nils-herrmann/clean_scirepeval huggingface dataset:
The inferior alveolar nerve's loop at the mental foramen and its implications for surgery.

In this study, the authors aimed to identify and measure the anterior extension of the alveolar loop (aAL) and the caudal extension of the alveolar loop (cAL) of the inferior alveolar nerve by using cone-beam computed tomography (CBCT). They also aimed to provide recommendations for surgery in the anterior mandible.



In this retrospective case study of the frequency and extension of aAL and cAL, the authors evaluated 1,384 mandibular sites in 694 CBCT scans of dentate and partly edentulous patients, performed mainly for further diagnosis before removal of the mandibular third molars between January 2009 and February 2013, by using multiplanar reconstructions.



The frequency of aAL was 69.73 percent and of cAL was 100 percent. The mean value for aAL was 1.16 millimeters, with a range of 0.3 to 5.6 mm; the mean value for cAL was 4.11 mm, with a range of 0.25 to 8.87 mm. For aAL, 95.81 percent of the sites showed values of 0 to 3 mm; for cAL, 93.78 percent of the sites showed values of 0.25 to 6 mm. Dentate patients showed statistically significantly higher values for cAL than did partly edentulous patients (P = .043). CBCT resolution had a statistically significant impact on cAL measurements (P = .001), with higher values at higher resolution.



This study showed a high frequency of and large variations in aAL and cAL. In contrast to panoramic radiography, CBCT has been shown to be a reliable tool for identifying and measuring the AL. Therefore, preoperative diagnosis with CBCT is recommended for planning three-dimensional tasks such as implant placement in the vicinity of the mental foramen.



Owing to the variability of aAL and cAL measurements, it is difficult to recommend reliable safety margins for surgical procedures such as implant placement, bone harvesting or genioplasty Depending on the indication, the clinician should consider preoperative diagnosis by means of CBCT.
BROADBAND COPLANAR WAVEGUIDE-FED WIDE-SLOT ANTENNA

A large bandwidth wide-slot antenna, fed by coplanar waveguide (CPW), is proposed. Experimental investigations and detailed simulations are conducted to understand its behavior and to optimize for broadband operation. The impedance bandwidth, determined by 10-dB return loss of the proposed slot antenna using both measurement and simulation, is about 131% (2.8 to 14GHz). In addition to be small in size, the antenna has low cross polarization, relatively high gain, and it exhibits stable far-field radiation characteristics in the entire operating bandwidth. The design with very wide operating bandwidth and improved radiation pattern is obtained by properly choosing the suitable slot shape, selecting similar feed patch shape, and tuning their dimensions. Numerical sensitivity analysis has been used to understand the effects of changes of various antenna dimensions and to optimize the performance of the designed antenna. Based on our computer simulations it is shown that the antenna dimensions parameters have uncorrelated effects on the upper edge of the bandwidth. Simulation results show that the impedance matching of this kind of antenna is sensitive to the feed-slot combination and feed gap width. The simulated and measured results for return loss, far-field E-plane and H-plane radiation patterns, and gain of the designed antenna are presented and discussed.
Social interaction based video recommendation: Recommending YouTube videos to facebook users

Online videos, e.g., YouTube videos, are important topics for social interactions among users of online social networking sites (OSN), e.g., Facebook. This opens up the possibility of exploiting video-related user social interaction information for better video recommendation. Towards this goal, we conduct a case study of recommending YouTube videos to Facebook users based on their social interactions. We first measure social interactions related to YouTube videos among Facebook users. We observe that the attention a video attracts on Facebook is not always well-aligned with its popularity on YouTube. Unpopular videos on YouTube can become popular on Facebook, while popular videos on YouTube often do not attract proportionally high attentions on Facebook. This finding motivates us to develop a simple top-k video recommendation algorithm that exploits user social interaction information to improve the recommendation accuracy for niche videos, that are globally unpopular, but highly relevant to a specific user or user group. Through experiments on the collected Facebook traces, we demonstrate that our recommendation algorithm significantly outperforms the YouTube-popularity based video recommendation algorithm as well as a collaborative filtering algorithm based on user similarities.
Design of spoof surface plasmon polariton based terahertz delay lines

We present the analysis and design of fixed physical length, spoof Surface Plasmon Polariton based waveguides with adjustable delay at terahertz frequencies. The adjustable delay is obtained using Corrugated Planar Goubau Lines (CPGL) by changing its corrugation depth without changing the total physical length of the waveguide. Our simulation results show that electrical lengths of 237.9°, 220.6°, and 310.6° can be achieved by physical lengths of 250 μm and 200 μm at 0.25, 0.275, and 0.3 THz, respectively, for demonstration purposes. These simulations results are also consistent with our analytical calculations using the physical parameter and material properties. When we combine pairs of same length delay lines as if they are two branches of a terahertz phase shifter, we achieved an error rate of relative phase shift estimation better than 5.8%. To the best of our knowledge, this is the first-time demonstration of adjustable spoof Surface Plasmon Polariton based CPGL delay lines. The idea can be used for obtaining tunable delay lines with fixed lengths and phase shifters for the terahertz band circuitry.
Botanical insecticides, deterrents, and repellents in modern agriculture and an increasingly regulated world.

