Miracle-dz/UniFAR-FTUs huggingface dataset:
A deficit of ultraluminous X-ray sources in luminous infrared galaxies

We present results from a Chandra study of ultraluminous X-ray sources (ULXs) in a sample of 17 nearby (DL < 60 Mpc) luminous infrared galaxies (LIRGs), selected to have star formation rates (SFRs) in excess of 7 M⊙ yr −1 and low foreground Galactic column densities (NH � 5 × 10 20 cm −2 ). A total of 53 ULXs were detected and we confirm that this is a complete catalogue of ULXs for the LIRG sample. We examine the evolution of ULX spectra with luminosity in these galaxies by stacking the spectra of individual objects in three luminosity bins, finding a distinct change in spectral index at luminosity � 2 × 10 39 erg s −1 . This may be a change in spectrum as 10 M⊙ black holes transit from aEddington to a super-Eddington accretion regime, and is supported by a plausible detection of partially-ionised absorption imprinted on the spectrum of the luminous ULX (LX � 5 × 10 39 erg s −1 ) CXOU J024238.9-000055 in NGC 1068, consistent with the highly ionised massive wind that we would expect to see driven by a super-Eddington accretion flow. This sample shows a large deficit in the number of ULXs detected per unit SFR (0.2 versus 2 ULXs, per M⊙ yr −1 ) compared to the detection rate in nearby (DL < 14.5 Mpc) normal star forming galaxies. This deficit also manifests itself as a lower differential X-ray luminosity function normalisation for the LIRG sample than for samples of other star forming galaxies. We show that it is unlikely that this deficit is a purely observational effect. Part of this deficit might be attributable to the high metallicity of the LIRGs impeding the production efficiency of ULXs and/or a lag between the star formation starting and the production of ULXs; however, we argue that the evidence - including very low NULX/LFIR, and an even lower ULX incidence in the central regions of the LIRGs - shows that the main culprit for this deficit is likely to be the high column of gas and dust in these galaxies, that fuels the high SFR but also acts to obscure many ULXs from our view.
SHGCN: a hypergraph-based deep learning model for spatiotemporal traffic flow prediction

Traffic flow prediction, as one of the prominent tasks in intelligent transportation systems, is challenging due to underlying complex spatiotemporal characteristics. Consideration of historical spatial and temporal dependencies is essential for the traffic prediction of a geographic unit for a future time period. Existing works mainly adopted graphs to represent the irregular layout of spatial units, where nodes are signal of spatial units and edges are link strengths between units. For contemporary deep learning based spatiotemporal prediction tasks, the temporal dependence can be well modeled via convolution neural network or recurrent neural network, and spatial dependence features are commonly captured using graph convolution networks. However, classic graph structures cannot fully represent the complex nature of spatial relationships in transportation networks, because the spatial pattern of a location might be influenced by multiple sets of contextual information simultaneously, while a graph edge can only describe the linkage between two nodes. In addition, most existing models ignore the synchronous dependence between temporal and spatial features, leading to a mismatch between the temporal and spatial features of a location. Based on such problems, a hypergraph-based deep learning model, namely synchronous hypergraph convolutional network (SHGCN), is proposed to better capture the complex relationships between spatial and temporal knowledge. A novel synchronous hypergraph cell (SH-Cell) is designed based on LSTM cells integrated in the form of a Seq2seq architecture. Then, we construct dynamic hypergraphs to capture the synchronous spatiotemporal dependence adaptively using SH-Cells. Experimental results demonstrate the superiority of SHGCN over well-known benchmarks on two real-world publicly-available traffic datasets. This research provides new insights for improving the traffic flow prediction accuracy and understanding complex spatiotemporal relationships towards a more reliable urban traffic management.
Bringing wifi localization to any wifi devices

Recent years have seen significant advances in WiFi Localization. However, existing systems require either multiple access points to cooperate with each other or a single access point to have multiple antennas and transceiver chains. Therefore, they cannot be integrated into most IoT WiFi chipsets which have only a single transceiver chain. This paper presents WiSight, a novel approach to bringing WiFi localization to any WiFi devices, especially those with a single RF chain. We propose a WiFi antenna design and use the inherent properties of the 802.11 protocol to measure Angle-of-Arrival (AoA) and Time-of-Flight (ToF) using a single transceiver chain. Our proof-of-concept simulation and real world experiments promise the feasibility of this approach.
Sidecar: in-network performance enhancements in the age of paranoid transport protocols

