Miracle-dz/UniFAR-FTUs huggingface dataset:
The trace embedding lemma and spinelessness.

We demonstrate new applications of the trace embedding lemma to the study of piecewise-linear surfaces and the detection of exotic phenomena in dimension four. We provide infinitely many pairs of homeomorphic 4-manifolds $W$ and $W'$ homotopy equivalent to $S^2$ which have smooth structures distinguished by several formal properties: $W'$ is diffeomorphic to a knot trace but $W$ is not, $W'$ contains $S^2$ as a smooth spine but $W$ does not even contain $S^2$ as a piecewise-linear spine, $W'$ is geometrically simply connected but $W$ is not, and $W'$ does not admit a Stein structure but $W$ does. In particular, the simple spineless 4-manifolds $W$ provide an alternative to Levine and Lidman's recent solution to Problem 4.25 in Kirby's list. We also show that all smooth 4-manifolds contain topological locally flat surfaces that cannot be approximated by piecewise-linear surfaces.
Thermal transport for probing quantum materials

In probing quantum materials, thermal transport is less appreciated than electrical transport. This article aims to show the pivotal role that thermal transport may play in understanding quantum materials—longitudinal thermal transport reflects itinerant quasiparticles, even in an electrical insulating phase, while transverse thermal transport such as the thermal Hall and Nernst effects is tightly linked to nontrivial topology. We discuss three examples—quantum spin liquids wherein thermal transport identifies its existence, superconductors wherein thermal transport reveals the superconducting gap structure, and topological Weyl semimetals where the anomalous Nernst effect is a consequence of nontrivial Berry curvature. We conclude with an outlook on the unique insights thermal transport may offer to probe a much broader category of quantum phenomena.
3D Sensing of a Moving Object with a Nodding 2D LIDAR and Reconfigurable Mirrors

Perception in 3D has become standard practice for a large part of robotics applications. High quality 3D perception is costly. Our previous work on a nodding 2D Lidar provides high quality 3D depth information with low cost, but the sparse data generated by this sensor poses challenges in understanding the characteristics of moving objects within an uncertain environment. This paper proposes a novel design of the nodding Lidar but provides dynamic reconfigurability in terms of limiting the field of view of the sensor using a set of optical mirrors. It not only provides denser scans, but it also achieves a three times higher scan update rate. Additionally, we propose a novel calibration mechanism for this sensor and prove its effectiveness for dynamic object detection and tracking.
Logic Bugs in IoT Platforms and Systems: A Review

In recent years, IoT platforms and systems have been rapidly emerging. Although IoT is a new technology, new does not mean simpler (than existing networked systems). Contrarily, the complexity (of IoT platforms and systems) is actually being increased in terms of the interactions between the physical world and cyberspace. The increased complexity indeed results in new vulnerabilities. This paper seeks to provide a review of the recently discovered logic bugs that are specific to IoT platforms and systems. In particular, 17 logic bugs and one weakness falling into seven categories of vulnerabilities are reviewed in this survey.
Unsupervised Detection of Sub-events in Large Scale Disasters

Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people “on the ground” post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency “event”, such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.
Classifying topological sector via machine learning

We employ a machine learning technique for an estimate of the topological charge $Q$ of gauge configurations in SU(3) Yang-Mills theory in vacuum. As a first trial, we feed the four-dimensional topological charge density with and without smoothing into the convolutional neural network and train it to estimate the value of $Q$. We find that the trained neural network can estimate the value of $Q$ from the topological charge density at small flow time with high accuracy. Next, we perform the dimensional reduction of the input data as a preprocessing and analyze lower dimensional data by the neural network. We find that the accuracy of the neural network does not have statistically-significant dependence on the dimension of the input data. From this result we argue that the neural network does not find characteristic features responsible for the determination of $Q$ in the higher dimensional space.
Be and Bn stars: Balmer discontinuity and stellar-class relationship

Context. A significant number of Be stars show a second Balmer discontinuity (sBD) attributed to an extended circumstellar envelope (CE). The fast rotational velocity of Be stars undoubtedly plays a significant role in the formation of the CE. However, Bn stars, which are also B-type rapidly rotating stars, do not all present clear evidence of being surrounded by circumstellar material. Aims. We aim to characterize the populations of Be and Bn stars, and discuss the appearance of the sBD as a function of the stellar parameters. We expect to find new indices characterizing the properties of CEs in Be stars and properties relating Be and Bn stars. Methods. We obtained low- and high-resolution spectra of a sample of Be and Bn stars, derived stellar parameters, characterized the sBD, and measured the emission in the Hα line. Results. Correlations of the aspect and intensity of the sBD and the emission in the Hα line with the stellar parameters and the V sin i are presented. Some Bn stars exhibit the sBD in absorption, which may indicate the presence of rather dense CEs. Six Bn stars show emission in the Hα line, so they are reclassified as Be stars. The sBD in emission appears in Be stars with V sin i ≲ 250 km s−1, and in absorption in both Be and Bn stars with V sin i ≳ 50 km s−1. Low-mass Be and Bn stars share the same region in the Hertzsprung-Russell diagram. The distributions of rotational to critical velocity ratios of Be and Bn stars corresponding to the current stellar evolutionary stage are similar, while distributions inferred for the zero-age main sequence have different skewness. Conclusions. We found emission in the Hα line and signs of a CE in some Bn stars, which motivated us to think that Bn and Be stars probably belong to the same population. It should be noted that some of the most massive Bn stars could display the Be phenomenon at any time. The similarities found among Be and Bn stars deserve to be more deeply pursued.
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers

