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Virtual Reality (VR) applications have a number of unique characteristics that set them apart from traditional video streaming. These characteristics have major implications on the design of VR rendering, adaptation, prefetching, caching, and transport mechanisms. This paper contrasts VR to video streaming, stored 2D video streaming in particular, and discusses how to rethink system and network support for VR.

Climate change and other anthropogenic pressures are likely to induce tipping points in marine ecosystems, potentially leading to declines in primary productivity and fisheries. Despite increasing attention to nature-related financial risks and opportunities within the ocean economy, the extent to which these tipping points could affect investors has remained largely unexplored. Here we used satellite data to track fishing vessels operating in areas prone to marine regime shifts, as identified by their loss of resilience and vulnerability to marine heatwaves, and uncovered their corporate beneficial owners and shareholders. Despite some data gaps, we identified key countries, companies, and shareholders exposed to tipping risk. We also outline the potential challenges and opportunities that these actors may face if marine ecosystems shift to less productive states., Comment: 10 pages, 5 figures

We locally classify all possible cosmological homogeneous and isotropic Landsberg-type Finsler structures, in 4-dimensions. Among them, we identify viable non-stationary Finsler spacetimes, i.e. those geometries leading to a physical causal structure and a dynamical universe. Noting that any non-stationary Landsberg metric must be actually non-Berwaldian (i.e., it should be a so-called 'unicorn'), we construct the unique Finsler, non-Berwaldian Landsberg generalization of Friedmann-Lemaitre-Robertson-Walker geometry.

The method of variational bootstrapping, based on canonical variational completion, allows one to construct a Lagrangian for a physical theory depending on two sets of field variables, starting from a guess of the field equations for only one such set. This setup is particularly appealing for the construction of modified theories of gravity since one can take lessons from GR for the "educated guess" of the metric field equations; the field equations for the other fields are then fixed by the obtained Lagrangian (up to terms completely independent from the metric tensor). In the present paper, we explore the applications of variational bootstrapping in the context of metric-affine theories of gravity. We find those metric-affine models which are, in a variational sense, closest to the $\Lambda$CDM model of cosmology. The method then allows to find "corrected" metric equations and to "bootstrap" the connection field equations. The Lagrangians obtained via this method, though imposing some restricting criteria, encompass a wide variety of metric-affine models. In particular, they allow for quadratic metric-affine theories only if these avoid non-linear terms in the curvature tensor., Comment: 15 pages + 1 page references + 3 pages appendix, 0 figure

In this work, La0.8Sr0.2MnO3 (LSMO) nanopowders are synthesized using two different methods: Pechini (LSMO-PC) and auto-combustion (LSMO-AC). Nanoparticle sizes, structural, magnetic, and magnetocaloric properties were determined and compared. The X-ray diffraction confirms the coexistence of two phases; rhombohedral symmetry with space group R-3c and orthorhombic symmetry with space group Pbnm, with the rhombohedral phase dominating. The scanning electron microscope images show that LSMO-PC has larger nanoparticle sizes (~495 nm) than LSMO-AC (~195 nm). The samples exhibit ferromagnetic properties with distinct hysteresis loops and Curie temperatures 340 K and 290 K for LSMO-PC and LSMO-AC respectively. The variation of the magnetic entropy was measured indirectly using the Maxwell approach with increasing magnetic field. For LSMO-PC it reaches a maximum -dSM=1.69 J/kg.K at 340 K and dH= 5 T. The associated adiabatic temperature change dTM is 1.04 K. While LSMO-PC demonstrates superior magnetic and magnetocaloric properties, LSMO-AC displays significant magnetocaloric thermal stability. The obtained values make LSMO-PC and LSMO-AC promising candidates for eco-friendly room-temperature magnetocaloric applications.

For a torsion-free affine connection on a given manifold, which does not necessarily arise as the Levi-Civita connection of any pseudo-Riemannian metric, it is still possible that it corresponds in a canonical way to a Finsler structure; this property is known as Finsler (or Berwald-Finsler) metrizability. In the present paper, we clarify, for 4-dimensional SO(3)-invariant, Berwald-Finsler metrizable connections, the issue of the existence of an affinely equivalent pseudo-Riemannian structure. In particular, we find all classes of SO(3)-invariant connections which are not Levi-Civita connections for any pseudo-Riemannian metric - hence, are non-metric in a conventional way - but can still be metrized by SO(3)-invariant Finsler functions. The implications for physics, together with some examples are briefly discussed., Comment: 22 pages

The method of variational completion allows one to transform an (in principle, arbitrary) system of partial differential equations -- based on an intuitive ``educated guess'' -- into the Euler-Lagrange one attached to a Lagrangian, by adding a canonical correction term. Here, we extend this technique to theories that involve at least two sets of dynamical variables : we show that an educated guess of the field equations with respect to one of these sets of variables only is sufficient to variationally complete these equations and recover a Lagrangian for the full theory, up to terms that do not involve the respective variables. Applying this idea to natural metric-affine theories of gravity, we prove that, starting from an educated guess of the metric equations only, one can find the full metric equations, together with a generally covariant Lagrangian for the theory, up to metric-independent terms; the latter terms (which can only involve the distortion of the connection) are then completely classified over 4-dimensional spacetimes, by techniques pertaining to differential invariants., Comment: 16 pages + 1 page references & acknowledgments + 5 pages appendix, 0 figure

