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The kinematic information of the extraplanar diffuse ionized gas (eDIG) around galaxies provides clues to the origin of the gas. The eDIG-CHANGES project studies the physical and kinematic properties of the eDIG around the CHANG-ES sample of nearby edge-on disk galaxies. We use a novel multi-slit narrow-band spectroscopy technique to obtain the spatial distribution of spectral properties of the ionized gas around NGC 891, which is often regarded as an analog of the Milky Way. We developed specific data reduction procedures for the multi-slit narrow-band spectroscopy data taken with the MDM 2.4m telescope. The data presented in this paper covers the H$\alpha$ and [N II]$\lambda\lambda$6548,6583\AA emission lines. The eDIG traced by the H$\alpha$ and [N II] lines shows an obvious asymmetric morphology, being brighter in the northeastern part of the galactic disk and extending a few kpc above and below the disk. Global variations in the [N II]/H$\alpha$ line ratio suggest additional heating mechanisms for the eDIG at large heights beyond photoionization. We also construct position-velocity (PV) diagrams of the eDIG based on our optical multi-slit spectroscopy data and compare them to similar PV diagrams constructed with the H I data. The dynamics of the two gas phases are generally consistent with each other. Modeling the rotation curves at different heights from the galactic mid-plane suggests a vertical negative gradient in turnover radius and maximum rotation velocity, with magnitudes of approximately $3$ kpc kpc$^{-1}$ and $22-25$ km s$^{-1}$ kpc$^{-1}$, respectively. Measured vertical gradients of the rotation curve parameters suggest significant differential rotation of the ionized gas in the halo, or often referred to as the lagging eDIG. Systematic study of the lagging eDIG in our eDIG-CHANGES project, will help us to better understand the dynamics of the ionized gas in the halo., Comment: 11 pages, 8 figures, accepted for publication in A&A

Recent research in zero-shot Relation Extraction (RE) has focused on using Large Language Models (LLMs) due to their impressive zero-shot capabilities. However, current methods often perform suboptimally, mainly due to a lack of detailed, context-specific prompts needed for understanding various sentences and relations. To address this, we introduce the Self-Prompting framework, a novel method designed to fully harness the embedded RE knowledge within LLMs. Specifically, our framework employs a three-stage diversity approach to prompt LLMs, generating multiple synthetic samples that encapsulate specific relations from scratch. These generated samples act as in-context learning samples, offering explicit and context-specific guidance to efficiently prompt LLMs for RE. Experimental evaluations on benchmark datasets show our approach outperforms existing LLM-based zero-shot RE methods. Additionally, our experiments confirm the effectiveness of our generation pipeline in producing high-quality synthetic data that enhances performance., Comment: EMNLP 2024 Short

Context. We present multi-wavelength observations of the Galactic SNR HB9 and the H II region G159.2+3.3 apparently projected nearby, in order to study their properties and potential physical connections. Results. HB9 is bright in $\gamma$-rays, but the $\gamma$-ray morphology is centrally filled and most of it is not clearly associated with the surrounding molecular clouds. There is a weak apparent connection of HB9 to the IR bright enclosing shell of G159.2+3.3 in $\gamma$-ray. The diffuse Balmer line has almost identical morphology as the radio emission in G159.2+3.3, indicating they two are thermal in origin. Using medium-band high-resolution optical spectra from selected regions of the southeast (SE) shell of HB9 and G159.2+3.3, we found the radial velocity dispersion of HB9 along the slit is significantly higher than the FWHM of the lines. In contrast, these two values are both smaller and comparable to each other in G159.2+3.3. This indicates that the gas in HB9 may have additional global motion triggered by the SNR shock. The [N II] $\lambda$6583A/H$\alpha$ line ratio of both objects can be interpreted with photo-ionisation by hot stars or low velocity shocks, except for the post-shock region in the SE shell of HB9, where the elevated [N II]/H$\alpha$ line ratio suggests contribution from shock ionisation. The measured electron density from the [S II] 6716/6730 line ratio is significantly lower in the brighter G159.2+3.3 compared to the SE shell of HB9. Conclusions. Our density estimate suggests that G159.2+3.3, although appearing brighter and more compact, is likely located at a much larger distance than HB9, so the two objects have no physical connections, unless the shock compressed gas in HB9 has a significantly lower filling factor., Comment: 14 pages, 9 figures, 2 tables, accepted for publication in A&A

Code-based Distributed Matrix Multiplication (DMM) has been extensively studied in distributed computing for efficiently performing large-scale matrix multiplication using coding theoretic techniques. The communication cost and recovery threshold (i.e., the least number of successful worker nodes required to recover the product of two matrices) are two major challenges in coded DMM research. Several constructions based on Reed-Solomon (RS) codes are known, including Polynomial codes, MatDot codes, and PolyDot codes. However, these RS-based schemes are not efficient for small finite fields because the distributed order (i.e., the total number of worker nodes) is limited by the size of the underlying finite field. Algebraic geometry (AG) codes can have a code length exceeding the size of the finite field, which helps solve this problem. Some work has been done to generalize Polynomial and MatDot codes to AG codes, but the generalization of PolyDot codes to AGcodes still remains an open problem as far as we know. This is because functions of an algebraic curve do not behave as nicely as polynomials. In this work, by using local expansions of functions, we are able to generalize the three DMM schemes based on RS codes to AG codes. Specifically, we provide a construction of AG-based PolyDot codes for the first time. In addition, our AG-based Polynomial and MatDot codes achieve better recovery thresholds compared to previous AG-based DMM schemes while maintaining similar communication costs. Our constructions are based on a novel basis of the Riemann-Roch space using local expansions, which naturally generalizes the standard monomial basis of the univariate polynomial space in RS codes. In contrast, previous work used the non-gap numbers to construct a basis of the Riemann-Roch space, which can cause cancellation problems that prevent the conditions of PolyDot codes from being satisfied.

Multimodal emotion recognition in conversation (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they fail to fully harness direct inter-modal cues, possibly leading to less-than-thorough cross-modal modeling; (2) they concurrently extract information from the same and different modalities at each network layer, potentially triggering conflicts from the fusion of multi-source data; (3) they lack the agility required to detect dynamic sentimental changes, perhaps resulting in inaccurate classification of utterances with abrupt sentiment shifts. To address these issues, a novel approach named GraphSmile is proposed for tracking intricate emotional cues in multimodal dialogues. GraphSmile comprises two key components, i.e., GSF and SDP modules. GSF ingeniously leverages graph structures to alternately assimilate inter-modal and intra-modal emotional dependencies layer by layer, adequately capturing cross-modal cues while effectively circumventing fusion conflicts. SDP is an auxiliary task to explicitly delineate the sentiment dynamics between utterances, promoting the model's ability to distinguish sentimental discrepancies. Furthermore, GraphSmile is effortlessly applied to multimodal sentiment analysis in conversation (MSAC), forging a unified multimodal affective model capable of executing MERC and MSAC tasks. Empirical results on multiple benchmarks demonstrate that GraphSmile can handle complex emotional and sentimental patterns, significantly outperforming baseline models., Comment: Submitted