One of the most significant challenges facing a few-shot learning task is the generalizability of the (meta-)model from the base to the novel categories. Most of existing few-shot learning models attempt to address this challenge by either learning the meta-knowledge from multiple simulated tasks on the base categories, or resorting to data augmentation by applying various transformations to training examples. However, the supervised nature of model training in these approaches limits their ability of exploring variations across different categories, thus restricting their cross-category generalizability in modeling novel concepts. To this end, we present a novel regularization mechanism by learning the change of feature representations induced by a distribution of transformations without using the labels of data examples. We expect this regularizer could expand the semantic space of base categories to cover that of novel categories through the transformation of feature representations. It could minimize the risk of overfitting into base categories by inspecting the transformation-augmented variations at the encoded feature level. This results in the proposed FLAT (Few-shot Learning via Autoencoding Transformations) approach by autoencoding the applied transformations. The experiment results show the superior performances to the current state-of-the-art methods in literature.
Shared Arrangements: practical inter-query sharing for streaming dataflows

Current systems for data-parallel, incremental processing and view maintenance over high-rate streams isolate the execution of independent queries. This creates unwanted redundancy and overhead in the presence of concurrent incrementally maintained queries: each query must independently maintain the same indexed state over the same input streams, and new queries must build this state from scratch before they can begin to emit their first results. This paper introduces shared arrangements : indexed views of maintained state that allow concurrent queries to reuse the same in-memory state without compromising data-parallel performance and scaling. We implement shared arrangements in a modern stream processor and show order-of-magnitude improvements in query response time and resource consumption for incremental, interactive queries against high-throughput streams, while also significantly improving performance in other domains including business analytics, graph processing, and program analysis.
Pipelined Training with Stale Weights of Deep Convolutional Neural Networks

The growth in the complexity of Convolutional Neural Networks (CNNs) is increasing interest in partitioning a network across multiple accelerators during training and pipelining the backpropagation computations over the accelerators. Existing approaches avoid or limit the use of stale weights through techniques such as micro-batching or weight stashing. These techniques either underutilize of accelerators or increase memory footprint. We explore the impact of stale weights on the statistical efficiency and performance in a pipelined backpropagation scheme that maximizes accelerator utilization and keeps memory overhead modest. We use 4 CNNs (LeNet-5, AlexNet, VGG and ResNet) and show that when pipelining is limited to early layers in a network, training with stale weights converges and results in models with comparable inference accuracies to those resulting from non-pipelined training on MNIST and CIFAR-10 datasets; a drop in accuracy of 0.4%, 4%, 0.83% and 1.45% for the 4 networks, respectively. However, when pipelining is deeper in the network, inference accuracies drop significantly. We propose combining pipelined and non-pipelined training in a hybrid scheme to address this drop. We demonstrate the implementation and performance of our pipelined backpropagation in PyTorch on 2 GPUs using ResNet, achieving speedups of up to 1.8X over a 1-GPU baseline, with a small drop in inference accuracy.
Anti-Ramsey properties of randomly perturbed dense graphs

