nils-herrmann/clean_scirepeval huggingface dataset:
Efficient deep learning for stereo matching with larger image patches

Stereo matching plays an important role in many applications, such as Advanced Driver Assistance Systems, 3D reconstruction, navigation, etc. However it is still an open problem with many difficult. Most difficult are often occlusions, object boundaries, and low or repetitive textures. In this paper, we propose a method for processing the stereo matching problem. We propose an efficient convolutional neural network to measure how likely the two patches matched or not and use the similarity as their stereo matching cost. Then the cost is refined by stereo methods, such as semiglobal maching, subpixel interpolation, median filter, etc. Our architecture uses large image patches which makes the results more robust to texture-less or repetitive textures areas. We experiment our approach on the KITTI2015 dataset which obtain an error rate of 4.42% and only needs 0.8 second for each image pairs.
Deep Belief Networks Used on High Resolution Multichannel Electroencephalography Data for Seizure Detection

Ubiquitous bio-sensing for personalized health monitoring is slowly becoming a reality with the increasing availability of small, diverse, robust, high fidelity sensors. This oncoming flood of data begs the question of how we will extract useful information from it. In this paper we explore the use of a variety of representations and machine learning algorithms applied to the task of seizure detection in high resolution, multichannel EEG data. We explore classification accuracy, computational complexity and memory requirements with a view toward understanding which approaches are most suitable for such tasks as the number of people involved and the amount of data they produce grows to be quite large. In particular, we show that layered learning approaches such as Deep Belief Networks excel along these dimensions.
Relationships Between Social Support, Loneliness, and Internet Addiction in Chinese Postsecondary Students: A Longitudinal Cross-Lagged Analysis

Using the Internet has become one of the most popular leisure activities among postsecondary students in China. Concern about the large number of students using the Internet has led to an increase in research on the influencing factors of Internet addiction and the negative consequences caused by it. This short-term longitudinal study examined the associations among three dimensions of social support [objective support (OS), subjective support (SS), and support utilization (SU)], loneliness, and the four dimensions of Internet addiction (compulsive Internet use [CIU] & withdrawal from Internet addiction [WIA], tolerance of Internet addiction [TIA], time-management problems [TMPs], and interpersonal and health problems [IHPs]) in a Chinese sample. A total of 169 postsecondary first-year students (88 girls and 81 boys; mean age = 18.31 years) participated in the study. The questionnaire measurements were taken at the beginning of the school year (T1), 6 months later (T2), and 1 year later (T3). Cross-lagged and structural equation modeling analyses indicated that (a) OS (T1) and SU (T1) negatively predicted loneliness (T2); and loneliness (T2) negatively predicted OS (T3) and SU(T3); (b) CIU & WIA (T1) and TMPs (T1) positively predicted loneliness (T2); and loneliness (T2) positively predicted CIU & WIA (T3), TIA (T3), TMP (T3), and IHP (T3); (c) SS (T1) directly affected TIA (T3) and TMP (T3); and (d) loneliness (T2) played a mediating role in the relationships between OS (T1) and CIU (T3), OS (T1) and TMP (T3), OS (T1) and IHP (T3), and SU (T1) and IHP (T3). Finally, interventions for Internet addiction and implications for future studies were discussed.
The impact of school daily schedule on adolescent sleep.

This study was initiated to examine the impact of starting school on adolescent sleep, to compare weekday and weekend sleep times, and to attempt to normalize the timing of the circadian sleep/wake cycle by administering bright light in the morning. This was a collaborative project involving high school students and their parents, as well as high school and university faculty members, for the purpose of contributing information to the scientific community while educating students about research processes and their own sleep/wake cycles and patterns.



Sixty incoming high school seniors kept sleep/wake diaries beginning in August and continuing through 2 weeks after the start of school in September. Sleep diaries were also kept for 1 month in November and 1 month in February. Early-morning light treatments were given to 19 students in the last 2 weeks of November and the last 2 weeks of February. Neuropsychologic performance was measured with computer-administered tests. Paper-and-pencil tests were used for assessment of mood and vigor. A testing period consisted of 2 consecutive days at the beginning and end of November and at the beginning and end of February. Tests were given 3 times per day, ie, in the morning before school (6:30-8:00 AM), during midday lunch periods (11:30 AM to 1:00 PM), and in the afternoon (3:00-4:30 PM), on each of the test days.