In response to ossification and privacy concerns, post-TCP transport protocols such as QUIC are designed to be "paranoid"---opaque to meddling middleboxes by encrypting and authenticating the header and payload---making it impossible for Performance-Enhancing Proxies (PEPs) to provide the same assistance as before. We propose a research agenda towards an alternate approach to PEPs, creating a sidecar protocol that is loosely-coupled to the unchanged and opaque, underlying transport protocol. The key technical challenge to sidecar protocols is how to usefully refer to the packets of the underlying connection without ossification. We have made progress on this problem by creating a tool we call a quACK (quick ACK), a concise representation of a multiset of numbers that can be used to efficiently decode the randomly-encrypted packet contents a sidecar has received. We implement the quACK and discuss how to achieve several applications with this approach: alternate congestion control, ACK reduction, and PEP-to-PEP retransmission across a lossy subpath.
SOFIA/FORCAST IMAGING OF THE CIRCUMNUCLEAR RING AT THE GALACTIC CENTER

We present 19.7, 31.5, and 37.1 μm images of the inner 6 pc of the Galactic center of the Milky Way with a spatial resolution of 3. ″2–4. ″6 taken by the Faint Object Infrared Camera on the Stratospheric Observatory for Infrared Astronomy. The images reveal in detail the “clumpy” structure of the circumnuclear ring (CNR)—the inner edge of the molecular torus orbiting the supermassive black hole at the Galactic center—and the prominent streamers of hot, ionized gas and dust within the CNR that compose the H ii region Sgr A West. The CNR exhibits features of a classic H ii region: the dust emission at 19.7 μm closely traces the ionized gas emission observed in the radio while the 31.5 and 37.1 μm emission traces the photo-dissociation region beyond the ionized gas. The 19.7/37.1 color temperature map reveals a radial temperature gradient across the CNR with temperatures ranging from 65 to 85 K, consistent with the prevailing paradigm in which the dust is centrally heated by the inner cluster of hot, young stars. We model the 37.1 μm intensity of the CNR as an inclined (θi = 67°) ring with a thickness and radius of 0.34 pc and 1.4 pc, respectively, and find that it is consistent with the observed 37.1 μm map of the CNR. The 37.1 μm optical depth map also reveals the clumpy dust distribution of the CNR and implies a total gas mass of ∼610 M☉. Dense (5–9 × 104 cm−3) clumps with an FWHM of ∼0.15 pc exist along the inner edge of the CNR and shadow the material deeper into the ring. We find that the clumps are unlikely to be long-lived structures since they are not dense enough to be stable against tidal shear from the supermassive black hole and will be sheared out on a timescale of an orbital period (∼105 yr).
BinoML: supervised ranking for automatic building labeling

Building numbers shown on building outlines of a map are important information for guiding delivery associates to the correct building of a package's recipient. Intuitively, the more labeled buildings are present in our map, the less likely to misplace an order in addition to other benefits such as delivery efficiency as drivers get better visual cues about building positions. Although there are free and collaborative projects for creating geographic database of the world, such as the OpenStreetMap (OSM) [2] which also supplies building outlines along with their building numbers, many building outlines still remain unlabeled in many U.S. regions and other countries. Hence, we are interested in developing models that can automatically add building numbers with ≥ 99% precision to unlabeled buildings across geographies with low to medium building number coverage. In this paper, we describe a ML model which in offline results showed 2% to 12% increase in building number coverage in some US regions compared to that of the OSM. The proposed model can also be applied to improve the building number coverage of other countries after fine-tuning to those new regions.
Regularizing a linearized EIT reconstruction method using a sensitivity-based factorization method

For electrical impedance tomography (EIT), most practical reconstruction methods are based on linearizing the underlying non-linear inverse problem. Recently, it has been shown that the linearized problem still contains the exact shape information. However, the stable reconstruction of shape information from measurements of finite accuracy on a limited number of electrodes remains a challenge. In this work, we propose to regularize the standard linearized reconstruction method (LM) for EIT using a non-iterative shape reconstruction method (the factorization method). Our main tool is a discrete-sensitivity-based variant of the factorization method (herein called S-FM) which allows us to formulate and combine both methods in terms of the sensitivity matrix. We give a heuristic motivation for this new method and show numerical examples that indicate its good performance in the localization of anomalies and the alleviation of ringing artifacts.
highway2vec: representing OpenStreetMap microregions with respect to their road network characteristics

Recent years brought advancements in using neural networks for representation learning of various language or visual phenomena. New methods freed data scientists from hand-crafting features for common tasks. Similarly, problems that require considering the spatial variable can benefit from pretrained map region representations instead of manually creating feature tables that one needs to prepare to solve a task. However, very few methods for map area representation exist, especially with respect to road network characteristics. In this paper, we propose a method for generating microregions' embeddings with respect to their road infrastructure characteristics. We base our representations on OpenStreetMap road networks in a selection of cities and use the H3 spatial index to allow reproducible and scalable representation learning. We obtained vector representations that detect how similar map hexagons are in the road networks they contain. Additionally, we observe that embeddings yield a latent space with meaningful arithmetic operations. Finally, clustering methods allowed us to draft a high-level typology of obtained representations. We are confident that this contribution will aid data scientists working on infrastructure-related prediction tasks with spatial variables.
The internet of things in a laptop: rapid prototyping for IoT applications with digibox

