nils-herrmann/clean_scirepeval huggingface dataset:
Passively Aligned Transmit Optical Subassembly Module Based on a WDM Incorporating VCSELs

A short-reach transmit optical subassembly module enabled by a passive alignment has been proposed and manufactured which takes advantage of a filter free coarse wavelength-division multiplexer (WDM). A four-channel data transmission was accomplished by employing only two plastic optical fibers and four vertical-cavity surface-emitting lasers (VCSELs). A multiaxis beam combination was introduced to overcome the use of WDM filters. The proposed module was constructed via a passive alignment; the overall alignment tolerance was examined by assessing the optical coupling efficiency in terms of the VCSEL displacement. The achieved tolerance was ~20 and ~150 μ m in the horizontal and vertical direction respectively, while the demonstrated peak coupling was ~41%. Finally, a high-speed 2.5-Gb/s signal was successfully delivered via the demonstrated module.
How many tiers?: pricing in the internet transit market

ISPs are increasingly selling "tiered" contracts, which offer Internet connectivity to wholesale customers in bundles, at rates based on the cost of the links that the traffic in the bundle is traversing. Although providers have already begun to implement and deploy tiered pricing contracts, little is known about how to structure them. While contracts that sell connectivity on finer granularities improve market efficiency, they are also more costly for ISPs to implement and more difficult for customers to understand. Our goal is to analyze whether current tiered pricing practices in the wholesale transit market yield optimal profits for ISPs and whether better bundling strategies might exist. In the process, we deliver two contributions: 1) we develop a novel way of mapping traffic and topology data to a demand and cost model, and 2) we fit this model on three large real-world networks: an European transit ISP, a content distribution network, and an academic research network, and run counterfactuals to evaluate the effects of different bundling strategies. Our results show that the common ISP practice of structuring tiered contracts according to the cost of carrying the traffic flows (e.g., offering a discount for traffic that is local) can be suboptimal and that dividing contracts based on both traffic demand and the cost of carrying it into only three or four tiers yields near-optimal profit for the ISP.
Making the Transition: Helping Teachers To Teach Online.

Teaching in the cyberspace classroom requires moving beyond old models of. pedagogy into new practices that are more facilitative. It involves much more than simply taking old models of pedagogy and transferring them to a different medium. Unlike the face-to-face classroom, in online distance education, attention needs to be paid to the development of a sense of community within the group of participants in order for the learning process to be successful. The transition to the cyberspace classroom can be successfully achieved if attention is paid to several key areas. These include: ensuring access to and familiarity with the technology in use; establishing guidelines and procedures which are relatively loose and free-flowing, and generated with significant input from participants; striving to achieve maximum participation and "buy-in" from the participants; promoting collaborative learning; and creating a double or triple loop in the learning process to enable participants to reflect on their learning process. All of these practices significantly contribute to the development of an online learning community, a powerful tool for enhancing the learning experience. Each of these is reviewed in detail in the paper. (AEF) Reproductions supplied by EDRS are the best that can be made from the original document.  Helping Teachers to Teach Online Rena M. Palloff, Ph.D. Crossroads Consulting Group and The Fielding Institute Alameda, CA
Fast and accurate classification of echocardiograms using deep learning

Echocardiography is essential to modern cardiology. However, human interpretation limits high throughput analysis, limiting echocardiography from reaching its full clinical and research potential for precision medicine. Deep learning is a cutting-edge machine-learning technique that has been useful in analyzing medical images but has not yet been widely applied to echocardiography, partly due to the complexity of echocardiograms' multi view, multi modality format. The essential first step toward comprehensive computer assisted echocardiographic interpretation is determining whether computers can learn to recognize standard views. To this end, we anonymized 834,267 transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51 percent female, 26 percent obese) seen between 2000 and 2017 and labeled them according to standard views. Images covered a range of real world clinical variation. We built a multilayer convolutional neural network and used supervised learning to simultaneously classify 15 standard views. Eighty percent of data used was randomly chosen for training and 20 percent reserved for validation and testing on never seen echocardiograms. Using multiple images from each clip, the model classified among 12 video views with 97.8 percent overall test accuracy without overfitting. Even on single low resolution images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5 percent for board-certified echocardiographers. Confusional matrices, occlusion experiments, and saliency mapping showed that the model finds recognizable similarities among related views and classifies using clinically relevant image features. In conclusion, deep neural networks can classify essential echocardiographic views simultaneously and with high accuracy. Our results provide a foundation for more complex deep learning assisted echocardiographic interpretation.
A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification

A novel learnable dictionary encoding layer is proposed in this paper for end-to-end language identification. It is inline with the conventional GMM i-vector approach both theoretically and practically. We imitate the mechanism of traditional GMM training and Supervector encoding procedure on the top of CNN. The proposed layer can accumulate high-order statistics from variable-length input sequence and generate an utterance level fixed-dimensional vector representation. Unlike the conventional methods, our new approach provides an end-to-end learning framework, where the inherent dictionary are learned directly from the loss function. The dictionaries and the encoding representation for the classifier are learned jointly. The representation is orderless and therefore appropriate for language identification. We conducted a preliminary experiment on NIST LRE07 closed-set task, and the results reveal that our proposed dictionary encoding layer achieves significant error reduction comparing with the simple average pooling.
Legion scribe: real-time captioning by non-experts

The promise of affordable, automatic approaches to real-time captioning imagines a future in which deaf and hard of hearing (DHH) users have immediate access to speech in the world around them my simply picking up their phone or other mobile device. While the challenges of processing highly variable natural language has prevented automated approaches from completing this task reliably enough for use in settings such as classrooms or workplaces [4], recent work in crowd-powered approaches have allowed groups of non-expert captionists to provide a similarly-flexible source of captions for DHH users. This is in contrast to current human-powered approaches, which use highly-trained professional captionists who can type up to 250 words per minute (WPM), but also can cost over $100/hr. In this paper, we describe a real-time demo of Legion:Scribe (or just "Scribe"), a crowd-powered captioning system that allows untrained participants and volunteers to provide reliable captions with less than 5 seconds of latency by computationally merging their input into a single collective answer that is more accurate and more complete than any one worker could have generated alone.
Bayesian Time Delay Estimation of GNSS Signals in Dynamic Multipath Environments

A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops, it builds upon complexity reduction concepts that previously have been applied within maximum likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both BPSK-modulated and BOC-(1,1) modulated navigation signals.
A meta-analysis of the impact of technology on learning effectiveness of elementary students

The existing studies suggest that if technology is interwoven comprehensively into pedagogy, it can act as a powerful tool for effective learnin g of the elementary students. This study conducted the meta-analysis by integrating the quan titative findings of 122 peer-reviewed academic papers that measured the impact of technol ogy on learning effectiveness of elementary students. The results confirmed that the technology has a medium effect on learning effectiveness of elementary students. Furt her, this study analysed the effect sizes of moderating variables such as domain subject, applic ation type, intervention duration, and learning environment. Finally, the impact of techno logy at different levels of moderating variables has been discussed and the implications f or theory and practice are provided.
Portfolio optimization with linear and fixed transaction costs

We consider the problem of portfolio selection, with transaction costs and constraints on exposure to risk. Linear transaction costs, bounds on the variance of the return, and bounds on different shortfall probabilities are efficiently handled by convex optimization methods. For such problems, the globally optimal portfolio can be computed very rapidly. Portfolio optimization problems with transaction costs that include a fixed fee, or discount breakpoints, cannot be directly solved by convex optimization. We describe a relaxation method which yields an easily computable upper bound via convex optimization. We also describe a heuristic method for finding a suboptimal portfolio, which is based on solving a small number of convex optimization problems (and hence can be done efficiently). Thus, we produce a suboptimal solution, and also an upper bound on the optimal solution. Numerical experiments suggest that for practical problems the gap between the two is small, even for large problems involving hundreds of assets. The same approach can be used for related problems, such as that of tracking an index with a portfolio consisting of a small number of assets.
Dynamics of African swine fever virus shedding and excretion in domestic pigs infected by intramuscular inoculation and contact transmission

African swine fever virus (ASFV) is a highly virulent swine pathogen that has spread across Eastern Europe since 2007 and for which there is no effective vaccine or treatment available. The dynamics of shedding and excretion is not well known for this currently circulating ASFV strain. Therefore, susceptible pigs were exposed to pigs intramuscularly infected with the Georgia 2007/1 ASFV strain to measure those dynamics through within- and between-pen transmission scenarios. Blood, oral, nasal and rectal fluid samples were tested for the presence of ASFV by virus titration (VT) and quantitative real-time polymerase chain reaction (qPCR). Serum was tested for the presence of ASFV-specific antibodies. Both intramuscular inoculation and contact transmission resulted in development of acute disease in all pigs although the experiments indicated that the pathogenesis of the disease might be different, depending on the route of infection. Infectious ASFV was first isolated in blood among the inoculated pigs by day 3, and then chronologically among the direct and indirect contact pigs, by day 10 and 13, respectively. Close to the onset of clinical signs, higher ASFV titres were found in blood compared with nasal and rectal fluid samples among all pigs. No infectious ASFV was isolated in oral fluid samples although ASFV genome copies were detected. Only one animal developed antibodies starting after 12 days post-inoculation. The results provide quantitative data on shedding and excretion of the Georgia 2007/1 ASFV strain among domestic pigs and suggest a limited potential of this isolate to cause persistent infection.
Intracellular imaging with a graphene-based fluorescent probe.