Adolescents lost as much as 120 minutes of sleep per night during the week after the start of school, and weekend sleep time was also significantly longer (approximately 30 minutes) than that seen before the start of school (August). No significant differences were found between weekday sleep in the summer and weekend sleep during the school year. Early-morning light treatments did not modify total minutes of sleep per night, mood, or computer-administered vigilance test results. All students performed better in the afternoon than in the morning. Students in early morning classes reported being wearier, being less alert, and having to expend greater effort.



The results of this study demonstrated that current high school start times contribute to sleep deprivation among adolescents. Consistent with a delay in circadian sleep phase, students performed better later in the day than in the early morning. However, exposure to bright light in the morning did not change the sleep/wake cycle or improve daytime performance during weekdays. Both short-term and long-term strategies that address the epidemic of sleep deprivation among adolescents will be necessary to improve health and maximize school performance.
Wireless Mesh Networking with XBee

We perform a performance evaluation of ZigBee mesh networks based on XBee modules, in terms of packet delay and received signal strength (RSSI). We evaluate the performance for both line-of-sight (LOS) and non-line-of-sight (NLOS) environments in buildings and a parking lot. The results of our study are useful for building wireless sensor networks and communication networks of mobile robots where the communication distances between nodes are greater than the single-hop range and hence communication between nodes requires multi-hop transmissions.
Efficient Algorithms for Temporal Path Computation

Shortest path is a fundamental graph problem with numerous applications. However, the concept of classic shortest path is insufficient. In this paper, we study various concepts of “shortest” path in temporal graphs, called minimum temporal paths. Computing these minimum temporal paths is challenging as subpaths of a “shortest” path may not be “shortest” in a temporal graph. We propose efficient algorithms to compute minimum temporal paths and verified their efficiency using large real-world temporal graphs.
Coupled-Inductor Filter: A Basic Filter Building Block

Coupled-magnetics and coupled-inductor filters provide smoothing in a power converter by steering ripple current away from the “quite port.” While this concept is not new, topologies claiming to be “new” or “novel” continue to be reported in the literature, suggesting that the ripple-steering phenomenon may not be well understood to be a property of the filter block itself. This paper describes a general coupled-inductor filter block and presents a brief history of its application starting with the earliest known version of the circuit, which dates back to a 1923 patent application. The coupled-inductor circuit model is reviewed, starting with the basic topology and ideal circuit elements. Real circuit elements are then considered along with the effects of equivalent series resistance and other parasitic elements. A circuit-based filtering approach is used to emphasize the filtering aspect of the coupled inductor. It will be seen that the coupled-inductor filter can exhibit a low pass with notch frequency response. This provides the ability to achieve extra attenuation at a particular frequency, such as the converter switching frequency, which can reduce the overall filtering burden without adding extra inductive components. When the filter components are treated as a functional circuit group, instead of an interconnection of inductors and capacitors, the coupled-inductor filter can be analyzed as a drop-in replacement wherever a smoothing choke would normally be used and does not depend on converter voltage waveforms. A topology transformation of the Ćuk converter shows that “zero ripple” in not a special property of the topology, but can be achieved using the coupled-inductor filter block.
The 11-13-week scan: diagnosis and outcome of holoprosencephaly, exomphalos and megacystis.

To determine the prevalence and outcome of fetuses with holoprosencephaly, exomphalos and megacystis diagnosed at 11-13 weeks of gestation.



As part of a prospective screening study for trisomy 21 in singleton pregnancies at 11 + 0 to 13 + 6 weeks' gestation, transabdominal ultrasound examination was performed to diagnose holoprosencephaly, exomphalos and megacystis. Fetal karyotype and pregnancy outcome in fetuses with these defects were examined.



Screening was carried out in 57 119 pregnancies. The prevalence of holoprosencephaly, exomphalos and megacystis was 1 : 1298, 1 : 381 and 1 : 1632, respectively. Chromosomal abnormalities, mainly trisomies 18 and 13, were found in 65.9% of fetuses with holoprosencephaly, in 55.3% with exomphalos and in 31.4% with megacystis. There was spontaneous resolution of the defect by 20 weeks in 92.5% of euploid fetuses with exomphalos containing only bowel and in 90% of the euploid fetuses with megacystis and bladder length of < or = 15 mm.



