Miracle-dz/UniFAR-FTUs huggingface dataset:
Swift-XRT Follow-up of Gravitational-wave Triggers in the Second Advanced LIGO/Virgo Observing Run

The Neil Gehrels Swift Observatory carried out prompt searches for gravitational-wave (GW) events detected by the LIGO/Virgo Collaboration (LVC) during the second observing run (“O2”). Swift performed extensive tiling of eight LVC triggers, two of which had very low false-alarm rates (GW170814 and the epochal GW170817), indicating a high confidence of being astrophysical in origin; the latter was the first GW event to have an electromagnetic counterpart detected. In this paper we describe the follow-up performed during O2 and the results of our searches. No GW electromagnetic counterparts were detected; this result is expected, as GW170817 remained the only astrophysical event containing at least one neutron star after LVC’s later retraction of some events. A number of X-ray sources were detected, with the majority of identified sources being active galactic nuclei. We discuss the detection rate of transient X-ray sources and their implications in the O2 tiling searches. Finally, we describe the lessons learned during O2 and how these are being used to improve the Swift follow-up of GW events. In particular, we simulate a population of gamma-ray burst afterglows to evaluate our source ranking system’s ability to differentiate them from unrelated and uncataloged X-ray sources. We find that ≈60%–70% of afterglows whose jets are oriented toward Earth will be given high rank (i.e., “interesting” designation) by the completion of our second follow-up phase (assuming that their location in the sky was observed), but that this fraction can be increased to nearly 100% by performing a third follow-up observation of sources exhibiting fading behavior.
Gradient-Based Multi-Area Distribution System State Estimation

The increasing distributed and renewable energy resources and controllable devices in distribution systems make fast distribution system state estimation (DSSE) crucial in system monitoring and control. We consider a large multi-phase distribution system and formulate DSSE as a weighted least squares (WLS) problem. We divide the large distribution system into smaller areas of subtree structure, and by jointly exploring the linearized power flow model and the network topology, we propose a gradient-based multi-area algorithm to exactly and efficiently solve the WLS problem. The proposed algorithm enables distributed and parallel computation of the state estimation problem without compromising any performance. Numerical results on a 4,521-node test feeder show that the designed algorithm features fast convergence and accurate estimation results. Comparison with traditional Gauss-Newton method shows that the proposed method has much better performance in distribution systems with a limited amount of reliable measurement. The real-time implementation of the algorithm tracks time-varying system states with high accuracy.
Path Planning in Unknown Environments Using Optimal Transport Theory

This paper introduces a graph-based, potential-guided method for path planning problems in unknown environments, where obstacles are unknown until the robots are in close proximity to the obstacle locations. Inspired by optimal transport theory, the proposed method generates a graph connecting the initial and target configurations, and then finds a path over the graph using the available environmental information. The graph and path are updated iteratively when newly encountered obstacle information becomes available. The resulting method is a deterministic procedure proven to be complete, i.e., it is guaranteed to find a feasible path, when one exists, in a finite number of iterations. The method is scalable to high-dimensional problems. In addition, our method does not search the entire domain for the path, instead, the algorithm only explores a sub-region that can be described by the evolution of the Fokker-Planck equation. We demonstrate the performance of our algorithm via several numerical examples with different environments and dimensions, including high-dimensional cases.
Manufacturability Oriented Model Correction and Build Direction Optimization for Additive Manufacturing

We introduce a method to analyze and modify a shape to make it manufacturable for a given additive manufacturing (AM) process. Different AM technologies, process parameters, or materials introduce geometric constraints on what is manufacturable or not. Given an input 3D model and minimum printable feature size dictated by the manufacturing process characteristics and parameters, our algorithm generates a corrected geometry that is printable with the intended AM process. A key issue in model correction for manufacturability is the identification of critical features that are affected by the printing process. To address this challenge, we propose a topology aware approach to construct the allowable space for a print head to traverse during the 3D printing process. Combined with our build orientation optimization algorithm, the amount of modifications performed on the shape is kept at minimum while providing an accurate approximation of the as-manufactured part. We demonstrate our method on a variety of 3D models and validate it by 3D printing the results.
Three-Dimensional Extended Object Tracking and Shape Learning Using Gaussian Processes