The [CII] 158 $\mu$m emission line and the underlying far-infrared (FIR) dust continuum are important tracers for studying star formation and kinematic properties of early galaxies. We present a survey of the [CII] emission lines and FIR continua of 31 luminous quasars at $z>6.5$ using the Atacama Large Millimeter Array (ALMA) and the NOrthern Extended Millimeter Array (NOEMA) at sub-arcsec resolution. This survey more than doubles the number of quasars with [CII] and FIR observations at these redshifts and enables statistical studies of quasar host galaxies deep into the epoch of reionization. We detect [CII] emission in 27 quasar hosts with a luminosity range of $L_{\rm [CII]}=(0.3-5.5)\times10^9~L_\odot$ and detect the FIR continuum of 28 quasar hosts with a luminosity range of $L_{\rm FIR}=(0.5-13.0)\times10^{12}~L_\odot$. Both $L_{\rm [CII]}$ and $L_{\rm FIR}$ are correlated ($\rho\simeq0.4$) with the quasar bolometric luminosity, albeit with substantial scatter. The quasar hosts detected by ALMA are clearly resolved with a median diameter of $\sim$5 kpc. About 40% of the quasar host galaxies show a velocity gradient in [CII] emission, while the rest show either dispersion-dominated or disturbed kinematics. Basic estimates of the dynamical masses of the rotation-dominated host galaxies yield $M_{\rm dyn}=(0.1-7.5)\times10^{11}~M_\odot$. Considering our findings alongside those of literature studies, we found that the ratio between $M_{\rm BH}$ and $M_{\rm dyn}$ is about ten times higher than that of local $M_{\rm BH}-M_{\rm dyn}$ relation on average but with substantial scatter (the ratio difference ranging from $\sim$0.6 to 60) and large uncertainties., Comment: accepted for publication in ApJ

Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task. However, we found that inherent heterogeneity among factor tensors in tensor decomposition significantly hinders the tensor fusion process and further limits the performance of link prediction. To overcome this limitation, we introduce a novel method that maps factor tensors onto a unified smooth Lie group manifold to make the distribution of factor tensors approximating homogeneous in tensor decomposition. We provide the theoretical proof of our motivation that homogeneous tensors are more effective than heterogeneous tensors in tensor fusion and approximating the target for tensor decomposition based TKGE methods. The proposed method can be directly integrated into existing tensor decomposition based TKGE methods without introducing extra parameters. Extensive experiments demonstrate the effectiveness of our method in mitigating the heterogeneity and in enhancing the tensor decomposition based TKGE models.

The extraplanar diffuse ionized gas (eDIG) represents ionized gases traced by optical/UV lines beyond the stellar extent of galaxies. We herein introduce a novel multi-slit narrow-band spectroscopy method to conduct spatially resolved spectroscopy of the eDIG around a sample of nearby edge-on disk galaxies (eDIG-CHANGES). In this paper, we introduce the project design and major scientific goals, as well as a pilot study of NGC 3556 (M108). The eDIG is detected to a vertical extent of a few kpc above the disk, comparable to the X-ray and radio images. We do not see significant vertical variation of the [N II]/H$\alpha$ line ratio. A rough examination of the pressure balance between different circum-galactic medium (CGM) phases indicates the magnetic field is in a rough pressure balance with the X-ray emitting hot gas, and may play an important role in the global motion of both the eDIG and the hot gas in the lower halo. At the location of an HST/COS observed UV bright background AGN $\sim29\rm~kpc$ from the center of NGC 3556, the magnetic pressure is much lower than that of the hot gas and the ionized gas traced by UV absorption lines, although the extrapolation of the pressure profiles may cause some biases in this comparison. By comparing the position-velocity diagrams of the optical and CO lines, we also find the dynamics of the two gas phases are consistent with each other, with no evidence of a global inflow/outflow and a maximum rotation velocity of $\sim150\rm~km~s^{-1}$., Comment: 19 pages, 9 figures, 1 table, accepted for publication in ApJ

Superbubbles in the nuclear region of galaxies could be produced by the AGN or nuclear starburst via different driving forces. We report analysis of the multi-wavelength data of the kpc-scale nuclear superbubble in NGC 3079, in order to probe the mechanisms driving the expansion of the superbubble. Based on the Chandra X-ray observations, we derive the hot gas thermal pressure inside the bubble, which is about one order of magnitude higher than that of the warm ionized gas traced by optical lines. We derive a [C II]-based star formation rate of ${\rm SFR}\sim1.3\rm~M_\odot~{\rm yr}^{-1}$ from the nuclear region using the SOFIA/FIFI-LS observation. This SFR infers a radiation pressure toward the bubble shells much lower than the thermal pressure of the gases. The VLA radio image infers a magnetic pressure at the northeast cap above the superbubble less than the thermal pressure of the hot gas enclosed in the bubble, but has a clearly larger extension. The magnetic field may thus still help to reconcile the expansion of the bubble. The observed thermal energy of the hot gas enclosed in the bubble requires an energy injection rate of $\gtrsim10^{42}\rm~ergs~s^{-1}$ within the bubble's dynamical age, which is probably larger than the power provided by the current nuclear starburst and the parsec-scale jet. If this is true, stronger past AGN activity may provide an alternative energy source to drive the observed bubble expansion., Comment: 12 pages, 3 figures, 1 table, ApJ in press

We report the discovery of the 10 kilo-parsec (kpc) scale radio lobes in the Sombrero galaxy (NGC 4594), using data from the Continuum Halos in Nearby Galaxies - an Expanded Very Large Array (VLA) Survey (CHANG-ES) project. We further examine the balance between the magnetic pressure inside the lobes and the thermal pressure of the ambient hot gas. At the radii $r$ of ~(1-10) kpc, the magnetic pressure inside the lobes and the thermal pressure of the ambient hot gas are generally in balance. This implies that the jets could expand into the surroundings at least to r ~ 10 kpc. The feedback from the active galactic nucleus (AGN) jet responsible for the large-scale lobes may help to explain the unusually high X-ray luminosity of this massive quiescent isolated disk galaxy, although more theoretical work is needed to further examine this possibility., Comment: 8 pages, 3 figures, 2 tables, accepted for publication in ApJ

Hierarchical massive quadruple systems are ideal laboratories for examining the theories of star formation, dynamical evolution, and stellar evolution. The successive mergers of hierarchical quadruple systems might explain the mass gap between neutron stars and black holes. Looking for light curves of O-type binaries identified by LAMOST, we find a (2+2) quadruple system: TYC 3340-2437-1, located in the stellar bow-shock nebula (SBN). It has a probability of over 99.99\% being a quadruple system derived from the surface density of the vicinity stars. Its inner orbital periods are 3.390602(89) days and 2.4378(16) days, respectively, and the total mass is about (11.47 + 5.79) + (5.2 + 2.02) = 24.48 $M_{\odot}$. The line-of-sight inclinations of the inner binaries, B$_1$ and B$_2$, are 55.94 and 78.2 degrees, respectively, indicating that they are not co-planar. Based on observations spanning 34 months and the significance of the astrometric excess noise ($D>2$) in Gaia DR3 data, we guess that its outer orbital period might be a few years. If it were true, the quadruple system might form through the disk fragmentation mechanism with outer eccentric greater than zero. This eccentricity could be the cause of both the arc-like feature of the SBN and the noncoplanarity of the inner orbit. The outer orbital period and outer eccentric could be determined with the release of future epoch astrometric data of Gaia.

