Miracle-dz/UniFAR-FTUs huggingface dataset:
The 1.28 GHz MeerKAT DEEP2 Image

We present the confusion-limited 1.28 GHz MeerKAT DEEP2 image covering one primary-beam area with θ = 7. ″6 FWHM resolution and rms noise. Its J2000 center position α = 04h 13m 26. ˢ4, δ = −80° 00′ 00″ was selected to minimize artifacts caused by bright sources. We introduce the new 64-element MeerKAT array and describe commissioning observations to measure the primary-beam attenuation pattern, estimate telescope pointing errors, and pinpoint (u, v) coordinate errors caused by offsets in frequency or time. We constructed a 1.4 GHz differential source count by combining a power-law count fit to the DEEP2 confusion P(D) distribution from 0.25 to 10 μJy with counts of individual DEEP2 sources between 10 μJy and 2.5 mJy. Most sources fainter than S ∼ 100 μJy are distant star-forming galaxies (SFGs) obeying the far-IR/radio correlation, and sources stronger than 0.25 μJy account for ∼93% of the radio background produced by SFGs. For the first time, the DEEP2 source count has reached the depth needed to reveal the majority of the star formation history of the universe. A pure luminosity evolution of the 1.4 GHz local luminosity function consistent with the Madau & Dickinson model for the evolution of SFGs based on UV and infrared data underpredicts our 1.4 GHz source count in the range .
Assessing effect heterogeneity of a randomized treatment using conditional inference trees

Treatment effect heterogeneity occurs when individual characteristics influence the effect of a treatment. We propose a novel approach that combines prognostic score matching and conditional inference trees to characterize effect heterogeneity of a randomized binary treatment. One key feature that distinguishes our method from alternative approaches is that it controls the Type I error rate, that is, the probability of identifying effect heterogeneity if none exists and retains the underlying subgroups. This feature makes our technique particularly appealing in the context of clinical trials, where there may be significant costs associated with erroneously declaring that effects differ across population subgroups. Treatment effect heterogeneity trees are able to identify heterogeneous subgroups, characterize the relevant subgroups and estimate the associated treatment effects. We demonstrate the efficacy of the proposed method using a comprehensive simulation study and illustrate our method using a nutrition trial dataset to evaluate effect heterogeneity within a patient population.
Mcity Data Collection for Automated Vehicles Study

The main goal of this paper is to introduce the data collection effort at Mcity targeting automated vehicle development. We captured a comprehensive set of data from a set of perception sensors (Lidars, Radars, Cameras) as well as vehicle steering/brake/throttle inputs and an RTK unit. Two in-cabin cameras record the human driver's behaviors for possible future use. The naturalistic driving on selected open roads is recorded at different time of day and weather conditions. We also perform designed choreography data collection inside the Mcity test facility focusing on vehicle to vehicle, and vehicle to vulnerable road user interactions which is quite unique among existing open-source datasets. The vehicle platform, data content, tags/labels, and selected analysis results are shown in this paper.
Fourth-Order Topological Insulator via Dimensional Reduction.

The properties of topological systems are inherently tied to their dimensionality. Higher-dimensional physical systems exhibit topological properties not shared by their lower dimensional counterparts and, in general, offer richer physics. One example is a d-dimensional quantized multipole topological insulator, which supports multipoles of order up to 2^d and a hierarchy of gapped boundary modes with topological 0-D corner modes at the top. While multipole topological insulators have been successfully realized in electromagnetic and mechanical 2D systems with quadrupole polarization, and a 3D octupole topological insulator was recently demonstrated in acoustics, going beyond the three physical dimensions of space is an intriguing and challenging task. In this work, we apply dimensional reduction to map a 4D higher-order topological insulator (HOTI) onto an equivalent aperiodic 1D array sharing the same spectrum, and emulate in this system the properties of a hexadecapole topological insulator. We observe the 1D counterpart of zero-energy states localized at 4D HOTI corners - the hallmark of multipole topological phase. Interestingly, the dimensional reduction guarantees that one of the 4D corner states remains localized to the edge of the 1D array, while all other localize in the bulk and retain their zero-energy eigenvalues. This discovery opens new directions in multi-dimensional topological physics arising in lower-dimensional aperiodic systems, and it unveils highly unusual resonances protected by topological properties inherited from higher dimensions.
Challenges in designing edge-based middlewares for the Internet of Things: A survey