In this article, we investigate the problem of tracking objects with unknown shapes using 3-D point cloud data. We propose a Gaussian process-based model to jointly estimate object kinematics, including position, orientation, and velocities, together with the shape of the object for online and offline applications. We describe the unknown shape by a radial function in 3-D, and induce a correlation structure via a Gaussian process. Furthermore, we propose an efficient algorithm to reduce the computational complexity of working with 3-D data. This is accomplished by casting the tracking problem into projection planes, which are attached to the object's local frame. The resulting algorithms can process 3-D point cloud data and accomplish tracking of a dynamic object. Furthermore, they provide analytical expressions for the representation of the object shape in 3-D, together with confidence intervals. The confidence intervals, which quantify the uncertainty in the shape estimate, can later be used for solving the gating and association problems inherent in object tracking. The performance of the methods is demonstrated both on simulated and real data. The results are compared with an existing random matrix model, which is commonly used for extended object tracking in the literature.
Asynchronous and Time-Varying Proximal Type Dynamics in Multiagent Network Games

In this article, we study proximal type dynamics in the context of multiagent network games. We analyze several conjugations of this class of games, providing convergence results. Specifically, we look into synchronous/asynchronous dynamics with time-invariant communication network and synchronous dynamics with time-varying communication networks. Finally, we validate the theoretical results via numerical simulations on opinion dynamics.
Random k-out Subgraph Leaves only O(n/k) Inter-Component Edges

Each vertex of an arbitrary simple graph on n vertices chooses k random incident edges. What is the expected number of edges in the original graph that connect different connected components of the sampled subgraph? We prove that the answer is O(n/k), when k ≥ c log n, for some large enough c. We conjecture that the same holds for smaller values of k, possibly for any k ≥ 2. Such a result is best possible for any k ≥ 2. As an application, we use this sampling result to obtain a one-way communication protocol with private randomness for finding a spanning forest of a graph in which each vertex sends only O (√n log n) bits to a referee.
On the Ultra-Reliable and Low-Latency Communications in Flexible TDD/FDD 5G Networks

The ultra-reliable and low-latency communication (URLLC) is the key driver of the current 5G new radio standardization. URLLC encompasses sporadic and small-payload transmissions that should be delivered within extremely tight radio latency and reliability bounds, i.e., a radio latency of 1 ms with 99.999% success probability. However, such URLLC targets are further challenging in the 5G dynamic time division duplexing (TDD) systems, due to the switching between the uplink and downlink transmission opportunities and the additional inter-cell cross-link interference (CLI). This paper presents a system level analysis of the URLLC outage performance within the 5G new radio flexible TDD systems. Specifically, we study the feasibility of the URLLC outage targets compared to the case with the 5G frequency division duplexing (FDD), and with numerous 5G design variants. The presented results therefore offer valuable observations on the URLLC outage performance in such deployments, and hence, introducing the state-of-the-art flexible-FDD technology.
Neutron Stars and Black Holes in the Small Magellanic Cloud: The SMC NuSTAR Legacy Survey