Optical frequency division based on bulk or fiber optics provides unprecedented spectral purity for microwave oscillators. To extend the applications of this approach, the big challenges are to develop miniaturized optical frequency division oscillators without trading off phase noise performance. In this paper, we report a chip-scale electro-optical frequency division microwave oscillator with ultra-low phase noise performance. Dual laser sources are co-self-injection-locked to a single silicon nitride spiral resonator to provide a record high-stability, fully on-chip optical reference. An integrated electro-optical frequency comb based on a novel thin-film lithium niobate phase modulator chip is incorporated for the first time to perform optical-to-microwave frequency division. The resulting chip-scale photonic microwave oscillator achieves a phase noise level of -129 dBc/Hz at 10 kHz offset for 37.7 GHz carrier. The results represent a major advance in high performance, integrated photonic microwave oscillators for applications including signal processing, radar, timing, and coherent communications.

Untrained networks inspired by deep image priors have shown promising capabilities in recovering high-quality images from noisy or partial measurements without requiring training sets. Their success is widely attributed to implicit regularization due to the spectral bias of suitable network architectures. However, the application of such network-based priors often entails superfluous architectural decisions, risks of overfitting, and lengthy optimization processes, all of which hinder their practicality. To address these challenges, we propose efficient architecture-agnostic techniques to directly modulate the spectral bias of network priors: 1) bandwidth-constrained input, 2) bandwidth-controllable upsamplers, and 3) Lipschitz-regularized convolutional layers. We show that, with just a few lines of code, we can reduce overfitting in underperforming architectures and close performance gaps with high-performing counterparts, minimizing the need for extensive architecture tuning. This makes it possible to employ a more compact model to achieve performance similar or superior to larger models while reducing runtime. Demonstrated on inpainting-like MRI reconstruction task, our results signify for the first time that architectural biases, overfitting, and runtime issues of untrained network priors can be simultaneously addressed without architectural modifications. Our code is publicly available., Comment: Accepted at ECCV 2024

The LUCE (LUtetium sCintillation Experiment) project will search for the 176Lu electron capture based on a milli-Kelvin calorimetric approach. This decay is of special interest in the field of nuclear structure, with implications for the s-process and for a better comprehension of the nuclear matrix elements of neutrinoless double beta decay (0{\nu}\b{eta}\b{eta}) and two-neutrino double beta decay (2{\nu}\b{eta}\b{eta}). Possible impacts also include the development of a new class of coherent elastic neutrino-nucleus scattering (CE{\nu}NS) and spin-dependent (independent) dark matter detectors. We report on the current status and design of a novel detector cryogenic-module for the measurement of the electron capture and detail a future measurement plan., Comment: proceedings

DNA monolayers with inherent chirality play a pivotal role across various domains, including biosensors, DNA chips, and bioelectronics. Nonetheless, conventional DNA chiral monolayers, typically constructed from single-stranded DNA (ssDNA) or double-stranded DNA (dsDNA), often lack structural orderliness and design flexibility at the interface. Structural DNA nanotechnology emerges as a promising solution to tackle these challenges. In this study, we present a strategy for crafting highly adaptable twisted DNA origami-based chiral monolayers. These structures exhibit distinct interfacial assembly characteristics and effectively mitigate the structural disorder of dsDNA monolayers, which is constrained by a limited persistence length of ~50 nm of dsDNA. We highlight the spin-filtering capabilities of four representative DNA origami-based chiral monolayers, demonstrating a maximal one-order-of-magnitude increase in spin-filtering efficiency per unit area compared to conventional dsDNA chiral monolayers. Intriguingly, our findings reveal that the higher-order, tertiary, chiral structure of twisted DNA origami further enhances the spin-filtering efficiency. This work paves the way for the rational design of DNA chiral monolayers.

The execution of deep neural network (DNN) algorithms suffers from significant bottlenecks due to the separation of the processing and memory units in traditional computer systems. Emerging memristive computing systems introduce an in situ approach that overcomes this bottleneck. The non-volatility of memristive devices, however, may expose the DNN weights stored in memristive crossbars to potential theft attacks. Therefore, this paper proposes a two-dimensional permutation-based protection (TDPP) method that thwarts such attacks. We first introduce the underlying concept that motivates the TDPP method: permuting both the rows and columns of the DNN weight matrices. This contrasts with previous methods, which focused solely on permuting a single dimension of the weight matrices, either the rows or columns. While it's possible for an adversary to access the matrix values, the original arrangement of rows and columns in the matrices remains concealed. As a result, the extracted DNN model from the accessed matrix values would fail to operate correctly. We consider two different memristive computing systems (designed for layer-by-layer and layer-parallel processing, respectively) and demonstrate the design of the TDPP method that could be embedded into the two systems. Finally, we present a security analysis. Our experiments demonstrate that TDPP can achieve comparable effectiveness to prior approaches, with a high level of security when appropriately parameterized. In addition, TDPP is more scalable than previous methods and results in reduced area and power overheads. The area and power are reduced by, respectively, 1218$\times$ and 2815$\times$ for the layer-by-layer system and by 178$\times$ and 203$\times$ for the layer-parallel system compared to prior works., Comment: 14 pages, 11 figures

Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss $\mathcal{L}_{\rm EC}$ is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plug-and-play nature of the EAE module in our method.

Molecular gas plays a critical role in explaining the quiescence of star formation (SF) in massive isolated spiral galaxies, which could be a result of either the low molecular gas content and/or the low SF efficiency. We present IRAM 30m observations of the CO lines in the Sombrero galaxy (NGC~4594), the most massive spiral at $d\lesssim30\rm~Mpc$. We detect at least one of the three CO lines covered by our observations in all 13 observed positions located at the galactic nucleus and along a $\sim25\rm~kpc$-diameter dusty ring. The total extrapolated molecular gas mass of the galaxy is $M_{\rm H_2}\approx4\times10^{8}\rm~M_\odot$. The measured maximum CO gas rotation velocity of $\approx379\rm~km~s^{-1}$ suggests that NGC~4594 locates in a dark matter halo with a mass $M_{\rm200}\gtrsim10^{13}\rm~M_\odot$. Comparing to other galaxy samples, NGC~4594 is extremely gas poor and SF inactive, but the SF efficiency is apparently not inconsistent with that predicted by the Kennicutt-Schmidt law, so there is no evidence of enhanced SF quenching in this extremely massive spiral with a huge bulge. We also calculate the predicted gas supply rate from various sources to replenish the cold gas consumed in SF, and find that the galaxy must experienced a starburst stage at high redshift, then the leftover or recycled gas provides SF fuels to maintain the gradual growth of the galactic disk at a gentle rate., Comment: 21 pages, 13 figures, accepted for publication in MNRAS