The Internet of Things paradigm connects edge devices via the Internet enabling them to be seamlessly integrated with a wide variety of applications. In recent years, the number of connected devices has grown significantly, along with the volume and variety of data that is being generated by these devices at the edge of the network. An edge-based middleware is defined as a software that serves as an interface between the computational resources and the IoT devices, making communication possible among elements. Such middleware is required to provide the necessary functional components for sensor registration, discovery, workflow composition, and data pre-processing. In recent years, the landscape of the edge middleware platforms has grown exponentially, each of them with different platform requirements, architectures, and features. The core of this survey is a comprehensive review of existing edge middleware solutions. In this regard, we propose a four-layer architecture for the design of edge-based middleware, along with some design goals for each of the proposed layer. The paper concludes with some open challenges and possible future research directions.
Physical Layer Security of Autonomous Driving: Secure Vehicle-to-Vehicle Communication in A Security Cluster

We suggest secure Vehicle-to-Vehicle communications in a secure cluster. Here, the security cluster refers to a group of vehicles having a certain level or more of secrecy capacity. Usually, there are many difficulties in defining secrecy capacity, but we define vehicular secrecy capacity for the vehicle defined only by SNR values. Defined vehicular secrecy capacity is practical and efficient in achieving physical layer security in V2V. Typically, secrecy capacity may be changed by antenna related parameters, path related parameters, and noise related parameters. In addition to these conventional parameters, we address unique vehicle-related parameters, such as vehicle speed, safety distance, speed limit, response time, etc. in connection with autonomous driving. We confirm the relationship between vehicle-related secrecy parameters and secrecy capacity through modeling in highway and urban traffic situations. These vehicular secrecy parameters enable real-time control of vehicle secrecy capacity of V2V communications. We can use vehicular secrecy capacity to achieve secure vehicle communications from attackers such as quantum computers. Our research enables economic, effective and efficient physical layer security in autonomous driving.
Synthetic observations of internal gravity waves in the solar atmosphere

Aims. We study the properties of internal gravity waves (IGWs) detected in synthetic observations that are obtained from realistic numerical simulation of the solar atmosphere. Methods. We used four different simulations of the solar magneto-convection performed using the CO5BOLD code. A magnetic-field-free model and three magnetic models were simulated. The latter three models start with an initial vertical, homogeneous field of 10, 50, and 100 G magnetic flux density, representing different regions of the quiet solar surface. We used the NICOLE code to compute synthetic spectral maps from all the simulated models for the two magnetically insensitive neutral iron lines Fe I λλ 5434 Å and 5576 Å. We carried out Fourier analyses of the intensity and Doppler velocities to derive the power, phase, and coherence in the kh − ω diagnostic diagram to study the properties of internal gravity waves. Results. We find the signatures of the internal gravity waves in the synthetic spectra to be consistent with observations of the real Sun. The effect of magnetic field on the wave spectra is not as clearly discernible in synthetic observations as in the case of numerical simulations. The phase differences obtained using the spectral lines are significantly different from the phase differences in the simulation. The phase coherency between two atmospheric layers in the gravity wave regime is height dependent and is seen to decrease with the travel distance between the observed layers. In the studied models, the lower atmosphere shows a phase coherency above the significance level for a height separation of ∼400 km, while in the chromospheric layers it reduces to ∼100–200 km depending on the average magnetic flux density. Conclusion. We conclude that the energy flux of IGWs determined from the phase difference analysis may be overestimated by an order of magnitude. Spectral lines that are weak and less temperature sensitive may be better suited to detecting internal waves and accurately determining their energy flux in the solar atmosphere.
Flexible Communication Avoiding Matrix Multiplication on FPGA with High-Level Synthesis

Data movement is the dominating factor affecting performance and energy in modern computing systems. Consequently, many algorithms have been developed to minimize the number of I/O operations for common computing patterns. Matrix multiplication is no exception, and lower bounds have been proven and implemented both for shared and distributed memory systems. Reconfigurable hardware platforms are a lucrative target for I/O minimizing algorithms, as they offer full control of memory accesses to the programmer. While bounds developed in the context of fixed architectures still apply to these platforms, the spatially distributed nature of their computational and memory resources requires a decentralized approach to optimize algorithms for maximum hardware utilization. We present a model to optimize matrix multiplication for FPGA platforms, simultaneously targeting maximum performance and minimum off-chip data movement, within constraints set by the hardware. We map the model to a concrete architecture using a high-level synthesis tool, maintaining a high level of abstraction, allowing us to support arbitrary data types, and enables maintainability and portability across FPGA devices. Kernels generated from our architecture are shown to offer competitive performance in practice, scaling with both compute and memory resources. We offer our design as an open source project to encourage the open development of linear algebra and I/O minimizing algorithms on reconfigurable hardware platforms.
Context-aware Entity Linking with Attentive Neural Networks on Wikidata Knowledge Graph