A high proportion of fetuses with holoprosencephaly, exomphalos and megacystis diagnosed at 11-13 weeks of gestation are aneuploid, but in the majority of cases exomphalos and megacystis represent temporary abnormalities that resolve spontaneously.
Energy Efficient Memory Decoder Design for Ultra-low Voltage Systems

This paper presents a low energy memory decoder architecture for ultra-low-voltage systems containing multiple voltage domains. Due to limitations in scalability of memory supply voltages, these systems typically contain a core operating at subthreshold voltages and memories operating at a higher voltage. This difference in voltage provides a timing slack on the memory path as the core supply is scaled. The paper analyzes the feasibility and trade-offs in utilizing this timing slack to operate a greater section of memory decoder circuitry at the lower supply. A 256x16-bit SRAM interface has been designed in UMC 65nm low-leakage process to evaluate the above technique with the core and memory operating at 280 mV and 500 mV respectively. The technique provides a reduction of up to 20% in energy/cycle of the row decoder without any penalty in area and system-delay.
A new six-port junction based on substrate integrated waveguide technology

A six-port junction based on the substrate integrated waveguide (SIW) technology is proposed and presented. In this design of such a junction, the SIW is first converted to an equivalent rectangular waveguide, then regular rectangular waveguide design techniques are used. In this structure, an SIW power divider and SIW hybrid 3-dB coupler are designed as fundamental building blocks. A six-port junction circuit operating at 24 GHz is fabricated and measured. Good agreement between simulated and measured results is found for the proposed six-port junction.
Multimodal Word Distributions

Word embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To learn these distributions, we propose an energy-based max-margin objective. We show that the resulting approach captures uniquely expressive semantic information, and outperforms alternatives, such as word2vec skip-grams, and Gaussian embeddings, on benchmark datasets such as word similarity and entailment.
A novel design method for blass matrix beam-forming networks

A novel design method for lossy Blass matrix beam-forming networks is presented. Compared to those formerly developed (Jones and DuFort, 1971), the new method allows the design of such circuits to generate an arbitrary number of simultaneous beams oriented in arbitrary positions. This is obtained by means of an equivalent circuit approach to compute the transmission parameters of the Blass matrix. The method has been applied to the design of a specific multi-beam waveguide application, which required the development and characterization of a novel waveguide cross coupler. Experimental results confirm the accuracy of the method showing high performance for all beams in terms of beam position, side lobe level and efficiency.
Orthogonal and Idempotent Transformations for Learning Deep Neural Networks

Identity transformations, used as skip-connections in residual networks, directly connect convolutional layers close to the input and those close to the output in deep neural networks, improving information flow and thus easing the training. In this paper, we introduce two alternative linear transforms, orthogonal transformation and idempotent transformation. According to the definition and property of orthogonal and idempotent matrices, the product of multiple orthogonal (same idempotent) matrices, used to form linear transformations, is equal to a single orthogonal (idempotent) matrix, resulting in that information flow is improved and the training is eased. One interesting point is that the success essentially stems from feature reuse and gradient reuse in forward and backward propagation for maintaining the information during flow and eliminating the gradient vanishing problem because of the express way through skip-connections. We empirically demonstrate the effectiveness of the proposed two transformations: similar performance in single-branch networks and even superior in multi-branch networks in comparison to identity transformations.
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition

This paper explores multi-task learning (MTL) for face recognition. First, we propose a multi-task convolutional neural network (CNN) for face recognition, where identity classification is the main task and pose, illumination, and expression (PIE) estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weights to each side task, which solves the crucial problem of balancing between different tasks in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses in a joint framework. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the PIE variations from the learnt identity features. Extensive experiments on the entire multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in multi-PIE for face recognition. Our approach is also applicable to in-the-wild data sets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.
Accuracy analysis of kinect depth data

This paper presents an investigation of the geometric quality of depth data obtained by the Kinect sensor. Based on the mathematical model of depth measurement by the sensor a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimetres up to about 4 cm at the maximum range of the sensor. The accuracy of the data is also found to be influenced by the low resolution of the depth measurements.
VINERy: A Visual IDE for Information Extraction