Emotion recognition in conversation (ERC) has emerged as a research hotspot in domains such as conversational robots and question-answer systems. How to efficiently and adequately retrieve contextual emotional cues has been one of the key challenges in the ERC task. Existing efforts do not fully model the context and employ complex network structures, resulting in limited performance gains. In this paper, we propose a novel emotion recognition network based on curriculum learning strategy (ERNetCL). The proposed ERNetCL primarily consists of temporal encoder (TE), spatial encoder (SE), and curriculum learning (CL) loss. We utilize TE and SE to combine the strengths of previous methods in a simplistic manner to efficiently capture temporal and spatial contextual information in the conversation. To ease the harmful influence resulting from emotion shift and simulate the way humans learn curriculum from easy to hard, we apply the idea of CL to the ERC task to progressively optimize the network parameters. At the beginning of training, we assign lower learning weights to difficult samples. As the epoch increases, the learning weights for these samples are gradually raised. Extensive experiments on four datasets exhibit that our proposed method is effective and dramatically beats other baseline models., Comment: Accepted by Knowledge-Based Systems (KBS)

Multimodal emotion recognition in conversation (ERC) has garnered growing attention from research communities in various fields. In this paper, we propose a Cross-modal Fusion Network with Emotion-Shift Awareness (CFN-ESA) for ERC. Extant approaches employ each modality equally without distinguishing the amount of emotional information in these modalities, rendering it hard to adequately extract complementary information from multimodal data. To cope with this problem, in CFN-ESA, we treat textual modality as the primary source of emotional information, while visual and acoustic modalities are taken as the secondary sources. Besides, most multimodal ERC models ignore emotion-shift information and overfocus on contextual information, leading to the failure of emotion recognition under emotion-shift scenario. We elaborate an emotion-shift module to address this challenge. CFN-ESA mainly consists of unimodal encoder (RUME), cross-modal encoder (ACME), and emotion-shift module (LESM). RUME is applied to extract conversation-level contextual emotional cues while pulling together data distributions between modalities; ACME is utilized to perform multimodal interaction centered on textual modality; LESM is used to model emotion shift and capture emotion-shift information, thereby guiding the learning of the main task. Experimental results demonstrate that CFN-ESA can effectively promote performance for ERC and remarkably outperform state-of-the-art models., Comment: Accepted by IEEE Transactions on Affective Computing (TAFFC)

In real-world dialog systems, the ability to understand the user's emotions and interact anthropomorphically is of great significance. Emotion Recognition in Conversation (ERC) is one of the key ways to accomplish this goal and has attracted growing attention. How to model the context in a conversation is a central aspect and a major challenge of ERC tasks. Most existing approaches struggle to adequately incorporate both global and local contextual information, and their network structures are overly sophisticated. For this reason, we propose a simple and effective Dual-stream Recurrence-Attention Network (DualRAN), which is based on Recurrent Neural Network (RNN) and Multi-head ATtention network (MAT). DualRAN eschews the complex components of current methods and focuses on combining recurrence-based methods with attention-based ones. DualRAN is a dual-stream structure mainly consisting of local- and global-aware modules, modeling a conversation simultaneously from distinct perspectives. In addition, we develop two single-stream network variants for DualRAN, i.e., SingleRANv1 and SingleRANv2. According to the experimental findings, DualRAN boosts the weighted F1 scores by 1.43% and 0.64% on the IEMOCAP and MELD datasets, respectively, in comparison to the strongest baseline. On two other datasets (i.e., EmoryNLP and DailyDialog), our method also attains competitive results., Comment: Accepted by Engineering Applications of Artificial Intelligence (EAAI)

This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models. Different from the previous translation-based models, TransERR encodes knowledge graphs in the hypercomplex-valued space, thus enabling it to possess a higher degree of translation freedom in mining latent information between the head and tail entities. To further minimize the translation distance, TransERR adaptively rotates the head entity and the tail entity with their corresponding unit quaternions, which are learnable in model training. We also provide mathematical proofs to demonstrate the ability of TransERR in modeling various relation patterns, including symmetry, antisymmetry, inversion, composition, and subrelation patterns. The experiments on 10 benchmark datasets validate the effectiveness and the generalization of TransERR. The results also indicate that TransERR can better encode large-scale datasets with fewer parameters than the previous translation-based models. Our code and datasets are available at~\url{https://github.com/dellixx/TransERR}.

We introduce the New-ANGELS program, an XMM-Newton survey of $\sim7.2\rm~deg^2$ area around M 31, which aims to study the X-ray populations in M 31 disk and the X-ray emitting hot gas in the inner halo of M 31 up to 30 kpc. In this first paper, we report the catalogue of 4506 detected X-ray sources, and attempt to cross-identify or roughly classify them. We identify 352 single stars in the foreground, 35 globular clusters and 27 supernova remnants associated with M 31, as well as 62 AGNs, 59 galaxies, and 1 galaxy clusters in the background. We uniquely classify 236 foreground stars and 17 supersoft sources based on their X-ray colors. X-ray binaries (83 LMXBs, 1 HMXBs) are classified based on their X-ray colors and X-ray variabilities. The remaining X-ray sources either have too low S/N to calculate their X-ray colors or do not have a unique classification, so are regarded as unclassified. The X-ray source catalogue is published online. Study of the X-ray source populations and the contribution of X-ray sources in the unresolved X-ray emissions based on this catalogue will be published in companion papers., Comment: 30 pages, 12 figures. Accepted for publication in APJS

Detecting safety clothing and helmets is paramount for ensuring the safety of construction workers. However, the development of deep learning models in this domain has been impeded by the scarcity of high-quality datasets. In this study, we construct a large, complex, and realistic safety clothing and helmet detection (SFCHD) dataset. SFCHD is derived from two authentic chemical plants, comprising 12,373 images, 7 categories, and 50,552 annotations. We partition the SFCHD dataset into training and testing sets with a ratio of 4:1 and validate its utility by applying several classic object detection algorithms. Furthermore, drawing inspiration from spatial and channel attention mechanisms, we design a spatial and channel attention-based low-light enhancement (SCALE) module. SCALE is a plug-and-play component with a high degree of flexibility. Extensive evaluations of the SCALE module on both the ExDark and SFCHD datasets have empirically demonstrated its efficacy in enhancing the performance of detectors under low-light conditions. The dataset and code are publicly available at https://github.com/lijfrank-open/SFCHD-SCALE., Comment: Submited

Deep models have demonstrated recent success in single-image dehazing. Most prior methods consider fully supervised training and learn from paired clean and hazy images, where a hazy image is synthesized based on a clean image and its estimated depth map. This paradigm, however, can produce low-quality hazy images due to inaccurate depth estimation, resulting in poor generalization of the trained models. In this paper, we explore an alternative approach for generating paired clean-hazy images by leveraging computer graphics. Using a modern game engine, our approach renders crisp clean images and their precise depth maps, based on which high-quality hazy images can be synthesized for training dehazing models. To this end, we present SimHaze: a new synthetic haze dataset. More importantly, we show that training with SimHaze alone allows the latest dehazing models to achieve significantly better performance in comparison to previous dehazing datasets. Our dataset and code will be made publicly available., Comment: Submitted to ICIP 2023