Botanical insecticides have long been touted as attractive alternatives to synthetic chemical insecticides for pest management because botanicals reputedly pose little threat to the environment or to human health. The body of scientific literature documenting bioactivity of plant derivatives to arthropod pests continues to expand, yet only a handful of botanicals are currently used in agriculture in the industrialized world, and there are few prospects for commercial development of new botanical products. Pyrethrum and neem are well established commercially, pesticides based on plant essential oils have recently entered the marketplace, and the use of rotenone appears to be waning. A number of plant substances have been considered for use as insect antifeedants or repellents, but apart from some natural mosquito repellents, little commercial success has ensued for plant substances that modify arthropod behavior. Several factors appear to limit the success of botanicals, most notably regulatory barriers and the availability of competing products (newer synthetics, fermentation products, microbials) that are cost-effective and relatively safe compared with their predecessors. In the context of agricultural pest management, botanical insecticides are best suited for use in organic food production in industrialized countries but can play a much greater role in the production and postharvest protection of food in developing countries.
Towards Literate Artificial Intelligence

Mrinmaya Sachan Thesis Committee: Eric P. Xing, Chair Jaime Carbonell Tom Mitchell Dan Roth (University of Pennsylvania) Thesis Proposal Jan. 22, 2018 1:00pm 8102 GHC Link to draft document:  Standardized tests are often used to test students as they progress in the formal education system. These tests are widely available and measurable with clear evaluation procedures and metrics. Hence, these can serve as good tests for AI. We propose approaches for solving some of these tests. We broadly categorize these tests into two categories: open domain question answering tests such as reading comprehensions and elementary school science tests, and closed domain question answering tests such as intermediate or advanced math and science tests. We present alignment based approach with multi-task learning for the former. For closed domain tests, we propose a parsing to programs approach which can be seen as a natural language interface to expert systems. We also describe approaches for question generation based on instructional material in both open domain as well as closed domain settings. Finally, we show that we can improve both the question answering and question generation models by learning them jointly. This mechanism also allows us to leverage cheap unlabelled data for learning the two models. Our work can potentially be applied for the social good in the education domain. We perform studies on human subjects who found our approaches useful as assistive tools in education.
On the implementation of BRS codes in Ceph

Ceph is a reliable, scalable, unified distributed storage system, and recently has become one of OpenStack's standard open source storage solutions. For the reason of the low performance, Ceph Filesystem (Cephfs) cannot employ erasure codes directly, especially for binary Reed-Solomon (BRS) codes, whose size of the parity block is larger than that of the data block. To address these problems, we implemented efficient BRS codes, and filled the gap between BRS codes and Cephfs by a simple conversion. In addition to that, an efficient framework consisting of filesystem, cache tier and storage tier was adopted in Ceph to ensure that the file data is finally stored with the erasure coding technology. The experimental results show that such a design spends a small amount of additional cost but obtains much better effects.
Innovation and practice of continuous auditing

August 2010 Received in revised form 23 December 2010 Accepted 6 January 2011 The traditional audit paradigm is outdated in the real time economy. Innovation of the traditional audit process is necessary to support real time assurance. Practitioners and academics are exploring continuous auditing as a potential successor to the traditional audit paradigm. Using technology and automation, continuous auditing methodology enhances the efficiency and effectiveness of the audit process to support real time assurance. This paper defines how continuous auditing methodology introduces innovation to practice in seven dimensions and proposes a four-stage paradigm to advance future research. In addition, we formulate a set of methodological propositions concerning the future of assurance for practitioners and academic researchers.
Application of Computational Intelligence to Improve Education in Smart Cities

According to UNESCO, education is a fundamental human right and every nation's citizens should be granted universal access with equal quality to it. Because this goal is yet to be achieved in most countries, in particular in the developing and underdeveloped countries, it is extremely important to find more effective ways to improve education. This paper presents a model based on the application of computational intelligence (data mining and data science) that leads to the development of the student's knowledge profile and that can help educators in their decision making for best orienting their students. This model also tries to establish key performance indicators to monitor objectives' achievement within individual strategic planning assembled for each student. The model uses random forest for classification and prediction, graph description for data structure visualization and recommendation systems to present relevant information to stakeholders. The results presented were built based on the real dataset obtained from a Brazilian private k-9 (elementary school). The obtained results include correlations among key data, a model to predict student performance and recommendations that were generated for the stakeholders.
Evaluation of Event-Based Algorithms for Optical Flow with Ground-Truth from Inertial Measurement Sensor