For two graphs $G$ and $H$, write $G \stackrel{\mathrm{rbw}}{\longrightarrow} H$ if $G$ has the property that every {\sl proper} colouring of its edges admits a {\sl rainbow} copy of $H$. We study the thresholds for such so called {\sl anti-Ramsey} properties in randomly perturbed dense graphs, by which we mean unions of the form $G \cup \mathbb{G}(n,p)$, where $G$ is an $n$-vertex graph with edge-density $d$, the latter being independent of $n$. For complete graphs, we determine the threshold for the property $G \cup \mathbb{G}(n,p) \stackrel{\mathrm{rbw}}{\longrightarrow} K_{2r-1}$ for all $r\geq 2$. In particular, if $r \geq 5$, then this threshold is $n^{-1/m_2(K_r)}$. For complete graphs of even order, we prove that the property $G \cup \mathbb{G}(n,p) \stackrel{\mathrm{rbw}}{\longrightarrow} K_{2r}$ holds a.a. s. whenever $r \geq 4$ and $p := p(n) = \omega \left(n^{-(r-2)/\binom{r}{2}} \right)$. We conjecture, that for every $r \geq 5$, the threshold for the property $G \cup \mathbb{G}(n,p) \stackrel{\mathrm{rbw}}{\longrightarrow} K_{2r}$ is $n^{-1/m_2(K_r)}$ (the latter is easily seen to be a lower bound for this threshold). We believe that $n^{-1/m_2(K_r)}$ is not the threshold for the property $G \cup \mathbb{G}(n,p) \stackrel{\mathrm{rbw}}{\longrightarrow} K_{2r}$ whenever $r \leq 4$. Clearly, no random edges are needed for $r=1$. For $r=2$, we prove that the threshold is $n^{-5/4}$. Lastly, we prove that for every $\ell \geq 1$, the threshold of the property $G \cup \mathbb{G}(n,p) \stackrel{\mathrm{rbw}}{\longrightarrow} C_{2\ell+1}$ is $n^{-2}$; in particular, it does not depend on the length of the cycle (clearly, for even cycles, no random perturbation is needed).
Denoising method based on intrascale correlation in nonsubsampled contourlet transform for synthetic aperture radar images

Abstract. Compared with speckle noise, the targets in synthetic aperture radar (SAR) images have strong directionality. Since the target and noise are different in the directional sub-bands on the same scale of nonsubsampled contourlet transform (NSCT), there are obvious differences in characteristics of the NSCT coefficients. Taking advantage of the differences, a denoising method for SAR image based on intrascale correlation of NSCT is proposed. The coefficients in different directional sub-bands in NSCT field are analyzed and the distribution law of the difference between maximum and minimum coefficients on the same scale is presented. Then, a threshold-determining strategy is proposed for identifying noise from targets. Finally, the proposed method is compared with some state-of-the-art denoising methods. It is observed from the results that our method presents the best performance in balance of noises and edge-preserving.
Remote sensing image change detection using a hybrid graphical model

Abstract. Semisupervised graph learning has a broad prospect in remote sensing (RS) image change detection. However, an improper graph model may result in a contradiction between the detection accuracy and computational efficiency. In order to effectively extract the structural information of changes and heavily reduce the computational burden, we propose a hybrid graphical model (HGM) for bitemporal RS image change detection. The HGM utilizes the hybrid superpixels (HSPs) as its vertices, and a hybrid graph kernel (HGK) function is proposed for measuring the similarities between the vertices. The HSPs are composed of the background superpixels and foreground isolated pixels of a subtraction image. The HGM effectively exploits the image structures, and the small graph size dramatically reduces the computational complexity. Moreover, the piecewise HGK function well detects the structures of the changed areas and heavily resists the background disturbances. A semisupervised label propagation algorithm is implemented with the HGK matrix for obtaining the final change detection results. Experimental results on real RS images demonstrate the effectiveness and efficiency of the proposed method and prove that it is a good candidate for RS image change detection.
Impact of boosting saturation on automatic human detection in imagery acquired by unmanned aerial vehicles

Abstract. We aim to investigate a potential impact of boosting saturation of aerial imagery on the performance of unsupervised human detection algorithms. The study is empirical since it is based on processing photographs taken during a full year experiment in the Izerskie Mountains (southwestern Poland) by a consumer-grade Canon S110 camera mounted onboard eBee, a fixed-wing micro-unmanned aerial vehicle (UAV). In the preliminary analysis, we used a few basic color adjustments (sharpening, hue–saturation–luminance, contrast, saturation, and vibrance) to process UAV-taken photographs prior to the automated human detection with the nested k-means algorithm. We found that saturation boost is an image preprocessing method that may potentially improve the performance of human detection. In the actual analysis, we investigate only the saturation effect by employing four saturation modification schemes (two versions of enhancements of unusual colors, additive boost of saturation, and multiplicative boost of saturation) and three human detection algorithms [three-dimensional (3-D) nested k-means on RGB, two-dimensional nested k-means on hue–saturation–value, morphological operations of erosion, and dilation with thresholding]. All the studied saturation boost techniques increase detection rates of the 3-D nested k-means on RGB, with particularly meaningful improvement for images acquired in the spring. Morphological operations of erosion and dilation are not found to be skillful in detecting persons. However, their performance is improved after the initial preprocessing by the original or modified enhancement of unusual colors.
Super-resolution reconstruction of remote sensing images based on convolutional neural network

