Miracle-dz/UniFAR-FTUs huggingface dataset:
An unusual transient following the short GRB 071227

We present X-ray and optical observations of the short duration gamma-ray burst GRB 071227 and its host at z = 0.381, obtained using Swift, Gemini South, and the Very Large Telescope. We identify a short-lived and moderately bright optical transient, with flux significantly in excess of that expected from a simple extrapolation of the X-ray spectrum at 0.2–0.3 d after burst. We fit the SED with afterglow models allowing for high extinction and thermal emission models that approximate a kilonova to assess the excess’ origins. While some kilonova contribution is plausible, it is not favoured due to the low temperature and high luminosity required, implying superluminal expansion and a large ejecta mass of ∼0.1 M$\odot$. We find, instead, that the transient is broadly consistent with power-law spectra with additional dust extinction of E(B − V) ∼ 0.4 mag, although a possibly thermal excess remains in the z band. We investigate the host, a spiral galaxy with an edge-on orientation, resolving its spectrum along its major axis to construct the galaxy rotation curve and analyse the star formation and chemical properties. The integrated host emission shows evidence for high extinction, consistent with the afterglow findings. The metallicity and extinction are consistent with previous studies of this host and indicate the galaxy is a typical, but dusty, late-type SGRB host.
Transport in two-dimensional topological materials: recent developments in experiment and theory

We review theoretical and experimental highlights in transport in two-dimensional topological materials over the last five years. Topological materials comprise topological insulators, Weyl semimetals and topological superconductors. This review focuses on key developments in the understanding of transport phenomena involving surfaces and interfaces of two-dimensional topological materials that have not been covered elsewhere. The review is structured around the following general topics: (i) topological insulators are finding applications in magnetic devices, while controversy continues to surround Hall transport in doped samples and the general issue of topological protection; (ii) state-of-the-art experiments on transition metal dichalcogenides have revealed new valley-dependent electrical and optical phenomena which have spin-dependent counterparts in topological insulators; (iii) in Weyl semimetals the manifestations of Fermi arcs in transport are actively investigated as well as controversial; (iv) a new and growing field centres on the non-linear electrical and optical responses of topological materials, where fundamental questions emerge about the interplay of the Berry curvature and disorder scattering; and (v) topological superconductor research is dominated by the quest for chiral superconductivity, Majorana fermions and topological quantum computing. Prospects for future research in this vast field are outlined in the closing section.
A Case for Stale Synchronous Distributed Model for Declarative Recursive Computation

Abstract A large class of traditional graph and data mining algorithms can be concisely expressed in Datalog, and other Logic-based languages, once aggregates are allowed in recursion. In fact, for most BigData algorithms, the difficult semantic issues raised by the use of non-monotonic aggregates in recursion are solved by Pre-Mappability ( ${\cal P}$ reM), a property that assures that for a program with aggregates in recursion there is an equivalent aggregate-stratified program. In this paper we show that, by bringing together the formal abstract semantics of stratified programs with the efficient operational one of unstratified programs, $\[{\cal P}\]$ reM can also facilitate and improve their parallel execution. We prove that $\[{\cal P}\]$ reM-optimized lock-free and decomposable parallel semi-naive evaluations produce the same results as the single executor programs. Therefore, $\[{\cal P}\]$ reM can be assimilated into the data-parallel computation plans of different distributed systems, irrespective of whether these follow bulk synchronous parallel (BSP) or asynchronous computing models. In addition, we show that non-linear recursive queries can be evaluated using a hybrid stale synchronous parallel (SSP) model on distributed environments. After providing a formal correctness proof for the recursive query evaluation with $\[{\cal P}\]$ reM under this relaxed synchronization model, we present experimental evidence of its benefits.
Next-generation Wireless Solutions for the Smart Factory, Smart Vehicles, the Smart Grid and Smart Cities

5G wireless systems will extend mobile communication services beyond mobile telephony, mobile broadband, and massive machine-type communication into new application domains, namely the so-called vertical domains including the smart factory, smart vehicles, smart grid, smart city, etc. Supporting these vertical domains comes with demanding requirements: high-availability, high-reliability, low-latency, and in some cases, high-accuracy positioning. In this survey, we first identify the potential key performance requirements of 5G communication in support of automation in the vertical domains and highlight the 5G enabling technologies conceived for meeting these requirements. We then discuss the key challenges faced both by industry and academia which have to be addressed in order to support automation in the vertical domains. We also provide a survey of the related research dedicated to automation in the vertical domains. Finally, our vision of 6G wireless systems is discussed briefly.
Improving user specifications for robot behavior through active preference learning: Framework and evaluation

