0 mm on the cross-sectional view of CBCT.



The second molar apex and apical 3 mm were located significantly deeper relative to the buccal bone surface compared with the first molar (p < 0.01). For the mandibular second molars, the distance from the buccal bone surface to the root apex was significantly shorter in patients over 70 years of age (p < 0.05). Furthermore, this distance was significantly shorter when the first molar was missing compared to nonmissing cases (p < 0.05). For the mandibular first molars, the distance to the distal root apex of one distal-rooted tooth was significantly greater than the distance to the disto-buccal root apex (p < 0.01). In mandibular second molar, the distance to the apex of C-shaped roots was significantly greater than the distance to the mesial root apex of non-C-shaped roots (p < 0.01).



For apical surgery in mandibular molars, the distance from the buccal bone surface to the apex and apical 3 mm is significantly affected by the location, patient age, an adjacent missing anterior tooth, and root configuration.
[Orbitozygomatic approaches to the skull base].

The paper is written in the lecture format and dedicated to one of the main basal approaches, the orbitozygomatic approach, that has been widely used by neurosurgeons for several decades. The authors describe the historical background of the approach development and the surgical technique features and also analyze the published data about application of the orbitozygomatic approach in surgery for skull base tumors and cerebral aneurysms.
Surgical anatomy and pathology of the middle ear.

Middle ear surgery is strongly influenced by anatomical and functional characteristics of the middle ear. The complex anatomy means a challenge for the otosurgeon who moves between preservation or improvement of highly important functions (hearing, balance, facial motion) and eradication of diseases. Of these, perforations of the tympanic membrane, chronic otitis media, tympanosclerosis and cholesteatoma are encountered most often in clinical practice. Modern techniques for reconstruction of the ossicular chain aim for best possible hearing improvement using delicate alloplastic titanium prostheses, but a number of prosthesis-unrelated factors work against this intent. Surgery is always individualized to the case and there is no one-fits-all strategy. Above all, both middle ear diseases and surgery can be associated with a number of complications; the most important ones being hearing deterioration or deafness, dizziness, facial palsy and life-threatening intracranial complications. To minimize risks, a solid knowledge of and respect for neurootologic structures is essential for an otosurgeon who must train him- or herself intensively on temporal bones before performing surgery on a patient.
Unsupervised adaptive transfer learning for Steady-State Visual Evoked Potential brain-computer interfaces

Recent advances in signal processing for the detection of Steady-State Visual Evoked Potentials (SSVEPs) have moved away from traditionally calibrationless methods, such as canonical correlation analysis, and towards algorithms that require substantial training data. In general, this has improved detection rates, but SSVEP-based brain-computer interfaces (BCIs) now suffer from the requirement of costly calibration sessions. Here, we address this issue by applying transfer learning techniques to SSVEP detection. Our novel Adaptive-C3A method incorporates an unsupervised adaptation algorithm that requires no calibration data. Our approach learns SSVEP templates for the target user and provides robust class separation in feature space leading to increased classification accuracy. Our method achieves significant improvements in performance over a standard CCA method as well as a transfer variant of the state-of-the art Combined-CCA method for calibrationless SSVEP detection.
A Stagewise Refinement Model for Detecting Salient Objects in Images

Deep convolutional neural networks (CNNs) have been successfully applied to a wide variety of problems in computer vision, including salient object detection. To detect and segment salient objects accurately, it is necessary to extract and combine high-level semantic features with low-levelfine details simultaneously. This happens to be a challenge for CNNs as repeated subsampling operations such as pooling and convolution lead to a significant decrease in the initial image resolution, which results in loss of spatial details and finer structures. To remedy this problem, here we propose to augment feedforward neural networks with a novel pyramid pooling module and a multi-stage refinement mechanism for saliency detection. First, our deep feedward net is used to generate a coarse prediction map with much detailed structures lost. Then, refinement nets are integrated with local context information to refine the preceding saliency maps generated in the master branch in a stagewise manner. Further, a pyramid pooling module is applied for different-region-based global context aggregation. Empirical evaluations over six benchmark datasets show that our proposed method compares favorably against the state-of-the-art approaches.
ForceHand Glove: A Wearable Force-Feedback Glove With Pneumatic Artificial Muscles (PAMs)