In this study we compare nine optical flow algorithms that locally measure the flow normal to edges according to accuracy and computation cost. In contrast to conventional, frame-based motion flow algorithms, our open-source implementations compute optical flow based on address-events from a neuromorphic Dynamic Vision Sensor (DVS). For this benchmarking we created a dataset of two synthesized and three real samples recorded from a 240 × 180 pixel Dynamic and Active-pixel Vision Sensor (DAVIS). This dataset contains events from the DVS as well as conventional frames to support testing state-of-the-art frame-based methods. We introduce a new source for the ground truth: In the special case that the perceived motion stems solely from a rotation of the vision sensor around its three camera axes, the true optical flow can be estimated using gyro data from the inertial measurement unit integrated with the DAVIS camera. This provides a ground-truth to which we can compare algorithms that measure optical flow by means of motion cues. An analysis of error sources led to the use of a refractory period, more accurate numerical derivatives and a Savitzky-Golay filter to achieve significant improvements in accuracy. Our pure Java implementations of two recently published algorithms reduce computational cost by up to 29% compared to the original implementations. Two of the algorithms introduced in this paper further speed up processing by a factor of 10 compared with the original implementations, at equal or better accuracy. On a desktop PC, they run in real-time on dense natural input recorded by a DAVIS camera.
Temporal Relational Reasoning in Videos

Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales. We evaluate TRN-equipped networks on activity recognition tasks using three recent video datasets SomethingSomething, Jester, and Charades which fundamentally depend on temporal relational reasoning. Our results demonstrate that the proposed TRN gives convolutional neural networks a remarkable capacity to discover temporal relations in videos. Through only sparsely sampled video frames, TRN-equipped networks can accurately predict human-object interactions in the Something-Something dataset and identify various human gestures on the Jester dataset with very competitive performance. TRN-equipped networks also outperform two-stream networks and 3D convolution networks in recognizing daily activities in the Charades dataset. Further analyses show that the models learn intuitive and interpretable visual common sense knowledge in videos.
Ecosystem or Echo-System? Exploring Content Sharing across Alternative Media Domains

This research examines the competing narratives about the role and function of Syria Civil Defence, a volunteer humanitarian organization popularly known as the White Helmets, working in war-torn Syria. Using a mixed-method approach based on seed data collected from Twitter, and then extending out to the websites cited in that data, we examine content sharing practices across distinct media domains that functioned to construct, shape, and propagate these narratives. We articulate a predominantly alternative media “echo-system” of websites that repeatedly share content about the White Helmets. Among other findings, our work reveals a small set of websites and authors generating content that is spread across diverse sites, drawing audiences from distinct communities into a shared narrative. This analysis also reveals the integration of governmentfunded media and geopolitical think tanks as source content for anti-White Helmets narratives. More broadly, the analysis demonstrates the role of alternative newswire-like services in providing content for alternative media websites. Though additional work is needed to understand these patterns over time and across topics, this paper provides insight into the dynamics of this multi-layered media ecosystem.
Attribute-based encryption with non-monotonic access structures

We construct an Attribute-Based Encryption (ABE) scheme that allows a user's private key to be expressed in terms of any access formula over attributes. Previous ABE schemes were limited to expressing only monotonic access structures. We provide a proof of security for our scheme based on the Decisional Bilinear Diffie-Hellman (BDH) assumption. Furthermore, the performance of our new scheme compares favorably with existing, less-expressive schemes.
Fast High-Accuracy Part-of-Speech Tagging by Independent Classifiers

Part-of-speech (POS) taggers can be quite accurate, but for practical use, accuracy often has to be sacrificed for speed. For example, the maintainers of the Stanford tagger (Toutanova et al., 2003; Manning, 2011) recommend tagging with a model whose per tag error rate is 17% higher, relatively, than their most accurate model, to gain a factor of 10 or more in speed. In this paper, we treat POS tagging as a single-token independent multiclass classification task. We show that by using a rich feature set we can obtain high tagging accuracy within this framework, and by employing some novel feature-weight-combination and hypothesis-pruning techniques we can also get very fast tagging with this model. A prototype tagger implemented in Perl is tested and found to be at least 8 times faster than any publicly available tagger reported to have comparable accuracy on the standard Penn Treebank Wall Street Journal test set.
A Modular IoT Platform for Real-Time Indoor Air Quality Monitoring

