Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiencyand therapeutic effect of modified pigtail probe in anastomosing lacerated lacrimal canaliculus

DOI: 10.3760/cma.j.issn.1008-1275.2009. 02.005 State Key Laboratory of Ophthalmology, Department of Ophthalmology, Affiliated Hospital, Medical College, Qingdao University, Qingdao 266003, China (Liang T, Zhao GQ, Li YL, Yang SS, Zhang LY and Wu Y) *Corresponding author: Tel: 86-532-82702869, E-mail: lt19722000@yahoo.com. cn Objective: To investigate the necessity of modification to the traditional pigtail probe and evaluate its efficiency and therapeutic effect in searching the nasal cut ends and anastomosing the lacerated lacrimal canaliculus. Methods: Eighty-seven patients (including 87 eyes) suffering from canalicular laceration were randomized into two groups: 41 patients treated with traditional pigtail probes (Group A) and 46 with modified pigtail probes (Group B). During the reconstruction of the lacerated canaliculi, the traditional pigtail probe and the modified pigtail probe were used respectively to seek for the nasal cut ends of lacerated lacrimal canaliculi. Peripherally inserted central catheter (PICCTM) silicone tube with diameter of 0.95 mm was intubated as a stent for 4-6 months. The surgical outcomes were retrospectively analyzed after stent removal. Results: In Group B, the primary success rate of searching the nasal cut ends of lacerated lacrimal canaliculi was 93.48% (43/46) and the final success rate was 97.83% (45/46). No false passage formed in Group B. Statistical significance was found between Group A and Group B as the primary success rates of searching the nasal cut ends ( = 10.522, P<0.01) and the false passage forming rates were concerned ( =4.704, P<0.05), whereas nosignificance was found between the two groups as the final success rates were concerned ( =0.007, P>0.05). The mean time of searching the nasal cut ends of lacerated lacrimal canaliculi in Group B was (5.02±2.73) minutes and the mean time of operation was(33.90±4.84)minutes,andboth weresignificantly shorter than those of Group A (t1=9.779, t2=10.700, P<0.01). The cure rate of Group Bwas 95.65%, though higher than that of Group A, no statistical significance was found (Z=–1.007, P>0.05). Totally, 2 patients (2.30%) were found to be absent of common canaliculus and underwent bicanalicular nasal intubation in the two groups. Conclusions: Pigtail probes are efficient and convenient apparatus for searching the nasal cut ends of the lacerated lacrimal canaliculi in the reconstruction of canalicular laceration. Necessary or proper modifications to the pigtail probes can minimize the risk of iatrogenic damagesor complications and enhance the efficiency and therapeutic effect of canalicular repair.
Selective Synthetic Augmentation with Quality Assurance

Supervised training of an automated medical image analysis system often requires a large amount of expert annotations that are hard to collect. Moreover, the proportions of data available across different classes may be highly imbalanced for rare diseases. To mitigate these issues, we investigate a novel data augmentation pipeline that selectively adds new synthetic images generated by conditional Adversarial Networks (cGANs), rather than extending directly the training set with synthetic images. The selection mechanisms that we introduce to the synthetic augmentation pipeline are motivated by the observation that, although cGAN-generated images can be visually appealing, they are not guaranteed to contain essential features for classification performance improvement. By selecting synthetic images based on the confidence of their assigned labels and their feature similarity to real labeled images, our framework provides quality assurance to synthetic augmentation by ensuring that adding the selected synthetic images to the training set will improve performance. We evaluate our model on a medical histopathology dataset, and two natural image classification benchmarks, CIFAR10 and SVHN. Results on these datasets show significant and consistent improvements in classification performance (with 6.8%, 3.9%, 1.6% higher accuracy, respectively) by leveraging cGAN generated images with selective augmentation.
PhoneBit: Efficient GPU-Accelerated Binary Neural Network Inference Engine for Mobile Phones

Over the last years, a great success of deep neural networks (DNNs) has been witnessed in computer vision and other fields. However, performance and power constraints make it still challenging to deploy DNNs on mobile devices due to their high computational complexity. Binary neural networks (BNNs) have been demonstrated as a promising solution to achieve this goal by using bit-wise operations to replace most arithmetic operations. Currently, existing GPU-accelerated implementations of BNNs are only tailored for desktop platforms. Due to architecture differences, mere porting of such implementations to mobile devices yields suboptimal performance or is impossible in some cases. In this paper, we propose PhoneBit, a GPU-accelerated BNN inference engine for Android-based mobile devices that fully exploits the computing power of BNNs on mobile GPUs. PhoneBit provides a set of operator-level optimizations including locality-friendly data layout, bit packing with vectorization and layers integration for efficient binary convolution. We also provide a detailed implementation and parallelization optimization for PhoneBit to optimally utilize the memory bandwidth and computing power of mobile GPUs. We evaluate PhoneBit with AlexNet, YOLOv2 Tiny and VGG16 with their binary version. Our experiment results show that PhoneBit can achieve significant speedup and energy efficiency compared with state-of-the-art frameworks for mobile devices.
All 2-neighborly d-polytopes with at most d + 9 facets