Graphene is an increasingly important nanomaterial that has shown great promise in the area of nanotechnology. In this study, fluorescein-functionalized graphene oxide (GO) is synthesized via a polyethylene glycol (PEG) bridge and its application in intracellular imaging is explored. GO is an oxide form of graphene that provides an ideal platform to prepare graphene-based functional nanomaterials via chemical modification. The PEG bridge was introduced to prevent GO-induced quenching of conjugated fluorescein. The fluorescein-PEG-GO conjugate thus prepared exhibits excellent pH-tunable fluorescent properties and, more significantly, can be efficiently taken up by cells and serve as a fluorescent nanoprobe for intracellular imaging.
NODE FEATURES ADJUSTED STOCHASTIC BLOCK MODEL

Stochastic block model (SBM) and its variants are popular models used in community detection for network data. In this paper, we propose a feature adjusted stochastic block model (FASBM) to capture the impact of node features on the network links as well as to detect the residual community structure beyond that explained by the node features. The proposed model can accommodate multiple node features and estimate the form of feature impacts from the data. Moreover, unlike many existing algorithms that are limited to binary-valued interactions, the proposed FASBM model and inference approaches are easily applied to relational data that generates from any exponential family distribution. We illustrate the methods on simulated networks and on two real world networks: a brain network and an US air-transportation network.
Persistence-based segmentation of deformable shapes

In this paper, we combine two ideas: persistence-based clustering and the Heat Kernel Signature (HKS) function to obtain a multi-scale isometry invariant mesh segmentation algorithm. The key advantages of this approach is that it is tunable through a few intuitive parameters and is stable under near-isometric deformations. Indeed the method comes with feedback on the stability of the number of segments in the form of a persistence diagram. There are also spatial guarantees on part of the segments. Finally, we present an extension to the method which first detects regions which are inherently unstable and segments them separately. Both approaches are reasonably scalable and come with strong guarantees. We show numerous examples and a comparison with the segmentation benchmark and the curvature function.
From driving simulation to virtual reality

Driving simulation from the very beginning of the advent of VR technology uses the very same technology for visualization and similar technology for head movement tracking and high end 3D vision. They also share the same or similar difficulties in rendering movements of the observer in the virtual environments. The visual-vestibular conflict, due to the discrepancies perceived by the human visual and vestibular systems, induce the so-called simulation sickness, when driving or displacing using a control device (ex. Joystick). Another cause for simulation sickness is the transport delay, the delay between the action and the corresponding rendering cues.
 Another similarity between driving simulation and VR is need for correct scale 1:1 perception. Correct perception of speed and acceleration in driving simulation is crucial for automotive experiments for Advances Driver Aid System (ADAS) as vehicle behavior has to be simulated correctly and anywhere where the correct mental workload is an issue as real immersion and driver attention is depending on it. Correct perception of distances and object size is crucial using HMDs or CAVEs, especially as their use is frequently involving digital mockup validation for design, architecture or interior and exterior lighting.
 Today, the advents of high resolution 4K digital display technology allows near eye resolution stereoscopic 3D walls and integrate them in high performance CAVEs. High performance CAVEs now can be used for vehicle ergonomics, styling, interior lighting and perceived quality. The first CAVE in France, built in 2001 at Arts et Metiers ParisTech, is a 4 sided CAVE with a modifiable geometry with now traditional display technology. The latest one is Renault's 70M 3D pixel 5 sides CAVE with 4K x 4K walls and floor and with a cluster of 20 PCs. Another equipment recently designed at Renault is the motion based CARDS driving simulator with CAVE like 4 sides display system providing full 3D immersion for the driver.
 The separation between driving simulation and digital mockup design review is now fading though different uses will require different simulation configurations.
 New application domains, such as automotive AR design, will bring combined features of VR and driving simulation technics, including CAVE like display system equipped driving simulators.
The contribution of chicken embryology to the understanding of vertebrate limb development

The chicken is an excellent model organism for studying vertebrate limb development, mainly because of the ease of manipulating the developing limb in vivo. Classical chicken embryology has provided fate maps and elucidated the cell-cell interactions that specify limb pattern. The first defined chemical that can mimic one of these interactions was discovered by experiments on developing chick limbs and, over the last 15 years or so, the role of an increasing number of developmentally important genes has been uncovered. The principles that underlie limb development in chickens are applicable to other vertebrates and there are growing links with clinical genetics. The sequence of the chicken genome, together with other recently assembled chicken genomic resources, will present new opportunities for exploiting the ease of manipulating the limb.
Evidence-Based Information on the Clinical Use of Neurofeedback for ADHD