Abstract. A method of super-resolution reconstruction of remote sensing images based on convolutional neural network is proposed to address the problems of low-resolution and poor visual quality of remote sensing images. In this method, a sample database with high-resolution (HR) and low-resolution (LR) remote sensing images was constructed. A convolutional neural network was then obtained by determining the intrinsic relationship between HR and LR remote sensing images in the sample database. Multiple pairs of HR and LR images were selected from the sample database and sent into a six-layer convolutional neural network. The experimental results showed that compared with other learning-based methods, such as the fast super-resolution convolutional neural network (FSRCNN), the image quality obtained by our method is improved both subjectively and objectively. Moreover, the training time was ∼17 % less than in the FSRCNN method.
Land cover classification of multispectral remote sensing images based on time-spectrum association features and multikernel boosting incremental learning

Abstract. In recent years, land cover classification technology based on multispectral remote sensing images has been applied to many fields of environmental monitoring. The existing methods generally rely on the information of spectral bands. However, the spectral information of monotemporal multispectral images cannot be generalized to describe the spectral characteristics of the ground objects at different times. In addition, the time-series samples will slightly change over time, and it is difficult to maintain classification performance continuously. To address these problems, we propose a method of land cover classification for multispectral images based on the time-spectrum association feature and multikernel boosting incremental learning. Our method is conducted in two main stages. (1) We propose the time-spectrum association features to acquire the seasonal spectral characteristics different ground objects. (2) To design the classifiers, we propose multikernel boosting method and introduce a multikernel boosting classification learning, which uses continuous new samples to update the weights of the classifier by low-computational and small-scale incremental learning. We test the proposed method on a public multispectral dataset from Landsat-5. The experimental results show that the extracted time-spectrum association features can better characterize the differences of different ground objects, and the proposed classifier can reach more accurate classification with gradually increasing samples over time.
Comparative study of sea clutter distribution and ship detectors’ performance for Sentinel-1 synthetic aperture radar image

Abstract. The constant false alarm rate (CFAR) detector is a classical algorithm for ship detection with synthetic aperture radar (SAR). However, the algorithm is susceptible to the accuracy of sea clutter modeling and the desired probability of false alarm, thus reducing detection performance. Therefore, a goodness-of-fit test and a certain number of ship detection experiments and theoretical analysis of false alarms have been extensively practiced as prior knowledge. Compared to earlier SAR sensors, the newly launched Sentinel-1 has nearly uniform signal-to-noise ratio, and the distributed-target-ambiguity ratio may provide additional capabilities for ship detection. Owing to the complex interaction between SAR system and sea surface, the previous work may not be completely suitable for Sentinel-1. As its application is in the beginning, further research is needed. We evaluate the effectiveness of model fitting among five commonly used distributions, and the influences of incident angle, polarization, and sea state on modeling are analyzed. In addition, CFAR detectors constructed by these distributions carried out the ship detection experiments. Moreover, the false alarms that are inevitably caused during the ship detection are classified and statistically analyzed. These aforementioned works can provide an important reference for Sentinel-1 to implement large-scale, as well as long-term, ship detection activities and for further improvement.
Validation of cutoffs for skeletal muscle mass index based on computed tomography analysis against dual energy X-ray absorptiometry in patients with cirrhosis: the KIRRHOS study

Background: Accurate assessments of muscle mass in patients with cirrhosis are necessary in clinical practice. Computed tomography (CT) of the upper abdomen has been proposed as a useful method for quantifying muscle mass. Recently, Carey et al developed specific cutoffs for muscle wasting based on the skeletal muscle index at the L3 vertebra (L3-SMI) for cirrhotic patients. The aim of the present study was to assess the concurrent validity of the newly proposed cutoffs of Carey et al, along with others widely used in several clinical contexts, using dual energy X-ray absorptiometry (DXA) as the reference method. Methods: Data were evaluated from 97 Caucasian patients (59.8% male, 59.1±11.6 years old, 45.4% decompensated) with cirrhosis of various etiologies. Muscle mass was assessed using the appendicular lean mass index (ALMI) by DXA and the L3-SMI by CT. Low L3-SMI was defined in relation to 5 different cutoffs. Results: Low muscle mass prevalence was 13.4% according to ALMI and 26.8-45.4% according to the different cutoffs applied for L3-SMI. The Carey et al, Prado et al and Montano-Loza et al cutoffs showed similar sensitivity (all 69.2%) and specificity (79.8%, 76.2% and 75.0%, respectively) and high accuracy (78.4%, 75.3% and 74.2%). The Carey et al cutoffs showed the highest diagnostic validity against DXA the multivariate odds ratio adjusted for age, sex, body mass index category, disease etiology and model for end-stage liver disease score (95% confidence interval) was 5.88 (1.36-25.4), P=0.018. Conclusion: Compared to DXA, the cutoffs for identifying muscle wasting proposed by Carey et al were proven to be the most accurate.