Deep Image Prior (DIP) shows that some network architectures naturally bias towards smooth images and resist noises, a phenomenon known as spectral bias. Image denoising is an immediate application of this property. Although DIP has removed the requirement of large training sets, it still presents two practical challenges for denoising: architectural design and noise-fitting, which are often intertwined. Existing methods mostly handcraft or search for the architecture from a large design space, due to the lack of understanding on how the architectural choice corresponds to the image. In this study, we analyze from a frequency perspective to demonstrate that the unlearnt upsampling is the main driving force behind the denoising phenomenon in DIP. This finding then leads to strategies for estimating a suitable architecture for every image without a laborious search. Extensive experiments show that the estimated architectures denoise and preserve the textural details better than current methods with up to 95% fewer parameters. The under-parameterized nature also makes them especially robust to a higher level of noise., Comment: Accepted to ICCV 2023

In the past several decades, various techniques have been developed and used for multiple-access (MA) communications. With the new applications for 6G, it is desirable to find new resources, physical or virtual, to confront the fast development of MA communication systems. For binary source transmission, this paper proposes an element-pair (EP) coding scheme for supporting massive users with short packet traffic, which solves the finite blocklength (FBL) of multiuser reliability transmission problem. Each user is assigned a unique EP, and the collection of EPs assigned to the users possesses the unique sum-pattern mapping (USPM) structural property. We present methods for constructing symbol-wise EP codes with USPM structural property based on the prime field and extension field of prime field, respectively. Based on the orthogonal EP code constructed over GF($2^m$), we propose finite-field MA (FFMA) systems over a Gaussian multiple-access channel (GMAC), including both the sparse-form and diagonal-from structures. The proposed FFMA is then applied to network layer and forms network FFMA systems for pure digital networks, in which an EP is viewed as a virtual resource block (VRB). Simulation results show that the error performance of the proposed FFMA over a GMAC can approach the error performance as that of the single-user transmission., Comment: 38 pages, 11 figures

Emotion Recognition in Conversation (ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper, we propose an emotional inertia and contagion-driven dependency modeling approach (EmotionIC) for ERC task. Our EmotionIC consists of three main components, i.e., Identity Masked Multi-Head Attention (IMMHA), Dialogue-based Gated Recurrent Unit (DiaGRU), and Skip-chain Conditional Random Field (SkipCRF). Compared to previous ERC models, EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels. The proposed model attempts to integrate the advantages of attention- and recurrence-based methods at the feature-extraction level. Specifically, IMMHA is applied to capture identity-based global contextual dependencies, while DiaGRU is utilized to extract speaker- and temporal-aware local contextual information. At the classification level, SkipCRF can explicitly mine complex emotional flows from higher-order neighboring utterances in the conversation. Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark datasets. The ablation studies confirm that our modules can effectively model emotional inertia and contagion., Comment: Accepted by SCIENCE CHINA Information Sciences (SCIS)

The extraplanar diffuse ionized gas (eDIG) represents the cool/warm ionized gas reservoir around galaxies. We present a spatial analysis of H$\alpha$ images of 22 nearby edge-on spiral galaxies from the CHANG-ES sample (the eDIG-CHANGES project), taken with the APO 3.5m telescope, in order to study their eDIG. We conduct an exponential fit to the vertical intensity profiles of the sample galaxies, of which 16 can be decomposed into a thin disk plus an extended thick disk component. The median value of the scale height (h) of the extended component is $1.13\pm 0.14$ kpc. We find a tight sublinear correlation between h and the SFR. Moreover, the offset of individual galaxies from the best-fit SFR-h relation shows significant anti-correlation with SFR_SD. This indicates that galaxies with more intense star formation tend to have disproportionately extended eDIG. Combined with data from the literature, we find that the correlations between the eDIG properties and the galaxies' properties extend to broader ranges. We further compare the vertical extension of the eDIG to multi-wavelength measurements of other CGM phases. We find the eDIG to be slightly more extended than the neutral gas (HI 21-cm line), indicating the existence of some extended ionizing sources. Most galaxies have an X-ray scale height smaller than the h, suggesting that the majority of the X-ray emission detected in shallow observations are actually from the thick disk. The h is comparable to the L-band radio continuum scale height, both slightly larger than that at higher frequencies (C-band), where the cooling is stronger and the thermal contribution may be larger. The comparable H$\alpha$ and L-band scale height indicates that the thermal and non-thermal electrons have similar spatial distributions. This further indicates that the thermal gas, the cosmics rays, and the magnetic field may be close to energy equipartition.

LAMOST-MRS-B is one of the sub-surveys of LAMOST medium-resolution (R~7500) spectroscopic survey. It aims at studying the statistical properties (e.g., binary fraction, orbital period distribution, mass ratio distribution) of binary stars and exotic stars. We intend to observe about 30000 stars (10 mag <= G <= 14.5 mag) with at least 10 visits in five years. We first planned to observe 25 plates around the galactic plane in 2018. Then the plates were reduced to 12 in 2019 because of the limitation of observation. At the same time, two new plates located at the high galactic latitude were added to explore binary properties influenced by the different environments. In this survey project, we set the identified exotic and low-metallicity stars with the highest observation priorities. For the rest of the selected stars, we gave higher priority to the relatively brighter stars in order to obtain high-quality spectra as many as possible. Spectra of 49129 stars have been obtained in LAMOST-MRS-B field and released in DR8, of which 28828 and 3375 stars have been visited more than twice and ten times with SNR >= 10, respectively. Most of the sources are B-, A-, and F-type stars with 0.6 < [Fe/H] < 0.4 dex. We also obtain 347 identified variable and exotic stars and about 250 stars with [Fe/H] < 1 dex. We measure radial velocities (RVs) by using 892233 spectra of the stars. The uncertainties of RV achieve about 1 km/s and 10 km/s1 for 95% of late- and early-type stars, respectively. The datasets presented in this paper are available at http://www.doi.org/10.57760/sciencedb.j00113.00035.

Duration of response (DOR) and time to response (TTR) are typically evaluated as secondary endpoints in early-stage clinical studies in oncology when efficacy is assessed by the best overall response (BOR) and presented as the overall response rate (ORR). Despite common use of DOR and TTR in particular in single-arm studies, the definition of these endpoints and the questions they are intended to answer remain unclear. Motivated by the estimand framework, we present relevant scientific questions of interest for DOR and TTR and propose corresponding estimand definitions. We elaborate on how to deal with relevant intercurrent events which should follow the same considerations as implemented for the primary response estimand. A case study in mantle cell lymphoma illustrates the implementation of relevant estimands of DOR and TTR. We close the paper with practical recommendations to implement DOR and TTR in clinical study protocols.

Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through the multi-head attention based intention fusion module to capture the speaker's intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human's psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multi-task learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression., Comment: 8 pages

Multimodal machine learning is an emerging area of research, which has received a great deal of scholarly attention in recent years. Up to now, there are few studies on multimodal Emotion Recognition in Conversation (ERC). Since Graph Neural Networks (GNNs) possess the powerful capacity of relational modeling, they have an inherent advantage in the field of multimodal learning. GNNs leverage the graph constructed from multimodal data to perform intra- and inter-modal information interaction, which effectively facilitates the integration and complementation of multimodal data. In this work, we propose a novel Graph network based Multimodal Fusion Technique (GraphMFT) for emotion recognition in conversation. Multimodal data can be modeled as a graph, where each data object is regarded as a node, and both intra- and inter-modal dependencies existing between data objects can be regarded as edges. GraphMFT utilizes multiple improved graph attention networks to capture intra-modal contextual information and inter-modal complementary information. In addition, the proposed GraphMFT attempts to address the challenges of existing graph-based multimodal conversational emotion recognition models such as MMGCN. Empirical results on two public multimodal datasets reveal that our model outperforms the State-Of-The-Art (SOTA) approaches with the accuracy of 67.90% and 61.30%., Comment: Accepted by Neurocomputing

Multimodal Emotion Recognition in Conversation (ERC) plays an influential role in the field of human-computer interaction and conversational robotics since it can motivate machines to provide empathetic services. Multimodal data modeling is an up-and-coming research area in recent years, which is inspired by human capability to integrate multiple senses. Several graph-based approaches claim to capture interactive information between modalities, but the heterogeneity of multimodal data makes these methods prohibit optimal solutions. In this work, we introduce a multimodal fusion approach named Graph and Attention based Two-stage Multi-source Information Fusion (GA2MIF) for emotion detection in conversation. Our proposed method circumvents the problem of taking heterogeneous graph as input to the model while eliminating complex redundant connections in the construction of graph. GA2MIF focuses on contextual modeling and cross-modal modeling through leveraging Multi-head Directed Graph ATtention networks (MDGATs) and Multi-head Pairwise Cross-modal ATtention networks (MPCATs), respectively. Extensive experiments on two public datasets (i.e., IEMOCAP and MELD) demonstrate that the proposed GA2MIF has the capacity to validly capture intra-modal long-range contextual information and inter-modal complementary information, as well as outperforms the prevalent State-Of-The-Art (SOTA) models by a remarkable margin., Comment: Accepted by IEEE Transactions on Affective Computing

Emotion Recognition in Conversation (ERC) plays a significant part in Human-Computer Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can mitigate the drawbacks of uni-modal approaches. Recently, Graph Neural Networks (GNNs) have been widely used in a variety of fields due to their superior performance in relation modeling. In multimodal ERC, GNNs are capable of extracting both long-distance contextual information and inter-modal interactive information. Unfortunately, since existing methods such as MMGCN directly fuse multiple modalities, redundant information may be generated and diverse information may be lost. In this work, we present a directed Graph based Cross-modal Feature Complementation (GraphCFC) module that can efficiently model contextual and interactive information. GraphCFC alleviates the problem of heterogeneity gap in multimodal fusion by utilizing multiple subspace extractors and Pair-wise Cross-modal Complementary (PairCC) strategy. We extract various types of edges from the constructed graph for encoding, thus enabling GNNs to extract crucial contextual and interactive information more accurately when performing message passing. Furthermore, we design a GNN structure called GAT-MLP, which can provide a new unified network framework for multimodal learning. The experimental results on two benchmark datasets show that our GraphCFC outperforms the state-of-the-art (SOTA) approaches., Comment: Accepted by IEEE Transactions on Multimedia (TMM)

Quasars behind the Galactic plane (GPQs) are important astrometric references and valuable probes of Galactic gas, yet the search for GPQs is difficult due to severe extinction and source crowding in the Galactic plane. In this paper, we present a sample of 204 spectroscopically confirmed GPQs at |b|<20{\deg}, 191 of which are new discoveries. This GPQ sample covers a wide redshift range from 0.069 to 4.487. For the subset of 230 observed GPQ candidates, the lower limit of the purity of quasars is 85.2%, and the lower limit of the fraction of stellar contaminants is 6.1%. Using a multicomponent spectral fitting, we measure the emission line and continuum flux of the GPQs, and estimate their single-epoch virial black hole masses. Due to selection effects raised from Galactic extinction and target magnitude, these GPQs have higher black hole masses and continuum luminosities in comparison to the SDSS DR7 quasar sample. The spectral-fitting results and black hole mass estimates are compiled into a main spectral catalog, and an extended spectral catalog of GPQs. The successful identifications prove the reliability of both our GPQ selection methods and the GPQ candidate catalog, shedding light on the astrometric and astrophysical programs that make use of a large sample of GPQs in the future., Comment: 17 pages, 10 figures, published in ApJS. Tables and spectra in this paper are available at https://doi.org/10.12149/101095

For the analysis of a time-to-event endpoint in a single-arm or randomized clinical trial it is generally perceived that interpretation of a given estimate of the survival function, or the comparison between two groups, hinges on some quantification of the amount of follow-up. Typically, a median of some loosely defined quantity is reported. However, whatever median is reported, is typically not answering the question(s) trialists actually have in terms of follow-up quantification. In this paper, inspired by the estimand framework, we formulate a comprehensive list of relevant scientific questions that trialists have when reporting time-to-event data. We illustrate how these questions should be answered, and that reference to an unclearly defined follow-up quantity is not needed at all. In drug development, key decisions are made based on randomized controlled trials, and we therefore also discuss relevant scientific questions not only when looking at a time-to-event endpoint in one group, but also for comparisons. We find that different thinking about some of the relevant scientific questions around follow-up is required depending on whether a proportional hazards assumption can be made or other patterns of survival functions are anticipated, e.g. delayed separation, crossing survival functions, or the potential for cure. We conclude the paper with practical recommendations., Comment: 31 pages

AGN bubbles could play an important role in accelerating high-energy CRs and galactic feedback. Only in nearby galaxies could we have high enough angular resolution in multi-wavelengths to study the sub-kpc environment of the AGN, where the bubbles are produced and strongly interact with the surrounding ISM. In this paper, we present the latest Chandra observations of the Virgo cluster galaxy NGC 4438, which hosts multi-scale bubbles detected in various bands. The galaxy also has low current star formation activity, so these bubbles are evidently produced by the AGN rather than a starburst. We present spatially resolved spectral analysis of the Chandra data of the $\sim3^{\prime\prime}\times5^{\prime\prime}$ ($\sim200{\rm~pc}\times350\rm~pc$) nuclear bubble of NGC 4438. The power law tail in the X-ray spectra can be most naturally explained as synchrotron emission from high-energy CR leptons. The hot gas temperature increases, while the overall contribution of the non-thermal X-ray emission decreases with the vertical distance from the galactic plane. We calculate the synchrotron cooling timescale of the CR leptons responsible for the non-thermal hard X-ray emission to be only a few tens to a few hundreds of years. The thermal pressure of the hot gas is about three times the magnetic pressure, but the current data cannot rule out the possibility that they are still in pressure balance. The spatially resolved spectroscopy presented in this paper may have important constraints on how the AGN accelerates CRs and drives outflows. We also discover a transient X-ray source only $\sim5^{\prime\prime}$ from the nucleus of NGC 4438. The source was not detected in 2002 and 2008, but became quite X-ray bright in March 2020, with an average 0.5-7 keV luminosity of $\sim10^{39}\rm~ergs~s^{-1}$., Comment: 20 pages, 7 figures, 2 tables, MNRAS in press