Information Extraction (IE) is the key technology enabling analytics over unstructured and semi-structured data. Not surprisingly, it is becoming a critical building block for a wide range of emerging applications. To satisfy the rising demands for information extraction in real-world applications, it is crucial to lower the barrier to entry for IE development and enable users with general computer science background to develop higher quality extractors. In this demonstration, we present VINERY, an intuitive yet expressive visual IDE for information extraction. We show how it supports the full cycle of IE development without requiring a single line of code and enables a wide range of users to develop high quality IE extractors with minimal efforts. The extractors visually built in VINERY are automatically translated into semantically equivalent extractors in a state-of-the-art declarative language for IE. We also demonstrate how the auto-generated extractors can then be imported into a conventional Eclipse-based IDE for further enhancement. The results of our user studies indicate that VINERY is a significant step forward in facilitating extractor development for both expert and novice IE developers.
Cellular-Enabled UAV Communication: Trajectory Optimization under Connectivity Constraint

In this paper, we study a cellular-enabled unmanned aerial vehicle (UAV) communication system consisting of one UAV and multiple ground base stations (GBSs). The UAV has a mission of flying from an initial location to a final location, during which it needs to maintain reliable wireless connection with the cellular network by associating with one of the GBSs at each time instant. We aim to minimize the UAV mission completion time by optimizing its trajectory, subject to a quality of connectivity constraint of the GBS-UAV link specified by a minimum received signal-to-noise ratio (SNR) target, which needs to be satisfied throughout the mission. This problem is non-convex and difficult to be optimally solved. We first propose an effective approach to check its feasibility based on graph connectivity verification. Then, by examining the GBS-UAV association sequence during the UAV mission, we obtain useful insights on the optimal UAV trajectory, based on which an efficient algorithm is proposed to find an approximate solution to the trajectory optimization problem by leveraging techniques in convex optimization and graph theory. Numerical results show that our proposed trajectory design achieves near-optimal performance.
Avoiding, finding and fixing spreadsheet errors - A survey of automated approaches for spreadsheet QA

Spreadsheet programs can be found everywhere in organizations and they are used for a variety of purposes, including financial calculations, planning, data aggregation and decision making tasks. A number of research surveys have however shown that such programs are particularly prone to errors. Some reasons for the error-proneness of spreadsheets are that spreadsheets are developed by end users and that standard software quality assurance processes are mostly not applied. Correspondingly, during the last two decades, researchers have proposed a number of techniques and automated tools aimed at supporting the end user in the development of error-free spreadsheets. In this paper, we provide a review of the research literature and develop a classification of automated spreadsheet quality assurance (QA) approaches, which range from spreadsheet visualization, static analysis and quality reports, over testing and support to model-based spreadsheet development. Based on this review, we outline possible opportunities for future work in the area of automated spreadsheet QA.
Atrial fibrillation detection using feature based algorithm and deep convolutional neural network

Electrocardiographic waveforms (ECG) are recognized as the most reliable method to detect abnormal heart rhythms such as atrial fibrillation. This task is challenging when the signals are distorted by noise. This paper presents an automatic classification algorithm to classify short lead ECGs in terms of abnormality of heart rhythm (AF or alternative rhythms) and quality (noisy recordings).  To meet this end, at first baseline wander removal and Butterworth filter for each signal are applied as a preprocessing stage. Due to the existence of noise in recordings, high quality beats are selected for any further analysis using cycle quality assessment. Then, three sets of features defined as correlation coefficient, fractal dimension and variance of R peaks are extracted to predict noisy recordings. Two separate approaches are employed to classify other three classes. The first approach is the feature based methodology and the second one is the applying deep neural networks. In the first approach, features from different domains are extracted. The method for AF detection utilizes and characterizes variability in RR-intervals which are extracted by applying classic Pan-Tompkins algorithm. To improve the accuracy of the AF-detection, atrial activity is analyzed by understanding whether the P-wave is present in signal. This is done by investigating the morphology of P-waves. Heart rate abnormality and the existence of premature beats in a signal are regarded as two characteristics to distinguish non-AF rhythms. The whole sets of features are fed into a neural network classifier. Another approach uses the segments with 600 samples as the input of a 1 dimensional convolutional neural network. The output obtained from both approaches are combined using a decision table and finally the recordings are classified into three classes.  The proposed method is evaluated using scoring function from 2017 PhysioNet/CinC Challenge and achieved an overall score of 80% and 71% on the training dataset and hidden test dataset, respectively.