We present a source catalog from the first deep hard X-ray (E > 10 keV) survey of the Small Magellanic Cloud (SMC), the Nuclear Spectroscopic Telescope Array (NuSTAR) Legacy Survey of the SMC. We observed three fields, for a total exposure time of 1 Ms, along the bar of this nearby star-forming galaxy. Fields were chosen for their young stellar and accreting binary populations. We detected 10 sources above a 3σ significance level (4–25 keV) and obtained upper limits on an additional 40 sources. We reached a 3σ limiting luminosity in the 4–25 keV band of ∼1035 erg s−1, allowing us to probe fainter X-ray binary (XRB) populations than has been possible with other extragalactic NuSTAR surveys. We used hard X-ray colors and luminosities to constrain the compact-object type, exploiting the spectral differences between accreting black holes and neutron stars at E > 10 keV. Several of our sources demonstrate variability consistent with previously observed behavior. We confirmed pulsations for seven pulsars in our 3σ sample. We present the first detection of pulsations from a Be-XRB, SXP 305 (CXO J005215.4–73191), with an X-ray pulse period of 305.69 ± 0.16 s and a likely orbital period of ∼1160–1180 days. Bright sources (≳5 × 1036 erg s−1) in our sample have compact-object classifications consistent with their previously reported types in the literature. Lower-luminosity sources (≲5 × 1036 erg s−1) have X-ray colors and luminosities consistent with multiple classifications. We raise questions about possible spectral differences at low luminosity between SMC pulsars and the Galactic pulsars used to create the diagnostic diagrams.
Negotiation-based Human-Robot Collaboration via Augmented Reality

Effective human-robot collaboration (HRC) requires extensive communication among the human and robot teammates, because their actions can potentially produce conflicts, synergies, or both. In this paper, we develop an augmented reality-driven, negotiation-based (ARN) framework for HRC, where ARN supports planning-phase negotiations within human-robot teams through a novel AR-based interface. We have conducted experiments in an office environment, where multiple mobile robots work on delivery tasks. The robots could not complete the tasks on their own, but sometimes need help from their human teammate, rendering human-robot collaboration necessary. From the experimental results, we observe that ARN significantly reduced the human-robot team's task completion time in comparison to a non-AR baseline.
20 Years of Evolution From Cognitive to Intelligent Communications

It has been 20 years since the concept of cognitive radio (CR) was proposed, which is an efficient approach to provide more access opportunities to connect massive wireless devices. To improve the spectrum efficiency, CR enables unlicensed usage of licensed spectrum resources. It has been regarded as the key enabler for intelligent communications. In this article, we will provide an overview on the intelligent communication in the past two decades to illustrate the revolution of its capability from cognition to artificial intelligence (AI). Particularly, this article starts from a comprehensive review of typical spectrum sensing and sharing, followed by the recent achievements on the AI-enabled intelligent radio. Moreover, research challenges in the future intelligent communications will be discussed to show a path to the real deployment of intelligent radio. After witnessing the glorious developments of CR in the past 20 years, we try to provide readers a clear picture on how intelligent radio could be further developed to smartly utilize the limited spectrum resources as well as to optimally configure wireless devices in the future communication systems.
Direct observation of Dirac states in Bi2Te3 nanoplatelets by 125Te NMR

Detection of the metallic Dirac electronic states on the surface of Topological Insulators (TIs) is a tribune for a small number of experimental techniques the most prominent of which is Angle Resolved Photoemission Spectroscopy. However, there is no experimental method showing at atomic scale resolution how the Dirac electrons extend inside TI systems. This is a critical issue in the study of important surface quantum properties, especially topological quasiparticle excitations. Herein, by applying advanced DFT-assisted solid-state 125Te Nuclear Magnetic Resonance on Bi2Te3 nanoplatelets, we succeeded in uncovering the hitherto invisible NMR signals with magnetic shielding influenced by the Dirac electrons, and subsequently showed how Dirac electrons spread and interact with the bulk interior of the nanoplatelets.
Privacy-preserving Searchable Databases with Controllable Leakage