The Entity Linking (EL) approaches have been a long-standing research field and find applicability in various use cases such as semantic search, text annotation, question answering, etc. Although effective and robust, current approaches are still limited to particular knowledge repositories (e.g. Wikipedia) or specific knowledge graphs (e.g. Freebase, DBpedia, and YAGO). The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entity titles, the knowledge graph is populated by non-standard, noisy, long or even sometimes awkward titles. The issue of long, implicit, and nonstandard entity representations is a challenge in EL approaches for gaining high precision and recall. In this paper, we advance the state-of-the-art approaches by developing a context-aware attentive neural network approach for entity linking on Wikidata. Our approach contributes by exploiting the sufficient context from a Knowledge Graph as a source of background knowledge, which is then fed into the neural network. This approach demonstrates merit to address challenges associated with entity titles (multi-word, long, implicit, case-sensitive). Our experimental study shows $\approx$8\% improvements over the baseline approach, and significantly outperform an end to end approach for Wikidata entity linking. This work, first of its kind, opens a new direction for the research community to pay attention to developing context-aware EL approaches for collaborative knowledge graphs.
Short-duration Speaker Verification (SdSV) Challenge 2020: the Challenge Evaluation Plan

This document describes the Short-duration Speaker Verification (SdSV) Challenge 2020. The main goal of the challenge is to evaluate new technologies for text-dependent (TD) and text-independent (TI) speaker verification (SV) in a short duration scenario. The proposed challenge evaluates SdSV with varying degree of phonetic overlap between the enrollment and test utterances (cross-lingual). It is the first challenge with a broad focus on systematic benchmark and analysis on varying degrees of phonetic variability on short-duration speaker recognition. We expect that modern methods (deep neural networks in particular) will play a key role.
A Method for Arbitrary Instance Style Transfer

The ability to synthesize style and content of different images to form a visually coherent image holds great promise in various applications such as stylistic painting, design prototyping, image editing, and augmented reality. However, the majority of works in image style transfer have focused on transferring the style of an image to the entirety of another image, and only a very small number of works have experimented on methods to transfer style to an instance of another image. Researchers have proposed methods to circumvent the difficulty of transferring style to an instance in an arbitrary shape. In this paper, we propose a topologically inspired algorithm called Forward Stretching to tackle this problem by transforming an instance into a tensor representation, which allows us to transfer style to this instance itself directly. Forward Stretching maps pixels to specific positions and interpolate values between pixels to transform an instance to a tensor. This algorithm allows us to introduce a method to transfer arbitrary style to an instance in an arbitrary shape. We showcase the results of our method in this paper.
Impossible measurements revisited

It is by now well-recognised that the na\"ive application of the projection postulate on composite quantum systems can induce signalling between their constituent components, indicative of a breakdown of causality in a relativistic spacetime context. Here we introduce a necessary and sufficient condition for a measurement of an observable on a composite system to be non-signalling. As well as being particularly simple, it generalises previous no-signalling conditions in that it allows for degeneracies and can be applied to all bounded self-adjoint operators. The condition is used to establish that arbitrary sums of local observables will not signal, in accordance with our expectations from relativistic quantum field theory. On the other hand, it is shown that the measurement of the tensor product of commuting local observables, for example bipartite operators of the form $A\otimes B$, will in fact signal, contrary to the widely-held belief that such measurements are always locally realisable. The implications for the notion of measurement in relativistic quantum field theory are addressed; it appears that the most straightforward application of the standard quantum formalism generically leads to violations of causality. We conclude that either the class of observables that can be measured should be severely restricted and/or that the na\"ive translation of the measurement framework of quantum theory, in particular the projection postulate, to quantum field theory must be re-evaluated.
Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds

Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design. Despite the recent progress in deep generative modelling, synthesis of finely detailed 3D surfaces, such as high-resolution point clouds, from scratch has not been achieved with existing approaches. In this work, we propose to employ the latent-space Laplacian pyramid representation within a hierarchical generative model for 3D point clouds. We combine the recently proposed latent-space GAN and Laplacian GAN architectures to form a multi-scale model capable of generating 3D point clouds at increasing levels of detail. Our evaluation demonstrates that our model outperforms the existing generative models for 3D point clouds.
Studying language in context using the temporal generalization method

