Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multiwavelength View of IC 860: What Is in Action inside Quenching Galaxies

We present a multiwavelength study of IC 860, a nearby post-starburst galaxy at the early stage of transitioning from blue and star forming to red and quiescent. Optical images reveal a galaxy-wide, dusty outflow originating from a compact core. We find evidence for a multiphase outflow in the molecular and neutral gas phase from the CO position–velocity diagram and NaD absorption features. We constrain the neutral mass outflow rate to be ∼0.5 M ⊙ yr−1, and the total hydrogen mass outflow rate to be ∼12 M ⊙ yr−1. Neither outflow component seems able to escape the galaxy. We also find evidence for a recent merger in the optical images, CO spatial distribution, and kinematics, and evidence for a buried active galactic nucleus in the optical emission line ratios, mid-IR properties, and radio spectral shape. The depletion time of the molecular gas reservoir under the current star formation rate is ∼7 Gyr, indicating that the galaxy could stay at the intermediate stage between the blue and red sequence for a long time. Thus the timescales for a significant decline in star formation rate (quenching) and gas depletion are not necessarily the same. Our analysis supports the quenching picture where outflows help suppress star formation by disturbing rather than expelling the gas and shed light on possible ongoing activities in similar quenching galaxies.
Highly dynamic locomotion control of biped robot enhanced by swing arms

Swing arms have an irreplaceable role in promoting highly dynamic locomotion on bipedal robots by a larger angular momentum control space from the viewpoint of biomechanics. Few bipedal robots utilize swing arms and its redundancy characteristic of multiple degrees of freedom due to the lack of appropriate locomotion control strategies to perfectly integrate modeling and control. This paper presents a kind of control strategy by modeling the bipedal robot as a flywheel-spring loaded inverted pendulum (F-SLIP) to extract characteristics of swing arms and using the whole-body controller (WBC) to achieve these characteristics, and also proposes a evaluation system including three aspects of agility defined by us, stability and energy consumption for the highly dynamic locomotion of bipedal robots. We design several sets of simulation experiments and analyze the effects of swing arms according to the evaluation system during the jumping motion of Purple (Purple energy rises in the east)V1.0, a kind of bipedal robot designed to test high explosive locomotion. Results show that Purple's agility is increased by more than 10 percent, stabilization time is reduced by a factor of two, and energy consumption is reduced by more than 20 percent after introducing swing arms.
Understanding Attention for Vision-and-Language Tasks

Attention mechanism has been used as an important component across Vision-and-Language(VL) tasks in order to bridge the semantic gap between visual and textual features. While attention has been widely used in VL tasks, it has not been examined the capability of different attention alignment calculation in bridging the semantic gap between visual and textual clues. In this research, we conduct a comprehensive analysis on understanding the role of attention alignment by looking into the attention score calculation methods and check how it actually represents the visual region’s and textual token’s significance for the global assessment. We also analyse the conditions which attention score calculation mechanism would be more (or less) interpretable, and which may impact the model performance on three different VL tasks, including visual question answering, text-to-image generation, text-and-image matching (both sentence and image retrieval). Our analysis is the first of its kind and provides useful insights of the importance of each attention alignment score calculation when applied at the training phase of VL tasks, commonly ignored in attention-based cross modal models, and/or pretrained models. Our code is available at: https://github.com/adlnlp/Attention_VL
Transceiver designs to attain the entanglement assisted communications capacity

Pre-shared entanglement can signiﬁcantly boost communication rates in the high thermal noise and low-brightness transmitter regime. In this regime, for a lossy-bosonic channel with additive thermal noise, the ratio between the entanglement-assisted capacity and the Holevo capacity—the maximum reliable-communications rate permitted by quantum mechanics without any pre-shared entanglement—scales as log(1 / ¯ N S ), where the mean transmitted photon number per mode, ¯ N S (cid:28) 1. Thus, pre-shared entanglement, e.g., distributed by the quantum internet or a satellite-assisted quantum link, promises to signiﬁcantly improve low-power radio-frequency communications. In this paper, we propose a pair of structured quantum transceiver designs that leverage continuous-variable pre-shared entanglement generated, e.g., from a downconversion source, binary phase modulation, and non-Gaussian joint detection over a codeword block, to achieve this scaling law of capacity enhancement. Further, we describe a modiﬁcation to the aforesaid receiver using a front-end that uses sum-frequency generation sandwiched with dynamically-programmable in-line two-mode squeezers, and a receiver back-end that takes full advantage of the output of the receiver’s front-end by employing a non-destructive multimode vacuum-or-not measurement to achieve the entanglement-assisted classical communications capacity.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment

The security and privacy of smart home systems, particularly from a home user’s perspective, have been a very active research area in recent years. However, via a meta-review of 52 review papers covering related topics (published between 2000 and 2021), this article shows a lack of a more recent literature review on user perspectives of smart home security and privacy since the 2010s. This identified gap motivated us to conduct a systematic literature review (SLR) covering 126 relevant research papers published from 2010 to 2021. Our SLR led to the discovery of a number of important areas where further research is needed; these include holistic methods that consider a more diverse and heterogeneous range of home devices, interactions between multiple home users, complicated data flow between multiple home devices and home users, some less studied demographic factors, and advanced conceptual frameworks. Based on these findings, we recommended key future research directions, e.g., research for a better understanding of security and privacy aspects in different multi-device and multi-user contexts, and a more comprehensive ontology on the security and privacy of the smart home covering varying types of home devices and behaviors of different types of home users.
I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies

Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as a disease, in a group of individuals. Unfortunately, careless sharing of GWAS statistics might give rise to privacy attacks. Several works attempted to reconcile secure processing with privacy-preserving releases of GWASes. However, we highlight that these approaches remain vulnerable if GWASes utilize overlapping sets of individuals and genomic variations. In such conditions, we show that even when relying on state-of-the-art techniques for protecting releases, an adversary could reconstruct the genomic variations of up to 28.6% of participants, and that the released statistics of up to 92.3% of the genomic variations would enable membership inference attacks. We introduce I-GWAS, a novel framework that securely computes and releases the results of multiple possibly interdependent GWASes. I-GWAS continuously releases privacy-preserving and noise-free GWAS results as new genomes become available.
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework Based on Semantic Blocks

Unsupervised summarization methods have achieved remarkable results by incorporating representations from pre-trained language models. However, existing methods fail to consider efficiency and effectiveness at the same time when the input document is extremely long. To tackle this problem, in this paper, we proposed an efficient Coarse-to-Fine Facet-Aware Ranking (C2F-FAR) framework for unsupervised long document summarization, which is based on the semantic block. The semantic block refers to continuous sentences in the document that describe the same facet. Specifically, we address this problem by converting the one-step ranking method into the hierarchical multi-granularity two-stage ranking. In the coarse-level stage, we proposed a new segment algorithm to split the document into facet-aware semantic blocks and then filter insignificant blocks. In the fine-level stage, we select salient sentences in each block and then extract the final summary from selected sentences. We evaluate our framework on four long document summarization datasets: Gov-Report, BillSum, arXiv, and PubMed. Our C2F-FAR can achieve new state-of-the-art unsupervised summarization results on Gov-Report and BillSum. In addition, our method speeds up 4-28 times more than previous methods.
Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning

Accurate geometry representation is essential in developing finite element models. Although generally good, deep-learning segmentation approaches with only few data have difficulties in accurately segmenting fine features, e.g., gaps and thin structures. Subsequently, segmented geometries need labor-intensive manual modifications to reach a quality where they can be used for simulation purposes. We propose a strategy that uses transfer learning to reuse datasets with poor segmentation combined with an interactive learning step where fine-tuning of the data results in anatomically accurate segmentations suitable for simulations. We use a modified MultiPlanar UNet that is pre-trained using inferior hip joint segmentation combined with a dedicated loss function to learn the gap regions and post-processing to correct tiny inaccuracies on symmetric classes due to rotational invariance. We demonstrate this robust yet conceptually simple approach applied with clinically validated results on publicly available computed tomography scans of hip joints. Code and resulting 3D models are available at: https://github.com/MICCAI2022-155/AuToSeg}
Evolution of the Hub-filament Structures in IC 5146 in the Context of the Energy Balance of Gravity, Turbulence, and Magnetic Field

We present the results of 850 μm polarization and C18O (3 − 2) line observations toward the western hub-filament structure (W-HFS) of the dark Streamer in IC 5146 using the James Clerk Maxwell Telescope SCUBA-2/POL-2 and HARP instruments. We aim to investigate how the relative importance of the magnetic field, gravity, and turbulence affects core formation in HFS by comparing the energy budget of this region. We identified four 850 μm cores and estimated the magnetic field strengths (B pos) of the cores and the hub and filament using the Davis–Chandrasekhar–Fermi method. The estimated B pos is ∼80 to 1200 μG. From Wang et al., B pos of E-47, a core in the eastern hub (E-hub), and E-hub were reestimated to be 500 and 320 μG, respectively, with the same method. We measured the gravitational (E G), kinematic (E K), and magnetic energies (E B) in the filament and hubs and compared the relative importance among them. We found that an E B-dominant filament has aligned fragmentation type, while E G-dominant hubs show no and clustered fragmentation types. In the E G dominant hubs, it seems that the portion of E K determines whether the hub becomes to have clustered (the portion of E K ∼ 20%) or no fragmentation type (∼10%). We propose an evolutionary scenario for the E- and W-HFSs, where the HFS forms first by the collision of turbulent flows, and then the hubs and filaments can go into various types of fragmentation depending on their energy balance of gravity, turbulence, and magnetic field.
Conformal Inference for Online Prediction with Arbitrary Distribution Shifts