Searchable Encryption (SE) is a technique that allows Cloud Service Providers (CSPs) to search over encrypted datasets without learning the content of queries and records. In recent years, many SE schemes have been proposed to protect outsourced data from CSPs. Unfortunately, most of them leak sensitive information, from which the CSPs could still infer the content of queries and records by mounting leakage-based inference attacks, such as the count attack and file injection attack. In this work, first we define the leakage in searchable encrypted databases and analyse how the leakage is leveraged in existing leakage-based attacks. Second, we propose a Privacy-preserving Multi-cloud based dynamic symmetric SE (SSE) scheme for relational Database (P-McDb). P-McDb has minimal leakage, which not only ensures confidentiality of queries and records, but also protects the search, access, and size patterns from CSPs. Moreover, P-McDb ensures both forward and backward privacy of the database. Thus, P-McDb could resist existing leakage-based attacks, e.g., active file/record-injection attacks. We give security definition and analysis to show how P-McDb hides the aforementioned patterns. Finally, we implemented a prototype of P-McDb and test it using the TPC-H benchmark dataset. Our evaluation results show the feasibility and practical efficiency of P-McDb.
A search for pulsars in subdwarf B binary systems and discovery of giant-pulse emitting PSR J0533−4524

Binary millisecond pulsars (MSPs) provide several opportunities for research of fundamental physics. However, finding them can be challenging. Several subdwarf B (sdB) binary systems with possible neutron star companions have been identified, allowing us to perform a targeted search for MSPs within these systems. Six sdBs with companions in the neutron star mass range, as determined from their optical light curves, were observed with the Green Bank and Westerbork radio telescopes. The data were searched for periodic signals as well as single pulses. No radio pulsations from sdB systems were detected, down to an average sensitivity limit of $0.11\,$mJy. We did, however, discover a pulsar in the field of sdB HE0532−4503. Follow-up observations with the Giant Metrewave Radio Telescope showed that this pulsar, J0533−4524, is not spatially coincident with the sdB system. The pulsar has a relatively low magnetic field but still emits giant pulses. We place an upper limit of three to the number of radio pulsars in the six sdB systems. The non-detections may be explained by a combination of the MSP beaming fraction, luminosity, and a recycling fraction <0.5. Alternatively, the assumption of corotation between the MSP and sdB may break down, which implies the systems are more edge-on than previously thought. This would shift the predicted companion masses into the white dwarf range. It would also explain the relative lack of edge-on sdB systems with massive companions.
Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is to predict the sentiment polarity towards a particular aspect in a sentence. Recently, this task has been widely addressed by the neural attention mechanism, which computes attention weights to softly select words for generating aspect-specific sentence representations. The attention is expected to concentrate on opinion words for accurate sentiment prediction. However, attention is prone to be distracted by noisy or misleading words, or opinion words from other aspects. In this paper, we propose an alternative hard-selection approach, which determines the start and end positions of the opinion snippet, and selects the words between these two positions for sentiment prediction. Specifically, we learn deep associations between the sentence and aspect, and the long-term dependencies within the sentence by leveraging the pre-trained BERT model. We further detect the opinion snippet by self-critical reinforcement learning. Especially, experimental results demonstrate the effectiveness of our method and prove that our hard-selection approach outperforms soft-selection approaches when handling multi-aspect sentences.
Metrics and pseudometrics on unitary groups with applications in quantum information processing

Metrics and pseudometrics are defined on the group of unitary operators in a Hilbert space. Several explicit formulas are derived. A special feature of the work is investigation of pseudometrics in unitary groups. The rich classes of pseudometrics have many interesting applications. Three such applications, distinguishibility of unitary operators, quantum coding and quantum search problems are discussed.
LANGUAGE-SPECIFIC PROSODIC STRUCTURAL MODULATION OF COARTICULATION VOWEL NASALIZATION IN #NV AND CVN# IN MANDARIN CHINESE

This study investigates prominence and boundary effects on acoustic realization on N-duration and Vnasalization in Mandarin Chinese. In both #NV and VN#, prominence was found to elongate N-related duration (enhancing N’s nasality), while V tended to resist coarticulatory influence from N (enhancing V’s orality). In phrase-initial position (#NV), boundary strength induced a shortening of N in line with CV contrast enhancement (C being less sonorant hence more consonant-like), while V was nasalized categorically regardless of boundary strength, different from a general coarticulatory pattern. In phrase-final position (CVN#), while there was no clear-cut acoustic distinction between V and N, the nasalized portion showed preboundary lengthening. Crucially, the degree of V-nasalization was not larger phrase-finally, deviating from a general increase in V-nasalization found in other languages in which the nasal-oral contrast in the coda is enhanced phrasefinally. The results are discussed in terms of distributional restrictions in Mandarin Chinese which allows only nasals in the coda which are lenited with no oral counterparts.
Cultural Diversity and Change in Post-Cold War Europe

