Miracle-dz/UniFAR-FTUs huggingface dataset:
Syllabus of Dental Materials

Abstract : This syllabus was prepared for military dental residents and clinicians. It presents a simplified review of the current status of modern restorative materials and devices, and offers alternative techniques, and examples of materials and devices which might improve the quality of military dental care while diminishing expense. As new materials appear upon the dental market and the military dentist is subjected to advertising claims, considerable misunderstanding of the proper use of restorative materials can be experienced. A publicity blitz by dental manufacturers is underway to convince the dental profession that posterior composite restorative materials are 'as durable as amalgam restorations'. Current dental research does not support this belief. Agar hydrocolloid and rubber base impression materials are the most commonly used dental impression materials in the military today; there are improvements in both techniques which can conserve resources while yielding excellent reproductions of prepared teeth. Originator-supplied keywords include: Dental amalgam/cements/bases/liners. Resin restoratives, porcelain casting alloys.
Prognostic Factor Analysis of Early Death in Stage II Breast Cancer

1 Department of Information Medicine, Medical Research Institute, Tokyo Medical and Dental University 1–5–45 Yushima, Bunkyo-ku, Tokyo 113–8510, Japan E-mail: shicom@mri.tmd. ac.jp 2 Cancer Information and Epidemiology Division, National Cancer Center Research Institute 3 Department of Surgery, National Cancer Center Hospital Received: November 2, 2000 Accepted: December 5, 2000 Original Article Jpn J Med Info 20 (5), 2000: 405–412
The interaction of two baroclinic geostrophic vortices on the β–plane

The interaction of two anticyclonic baroclinic vortices on the β–plane is studied, taking into account the effects of vortex–vortex advection and the radiation of barotropic Rossby waves. Quasi–geostrophic dynamics is assumed in each of the two layers and the vortices are represented by delta–function anomalies in the upper–layer potential vorticity. The vortices are strong (i.e. their swirl velocities are large compared with the long baroclinic Rossby wave speed), but are sufficiently widely separated so that the advection of one vortex due to another is of the same order as the meridional drift caused by Rossby wave radiation. Centre of mass arguments are used to demonstrate that the velocity of a vortex is due to the sum of (a) advection due to the other vortex and (b) drift due to wave radiation. The far–field structure of the Rossby wave wake of two zonally aligned vortices (i.e. they have zero meridional displacement relative to each other) is found as a function of their relative position enabling the total drag on the centre of mass to be found. The drag and subsequent meridional drift of each vortex due to the wave radiation alone is then deduced. Equilibria are found in which the vortices have the same southward and westward drift, thus preserving their zonal alignment. Depending on the vortex separation distance, the equilibrium configuration may have meridional drift significantly different from that of a single vortex by itself. For example, when the ratio of the upper–layer depth to bottom–layer depth is 0.4, the meridional drift may be as little as 35% or as much as 117% of the single–vortex drift speed. The stability of the equilibria as a function of the vortex separation distance is found.
Invasive Imaging of Coronary Atherosclerosis

markdownabstract__Abstract__ Unheralded acute coronary syndromes are a common initial manifestation of coronary atherosclerosis, and most of these events arise from coronary lesions which are not flow limiting. Pathological studies have retrospectively proposed that plaque composition is a crucial determinant of the propensity of an atherosclerotic lesion to rupture. A large study of victims of sudden cardiac death showed that 60% of acute coronary thrombi had as a substrate, a ruptured thincap fibroatheromatous (TCFA) lesion. Furthermore, 70% of those patients had additional TCF As in their coronary tree that had not ruptured. A large necrotic core (avascular, hypocellular, lipid-rich area), a thin fibrous cap with inflammatOry infiltration and pauciry of smooth muscle cells, and the presence of expansive (positive) remodelling have been identified as the major criteria to define TCFA lesions. In vivo early detection of these non-obstructive, lipid necrotic-rich, highrisk plaques may have an important impact on the prevention of acute myocardial infarction and sudden death.
Sensory Aids for Blind Persons