An important challenge in human–robot interaction (HRI) is enabling non-expert users to specify complex tasks for autonomous robots. Recently, active preference learning has been applied in HRI to interactively shape a robot’s behavior. We study a framework where users specify constraints on allowable robot movements on a graphical interface, yielding a robot task specification. However, users may not be able to accurately assess the impact of such constraints on the performance of a robot. Thus, we revise the specification by iteratively presenting users with alternative solutions where some constraints might be violated, and learn about the importance of the constraints from the users’ choices between these alternatives. We demonstrate our framework in a user study with a material transport task in an industrial facility. We show that nearly all users accept alternative solutions and thus obtain a revised specification through the learning process, and that the revision leads to a substantial improvement in robot performance. Further, the learning process reduces the variances between the specifications from different users and, thus, makes the specifications more similar. As a result, the users whose initial specifications had the largest impact on performance benefit the most from the interactive learning.
Scalable and Secure Computation Among Strangers: Resource-Competitive Byzantine Protocols

Motivated, in part, by the rise of permissionless systems such as Bitcoin where arbitrary nodes (whose identities are not known apriori) can join and leave at will, we extend established research in scalable Byzantine agreement to a more practical model where each node (initially) does not know the identity of other nodes. A node can send to new destinations only by sending to random (or arbitrary) nodes, or responding (if it chooses) to messages received from those destinations. We assume a synchronous and fully-connected network, with a full-information, but static Byzantine adversary. A general drawback of existing Byzantine protocols is that the communication cost incurred by the honest nodes may not be proportional to those incurred by the Byzantine nodes; in fact, they can be significantly higher. Our goal is to design Byzantine protocols for fundamental problems which are {\em resource competitive}, i.e., the number of bits sent by honest nodes is not much more than those sent by Byzantine nodes. We describe a randomized scalable algorithm to solve Byzantine agreement, leader election, and committee election in this model. Our algorithm sends an expected $O((T+n)\log n)$ bits and has latency $O(polylog(n))$, where $n$ is the number of nodes, and $T$ is the minimum of $n^2$ and the number of bits sent by adversarially controlled nodes. The algorithm is resilient to $(1/4-\epsilon)n$ Byzantine nodes for any fixed $\epsilon > 0$, and succeeds with high probability. Our work can be considered as a first application of resource-competitive analysis to fundamental Byzantine problems. To complement our algorithm we also show lower bounds for resource-competitive Byzantine agreement. We prove that, in general, one cannot hope to design Byzantine protocols that have communication cost that is significantly smaller than the cost of the Byzantine adversary.
Data-Driven Physical Face Inversion

Facial animation is one of the most challenging problems in computer graphics, and it is often solved using linear heuristics like blend-shape rigging. More expressive approaches like physical simulation have emerged, but these methods are very difficult to tune, especially when simulating a real actor's face. We propose to use a simple finite element simulation approach for face animation, and present a novel method for recovering the required simulation parameters in order to best match a real actor's face motion. Our method involves reconstructing a very small number of head poses of the actor in 3D, where the head poses span different configurations of force directions due to gravity. Our algorithm can then automatically recover both the gravity-free rest shape of the face as well as the spatially-varying physical material stiffness such that a forward simulation will match the captured targets as closely as possible. As a result, our system can produce actor-specific, physical parameters that can be immediately used in recent physical simulation methods for faces. Furthermore, as the simulation results depend heavily on the chosen spatial layout of material clusters, we analyze and compare different spatial layouts.
A Dual Black Hole Associated with Obscured and Unobscured AGNs: CXO J101527.2+625911

We report the results of an investigation to determine the nature of the offset active galactic nucleus (AGN) found in the source CXO J101527.2+625911. Hubble Space Telescope and Chandra X-ray observatory data have suggested that the offset AGN, which has an angular separation of only 0. ″26 from the center of the host galaxy, is a recoiled supermassive black hole (rSMBH). We carried out high angular resolution observations with both the Very Long Baseline Array (1.54 GHz) and the Very Large Array (10.0 GHz and 33.0 GHz) and detected a single compact radio source in the center of the host galaxy, with no radio continuum emission associated with the offset AGN. The detected radio source has a high brightness temperature value of Tb = 7.2 × 107 K, indicating that the radio emission is associated with an AGN. Furthermore, we present the decomposition of high-resolution KECK spectra of the [O iii] 5007 Å line into two narrow emission line components, which is a characteristic sign of a dual black hole system. These new radio and optical wavelength results suggest that CXO J101527.2+625911 is the host of a dual black hole system rather than an rSMBH.
SleepNet: automated sleep analysis via dense convolutional neural network using physiological time series