Why are the cultures of European societies different? Are any of these differences increasing or diminishing in the wake of the Cold War? These questions are important with regard to the processes of European integration, but the empirical investigations thus far have been surprisingly scarce. Universalist and diffusion theories assume that with modernization and closer cross-country cooperation, the cultural differences between nations will gradually diminish. Cultural inheritance theories, by contrast, claim that deeply ingrained historical legacies provide resistance to cultural change and may even lead to a culture clash that will push societies further apart. The unprecedented integration of European states into the EU offers a unique opportunity to study the dynamics of cultural change. This dissertation reevaluates and builds upon these theories to provide a revised framework for analyzing cultural change at a societal level. The empirical analyses are based on repeated cross-sectional nationally representative surveys in the World Values Survey and European Values Study series taken from 1990 to 2014.
Utilization of Edge Information in Handwritten Numerals Recognition

The recognition of handwritten numerals has many applications in automatic identification and cognition. This research contains three experimented scenarios to recognize the handwritten English (i.e. Arabic) numerals. In the first scenario the bilinear interpolation of the image is used, while in the second scenario and after the bilinear interpolation is being applied, the Sobel operators are applied on the resulted interpolated image. In the third scenario which represents the last one, the effect of normalization of image dimensions is tested. 550 images of handwritten numerals were tested. Three types of tests were conducted for each scenario namely: trained-set test, not-trained-set test and comprehensive-set test. Depending on the results obtained from the comprehensive-set test, the best scenario is the second scenario of bilinear interpolation followed by Sobel operators which leads to excellent success rate reaches to 97.63%.
Predictive Factors of Intracranial Response of Immune Checkpoint Inhibitors in Patients with Brain Metastasis from Non-Small Cell Lung Cancer

Background: Immune checkpoint inhibitors (ICI)s were recently approved for the treatment of advanced non-small cell lung cancer (NSCLC). Whereas brain metastases (BM) are frequent in NSCLC patients, data are missing regarding ICIs intracranial efficacy and tolerance in patients with BM from NSCLC. Methods: This retrospective study was performed in the Multidisciplinary Oncology and Therapeutic Innovation department, Marseille, France between April 2013 and February 2016. Data from patients with NSCLC with at least one BM, and treated with ICIs (anti-PD1, anti-PDL1 or anti-CTL4) were analyzed. Clinical, biological data and outcomes were retrieved from electronic patients’ records. We assessed ICIs intracranial efficacy and tolerance. Results: Data from 55 patients were analyzed. Objective Response Rate (ORR) and Disease Control Rate (DCR) were respectively of 1.8 and 36.4%. Median overall survival was 17.2 months and median progression free survival was 2.9 months. Intracranial ORR (icORR) and intracranial DCR (icDCR) were respectively 16.4% and 45.5%. Both were independent of smoking status, intracranial treatment, performance status, pathology, molecular profile and the presence or number of BM at diagnosis. However, there was a trend towards an association between icORR and ECOG PS (p = 0.05), tobacco status (p = 0.057) and intracranial treatment. Adverse events were seen in 38.2% patients without identified predictive factor. Neurological symptoms appeared in 5.5% patients during immunotherapy and improved in 3.63% patients. Conclusions: ICIs can be used safely on patients with BM from NSCLC. However, intracranial response is heterogeneous in such patients and we showed ECOG PS, tobacco smoking and intracranial treatment to be associated with an improved icORR. This is the first study looking for predictive factors of intracranial response of ICIs in patients with BM from NSCLC.