This state-of-the-art report includes descriptions of travel aids (Russell's Pathsounder, Kay's ultrasonic spectacles, Benjamin's Laser Cane, and Mims’ Seeing Aid), electronic reading machines (the Optacon and the Cognodictor), and vision substitution systems (the research of Collins and of Brindley). The future prospects for sensory aids are discussed in the light of marketing problems, the interest (and disinterest) of large companies in such research, and present levels of funding.
PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation

LiDAR sensors can provide dependable 3D spatial information at a low frequency (around 10 Hz) and have been widely applied in the field of autonomous driving and unmanned aerial vehicle (UAV). However, the camera with a higher frequency (around 20 Hz) has to be decreased so as to match with LiDAR in a multi-sensor system. In this paper, we propose a novel Pseudo-LiDAR interpolation network (PLIN) to increase the frequency of LiDAR sensor data. PLIN can generate temporally and spatially high-quality point cloud sequences to match the high frequency of cameras. To achieve this goal, we design a coarse interpolation stage guided by consecutive sparse depth maps and motion relationship. We also propose a refined interpolation stage guided by the realistic scene. Using this coarse-to-fine cascade structure, our method can progressively perceive multi-modal information and generate accurate intermediate point clouds. To the best of our knowledge, this is the first deep framework for Pseudo-LiDAR point cloud interpolation, which shows appealing applications in navigation systems equipped with LiDAR and cameras. Experimental results demonstrate that PLIN achieves promising performance on the KITTI dataset, significantly outperforming the traditional interpolation method and the state-of-the-art video interpolation technique.
A Weighted $\ell_1$-Minimization Approach For Wavelet Reconstruction of Signals and Images

In this effort, we propose a convex optimization approach based on weighted $\ell_1$-regularization for reconstructing objects of interest, such as signals or images, that are sparse or compressible in a wavelet basis. We recover the wavelet coefficients associated to the functional representation of the object of interest by solving our proposed optimization problem. We give a specific choice of weights and show numerically that the chosen weights admit efficient recovery of objects of interest from either a set of sub-samples or a noisy version. Our method not only exploits sparsity but also helps promote a particular kind of structured sparsity often exhibited by many signals and images. Furthermore, we illustrate the effectiveness of the proposed convex optimization problem by providing numerical examples using both orthonormal wavelets and a frame of wavelets. We also provide an adaptive choice of weights which is a modification of the iteratively reweighted $\ell_1$-minimization method.
MinneApple: A Benchmark Dataset for Apple Detection and Segmentation

In this work, we present a new dataset to advance the state-of-the-art in fruit detection, segmentation, and counting in orchard environments. While there has been significant recent interest in solving these problems, the lack of a unified dataset has made it difficult to compare results. We hope to enable direct comparisons by providing a large variety of high-resolution images acquired in orchards, together with human annotations of the fruit on trees. The fruits are labeled using polygonal masks for each object instance to aid in precise object detection, localization, and segmentation. Additionally, we provide data for patch-based counting of clustered fruits. Our dataset contains over 41’0000 annotated object instances in 1000 images. We present a detailed overview of the dataset together with baseline performance analysis for bounding box detection, segmentation, and fruit counting as well as representative results for yield estimation. We make this dataset publicly available and host a CodaLab challenge to encourage a comparison of results on a common dataset. To download the data and learn more about the MinneApple dataset, please see the project website: http://rsn.cs. umn.edu/index.php/MinneApple. Up to date information is available online.
Out the Window: A Crowd-Sourced Dataset for Activity Classification in Security Video

The Out the Window (OTW) dataset is a crowdsourced activity dataset containing 5,668 instances of 17 activities from the NIST Activities in Extended Video (ActEV) challenge. These videos are crowdsourced from workers on the Amazon Mechanical Turk using a novel scenario acting strategy, which collects multiple instances of natural activities per scenario. Turkers are instructed to lean their mobile device against an upper story window overlooking an outdoor space, walk outside to perform a scenario involving people, vehicles and objects, and finally upload the video to us for annotation. Performance evaluation for activity classification on VIRAT Ground 2.0 shows that the OTW dataset provides an 8.3% improvement in mean classification accuracy, and a 12.5% improvement on the most challenging activities involving people with vehicles.
Measures of maximal entropy on subsystems of topological suspension semiflows