Objective: In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep disorders including arousal, apnea and hypopnea using polysomnography (PSG) measurement channels provided in the 2018 PhysioNet Challenge database. Approach: Our model structure is composed of multiple dense convolutional units (DCU) followed by a bidirectional long-short term memory (LSTM) layer followed by a softmax output layer. The sleep events, including sleep stages, arousal regions and multiple types of apnea and hypopnea, are manually annotated by experts, which enables us to train our proposed network using a multi-task learning mechanism. Three binary cross-entropy loss functions, corresponding to sleep/wake, target arousal and apnea-hypopnea/normal detection tasks, are summed up to generate our overall network loss function that is optimized using the Adam method. Our model performance was evaluated using two metrics: the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC). To measure our model generalization, 4-fold cross-validation was also performed. For training, our model was applied to full night recording data. Main results: Finally, the average AUPRC and AUROC values associated with the arousal detection task were 0.505 and 0.922, respectively, on our testing dataset. An ensemble of four models trained on different data folds improved the AUPRC and AUROC to 0.543 and 0.931, respectively. Significance: Our proposed algorithm achieved the first place in the official stage of the 2018 PhysioNet Challenge for detecting sleep arousals with an AUPRC of 0.54 on the blind testing dataset.
Reduced-state sequence estimation for trellis-coded modulation on intersymbol interference channels

The authors consider the detection of trellis codes designed for ideal channels when they operate in the presence of intersymbol interference (ISI). A well-structured reduced-state sequence-estimation (RSSE) algorithm is described which can achieve the performance of maximum-likelihood sequence estimation (MLSE) at drastically reduced complexity. The authors start by constructing proper reduced-state trellises by merging the states of the ML supertrellis using set-partitioning principles. Then the Viterbi algorithm (VA) searches these trellises using a modified ML metric. The authors examine the performance and complexity of RSSE and compare it to that of MLSE and DFE (decision-feedback equalization). They also discuss the application of RSSE to higher dimensional trellis codes and to trellis coding with interleaving. <<ETX>>
AN APERIODIC TILE WITH EDGE-TO-EDGE ORIENTATIONAL MATCHING RULES

Abstract We present a single, connected tile which can tile the plane but only nonperiodically. The tile is hexagonal with edge markings, which impose simple rules as to how adjacent tiles are allowed to meet across edges. The first of these rules is a standard matching rule, that certain decorations match across edges. The second condition is a new type of matching rule, which allows tiles to meet only when certain decorations in a particular orientation are given the opposite charge. This forces the tiles to form a hierarchy of triangles, following a central idea of the Socolar–Taylor tilings. However, the new edge-to-edge orientational matching rule forces this structure in a very different way, which allows for a surprisingly simple proof of aperiodicity. We show that the hull of all tilings satisfying our rules is uniquely ergodic and that almost all tilings in the hull belong to a minimal core of tilings generated by substitution. Identifying tilings which are charge-flips of each other, these tilings are shown to have pure point dynamical spectrum and a regular model set structure.
EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings

Despite remarkable advances in emotion recognition, they are severely restrained from either the essentially limited property of the employed single modality, or the synchronous presence of all involved multiple modalities. Motivated by this, we propose a novel crossmodal emotion embedding framework called EmoBed, which aims to leverage the knowledge from other auxiliary modalities to improve the performance of an emotion recognition system at hand. The framework generally includes two main learning components, i.e., joint multimodal training and crossmodal training. Both of them tend to explore the underlying semantic emotion information but with a shared recognition network or with a shared emotion embedding space, respectively. In doing this, the enhanced system trained with this approach can efficiently make use of the complementary information from other modalities. Nevertheless, the presence of these auxiliary modalities is not demanded during inference. To empirically investigate the effectiveness and robustness of the proposed framework, we perform extensive experiments on the two benchmark databases RECOLA and OMG-Emotion for the tasks of dimensional emotion regression and categorical emotion classification, respectively. The obtained results show that the proposed framework significantly outperforms related baselines in monomodal inference, and are also competitive or superior to the recently reported systems, which emphasises the importance of the proposed crossmodal learning for emotion recognition.
Intelligent Engine Room IoT System Based on Multi-Processors

An intelligent engine room IoT system is designed based on SOPC technology with the help of Niche Stack TCP/IP stack,μC/OS-Ⅱ,zip file system,ZigBee technology and multi-processor technology.In the system,three Nios II processors are integrated in a FPGA development board,which aim to create Web server,to monitor and to play audio,as well as to transmit the real-time data of video,audio and picture.There are several schemes to protect network security.It is proved that the system is stable,reliable,and flexible,and is convenient for upgrading and maintenance.
Analisis Opini Terhadap Fitur Smartphone Pada Ulasan Website Berbahasa Indonesia