The temporal generalization method (TGM) is a data analysis technique that can be used to test if the brain’s representation for particular stimuli (e.g. sounds, images) is maintained, or if it changes as a function of time (King J-R, Dehaene S. 2014 Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci. 18, 203–210. (doi:10.1016/j.tics.2014.01.002)). The TGM involves training models to predict the stimuli or condition using a time window from a recording of brain activity, and testing the resulting models at all possible time windows. This is repeated for all possible training windows to create a full matrix of accuracy for every combination of train/test window. The results of a TGM indicate when brain activity patterns are consistent (i.e. the trained model performs well even when tested on a different time window), and when they are inconsistent, allowing us to track neural representations over time. The TGM has been used to study the representation of images and sounds during a variety of tasks, but has been less readily applied to studies of language. Here, we give an overview of the method itself, discuss how the TGM has been used to analyse two studies of language in context and explore how the TGM could be applied to further our understanding of semantic composition. This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.
Sheaving—a universal construction for semantic compositionality

Semantic compositionality—the way that meanings of complex entities obtain from meanings of constituent entities and their structural relations—is supposed to explain certain concomitant cognitive capacities, such as systematicity. Yet, cognitive scientists are divided on mechanisms for compositionality: e.g. a language of thought on one side versus a geometry of thought on the other. Category theory is a field of (meta)mathematics invented to bridge formal divides. We focus on sheaving—a construction at the nexus of algebra and geometry/topology, alluding to an integrative view, to sketch out a category theory perspective on the semantics of compositionality. Sheaving is a universal construction for making inferences from local knowledge, where meaning is grounded by the underlying topological space. Three examples illustrate how topology conveys meaning, in terms of the inclusion relations between the open sets that constitute the space, though the topology is not regarded as the only source of semantic information. In this sense, category (sheaf) theory provides a general framework for semantic compositionality. This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.
The acquisition of compositional meaning

How do people create meaning from a string of sounds or pattern of dots? Insights into this process can be obtained from the way children acquire sentence meanings. According to the well-known principle of compositionality, the meaning of an expression is a function of the meanings of its parts and the way they are syntactically combined. However, children frequently seem to ignore syntactic structure in their sentence interpretations, suggesting that syntax is merely one of the sources of information constraining meaning and does not have a special status. A fundamental assumption in the argument in favour of compositionality is that speakers and listeners generally agree upon the meanings of sentences. Remarkably, however, children as listeners do not always understand what they are able to produce as speakers, and vice versa. For example, children's production of word order appears to develop ahead of their comprehension of word order in the acquisition of languages like English and Dutch. Such production–comprehension asymmetries are not uncommon in child language and motivate a view of compositionality as a principle pertaining to the result of perspective taking, and of meaning composition as a process of speaker–listener coordination. This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.
Increased Soluble Epoxide Hydrolase in Human Gestational Tissues from Pregnancies Complicated by Acute Chorioamnionitis

Chorioamnionitis (CAM) is primarily a polymicrobial bacterial infection involving chorionic and amniotic membranes that is associated with increased risk of preterm delivery. Epoxyeicosatrienoic acids (EETs) are eicosanoids generated from arachidonic acid by cytochrome P450 enzymes and further metabolized mainly by soluble epoxide hydrolase (sEH) to produce dihydroxyeicosatrienoic acids (DHETs). As a consequence of this metabolism of EETs, sEH reportedly exacerbates several disease states; however, its role in CAM remains unclear. The objectives of this study were to (1) determine the localization of sEH and compare the changes it undergoes in the gestational tissues (placentas and fetal membranes) of women with normal-term pregnancies and those with pregnancies complicated by acute CAM; (2) study the effects of lipopolysaccharide (LPS) on the expression of sEH in the human gestational tissues; and (3) investigate the effect of 12-(3-adamantan-1-yl-ureido)-dodecanoic acid (AUDA), a specific sEH inhibitor, on LPS-induced changes in 14,15-DHET and cytokines such as interleukin- (IL-) 1β and IL-6 in human gestational tissues in vitro and in pregnant mice. We found that women with pregnancies complicated by acute CAM had higher levels of sEH mRNA and protein in fetal membranes and villous tissues compared to those in women with normal-term pregnancies without CAM. Furthermore, fetal membrane and villous explants treated with LPS had higher tissue levels of sEH mRNA and protein and 14,15-DHET than those present in the vehicle controls, while the administration of AUDA in the media attenuated the LPS-induced production of 14,15-DHET in tissue homogenates and IL-1β and IL-6 in the media of explant cultures. Administration of AUDA also reduced the LPS-induced changes of 14,15-DHET, IL-1β, and IL-6 in the placentas of pregnant mice. Together, these results suggest that sEH participates in the inflammatory changes in human gestational tissues in pregnancies complicated by acute CAM.