We consider the problem of forming prediction sets in an online setting where the distribution generating the data is allowed to vary over time. Previous approaches to this problem suffer from over-weighting historical data and thus may fail to quickly react to the underlying dynamics. Here we correct this issue and develop a novel procedure with provably small regret over all local time intervals of a given width. We achieve this by modifying the adaptive conformal inference (ACI) algorithm of Gibbs and Cand\`{e}s (2021) to contain an additional step in which the step-size parameter of ACI's gradient descent update is tuned over time. Crucially, this means that unlike ACI, which requires knowledge of the rate of change of the data-generating mechanism, our new procedure is adaptive to both the size and type of the distribution shift. Our methods are highly flexible and can be used in combination with any baseline predictive algorithm that produces point estimates or estimated quantiles of the target without the need for distributional assumptions. We test our techniques on two real-world datasets aimed at predicting stock market volatility and COVID-19 case counts and find that they are robust and adaptive to real-world distribution shifts.
Discovering Agents

Causal models of agents have been used to analyse the safety aspects of machine learning systems. But identifying agents is non-trivial -- often the causal model is just assumed by the modeler without much justification -- and modelling failures can lead to mistakes in the safety analysis. This paper proposes the first formal causal definition of agents -- roughly that agents are systems that would adapt their policy if their actions influenced the world in a different way. From this we derive the first causal discovery algorithm for discovering agents from empirical data, and give algorithms for translating between causal models and game-theoretic influence diagrams. We demonstrate our approach by resolving some previous confusions caused by incorrect causal modelling of agents.
ASTRO: An AST-Assisted Approach for Generalizable Neural Clone Detection

Neural clone detection has attracted the attention of software engineering researchers and practitioners. However, most neural clone detection methods do not generalize beyond the scope of clones that appear in the training dataset. This results in poor model performance, especially in terms of model recall. In this paper, we present an A bstract S yntax T ree (AST) assisted approach for gene R alizable neural clone detecti O n, or ASTRO , a framework for finding clones in codebases reflecting industry practices. We present three main components: (1) an AST-inspired representation for source code that leverages program structure and semantics, (2) a global graph representation that captures the context of an AST among a corpus of programs, and (3) a graph embedding for programs that, in combination with extant large-scale language models, improves state-of-the-art code clone detection. Our experimental results show that ASTRO improves state-of-the-art neural clone detection approaches in both recall and F-1 scores.
Categorified Crystal Structure on Localized Quantum Coordinate Rings

A bstract . For the quiver Hecke algebra R associated with a simple Lie algebra, let R -gmod be the category of ﬁnite-dimensional graded R -modules. It is well-known that it categoriﬁes the unipotent quantum coordinate ring A q ( n ). The localization of R -gmod has been deﬁned in [12], which will be denoted by e R -gmod. Its Grothendieck ring K ( e R -gmod) deﬁnes the localized (unipotent) quantum coordinate ring ] A q ( n ). We shall give a certain crystal structure on the localized quantum coordinate ring by regarding the set of self-dual simple objects B ( e R -gmod) in e R -gmod. We also give the isomorphism of crystals from B ( e R -gmod) to the cellular crystal B i for an arbitrary reduced word i of the longest Weyl group element. This result can be seen as a localized version for the categoriﬁcation of the crystal B ( ∞ ) by Lauda-Vazirani [19] since the crystal B ( ∞ ) is realized in the cellular crystal B i .
Urban feature analysis from aerial remote sensing imagery using self-supervised and semi-supervised computer vision

Analysis of overhead imagery using computer vision is a problem that has received considerable attention in academic literature. Most techniques that operate in this space are both highly specialised and require expensive manual annotation of large datasets. These problems are addressed here through the development of a more generic framework, incorporating advances in representation learning which allows for more flexibility in analysing new categories of imagery with limited labeled data. First, a robust representation of an unlabeled aerial imagery dataset was created based on the momentum contrast mechanism. This was subsequently specialised for different tasks by building accurate classifiers with as few as 200 labeled images. The successful low-level detection of urban infrastructure evolution over a 10-year period from 60 million unlabeled images, exemplifies the substantial potential of our approach to advance quantitative urban research.
Wavelets for the Fast Solution of Second-Kind Integral Equations