Through online stores, consumers can give an opinion of a product , one of the best-selling products is smartphone. Their opinions become valuable and can be worthwhile to know the advantages or disadvantages of products based on the use r’s experience. Therefore, in order to utilize the data of customers' opinions, it is necessary to create a system that automatically performs mining and summarizing opinions on smartphone product . The system consist of five parts: data collection , preprocessing review, feature mining, analysis of opinions and then visualize the results . Data collection is taking data reviews website using web scraping , preprocessing review is for cleaning data reviews . F eature mining stage will find features in the reviews with apriori algorithm to produce frequent item set, then analyze the opinion using lexicon based, language rule and score function. The result will be shown in graphical form. From the testin g of feature mining o btained average recall score at 0.63 and precision at 0.72. It depends on good or bad quality of reviews. The results of testing accuracy opinion analysi s shows high value with accuracy 81.76 %. The technique showed good results with opinion data which is labeled, using l anguage rule and the implementation of score function.
Bell inequalities tailored to the Greenberger–Horne–Zeilinger states of arbitrary local dimension

In device-independent quantum information processing Bell inequalities are not only used as detectors of nonlocality, but also as certificates of relevant quantum properties. In order for these certificates to work, one very often needs Bell inequalities that are maximally violated by specific quantum states. Recently, in Salavrakos et al (2017 Phys. Rev. Lett. 119 040402) a general class of Bell inequalities, with arbitrary numbers of measurements and outcomes, has been designed, which are maximally violated by the maximally entangled states of two quantum systems of arbitrary dimension. In this work, we generalize these results to the multipartite scenario and obtain a general class of Bell inequalities maximally violated by the Greenberger–Horne–Zeilinger states of any number of parties and any local dimension. We then derive analytically their maximal quantum and nonsignaling values. We also obtain analytically the bound for detecting genuine nonlocality and compute the fully local bound for a few exemplary cases. Moreover, we consider the question of adapting this class of inequalities to partially entangled Greenberger–Horne–Zeilinger-like states for some special cases of low dimension and small number of parties. Through numerical methods, we find classes of inequalities maximally violated by these partially entangled states.
When Cold Radial Migration is Hot: Constraints from Resonant Overlap

It is widely accepted that stars in a spiral disk, like the Milky Way’s, can radially migrate on the order of a scale length over the disk’s lifetime. With the exception of cold torquing, also known as “churning,” processes that contribute to the radial migration of stars are necessarily associated with kinematic heating. Additionally, it is an open question as to whether or not an episode of cold torquing is kinematically cold over long radial distances. This study uses a suite of analytically based simulations to investigate the dynamical response when stars are subject to cold torquing and are also resonant with an ultraharmonic. Model results demonstrate that these populations are kinematically heated and have rms changes in orbital angular momentum around corotation that can exceed those of populations that do not experience resonant overlap. Thus, kinematic heating can occur during episodes of cold torquing. In a case study of a Milky Way-like disk with an exponential surface density profile and flat rotation curve, up to 40% of cold torqued stars in the solar cylinder experience resonant overlap. This fraction increases toward the galactic center. To first approximation, the maximum radial excursions from cold torquing depend only on the strength of the spiral pattern and the underlying rotation curve. This work places an upper limit to these excursions to be the distance between the ultraharmonics, otherwise radial migration near corotation can kinematically heat. The diffusion rate for kinematically cold radial migration is thus constrained by limiting the step size in the random walk approximation.
One-Shot Mutual Affine-Transfer for Photorealistic Stylization

Photorealistic style transfer aims to transfer the style of a reference photo onto a content photo naturally, such that the stylized image looks like a real photo taken by a camera. Existing state-of-the-art methods are prone to spatial structure distortion of the content image and global color inconsistency across different semantic objects, making the results less photorealistic. In this paper, we propose a one-shot mutual Dirichlet network, to address these challenging issues. The essential contribution of the work is the realization of a representation scheme that successfully decouples the spatial structure and color information of images, such that the spatial structure can be well preserved during stylization. This representation is discriminative and context-sensitive with respect to semantic objects. It is extracted with a shared sparse Dirichlet encoder. Moreover, such representation is encouraged to be matched between the content and style images for faithful color transfer. The affine-transfer model is embedded in the decoder of the network to facilitate the color transfer. The strong representative and discriminative power of the proposed network enables one-shot learning given only one content-style image pair. Experimental results demonstrate that the proposed method is able to generate photorealistic photos without spatial distortion or abrupt color changes.