The upper limb of the human body has a higher likeliness of getting affected by muscle fatigue and injury, due to repetitive motion. This letter presents an interactive glove for the wrist of the human body. The assistive model is enabled with pneumatic actuators and stretch sensors, which support the user in performing wrist flexion, extension, pronation, and supination. The purpose of this glove is to provide the subject with a force-feedback feeling, and while a repetitive practice of such training is done, rehabilitation can be achieved. For evaluation, surface electromyographical signals were first used to study the effect of force feedback on associated muscle activation. Average % maximum voluntary isometric contraction of all subjects showed statistically reduced muscle activity in at least one associated muscle while performing all motions, enabled with actuators. Second, the force feedback generated by the glove at the user's wrist has been modeled using experiments with a force transducer. An application was then developed where the user was given the ability to interact with virtual objects and receive feedback through the glove. The evaluation of the feasibility of this application was done by measuring and comparing force profiles while generating an equal force in an ideal and a predicted environment.
Improvements of image sharing with steganography and authentication

Recently, Lin and Tsai proposed an image secret sharing scheme with steganography and authentication to prevent participants from the incidental or intentional provision of a false stego-image (an image containing the hidden secret image). However, dishonest participants can easily manipulate the stego-image for successful authentication but cannot recover the secret image, i.e., compromise the steganography. In this paper, we present a scheme to improve authentication ability that prevents dishonest participants from cheating. The proposed scheme also defines the arrangement of embedded bits to improve the quality of stego-image. Furthermore, by means of the Galois Field GF(2), we improve the scheme to a lossless version without additional pixels.
A high-speed, hierarchical 16×16 array of array multiplier design

Array multipliers are preferred for smaller operand sizes due to their simpler VLSI implementation, in-spite of their linear time complexity. The tree multipliers have time complexity of O (log n) but are less suitable for VLSI implementation since, being less regular, they require larger total routing length, which may degrade their performance. Some hybrid architectures called ‘array of array’ multipliers have intermediate performance. These multipliers have a time complexity better than array multipliers, and therefore becomes an obvious choice for higher performance multiplier designs of moderate operand sizes. In this paper a 16×16 unsigned ‘array of array’ multiplier circuit is designed with hierarchical structure and implemented using conventional CMOS logic in 0.6µm, N-well CMOS process (SCN_SUBM, lambda=0.3) of MOSIS. The proposed multiplier implementation shows large reduction in propagation delay and the average power consumption (at 20MHz) as compared to 16-bit Booth encoded Wallace tree multiplier by F Jalil . The total transistor count, maximum instantaneous power, leakage power, core area, total routing length and number of vias are also presented.
Error analysis of multi-hop free-space optical communication

In this paper we analyze the error performance of free-space optical (FSO) communication over multiple hops. We first develop an error model for a single hop based on visibility, atmospheric attenuation, and geometric spread of the light beam. We model atmospheric visibility by Gaussian distributions with mean and variance values to reflect clear and adverse weather conditions. Based on this, we find the end-to-end bit error distribution of the FSO link for single hop and multi-hop scenarios. We present simulation results for decoded relaying, where each hop decodes the signal before retransmitting. We demonstrate that multi-hop FSO communication achieves a significant reduction in the mean bit error rate and also reduces the variance of the bit error rate. We argue that by lowering mean error and error variance, multi-hop operation facilitates an efficient system design and improves the reliability of the FSO link by application of specific coding schemes (such as forward error correction techniques).
A knowledge creation info-structure to acquire and crystallize the tacit knowledge of health-care experts

Tacit knowledge of health-care experts is an important source of experiential know-how, yet due to various operational and technical reasons, such health-care knowledge is not entirely harnessed and put into professional practice. Emerging knowledge-management (KM) solutions suggest strategies to acquire the seemingly intractable and nonarticulated tacit knowledge of health-care experts. This paper presents a KM methodology, together with its computational implementation, to 1) acquire the tacit knowledge possessed by health-care experts; 2) represent the acquired tacit health-care knowledge in a computational formalism-i.e., clinical scenarios-that allows the reuse of stored knowledge to acquire tacit knowledge; and 3) crystallize the acquired tacit knowledge so that it is validated for health-care decision-support and medical education systems.
Deep Learning and Ensemble Methods for Domain Adaptation