Given a compact topological dynamical system (X, f) with positive entropy and upper semi-continuous entropy map, and any closed invariant subset $Y \subset X$ with positive entropy, we show that there exists a continuous roof function such that the set of measures of maximal entropy for the suspension semi-flow over (X,f) consists precisely of the lifts of measures which maximize entropy on Y. This result has a number of implications for the possible size of the set of measures of maximal entropy for topological suspension flows. In particular, for a suspension flow on the full shift on a finite alphabet, the set of ergodic measures of maximal entropy may be countable, uncountable, or have any finite cardinality.
Delivering Cognitive Behavioral Therapy Using A Conversational Social Robot

Social robots are becoming an integrated part of our daily life due to their ability to provide companionship and entertainment. A subfield of robotics, Socially Assistive Robotics (SAR), is particularly suitable for expanding these benefits into the healthcare setting because of its unique ability to provide cognitive, social, and emotional support. This paper presents our recent research on developing SAR by evaluating the ability of a life-like conversational social robot, called Ryan, to administer internet-delivered cognitive behavioral therapy (iCBT) to older adults with depression. For Ryan to administer the therapy, we developed a dialogue-management system, called Program-R. Using an accredited CBT manual for the treatment of depression, we created seven hour-long iCBT dialogues and integrated them into Program-R using Artificial Intelligence Markup Language (AIML). To assess the effectiveness of Robot-based iCBT and users’ likability of our approach, we conducted an HRI study with a cohort of elderly people with mild-to-moderate depression over a period of four weeks. Quantitative analyses of participant’s spoken responses (e.g. word count and sentiment analysis), face-scale mood scores, and exit surveys, strongly support the notion robot-based iCBT is a viable alternative to traditional human-delivered therapy.
2-knot homology and Roseman move

Ng constructed an invariant of knots in ${\mathbb{R}}^3$, a combinatorial knot contact homology. Extending his study, we construct an invariant of surface-knots in ${\mathbb{R}}^4$ using diagrams in ${\mathbb{R}}^3$.
Phase shift of electron spin echo signal caused by polarization of nuclear spins: revealing the quantum nature of spin environment

We consider the spin echo decay of a nitrogen-vacancy center spin qubit, based on $m\! =\! 0$ and $m\! =\! 1$ levels of the $S\! = \! 1$ ground state spin manifold of the center, in presence of finite dynamic nuclear polarization of the nuclear environment. We show that the signal acquires a nontrivially time-dependent phase shift in presence of such polarization. In Gaussian approximation to calculation of dephasing this phase shift is absent when the NV center is prepared in a superposition of $m\! = \! 1$ and $m\! =\! -1$ states, i.e. when the qubit couples to the spin environment in a way analogous to that of spin-$1/2$. For nuclear environment devoid of spins strongly coupled to the qubit, the phase shift can be quite well described in Gaussian approximation, which makes it clear that it is related to a linear response function of the environment affected by an evolving qubit. Consequently, its observation signifies the presence of back-action of the qubit on the environment, and it thus excludes the possibility of physically well-motivated treatment of environment as a source of external classical noise affecting the qubit. Furthermore, when exact coherence dynamics is well described by weak coupling approximation, for qubit based on $m\! =\! 0$ and $m\! =\! 1$ levels observation of dependence of modulus of echo signal on nuclear polarization signifies non-Gaussian character of environmental influence. The same conclusion follows from observation of nonzero phase shift of spin echo signal of a qubit based on $m\! = \! \pm 1$ levels.
P2IM: Scalable and Hardware-independent Firmware Testing via Automatic Peripheral Interface Modeling (extended version)

