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We present a study of the galaxy merger and interaction activity within the Hyperion Proto-supercluster atz ∼ 2.5 in an effort to assess the occurrence of galaxy mergers and interactions in contrast to the coeval field and their impact on the buildup of stellar mass in high-density environments at higher redshifts. For this work, we utilized data from the Charting Cluster Construction with VUDS and ORELSE Survey (C3VO) along with extensive spectroscopic and photometric datasets available for the COSMOS field – including theHST-Hyperion Survey. To evaluate potential merger and interaction activity, we measured the fraction of galaxies with close kinematic companions (f_ckc) both within Hyperion and the coeval field by means of a Monte Carlo (MC) methodology developed in this work that probabilistically employs our entire combined spectroscopic and photometric dataset. We validated ourf_ckcMC methodology on a simulated lightcone built from the GAlaxy Evolution and Assembly (GAEA) semi-analytic model, and we determined correction factors that account for the underlying spectroscopic sampling rate of our dataset. We find that galaxies in Hyperion have close kinematic companions ≳2.5× more than galaxies in the field and measure a correctedf_ckc= 59⁺⁹₋₁₀% for Hyperion and a correctedf_ckc= 23^+1.7_−1.8% for the surrounding field; a ≳3σdifference. The enhancement inf_ckclikely correlates to an enhancement in the merger and interaction activity within the high-density environment of Hyperion and matches the trend seen in other structures. The rate of merger and interactions within the field implied from our fieldf_ckcmeasurement is well aligned with values measured from other observations in similar redshift ranges. The enhancedf_ckcmeasured within Hyperion suggests that merger and interaction activity play an important role in the mass growth of galaxies in denser environments at higher redshifts. 

Small, low-cost IoT devices are typically equipped with only a single, low-quality antenna, significantly limiting communication range and link quality. In particular, these antennas are typically linearly polarized and therefore susceptible to polarization mismatch, which can easily cause 10-15 dBm of link loss on communication to and from such devices. In this work, we highlight this under-appreciated issue and propose the augmentation of IoT deployment environments with programmable, RF-sensitive surfaces made of metamaterials. Our smart meta-surface mitigates polarization mismatch by rotating the polarization of signals that pass through or reflect off the surface. We integrate our metasurface into an IoT network as LLAMA, a Low-power Lattice of Actuated Metasurface Antennas, designed for the pervasively used 2.4 GHz ISM band. We optimize LLAMA’s metasurface design for both low transmission loss and low cost, to facilitate deployment at scale. We then build an end-to-end system that actuates the metasurface structure to optimize for link performance in real time. Our experimental prototype-based evaluation demonstrates gains in link power of up to 15 dBm, and wireless capacity improvements of 100 and 180 Kbit/s/Hz in through-surface and surface-reflective scenarios, respectively, attributable to the polarization rotation properties of LLAMA’s metasurface. 

Studies have shown that combinatorial testing (CT) can be effective for detecting faults in software systems. By focusing on the interactions between different factors of a system, CT shows its potential for detecting faults, especially those that can be revealed only by the specific combinations of values of multiple factors (multi-factor faults). However, is CT practical enough to be applied in the industry? Can it be more effective than other industry-favored techniques? Are there any challenges when applying CT in practice? These research questions remain in the context of industrial settings. In this paper, we present an empirical study of CT on five industrial systems with real faults. The details of the input space model (ISM) construction, such as factor identification and value assignment, are included. We compared the faults detected by CT with those detected by the inhouse testing teams using other methods, and the results suggest that despite some challenges, CT is an effective technique to detect real faults, especially multi-factor faults, of software systems in industrial settings. Observations and lessons learned are provided to further improve the fault detection effectiveness and overcome various challenges. 

Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query. However, prior work models queries as single points in the vector space, which is problematic because a complex query represents a potentially large set of its answer entities, but it is unclear how such a set can be represented as a single point. Furthermore, prior work can only handle queries that use conjunctions (^) and existential quantifiers (9). Handling queries with logical disjunctions (_) remains an open problem. Here we propose QUERY2BOX, an embedding-based framework for reasoning over arbitrary queries with ^, _, and 9 operators in massive and incomplete KGs. Our main insight is that queries can be embedded as boxes (i.e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query. We show that conjunctions can be naturally represented as intersections of boxes and also prove a negative result that handling disjunctions would require embedding with dimension proportional to the number of KG entities. However, we show that by transforming queries into a Disjunctive Normal Form, QUERY2BOX is capable of handling arbitrary logical queries with ^, _, 9 in a scalable manner. We demonstrate the effectiveness of QUERY2BOX on three large KGs and show that QUERY2BOX achieves up to 25% relative improvement over the state of the art. 

Abstract Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition to structural and integral magnetic characterisations, imaging of magnetisation patterns, current distributions and magnetic fields at nano- and microscale is of major importance to understand the material responses and qualify them for specific applications. In this roadmap, we aim to cover a broad portfolio of techniques to perform nano- and microscale magnetic imaging using superconducting quantum interference devices, spin centre and Hall effect magnetometries, scanning probe microscopies, x-ray- and electron-based methods as well as magnetooptics and nanoscale magnetic resonance imaging. The roadmap is aimed as a single access point of information for experts in the field as well as the young generation of students outlining prospects of the development of magnetic imaging technologies for the upcoming decade with a focus on physics, materials science, and chemistry of planar, three-dimensional and geometrically curved objects of different material classes including two-dimensional materials, complex oxides, semi-metals, multiferroics, skyrmions, antiferromagnets, frustrated magnets, magnetic molecules/nanoparticles, ionic conductors, superconductors, spintronic and spinorbitronic materials.