We give a complete enumeration of all 2-neighborly $d$-polytopes with $d+9$ and less facets. All of them are realized as 0/1-polytopes, except a 6-polytope $P_{6,10,15}$ with 10 vertices and 15 facets, and pyramids over $P_{6,10,15}$. In particular, we update the lower bounds for the number of facets of a 2-neighborly $d$-polytope $P$ and showed that the number of facets of $P$ is not less than the number of its vertices $f_0(P)$ for $f_0(P) \le d + 10$.
Accepted or Abandoned? Predicting the Fate of Code Changes

Many mature Open-Source Software (OSS), as well as commercial, organizations have adopted peer code review as an integral part of the development process to ensure the quality of the product. Of particular interest are code changes that end up "abandoned," either because they are rejected, or (more commonly) because they are never accepted at all (at least not through the review tool). Several factors such as resource allocation, job environment, and efficiency mismatch between the author and the reviewer may cause a code change to be abandoned even after months of efforts from the developers and the reviewers. Predicting the review outcome of such code changes can ease the prioritization of tasks and the utilization of limited resources by saving time spent on low-quality code changes. In this paper, we conducted a comprehensive study to predict whether a code change is merged or abandoned and applied various well-known supervised machine learning algorithms. We propose PredCR, a Random Forest based model that predicts the review outcome of a code change with 0.91 f-measure at the beginning of the code change on the test set. Also, it improves predictions of abandoned changes by 27\%-103\% and merged changes by 5\%-11\%. Our model accurately classifies 93\% of the top 25\% code changes (with average 196 days duration) that go longest without being merged. PredCR can also adapt to the changes in feature values at different stages of the review process although it achieves very high performance at the very early stage (within 10\% of the review process). This way, prediction quality for a particular code change can improve as the code review progresses. We also conducted a study to find out the properties of an ideal training set for our tool. We found that training with the instances from the same projects ensures 9\%-25\% performance increase.
VoronoiNet : General Functional Approximators with Local Support

Voronoi diagrams are highly compact representations that are used in various Graphics applications. In this work, we show how to embed a differentiable version of it – via a novel deep architecture – into a generative deep network. By doing so, we achieve a highly compact latent embedding that is able to provide much more detailed reconstructions, both in 2D and 3D, for various shapes. In this tech report, we introduce our representation and present a set of preliminary results comparing it with recently proposed implicit occupancy networks.
Deep Learning-based Hybrid Graph-Coloring Algorithm for Register Allocation

Register allocation, which is a crucial phase of a good optimizing compiler, relies on graph coloring. Hence, an efficient graph coloring algorithm is of paramount importance. In this work we try to learn a good heuristic for coloring interference graphs that are used in the register allocation phase. We aim to handle moderate sized interference graphs which have 100 nodes or less. For such graphs we can get the optimal allocation of colors to the nodes. Such optimal coloring is then used to train our Deep Learning network which is based on several layers of LSTM that output a color for each node of the graph. However, the current network may allocate the same color to the nodes connected by an edge resulting in an invalid coloring of the interference graph. Since it is difficult to encode constraints in an LSTM to avoid invalid coloring, we augment our deep learning network with a color correction phase that runs after the colors have been allocated by the network. Thus, our algorithm is hybrid in nature consisting of a mix of a deep learning algorithm followed by a more traditional correction phase. We have trained our network using several thousand random graphs of varying sparsity. On application of our hybrid algorithm to various popular graphs found in literature we see that our algorithm does very well when compared to the optimal coloring of these graphs. We have also run our algorithm against LLVMs popular greedy register allocator for several SPEC CPU 2017 benchmarks and notice that the hybrid algorithm performs on par or better than such a well-tuned allocator for most of these benchmarks.
Hand and Body Blockage Measurements With Form-Factor User Equipment at 28 GHz

Blockage by the human hand/body is an important impairment in realizing practical millimeter-wave wireless systems. Prior works on blockage modeling are either based on theoretical studies of double knife-edge diffraction or its modifications, high-frequency simulations of electromagnetic effects, or measurements with experimental millimeter-wave prototypes. While such studies are useful, they do not capture the form-factor constraints of user equipments (UEs), such as moderate array sizes, gains, and beamwidths. In this work, we study the impact of hand/body blockage with a UE at 28 GHz built on commercial millimeter-wave components. We report five controlled studies with different types of hand holdings/grips, antenna types, and directional/narrow beams. For both hard and loose hand grips, we report considerably lower blockage loss estimates than prior works. Critical in estimating the loss is the definition of a “region of interest” (RoI) around the UE where the impact of the hand/body is seen. Toward this goal, we define an RoI that includes the spatial area where significant energy is seen in either the no blockage or blockage modes. Our studies show that significant spatial area coverage improvement can be seen with a loose hand grip due to hand reflections.
Performance and Comparisons of STDP based and Non-STDP based Memristive Neural Networks on Hardware