Dynamic testing or fuzzing of embedded firmware is severely limited by hardware-dependence and poor scalability, partly contributing to the widespread vulnerable IoT devices. We propose a software framework that continuously executes a given firmware binary while channeling inputs from an off-the-shelf fuzzer, enabling hardware-independent and scalable firmware testing. Our framework, using a novel technique called P$^2$IM, abstracts diverse peripherals and handles firmware I/O on the fly based on automatically generated models. P$^2$IM is oblivious to peripheral designs and generic to firmware implementations, and therefore, applicable to a wide range of embedded devices. We evaluated our framework using 70 sample firmware and 10 firmware from real devices, including a drone, a robot, and a PLC. It successfully executed 79% of the sample firmware without any manual assistance. We also performed a limited fuzzing test on the real firmware, which unveiled 7 unique unknown bugs.
Multitask Learning to Improve Egocentric Action Recognition

In this work we employ multitask learning to capitalize on the structure that exists in related supervised tasks to train complex neural networks. It allows training a network for multiple objectives in parallel, in order to improve performance on at least one of them by capitalizing on a shared representation that is developed to accommodate more information than it otherwise would for a single task. We employ this idea to tackle action recognition in egocentric videos by introducing additional supervised tasks. We consider learning the verbs and nouns from which action labels consist of and predict coordinates that capture the hand locations and the gaze-based visual saliency for all the frames of the input video segments. This forces the network to explicitly focus on cues from secondary tasks that it might otherwise have missed resulting in improved inference. Our experiments on EPIC-Kitchens and EGTEA Gaze+ show consistent improvements when training with multiple tasks over the single-task baseline. Furthermore, in EGTEA Gaze+ we outperform the state-of-the-art in action recognition by 3.84%. Apart from actions, our method produces accurate hand and gaze estimations as side tasks, without requiring any additional input at test time other than the RGB video clips.
Asteroseismic Constraints on the Cosmic-time Variation of the Gravitational Constant from an Ancient Main-sequence Star

We investigate the variation of the gravitational constant G over the history of the universe by modeling the effects on the evolution and asteroseismology of the low-mass star KIC 7970740, which is one of the oldest (∼11 Gyr) and best-observed solar-like oscillators in the Galaxy. From these data we find G ˙ / G = ( 1.2 ± 2.6 ) × 10 − 12 yr − 1 , that is, no evidence for any variation in G. We also find a Bayesian asteroseismic estimate of the age of the universe as well as astrophysical S-factors for five nuclear reactions obtained through a 12-dimensional stellar evolution Markov Chain Monte Carlo simulation.
A conjectural bound on the second Betti number for hyper-Kaehler manifolds

In previous work, we noted that the known cases of hyper-Kahler manifolds satisfy a natural condition on the LLV decomposition of the cohomology; informally, the Verbitsky component is the dominant representation in the LLV decomposition. Assuming this condition holds for all hyper-Kahler manifolds, we obtain an upper bound for the second Betti number in terms of the dimension.
Boosting Real-Time Driving Scene Parsing With Shared Semantics

Real-time scene parsing is a fundamental feature for autonomous driving vehicles with multiple cameras. In this letter we demonstrate that sharing semantics between cameras with different perspectives and overlapping views can boost the parsing performance when compared with traditional methods, which individually process the frames from each camera. Our framework is based on a deep neural network for semantic segmentation but with two kinds of additional modules for sharing and fusing semantics. On the one hand, a semantics sharing module is designed to establish the pixel-wise mapping between the input images. Features as well as semantics are shared by the map to reduce duplicated workload, which leads to more efficient computation. On the other hand, feature fusion modules are designed to combine different modalities of semantic features, which leverages the information from both inputs for better accuracy. To evaluate the effectiveness of the proposed framework, we have applied our network to a dual-camera vision system for driving scene parsing. Experimental results show that our network outperforms the baseline method on the parsing accuracy with comparable computations.
$360^o$ Surface Regression with a Hyper-Sphere Loss