Neurofeedback (NF) is a training to enhance self-regulatory capacity over brain activity patterns and consequently over brain mental states. Recent findings suggest that NF is a promising alternative for the treatment of attention-deficit/hyperactivity disorder (ADHD). We comprehensively reviewed literature searching for studies on the effectiveness and specificity of NF for the treatment of ADHD. In addition, clinically informative evidence-based data are discussed. We found 3 systematic review on the use of NF for ADHD and 6 randomized controlled trials that have not been included in these reviews. Most nonrandomized controlled trials found positive results with medium-to-large effect sizes, but the evidence for effectiveness are less robust when only randomized controlled studies are considered. The direct comparison of NF and sham-NF in 3 published studies have found no group differences, nevertheless methodological caveats, such as the quality of the training protocol used, sample size, and sample selection may have contributed to the negative results. Further data on specificity comes from electrophysiological studies reporting that NF effectively changes brain activity patterns. No safety issues have emerged from clinical trials and NF seems to be well tolerated and accepted. Follow-up studies support long-term effects of NF. Currently there is no available data to guide clinicians on the predictors of response to NF and on optimal treatment protocol. In conclusion, NF is a valid option for the treatment for ADHD, but further evidence is required to guide its use.
Detection and Recognition of Traffic Signs from Road Scene Images

Automatic detection and recognition of road signs is an important component of automated driver assistance systems contributing to the safety of the drivers, pedestrians and vehicles. Despite significant research, the problem of detecting and recognizing road signs still remains challenging due to varying lighting conditions, complex backgrounds and different viewing angles. We present an effective and efficient method for detection and recognition of traffic signs from images. Detection is carried out by performing color based segmentation followed by application of Hough transform to find circles, triangles or rectangles. Recognition is carried out using three state-of-the-art feature matching techniques, SIFT, SURF and BRISK. The proposed system evaluated on a custom developed dataset reported promising detection and recognition results. A comparative analysis of the three descriptors reveal that while SIFT achieves the best recognition rates, BRISK is the most efficient of the three descriptors in terms of computation time.
Osgood-Schlatter's disease, an emphasis on soft tissue changes in roentgen diagnosis

Osgood-Schlatter's disease is a well known entity affecting the adolescent knee. Radiologic examination of the knee has been an integral part of the diagnosis of this condition for decades. However, the soft tissue changes have not been appreciated sufficiently. Emphasis is placed on the use of optimum radiographic technique and xeroradiography in the examination of the soft tissues of the knee.
High-Resolution Bubble Printing of Quantum Dots.

Semiconductor quantum dots (QDs) feature excellent properties, such as high quantum efficiency, tunable emission frequency, and good fluorescence stability. Incorporation of QDs into new devices relies upon high-resolution and high-throughput patterning techniques. Herein, we report a new printing technique known as bubble printing (BP), which exploits a light-generated microbubble at the interface of colloidal QD solution and a substrate to directly write QDs into arbitrary patterns. With the uniform plasmonic hot spot distribution for high bubble stability and the optimum light-scanning parameters, we have achieved full-color QD printing with submicron resolution (650 nm), high throughput (scanning rate of ∼10-2 m/s), and high adhesion of the QDs to the substrates. The printing parameters can be optimized to further control the fluorescence properties of the patterned QDs, such as emission wavelength and lifetime. The patterning of QDs on flexible substrates further demonstrates the wide applicability of this new technique. Thus, BP technique addresses the barrier of achieving a widely applicable, high-throughput and user-friendly patterning technique in the submicrometer regime, along with simultaneous fluorescence modification capability.
Gaining IS Business Value through Big Data Analytics: A Case Study of the Energy Sector

Following decades of stability and comfortable margins, utility companies today face strong pressure from regulatory bodies and competitors. As a response to the market dynamics, many have initiated a transformation from a “provider” to a service company, yet realize that their customer insights that would be necessary to successfully develop and market new services are sparse. We argue that the required information is contained in consumption data that is available to utility companies. We demonstrate how data analytics and machine learning make sense out of such data and add value to organizations. Using datasets containing annual electricity consumption information of private households, we apply and test in field experiments a Support Vector Machines algorithm that predicts probabilities of individual costumers to sign up on an energy efficiency portal. We show that signup rates can be doubled and argue that classification tools provide customer insights at low cost and at scale.