In the past decade, many scientific news media that report scientific breakthroughs and discoveries emerged, bringing science and technology closer to the general public. However, not all scientific news article cites proper sources, such as original scientific papers. A portion of scientific news articles contain misinterpreted, exaggerated, or distorted information that deviates from facts asserted in the original papers. Manually identifying proper citations is laborious and costly. Therefore, it is necessary to automatically search for pertinent scientific papers that could be used as evidence for a given piece of scientific news. We propose a system called SciEv that searches for scientific evidence papers given a scientific news article. The system employs a 2-stage query paradigm with the first stage retrieving candidate papers and the second stage reranking them. The key feature of SciEv is it uses domain knowledge entities (DKEs) to find candidates in the first stage, which proved to be more effective than regular keyphrases. In the reranking stage, we explore different document representations for news articles and candidate papers. To evaluate our system, we compiled a pilot dataset consisting of 100 manually curated (news,paper) pairs from ScienceAlert and similar websites. To our best knowledge, this is the first dataset of this kind. Our experiments indicate that the transformer model performs the best for DKE extraction. The system achieves a P@1=50%, P@5=71%, and P@10=74% when it uses a TFIDF-based text representation. The transformer-based re-ranker achieves a comparable performance but costs twice as much time. We will collect more data and test the system for user experience., Comment: 12 pages, 1 table, 4 figures

We report the results of near-infrared spectroscopic observations of 37 quasars in the redshift range $6.3< z\le7.64$, including 32 quasars at $z>6.5$, forming the largest quasar near-infrared spectral sample at this redshift. The spectra, taken with Keck, Gemini, VLT, and Magellan, allow investigations of central black hole mass and quasar rest-frame ultraviolet spectral properties. The black hole masses derived from the MgII emission lines are in the range $(0.3-3.6)\times10^{9}\,M_{\odot}$, which requires massive seed black holes with masses $\gtrsim10^{3-4}\,M_{\odot}$, assuming Eddington accretion since $z=30$. The Eddington ratio distribution peaks at $\lambda_{\rm Edd}\sim0.8$ and has a mean of 1.08, suggesting high accretion rates for these quasars. The CIV - MgII emission line velocity differences in our sample show an increase of CIV blueshift towards higher redshift, but the evolutionary trend observed from this sample is weaker than the previous results from smaller samples at similar redshift. The FeII/MgII flux ratios derived for these quasars up to $z=7.6$, compared with previous measurements at different redshifts, do not show any evidence of strong redshift evolution, suggesting metal-enriched environments in these quasars. Using this quasar sample, we create a quasar composite spectrum for $z>6.5$ quasars and find no significant redshift evolution of quasar broad emission lines and continuum slope, except for a blueshift of the CIV line. Our sample yields a strong broad absorption line quasar fraction of $\sim$24%, higher than the fractions in lower redshift quasar samples, although this could be affected by small sample statistics and selection effects., Comment: Accepted for publication in ApJ. 26 pages, 9 figures

Most of the baryons in L* galaxies are unaccounted for and are predicted to lie in hot gaseous halos (T ~ 3E6 K) that may extend beyond R200. A hot gaseous halo will produce a thermal Sunyaev-Zeldovich signal that is proportional to the product of the gas mass and the mass-weighted temperature. To best detect this signal, we used a Needlet Independent Linear Combination all-sky Planck map that we produced from the most recent Planck data release, also incorporating WMAP data. The sample is 12 L* spiral galaxies with distances of 3-10 Mpc, which are spatially resolved so that contamination from the optical galaxy can be excluded. One galaxy, NGC 891, has a particularly strong SZ signal, and when excluding it, the stack of 11 galaxies is detected at about 4sigma (declining with radius) and is extended to at least 250 kpc (~R_{200}) at > 99% confidence. The gas mass within a spherical volume to a radius of 250 kpc is 9.8 +/- 2.8 E10 Msun, for Tavg = 3E6 K. This is about 30% of the cosmic baryon content of the average galaxy (3.1E11 Msun), and about equal to the mass of stars, disk gas, and warm halo gas. The remaining missing baryons (~ 1.4E11 Msun, 40-50% of the total baryon content) are likely to be hot and extend to the 400-500 kpc volume, if not beyond. The result is higher than predictions, but within the uncertainties., Comment: 25 pages, 11 figures, accepted by ApJ (30 Jan. 2022)

In modern internet industries, deep learning based recommender systems have became an indispensable building block for a wide spectrum of applications, such as search engine, news feed, and short video clips. However, it remains challenging to carry the well-trained deep models for online real-time inference serving, with respect to the time-varying web-scale traffics from billions of users, in a cost-effective manner. In this work, we present JIZHI - a Model-as-a-Service system - that per second handles hundreds of millions of online inference requests to huge deep models with more than trillions of sparse parameters, for over twenty real-time recommendation services at Baidu, Inc. In JIZHI, the inference workflow of every recommendation request is transformed to a Staged Event-Driven Pipeline (SEDP), where each node in the pipeline refers to a staged computation or I/O intensive task processor. With traffics of real-time inference requests arrived, each modularized processor can be run in a fully asynchronized way and managed separately. Besides, JIZHI introduces heterogeneous and hierarchical storage to further accelerate the online inference process by reducing unnecessary computations and potential data access latency induced by ultra-sparse model parameters. Moreover, an intelligent resource manager has been deployed to maximize the throughput of JIZHI over the shared infrastructure by searching the optimal resource allocation plan from historical logs and fine-tuning the load shedding policies over intermediate system feedback. Extensive experiments have been done to demonstrate the advantages of JIZHI from the perspectives of end-to-end service latency, system-wide throughput, and resource consumption. JIZHI has helped Baidu saved more than ten million US dollars in hardware and utility costs while handling 200% more traffics without sacrificing inference efficiency., Comment: Accepted to SIGKDD 2021 applied data science track

Outflows from super-massive black holes (SMBHs) play an important role in the co-evolution of themselves, their host galaxies, and the larger scale environments. Such outflows are often characterized by emission and absorption lines in various bands and in a wide velocity range blueshifted from the systematic redshift of the host quasar. In this paper, we report a strong broad line region (BLR) outflow from the z~4.7 quasar BR 1202-0725 based on the high-resolution optical spectrum taken with the Magellan Inamori Kyocera Echelle (MIKE) spectrograph installed on the 6.5m Magellan/Clay telescope, obtained from the `Probing the He II re-Ionization ERa via Absorbing C IV Historical Yield' (HIERACHY) project. This rest-frame ultraviolet (UV) spectrum is characterized by a few significantly blueshifted broad emission lines from high ions; the most significant one is the C IV line at a velocity of -6500 km/s relative to the H{\alpha} emission line, which is among the highest velocity BLR outflows in observed quasars at z > 4. The measured properties of UV emission lines from different ions, except for O I and Ly{\alpha}, also follow a clear trend that higher ions tend to be broader and outflow at higher average velocities. There are multiple C IV and Si IV absorbing components identified on the blue wings of the corresponding emission lines, which may be produced by either the outflow or the intervening absorbers., Comment: Accepted for publication in the MNRAS. 15 pages, 7 figures, 3 tables