Digibox is a prototyping environment for IoT applications. It enables a novel scene-centric prototyping where developers can program an ensemble of simulated devices to capture not only their individual but also their coordinated behaviors, making it possible to test, debug, and evaluate the behaviors of an IoT application. Using Digibox, developers can download and reuse existing scenes, customize, and repurpose them towards developing new applications; or replicate others' experiment results from scientific research. Digibox's Kubernetes-based runtime further allows developers to easily scale the prototyping environment from a single laptop to a cluster running simulated devices and scenes at a scale appropriate to the application.
TiltWalker: Operating a Telepresence Robot with One-Hand by Tilt Controls on a Smartphone

Mobile clients for telepresence robots are cluttered with interactive elements that either leave a little room for the camera feeds or occlude them. Many do not provide meaningful feedback on the robot's state and most require the use of both hands. These make maneuvering telepresence robots difficult with mobile devices. TiltWalker enables controlling a telepresence robot with one hand using tilt gestures with a smartphone. In a series of studies, we first justify the use of a Web platform, determine how far and fast users can tilt without compromising the comfort and the legibility of the display content, and identify a velocity-based function well-suited for control-display mapping. We refine TiltWalker based on the findings of these studies, then compare it with a default method in the final study. Results revealed that TiltWalker is significantly faster and more accurate than the default method. Besides, participants preferred TiltWalker's interaction methods and graphical feedback significantly more than those of the default method.
Making links on your web pages last longer than you

It is common for the authors of a web page to include links to related pages on other sites. However, when users visit a page several years after it was last updated, they often find that some of the external links either do not work or point to unrelated content. To combat these problems of link rot and content drift, the solution used today is to capture a copy of the linked page when a link is created and serve this copy to users who choose to visit the link. We argue that this status quo ignores the reality that one does not always link to a page in order to point visitors to the content that existed on that page when the link was created. The utility of linking to a web page by simply directing users to that page's URL is that they can benefit from any updates to the page's content (e.g., corrections to news articles and new comments on a blog post) or access rich app-like functionality on the page (e.g., search). In this paper, we present a sketch of what it would take to make web links resilient while accounting for the dynamism of web pages.
SPEMI: normalizing spatial imbalance with spatial eminence transformer for citywide region embedding

Region embedding is a primary task for a wide variety of urban-related downstream applications. However, many existing embedding techniques neglected the fact that the regions in a city have been developed differently by many factors such as planning policies, economic, and population mitigation. Such a spatial imbalance problem may result in a quite different region embedding to distinguish differences between regions, even though the regions could be similar in terms of the certain application tasks. In this paper, we propose a SPatial EMInence (SPEMI) model that normalizes region embeddings to mitigate the effects from spatial imbalance. In particular, we present a context-aware spatial feature, called spatial eminence, that measures a region's importance to its spatial context. The experimental results of store placement recommendation using real-world urban data show that SPEMI improves the performance of citywide region embeddings by up to 27.92%.
Investigating Pointing Performance for Tangible Surfaces with Physical 3D Targets

One of the most fundamental interactions –pointing– is well understood on flat surfaces. However, pointing performance on tangible surfaces with physical targets is still limited for Tangible User Interfaces (TUIs). We investigate the effect of a target’s physical width, height, and distance on user pointing performance. We conducted a study using a reciprocal tapping task (n=19) with physical rods arranged in a circle. We compared our data with five conventional interaction models designed for 2D/3D tasks rather than tangible targets. We show that variance in the movement times was only satisfactorily explained by a model established for volumetric displays (r2=0.954). Analysis shows that movement direction and height should be included as parameters to this model to generalize for 3D tangible targets. Qualitative feedback from participants suggests that pointing at physical targets involves additional human factors (e.g., perception of sharpness or robustness) that need to be investigated further to understand how performance with tangible objects is affected.
The case for an internet primitive for fault localization

Modern distributed applications run across numerous microservices and components deployed in cloud datacenters, using shared cloud services for computing and storage, edge services such as content distribution networks, network functions such as rate limiters and firewalls, security infrastructures, network routers, and physical links. When a user-visible fault occurs, the first step toward diagnosis is localization to determine where the fault has occurred. However, because application delivery spans different layers and different organizations, no entity has complete visibility or access to the information required to localize faults quickly. This paper proposes a cross-layer, cross-domain, and cross-application fault localization primitive with a simple and standardized information interface for the Internet.
Implementing topological quantum manipulation with superconducting circuits