With the development of research on memristor, memristive neural networks (MNNs) have become a hot research topic recently. Because memristor can mimic the spike timing-dependent plasticity (STDP), the research on STDP based MNNs is rapidly increasing. However, although state-of-the-art works on STDP based MNNs have many applications such as pattern recognition, STDP mechanism brings relatively complex hardware framework and low processing speed, which block MNNs' hardware realization. A non-STDP based unsupervised MNN is constructed in this paper. Through the comparison with STDP method on the basis of two common structures including feedforward and crossbar, non-STDP based MNNs not only remain the same advantages as STDP based MNNs including high accuracy and convergence speed in pattern recognition, but also better hardware performance as few hardware resources and higher processing speed. By virtue of the combination of memristive character and simple mechanism, non-STDP based MNNs have better hardware compatibility, which may give a new viewpoint for memristive neural networks' engineering applications.
Control-Oriented, Data-Driven Models of Thermal Dynamics

We investigate data-driven, simple-to-implement residential environmental models that can serve as the basis for energy saving algorithms in both retrofits and new designs of residential buildings. Despite the nonlinearity of the underlying dynamics, using Koopman operator theory framework in this study we show that a linear second order model embedding, that captures the physics that occur inside a single or multi zone space does well when compared with data simulated using EnergyPlus. This class of models has low complexity. We show that their parameters have physical significance for the large-scale dynamics of a building and are correlated to concepts such as the thermal mass. We investigate consequences of changing the thermal mass on the energy behavior of a building system and provide best practice design suggestions.
MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population

Performing inference on data obtained through observational studies is becoming extremely relevant due to the widespread availability of data in fields such as healthcare, education, retail, etc. Furthermore, this data is accrued from multiple homogeneous subgroups of a heterogeneous population, and hence, generalizing the inference mechanism over such data is essential. We propose the MetaCI framework with the goal of answering counterfactual questions in the context of causal inference (CI), where the factual observations are obtained from several homogeneous subgroups. While the CI network is designed to generalize from factual to counterfactual distribution in order to tackle covariate shift, MetaCI employs the meta-learning paradigm to tackle the shift in data distributions between training and test phase due to the presence of heterogeneity in the population, and due to drifts in the target distribution, also known as concept shift. We benchmark the performance of the MetaCI algorithm using the mean absolute percentage error over the average treatment effect as the metric, and demonstrate that meta initialization has significant gains compared to randomly initialized networks, and other methods.
Magnetic activity and evolution of the four Hyades K giants

We determine the exact physical parameters of the four Hyades cluster K giants, using their parallaxes and atmospheric modelling of our red-channel TIGRE high-resolution spectra. Performing a comparison with well-tested evolutionary tracks, we derive exact masses and evolutionary stages. At an age of 588 (±60) Myr and with a metallicity of Z = 0.03 (consistent with the spectroscopic abundances), we find HD 27371 and HD 28307, the two less bright K giants, at the onset of central helium burning, entering their blue loops with a mass of 2.62 M⊙, while the slightly brighter stars HD 28305 and HD 27697 are already exiting their blue loop. Their more advanced evolution suggests a higher mass of 2.75 M⊙. Notably, this pairing coincides with the different activity levels, which we find for these four stars from chromospheric activity monitoring with TIGRE and archival Mount Wilson data as well as from ROSAT coronal detections. The two less evolved K giants are the far more active pair, and we confidently confirm their rotation with periods of about 142 d. This work therefore provides some first, direct evidence of magnetic braking during the 130 Myr lasting phase of central helium-burning, similar to what has long been known to occur to cool main-sequence stars.
A Generalized Expression for the Gradient of Mutual Information with the Application in Multiple Access Channels

Taking a functional approach, we derive a general expression for the gradient of the Mutual Information (MI) with respect to the system parameters in the stochastic systems. This expression covers the cases in which the system input depends on the system parameters. As an application, we consider the K-user Multiple Access Channels (MAC) with feedback and utilize the obtained results to explore the behavior of these systems in terms of the MI. Specializing the results to the additive Gaussian noise MAC, we extend the MI and Minimum Mean Square Error (MMSE) relationship, i.e., I-MMSE to the K-user Gaussian MAC with feedback. In this derivation, we show that the gradient of MI can be decomposed into three distinct parts, where the first part is the MMSE term originated from noise, and the second and third parts reflect the effects of the interference and feedback, respectively. Then, considering the capacity achieving Fourier-Modulated Estimate Correction (F-MEC) strategy of Kramer, we show how feedback compensates the destructive effects of the users' interference in the K-user symmetric Gaussian MAC.
Naive Gabor Networks