X-ray observations provide a unique probe of the accretion disk corona of supermassive black holes (SMBHs). In this paper, we present a uniform \emph{Chandra} X-ray data analysis of a sample of 152 $z\geq4.5$ quasars. We firmly detect 46 quasars of this sample in 0.5-2~keV above 3~$\sigma$ and calculate the upper limits of the X-ray flux of the remaining. We also estimate the power law photon index of the X-ray spectrum of 31 quasars. 24 of our sample quasars are detected in the FIRST or NVSS radio surveys; all of them are radio-loud. We statistically compare the X-ray properties of our $z\geq4.5$ quasars to other X-ray samples of AGN at different redshifts. The relation between the rest-frame X-ray luminosity and other quasar parameters, such as the bolometric luminosity, UV luminosity, or SMBH mass, show large scatters. These large scatters can be attributed to the narrow luminosity range at the highest redshift, the large measurement error based on relatively poor X-ray data, and the inclusion of radio-loud quasars in the sample. The $L_{\rm X}-L_{\rm UV}$ relationship is significantly sub-linear. We do not find a significant redshift evolution of the $L_{\rm X}-L_{\rm UV}$ relation, expressed either in the slope of this relation, or the departure of individual AGNs from the best-fit $\alpha_{\rm OX}-L_{\rm UV}$ relation ($\Delta\alpha_{\rm OX}$). The median value of the X-ray photon index is $\Gamma\approx1.79$, which does not show redshift evolution from $z=0$ to $z\sim7$. The X-ray and UV properties of the most distant quasars could potentially be used as a standard candle to constrain cosmological models. The large scatter of our sample on the Hubble diagram highlights the importance of future large unbiased deep X-ray and radio surveys in using quasars in cosmological studies., Comment: 26 pages, 13 figures, 3 tables. MNRAS in press. Online data temporarily available at the following link before the formal publication of the paper: https://sites.google.com/a/umich.edu/jiangtaoli2/research/online-data?authuser=0 Please contact the first author if you have troubles downloading the data

Biological materials are self-assembled with near-atomic precision in living cells, whereas synthetic 3D structures generally lack such precision and controllability. Recently, DNA nanotechnology, especially DNA origami technology, has been useful in the bottom-up fabrication of well-defined nanostructures ranging from tens of nanometres to sub-micrometres. In this Primer, we summarize the methodologies of DNA origami technology, including origami design, synthesis, functionalization and characterization. We highlight applications of origami structures in nanofabrication, nanophotonics and nanoelectronics, catalysis, computation, molecular machines, bioimaging, drug delivery and biophysics. We identify challenges for the field, including size limits, stability issues and the scale of production, and discuss their possible solutions. We further provide an outlook on next-generation DNA origami techniques that will allow in vivo synthesis and multiscale manufacturing.

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets at enhancing the perceptual quality. The three tracks totally attract 482 registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the final results of Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of video quality enhancement. The homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh, Comment: Corrected the MOS values in Table 2, and corrected some minor typos

The mean square size of topologically interlocked molecules (TIMs) is presented as a linear combination of contributions from the backbone and subcomponents. Using scaling analyses and extensive molecular dynamics simulations of polycatenanes, as a typical example of TIMs, we show that the effective exponent $\nu(m)$ for the size dependence of the backbone on the monomer number of subcomponent $m$ is asymptotic to a value $\nu$ (approximately 0.588 in good solvents) with a correction of $m^{-0.47}$, which is the same as for the covalently linked polymer. However, the effective exponent for the size dependence of subcomponents on $m$ is asymptotic to the same value $\nu$ but with a new correction of $m^{-1.0}$. The different corrections to the scaling on the backbone and subcomponent structure induce a surprising double asymptotic behavior for the architecture of the TIMs. The scaling model that takes into account the double asymptotic behavior is in good quantitative agreement with the simulation result that the effective exponent for the size dependence of TIMs on $m$ increases with the subcomponent number $n$. The full scaling functional form of the size dependence on $m$ and $n$ for polycatenanes in a good solvent is well described by a simple sum of two limiting behaviors with different corrections., Comment: 5 pages, 5 figures

The charge, spin, and composition degrees of freedom in high-entropy alloy endow it with tunable valence and spin states, infinite combinations and excellent mechanical performance. Meanwhile, the stacking, interlayer, and angle degrees of freedom in van der Waals material bring it with exceptional features and technological applications. Integration of these two distinct material categories while keeping their merits would be tempting. Based on this heuristic thinking, we design and explore a new range of materials (i.e., dichalcogenides, halides and phosphorus trisulfides) with multiple metallic constitutions and intrinsic layered structure, which are coined as high-entropy van der Waals materials. Millimeter-scale single crystals with homogeneous element distribution can be efficiently acquired and easily exfoliated or intercalated in this materials category. Multifarious physical properties like superconductivity, magnetic ordering, metal-insulator transition and corrosion resistance have been exploited. Further research based on the concept of high-entropy van der Waals materials will enrich the high-throughput design of new systems with intriguing properties and practical applications., Comment: 10 pages, 3 figures, and 1 table

We investigate the optical Kerr nonlinearity and multi-photon absorption (MPA) properties of DSTMS excited by femtosecond pulses at a wavelengths of 1.43 {\mu}m, which is optimal for terahertz generation via difference frequency mixing. The MPA and the optical Kerr coefficients of DSTMS at 1.43 {\mu}m are strongly anisotropic indicating a dominating contribution from cascaded 2nd-order nonlinearity. These results suggest that the saturation of the THz generation efficiency is mainly related to the MPA process and to a spectral broadening caused by cascaded 2nd-order frequency mixing within DSTMS, Comment: 5pages, 4 figures

Distant quasars are unique tracers to study the formation of the earliest supermassive black holes (SMBHs) and the history of cosmic reionization. Despite extensive efforts, only two quasars have been found at $z\ge7.5$, due to a combination of their low spatial density and the high contamination rate in quasar selection. We report the discovery of a luminous quasar at $z=7.642$, J0313$-$1806, the most distant quasar yet known. This quasar has a bolometric luminosity of $3.6\times10^{13} L_\odot$. Deep spectroscopic observations reveal a SMBH with a mass of $(1.6\pm0.4) \times10^9M_\odot$ in this quasar. The existence of such a massive SMBH just $\sim$670 million years after the Big Bang challenges significantly theoretical models of SMBH growth. In addition, the quasar spectrum exhibits strong broad absorption line (BAL) features in CIV and SiIV, with a maximum velocity close to 20% of the speed of light. The relativistic BAL features, combined with a strongly blueshifted CIV emission line, indicate that there is a strong active galactic nucleus (AGN) driven outflow in this system. ALMA observations detect the dust continuum and [CII] emission from the quasar host galaxy, yielding an accurate redshift of $7.6423 \pm 0.0013$ and suggesting that the quasar is hosted by an intensely star-forming galaxy, with a star formation rate of $\rm\sim 200 ~M_\odot ~yr^{-1}$ and a dust mass of $\sim7\times10^7~M_\odot$. Followup observations of this reionization-era BAL quasar will provide a powerful probe of the effects of AGN feedback on the growth of the earliest massive galaxies., Comment: 4 figures, 1 table; accepted for publication in ApJL