Real world applications of machine learning in natural language processing can span many different domains and usually require a huge effort for the annotation of domain specific training data. For this reason, domain adaptation techniques have gained a lot of attention in the last years. In order to derive an effective domain adaptation, a good feature representation across domains is crucial as well as the generalisation ability of the predictive model. In this paper we address the problem of domain adaptation for sentiment classification by combining deep learning, for acquiring a cross-domain high-level feature representation, and ensemble methods, for reducing the cross-domain generalization error. The proposed adaptation framework has been evaluated on a benchmark dataset composed of reviews of four different Amazon category of products, significantly outperforming the state of the art methods.
Differential flatness-based trajectory planning for multiple unmanned aerial vehicles using mixed-integer linear programming

This paper provides a method for planning fuel-optimal trajectories for multiple unmanned aerial vehicles to reconfigure and traverse between goal points in a dynamic environment in real-time. Recent developments in robot motion planning have shown that trajectory optimization of linear vehicle systems including collision avoidance can be written as a linear program subject to mixed integer constraints, known as a mixed integer linear program (MILP). This paper extends the trajectory optimization to a class of nonlinear systems: differentially flat systems using MILP. A polynomial basis for a Ritz approximation of the optimal solution reduces the optimization variables and computation time without discretizing the systems. Based on the differential flatness property of unmanned vehicle systems, the trajectory planner satisfies the kinematic constraints of the individual vehicles while accounting for inter-vehicle collision and path constraints. The analytical fuel-optimal trajectories are smooth and continuous. Illustrative trajectory planning examples of multiple unmanned aerial vehicles are presented.
Mechanisms of the anterior cruciate ligament injury in sports activities: a twenty-year clinical research of 1,700 athletes.

The mechanisms of anterior cruciate ligament (ACL) injuries are still inconclusive from an epidemiological standpoint. An epidemiological approach in a large sample group over an appropriate period of years will be necessary to enhance the current knowledge of the ACL injury mechanism. The objective of the study was to investigate the ACL injury occurrence in a large sample over twenty years and demonstrate the relationships between the ACL injury occurrence and the dynamic knee alignment at the time of the injury. We investigated the activity, the injury mechanism, and the dynamic knee alignment at the time of the injury in 1,718 patients diagnosed as having the ACL injuries. Regarding the activity at the time of the injury, "competition "was the most common, accounting for about half of all the injuries. The current result also showed that the noncontact injury was the most common, which was observed especially in many female athletes. Finally, the dynamic alignment of "Knee-in & Toe- out "(i.e. dynamic knee valgus) was the most common, accounting for about half. These results enhance our understanding of the ACL injury mechanism and may be used to guide future injury prevention strategies. Key pointsWe investigated the situation of ACL injury occurrence, especially dynamic alignments at the time of injury, in 1,718 patients who had visited our institution for surgery and physical therapy for twenty years.Our epidemiological study of the large patient group revealed that "knee-in & toe-out "alignment was the most frequently seen at the time of the ACL injury.From an epidemiological standpoint, we need to pay much attention to avoiding "Knee-in & Toe-out "alignment during sports activities.
Collective Behavior of Place and Non-place Neurons in the Hippocampal Network

Discussions of the hippocampus often focus on place cells, but many neurons are not place cells in any given environment. Here we describe the collective activity in such mixed populations, treating place and non-place cells on the same footing. We start with optical imaging experiments on CA1 in mice as they run along a virtual linear track and use maximum entropy methods to approximate the distribution of patterns of activity in the population, matching the correlations between pairs of cells but otherwise assuming as little structure as possible. We find that these simple models accurately predict the activity of each neuron from the state of all the other neurons in the network, regardless of how well that neuron codes for position. Our results suggest that understanding the neural activity may require not only knowledge of the external variables modulating it but also of the internal network state.
Loneliness and social support in adolescent boys with autism spectrum disorders.