A two-component fermion model with conventional two-body interactions was recently shown to have anyonic excitations. We here propose a scheme to physically implement this model by transforming each chain of two two-component fermions to the two capacitively coupled chains of superconducting devices. In particular, we elaborate how to achieve the wanted operations to create and manipulate the topological quantum states, providing an experimentally feasible scenario to access the topological memory and to build the anyonic interferometry.
Testing the blazar sequence with the least luminous BL Lacertae objects

In a previous paper, we proposed a new method to select low-power BL Lacs (LPBLs) based on mid-infrared emission and flux contrast through the Ca II spectral break; that study led to the selection of a complete sample formed by 34 LPBLs with 0.05<z<=0.15 and radio luminosities spanning the range log(L_r) = 39.2-41.5 [erg/s]. We now assemble the broadband spectral energy distributions (SEDs) of these sources to investigate their nature and compare them with brighter BL Lacs. We find that the ratios between the X-ray and radio luminosities range from ~20 to ~30000 and that the synchrotron peak frequencies span a wide energy interval, from log(nu_peak)~13.5 to ~20 [Hz]. This indicates a broad variety of SED shapes and a mixture of BL Lac flavors. Indeed, although the majority of our LPBLs are high-energy peaked BL Lacs (HBLs), we find that a quarter of them are low-energy peaked BL Lacs (LBLs), despite the fact that the sample is biased against the selection of LBLs. The analysis of the median LPBL SED confirms disagreement with the blazar sequence at low radio luminosities. Furthermore, if we limit the sample to the LBLs subsample, we find that their median SED shape is essentially indistinguishable from that of the most luminous BL Lacs. We conclude that the observed radio power is not the main driving parameter of the multiwavelength properties of BL Lacs.
Phylogenetic Analysis of Small Ruminant Lentiviruses Originating from Naturally Infected Sheep and Goats from Poland Based on the Long Terminal Repeat Sequences

Abstract Introduction Previous gag and env sequence studies placed Polish small ruminant lentiviruses (SRLVs) isolated from sheep and goats in subtypes B1, B2, A1, A5, A12, A13, A16–A18, A23, A24 and A27. This study extended the genetic/phylogenetic analysis of previously identified Polish SRLV strains by contributing long terminal repeat (LTR) sequences. Material and Methods A total of 112 samples were analysed. Phylogenetic analyses were carried out on the LTR fragment using the neighbour-joining, maximum likelihood, and unweighted pair group method with arithmetic mean methods. Results Polish caprine and ovine LTR sequences clustered within group A and grouped in at least 10 clusters (subtypes A1, A5, A12, A13, A16–A18, A23, A24 and A27). Most of the Polish strains (78%) belonged to the same subtype by the indication of the gag, env and LTR genomic regions. Discrepancies in affiliation depending on the particular sequence were observed in 24 (21%) strains, most of which came from mixed-species flocks where more than one SRLV genotype circulated. Sequences of the LTR reflected subtype-specific patterns. Several subtype-specific markers were identified, e.g. a unique substitution of T to A in the fifth position of the TATA box in A17, A27, A20 and B3. Conclusion This study provides valuable insights into the genetic diversity of SRLV field strains in Poland, their phylogenetic relationships and their position in the recently established SRLV classification. Our results confirmed the existence of the ten subtypes listed and the readier emergence of new SRLV variants in mixed-species flocks.
Multicast routing and virtual network function placement in NFV-SDN networks: a genetic algorithms approach

Communication trends demand services that require flexible multicast transmission with requirements for data processing functions at the network nodes. This challenge is achieved with software-defined networking (SDN) and network functions virtualization (NFV) technologies. In the multicast routing problem in NFV-SDN networks, the goal is to compute a multicast tree and the location of virtual network functions (VNFs) at nodes, satisfying the traffic demand and processing functions with the least possible resource. The traffic flow must pass through a VNF node before reaching a destination node. Since the problem has high computational complexity and current proposals consider only one type of virtual function in all demands, the development of scalable solutions for multiple sessions with different virtual functions is necessary. This paper addresses the multicast session routing and VNF placement (MSVNFP) problem in NFV-SDN networks as a joint optimization problem. In this context, we propose an approach based on Genetic Algorithms (GA) called MSVNFP-GA. Given a set of multicast demands, the proposed algorithm calculates for each demand a tree and the location of VNFs seeking to minimize the total cost of links and node activation. Numerical simulations on different network topologies and traffic loads show that MSVNFP-GA is promising compared to the state-of-the-art competitive algorithms.