The impact of air quality on health and on life comfort is well established. In many societies, vulnerable elderly and young populations spend most of their time indoors. Therefore, indoor air quality monitoring (IAQM) is of great importance to human health. Engineers and researchers are increasingly focusing their efforts on the design of real-time IAQM systems using wireless sensor networks. This paper presents an end-to-end IAQM system enabling measurement of CO₂, CO, SO₂, NO₂, O₃, Cl₂, ambient temperature, and relative humidity. In IAQM systems, remote users usually use a local gateway to connect wireless sensor nodes in a given monitoring site to the external world for ubiquitous access of data. In this work, the role of the gateway in processing collected air quality data and its reliable dissemination to end-users through a web-server is emphasized. A mechanism for the backup and the restoration of the collected data in the case of Internet outage is presented. The system is adapted to an open-source Internet-of-Things (IoT) web-server platform, called Emoncms, for live monitoring and long-term storage of the collected IAQM data. A modular IAQM architecture is adopted, which results in a smart scalable system that allows seamless integration of various sensing technologies, wireless sensor networks (WSNs) and smart mobile standards. The paper gives full hardware and software details of the proposed solution. Sample IAQM results collected in various locations are also presented to demonstrate the abilities of the system.
Compact Dual-Band Branch-Line and Rat-Race Couplers With Stepped-Impedance-Stub Lines

This study constructs stepped-impedance-stub lines for a dual-band branch-line coupler design with improved design flexibility. The proposed structure demonstrates dual-band performance and a compact size due to additional stepped-impedance stubs to branches. The developed synthesis method has two degrees of freedom which can be exploited to miniaturize circuit size and/or replace impractical impedances with more realizable ones. Observations also show the advantage of a wide-range realizable frequency ratio of dual bands. The current work fabricates three experimental dual-band branch-line couplers, including a two-section coupler, and achieves a size reduction up to 21.7%, compared with conventional structures. The measured results validate good dual-band performance at 2.4/5.8 GHz with enhanced bandwidths up to 21% and 12%, respectively. This research also successfully applies the proposed circuit to synthesize a dual-band rat-race coupler.
Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography.

Brain-computer interfaces (BCIs) based on electroencephalography (EEG) have been shown to accurately detect mental activities, but the acquisition of high levels of control require extensive user training. Furthermore, EEG has low signal-to-noise ratio and low spatial resolution. The objective of the present study was to compare the accuracy between two types of BCIs during the first recording session. EEG and tripolar concentric ring electrode (TCRE) EEG (tEEG) brain signals were recorded and used to control one-dimensional cursor movements.



Eight human subjects were asked to imagine either 'left' or 'right' hand movement during one recording session to control the computer cursor using TCRE and disc electrodes.



The obtained results show a significant improvement in accuracies using TCREs (44%-100%) compared to disc electrodes (30%-86%).



This study developed the first tEEG-based BCI system for real-time one-dimensional cursor movements and showed high accuracies with little training.
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network

Recently, Generative Adversarial Network (GAN) has been found wide applications in style transfer, image-to-image translation and image super-resolution. In this paper, a colordepth conditional GAN is proposed to concurrently resolve the problems of depth super-resolution and color super-resolution in 3D videos. Firstly, given the low-resolution depth image and low-resolution color image, a generative network is proposed to leverage mutual information of color image and depth image to enhance each other in consideration of the geometry structural dependency of color-depth image in the same scene. Secondly, three loss functions, including data loss, total variation loss, and 8-connected gradient difference loss are introduced to train this generative network in order to keep generated images close to the real ones, in addition to the adversarial loss. Experimental results demonstrate that the proposed approach produces highquality color image and depth image from low-quality image pair, and it is superior to several other leading methods. Besides, the applications of the proposed method in other tasks are image smoothing and edge detection at the same time.
A novel LLC resonant controller with best-in-class transient performance and low standby power consumption

This paper introduces a novel LLC resonant controller with better light load efficiency and best-in-class transient performance. A new control algorithm — hybrid hysteretic control, is proposed which combines the benefit of charge control and frequency control. It maintains the good transient performance of charge control, but avoids the related stability issues by adding slope compensation. The slope compensation also helps to sense the resonant capacitor voltage by only using lossless capacitor divider. Burst mode operation is developed to improve the light load efficiency. By turning on the power devices for only a short period during light load, it helps to reduce the equivalent switching frequency and achieve high efficiency. The effectiveness of the proposed controller has been validated in a 12 V/120 W half-bridge LLC converter, as well as a commercial power supply unit.