Abstract : A class of vector-space bases is introduced for the sparse representation of discretizations of integral operators. An operator with a smooth, non-oscillatory kernel possessing a finite number of singularities in each row or column is represented in these bases as a sparse matrix, to high precision. A method is presented that employs these bases for the numerical solution of second-kind integral equations in time bounded by O(nlog squared n) , where n is the number of points in the discretization. Numerical results are given which demonstrate the effectiveness of the approach, and several generalizations and applications of the method are discussed.
Observed Reductions in the Infectivity of Bioaerosols Containing Bovine Coronavirus Under Repetitively Pulsed RF Exposure

Objective: The purpose of the present study is to investigate the inactivation of bioaerosols containing Bovine Coronavirus, BCoV, under repetitively pulsed radio frequency (RF) electromagnetic exposure. Methods: These experiments were performed in a waveguide containing a flowing aerosol stream and were limited to a single RF waveform: ∼2 μs square envelope, 5.6 GHz, 4.8 kHz repetition rate. Aerosol streams were exposed to RF electric field amplitudes in the range of 41.9 +/−6.2 kV/m. Under laminar flow conditions, 75% of the total collected aerosol stream spends 0.85 seconds or less in the RF exposure region. Results: Application of the RF waveform changed mean survival rate of the aerosolized BCoV by −0.58 decades (roughly a 74% reduction) and impacted the variance and standard deviation of the experimental results, with the RF exposure data showing an 800% increase in variance and 196% increase in standard deviation over the control results. Experimental results were compared to those from an analytic electromagnetic-heating inactivation model. Conclusion: The comparison indicated the feasibility that the observed reduction in BCoV survival rate might be due to a combination of thermal effects and non-thermal electric field effects. Significance: Developing better insight into the mechanisms of inactivation is important for understanding the potential limits of efficacy for this method. Additionally, these results contribute an important baseline for the impact of electromagnetic fields on aerosolized pathogens.
BACK TO LIFE: IS IT POSSIBLE TO BE MYSELF AGAIN? A QUALITATIVE STUDY WITH PERSONS INITIALLY HOSPITALIZED DUE TO COVID-19

Objective To gain a deeper understanding of the lived experiences of patients with COVID-19, regarding rehabilitation, work and social life 6 months after hospital discharge. Design An explorative qualitative study with individual interviews. Subjects Patients of working age with persistent self-reported symptoms at a 3-month follow-up who had received inpatient hospital care with discharge approximately 6 months previously were purposively sampled. Methods Semi-structured interviews were performed with 10 men and 5 women. The interviews were transcribed verbatim and analysed with inductive thematic analysis. Results Four themes were identified: “Social support – crucial, but decreased over time”, “Varying needs of, and access to, rehabilitation”, “Returning to work after COVID-19 – crucial for future prospects” and “An overwhelming experience that essentially changed one’s personality”. Conclusion Rehabilitation provided participants with the valuable tools for recovery, giving them hope for future recovery. Support from next of kin was highly valued, creating stronger family bonds. A new meaning and greater appreciation of life was expressed. LAY ABSTRACT The aim of this study was to gain a deeper understanding of the lived experiences of COVID-19 patients regarding rehabilitation, work and social life 6 months after hospital discharge. Patients of working age with persistent self-reported symptoms at a 3-month follow-up, who had received inpatient care at hospital with discharge approximately 6 months previously, were recruited. Individual interviews were performed with 10 men and 5 women, and the interviews were analysed according to thematic analysis. The study found that the rehabilitation team provided participants with valuable tools for recovery, which made them feel they were improving and gave them hope of future recovery. Support from next of kin was highly valued, and this created stronger family bonds. Participants also expressed a new meaning to, and greater appreciation for, life.
Resource Allocation in Wireless-Powered Mobile Edge Computing Systems for Internet of Things Applications

Wireless devices in Internet of Things (IoT) applications, such as wireless sensors and Radio Frequency Identifications (RFIDs), are faced with challenges of heavy computation tasks and limited energy, which can be solved by the importation of mobile edge computing (MEC) and wireless power transfer (WPT) techniques. As MEC can effectively enhance computation capability, and the wireless power transfer can ensure a sustainable supply of energy, it has drawn significant research interest in IoT applications. In this paper, we will study the resource allocation problem in the wireless-powered MEC system for IoT applications with one access point (AP) and many other wireless devices, and propose a Stackelberg dynamic game model to obtain the optimal allocated resource for the nodes in the IoT environment. The AP is a wireless power source that can charge wireless devices based on wireless power transfer techniques. The AP is also integrated with a MEC server that can carry out computation tasks that offload from wireless devices. The wireless devices can use the harvested energy to execute and offload computation tasks to the AP. Based on the proposed game model, the AP and wireless devices can control their optimal transmit power for energy transfer, and computation tasks offloading to the AP, respectively. The numerical simulation results show the correctness and effectiveness of the proposed model.