Omnidirectional vision is becoming increasingly relevant as more efficient $360^o$ image acquisition is now possible. However, the lack of annotated $360^o$ datasets has hindered the application of deep learning techniques on spherical content. This is further exaggerated on tasks where ground truth acquisition is difficult, such as monocular surface estimation. While recent research approaches on the 2D domain overcome this challenge by relying on generating normals from depth cues using RGB-D sensors, this is very difficult to apply on the spherical domain. In this work, we address the unavailability of sufficient $360^o$ ground truth normal data, by leveraging existing 3D datasets and remodelling them via rendering. We present a dataset of $360^o$ images of indoor spaces with their corresponding ground truth surface normal, and train a deep convolutional neural network (CNN) on the task of monocular 360 surface estimation. We achieve this by minimizing a novel angular loss function defined on the hyper-sphere using simple quaternion algebra. We put an effort to appropriately compare with other state of the art methods trained on planar datasets and finally, present the practical applicability of our trained model on a spherical image re-lighting task using completely unseen data by qualitatively showing the promising generalization ability of our dataset and model. The dataset is available at: this http URL.
Physical-Layer Security for Untrusted UAV-Assisted Full-Duplex Wireless Networks

The paper considers physical layer security (PLS) of an untrusted unmanned aerial vehicle (UAV) network, where a multitude of UAVs communicate in full-duplex (FD) mode. A source-based jamming (SBJ) scheme is exploited for secure communication without utilizing any external jammers. Firstly, the optimal power allocation between the confidential signal and the jamming signal is derived to maximize the secrecy rate of each link between the source and the destination. Then, the best UAV selection scheme is proposed to maximize the overall secrecy rate of the network. The corresponding secrecy outage probability (SOP) and the average secrecy rate (ASR) of the network are analyzed based on the proposed UAV selection and the optimal power allocation schemes. Asymptotic results are also obtained to derive the achievable diversity order. Results are validated through numerical evaluations while providing useful network design insights such as locations and altitudes of the UAVs.
Global Aggregation then Local Distribution in Fully Convolutional Networks

It has been widely proven that modelling long-range dependencies in fully convolutional networks (FCNs) via global aggregation modules is critical for complex scene understanding tasks such as semantic segmentation and object detection. However, global aggregation is often dominated by features of large patterns and tends to oversmooth regions that contain small patterns (e.g., boundaries and small objects). To resolve this problem, we propose to first use \emph{Global Aggregation} and then \emph{Local Distribution}, which is called GALD, where long-range dependencies are more confidently used inside large pattern regions and vice versa. The size of each pattern at each position is estimated in the network as a per-channel mask map. GALD is end-to-end trainable and can be easily plugged into existing FCNs with various global aggregation modules for a wide range of vision tasks, and consistently improves the performance of state-of-the-art object detection and instance segmentation approaches. In particular, GALD used in semantic segmentation achieves new state-of-the-art performance on Cityscapes test set with mIoU 83.3\%. Code is available at: \url{this https URL}
HyEdge: Optimal Request Scheduling in Hybrid Edge Computing Environment

With the widespread use of Internet of Things (IoT) devices and the arrival of the 5G era, edge computing has become an attractive paradigm to serve end-users and provide better QoS. Many efforts have been done to provision some merging public network services at the edge. We reveal that it is very common that specific users call for private and isolated edge services to preserve data privacy and enable other security intentions. However, it still remains open to fulfill such kind of mixed requests in edge computing. In this paper, we propose the framework of hybrid edge computing to offer both public and private edge services systematically. To fully exploit the benefits of this novel framework, we define the problem of optimal request scheduling over a given placement solution of hybrid edge servers, so as to minimize the response delay. This problem is further modeled as a mixed integer non-linear problem (MINLP), which is typically NP-hard. Accordingly, we propose the partition-based optimization method, which can efficiently solve this NP-hard problem via the problem decomposition and the branch and bound strategies. We finally conduct extensive evaluations with a real-world dataset to measure the performance of our methods. The results indicate that the proposed method achieves elegant performance with low computation complexity.