In this paper, we introduce naive Gabor Networks or Gabor-Nets which, for the first time in the literature, design and learn convolutional kernels strictly in the form of Gabor filters, aiming to reduce the number of parameters and constrain the solution space for convolutional neural networks (CNNs). In comparison with other Gabor-based methods, Gabor-Nets exploit the phase offset of the sinusoid harmonic to control the frequency characteristics of Gabor kernels, being able to adjust the convolutional kernels in accordance with the data from a frequency perspective. Furthermore, a fast 1-D decomposition of the Gabor kernel is implemented, bringing the original quadratic computational complexity of 2-D convolutions to a linear one. We evaluated our newly developed Gabor-Nets on two remotely sensed hyperspectral benchmarks, showing that our model architecture can significantly improve the convergence speed and the performance of CNNs, particularly when very limited training samples are available.
Deep CMST Framework for the Autonomous Recognition of Heavily Occluded and Cluttered Baggage Items from Multivendor Security Radiographs

—Since the last two decades, luggage scanning has become one of the prime aviation security concerns all over the world. Manual screening of the baggage items is a cumbersome, subjective and inefficient process and many researchers have developed x-ray imagery based autonomous systems for this purpose. However, to the best of our knowledge, there is no framework till date which can recognize heavily occluded and cluttered baggage items from x-ray scans irrespective of the acquisition machinery or the scan modality. This paper presents a deep cascaded multiscale structure tensor-based framework which can automatically extract and recognize normal as well as suspicious items irrespective of their position and orientation from the multivendor x-ray scans. The proposed framework is unique in its kind as it intelligently extracts each object by iteratively picking contour based transitional information from different orientations and uses only a single feedforward convolutional neural network for the recognition. The proposed framework has been rigorously tested on two publicly available datasets containing cumulative of 1,067,381 x-ray scans where it significantly outperformed the existing state-of-the-art solutions by achieving the mean intersection-over-union ratings of up to 0.9689, area under the curve of up to 0.9950, accuracy of up to 0.9955 and the mean average precision score of up to 0.9453 for detecting normal as well as suspicious baggage items. Furthermore, the proposed framework has achieved 15.78% better time performance as compared to the popular object detectors.
Spectral characterization of the non-thermal X-ray emission of gamma-ray pulsars

We report on a detailed spectral characterization of the non-thermal X-ray emission for a large sample of gamma-ray pulsars in the second Fermi Large Area Telescope catalogue. We outline the criteria adopted for the selection of our sample, its completeness, and critically describe different approaches to estimate the spectral shape and flux of pulsars. We perform a systematic modelling of the pulsars’ X-ray spectra using archival observations with XMM–Newton, Chandra, and NuSTAR and extract the corresponding non-thermal X-ray spectral distributions. This set of data is made available online and is useful to confront with predictions of theoretical models.
Towards countering hate speech and personal attack in social media

The damaging effects of hate speech in social media are evident during the last few years, and several organizations, researchers and the social media platforms themselves have tried to harness them without great success. Recently, following the advent of deep learning, several novel approaches appeared in the field of hate speech detection. However, it is apparent that such approaches depend on large-scale datasets in order to exhibit competitive performance. In this paper, we present a novel, publicly available collection of datasets in five different languages, that consists of tweets referring to journalism-related accounts, including high-quality human annotations for hate speech and personal attack. To build the datasets we follow a concise annotation strategy and employ an active learning approach. Additionally, we present a number of state-of-the-art deep learning architectures for hate speech detection and use these datasets to train and evaluate them. Finally, we propose an ensemble model that outperforms all individual models.
Recording of smoking status: a survey of general practice records of those with and without smoking-related disease

Recording of smoking status in general practice was compared according to the presence or absence of four diseases in which smoking cessation is important (asthma, hypertension, ischaemic heart disease and diabetes). Five of seven practices in one locality of Leicester took part. Our aims were to discover whether patients with these diseases were more likely to have a record of smoking status in their general practice notes than patients without them, and to examine the accuracy of this record compared with results from a patient questionnaire. For all diseases included, the proportion of records containing data on smoking status was higher if the disease was present than if it was not. When adjusted for age and sex, differences in recording reached statistical significance for asthma, diabetes and ischaemic heart disease, but not for hypertension. Smoking rates, as estimated from both the GP record and the postal questionnaire, were lower for all disease groups, but for asthma this did not reach statistical significance. In all disease groups GP data overestimated smoking prevalence compared with the postal questionnaire. We conclude that practices targeted their recording of smoking to those with smoking-related conditions, but achieving higher and more accurate rates of recording in those with smoking-related diseases remains a priority.