Loneliness and perceived social support were examined in 39 adolescent boys with autism spectrum disorders (ASD) by means of a self-labeling loneliness measure, the UCLA Loneliness Scale (third version), and the Social Support Scale for Children. Twenty-one percent of the boys with ASD described themselves as often or always feeling lonely. Compared with 199 boys from regular schools in a national probability study, ASD was strongly associated with often or always feeling lonely (OR: 7.08, p < .0005), as well as with a higher degree of loneliness (F(1,229) = 11.1, p < .005). Perceived social support from classmates, parents, and a close friend correlated negatively with loneliness in ASD. The study, therefore, indicates a high occurrence of loneliness among adolescent boys with ASD and points at perceived social support as an important protective factor.
Channel refined directional color filter array interpolation

In this paper, our proposed method has two contributions to demosaicking: first, different from conventional interpolation methods based on two directions or four directions, the proposed method exploits to a greater degree correlations among neighboring pixels along eight directions to improve the interpolation performance. Second, we propose an efficient post-processing method to reduce interpolation artifacts based on the color difference planes. As compared with the latest demosaicking algorithms, experiments show that the proposed algorithm provides superior performance in terms of both objective and subjective image qualities.
Does Human Action Recognition Benefit from Pose Estimation?

Early works on human action recognition focused on tracking and classifying articulated body motions. Such methods required accurate localisation of body parts, which is a difficult task, particularly under realistic imaging conditions. As such, recent trends have shifted towards the use of more abstract, low-level appearance features such as spatio-temporal interest points. Motivated by the recent progress in pose estimation, we feel that pose-based action recognition systems warrant a second look. In this paper, we address the question of whether pose estimation is useful for action recognition or if it is better to train a classifier only on low-level appearance features drawn from video data. We compare pose-based, appearance-based and combined pose and appearance features for action recognition in a home-monitoring scenario. Our experiments show that posebased features outperform low-level appearance features, even when heavily corrupted by noise, suggesting that pose estimation is beneficial for the action recognition task.
sWSI: A Low-cost and Commercial-quality Whole Slide Imaging System on Android and iOS Smartphones

In this paper, scalable Whole Slide Imaging (sWSI), a novel high-throughput, cost-effective and robust whole slide imaging system on both Android and iOS platforms is introduced and analyzed. With sWSI, most mainstream smartphone connected to a optical eyepiece of any manually controlled microscope can be automatically controlled to capture sequences of mega-pixel fields of views that are synthesized into giga-pixel virtual slides. Remote servers carry out the majority of computation asynchronously to support clients running at satisfying frame rates without sacrificing image quality nor robustness. A typical 15x15mm sample can be digitized in 30 seconds with 4X or in 3 minutes with 10X object magnification, costing under $1. The virtual slide quality is considered comparable to existing high-end scanners thus satisfying for clinical usage by surveyed pathologies. The scan procedure with features such as supporting magnification up to 100x, recoding z-stacks, specimen-type-neutral and giving real-time feedback, is deemed work-flow-friendly and reliable.
Design study of efficient digital order-based STDP neuron implementations for extracting temporal features

Spiking neural networks are naturally asynchronous and use pulses to carry information. In this paper, we consider implementing such networks on a digital chip. We used an event-based simulator and we started from a previously established simulation, which emulates an analog spiking neural network, that can extract complex and overlapping, temporally correlated features. We modified this simulation to allow an easier integration in an embedded digital implementation. We first show that a four bits synaptic weight resolution is enough to achieve the best performance, although the network remains functional down to a 2 bits weight resolution. Then we show that a linear leak could be implemented to simplify the neurons leakage calculation. Finally, we demonstrate that an order-based STDP with a fixed number of potentiated synapses as low as 200 is efficient for features extraction. A simulation including these modifications, which lighten and increase the efficiency of digital spiking neural network implementation shows that the learning behavior is not affected, with a recognition rate of 98% in a cars trajectories detection application.
Recursive Sampling for the Nystrom Method

We give the first algorithm for kernel Nyström approximation that runs in linear time in the number of training points and is provably accurate for all kernel matrices, without dependence on regularity or incoherence conditions. The algorithm projects the kernel onto a set of s landmark points sampled by their ridge leverage scores, requiring just O(ns) kernel evaluations and O(ns2) additional runtime. While leverage score sampling has long been known to give strong theoretical guarantees for Nyström approximation, by employing a fast recursive sampling scheme, our algorithm is the first to make the approach scalable. Empirically we show that it finds more accurate kernel approximations in less time than popular techniques such as classic Nyström approximation and the random Fourier features method.
Differential effects of seating arrangements on disruptive behavior of fifth grade students during independent seatwork.

We investigated teacher versus student seat selection in the context of group and individual seating arrangements. Disruptive behavior during group seating occurred at twice the rate when students chose their seats than when the teacher chose. During individual seating, disruptive behavior occurred more than three times as often when the students chose their seats. The results are discussed in relation to choice and the matching law.