The paper introduces Hands-Free VR, a voice-based natural-language interface for VR. The user gives a command using their voice, the speech audio data is converted to text using a speech-to-text deep learning model that is fine-tuned for robustness to word phonetic similarity and to spoken English accents, and the text is mapped to an executable VR command using a large language model that is robust to natural language diversity. Hands-Free VR was evaluated in a controlled within-subjects study (N = 22) that asked participants to find specific objects and to place them in various configurations. In the control condition participants used a conventional VR user interface to grab, carry, and position the objects using the handheld controllers. In the experimental condition participants used Hands-Free VR. The results confirm that: (1) Hands-Free VR is robust to spoken English accents, as for 20 of our participants English was not their first language, and to word phonetic similarity, correctly transcribing the voice command 96.71% of the time; (2) Hands-Free VR is robust to natural language diversity, correctly mapping the transcribed command to an executable command in 97.83% of the time; (3) Hands-Free VR had a significant efficiency advantage over the conventional VR interface in terms of task completion time, total viewpoint translation, total view direction rotation, and total left and right hand translations; (4) Hands-Free VR received high user preference ratings in terms of ease of use, intuitiveness, ergonomics, reliability, and desirability.

Multiferroic CoFe2O4-Ba0.95Ca0.05Ti0.89Sn0.11O3 core-shell nanofibers (CFO@BCTSn NFs) were synthesized by a sol-gel co-axial electrospinning technique. The scanning electron microscope and transmission electron microscope were used to check nanofibers' core-shell structure/configuration. X-ray diffraction and a high-resolution transmission electron microscope were used to confirm the spinel structure of CFO and the perovskite structure of BCTSn. The magnetic character of the resultant CFO@BCTSn NFs was determined by SQUID magnetometry. The piezoelectricity was verified using piezo-response force microscopy, which revealed an entirely covered ferroelectric shell outline, in accordance with SEM and TEM observations. The magnetoelectric (ME) coefficient was measured as a function of the applied external DC magnetic field. The maximum ME coefficient obtained for the CFO@BCTSn NFs was 346 mV cm-1 Oe-1. The high magnetoelectric coupling suggests that CFO@BCTSn NFs could be a promising candidate for magnetic field sensor and magnetoelectric device applications.

We prove that all Ricci-flat, spatially spherically symmetric Berwald spacetime structures are either pseudo-Riemannian (Lorentzian), or flat. This insight enables us to formulate a Jebsen-Birkhoff type theorem for Berwald spacetimes.

The burgeoning fields of machine learning (ML) and quantum machine learning (QML) have shown remarkable potential in tackling complex problems across various domains. However, their susceptibility to adversarial attacks raises concerns when deploying these systems in security sensitive applications. In this study, we present a comparative analysis of the vulnerability of ML and QML models, specifically conventional neural networks (NN) and quantum neural networks (QNN), to adversarial attacks using a malware dataset. We utilize a software supply chain attack dataset known as ClaMP and develop two distinct models for QNN and NN, employing Pennylane for quantum implementations and TensorFlow and Keras for traditional implementations. Our methodology involves crafting adversarial samples by introducing random noise to a small portion of the dataset and evaluating the impact on the models performance using accuracy, precision, recall, and F1 score metrics. Based on our observations, both ML and QML models exhibit vulnerability to adversarial attacks. While the QNNs accuracy decreases more significantly compared to the NN after the attack, it demonstrates better performance in terms of precision and recall, indicating higher resilience in detecting true positives under adversarial conditions. We also find that adversarial samples crafted for one model type can impair the performance of the other, highlighting the need for robust defense mechanisms. Our study serves as a foundation for future research focused on enhancing the security and resilience of ML and QML models, particularly QNN, given its recent advancements. A more extensive range of experiments will be conducted to better understand the performance and robustness of both models in the face of adversarial attacks.

For the general class of pseudo-Finsler spaces with $(\alpha,\beta)$-metrics, we establish necessary and sufficient conditions such that these admit a Finsler spacetime structure. This means that the fundamental tensor has Lorentzian signature on a conic subbundle of the tangent bundle and thus the existence of a cone of future pointing timelike vectors is ensured. The identified $(\alpha,\beta)$-Finsler spacetimes are candidates for applications in gravitational physics. Moreover, we completely determine the relation between the isometries of an $(\alpha,\beta)$-metric and the isometries of the underlying pseudo-Riemannian metric $a$; in particular, we list all $(\alpha,\beta)$-metrics which admit isometries that are not isometries of $a$., Comment: 25 pages

We locally classify all SO(3)-invariant 4-dimensional pseudo-Finsler Berwald structures. These are Finslerian geometries which are closest to (spatially, or SO(3))-spherically symmetric pseudo-Riemannian ones - and serve as ansatz to find solutions of Finsler gravity equations which generalize the Einstein equations. We find that there exist six classes of non pseudo-Riemannian (i.e., non-quadratic in the velocities) SO(3)-spherically symmetric pseudo-Finsler Berwald functions, which have either: a power law, an exponential law, or a one- or two-variable dependence on the velocities., Comment: 24 pages

This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with cooling Google's data centers more efficiently, we recently conducted live experiments on two real-world facilities in partnership with Trane Technologies, a building management system provider. These live experiments had a variety of challenges in areas such as evaluation, learning from offline data, and constraint satisfaction. Our paper describes these challenges in the hope that awareness of them will benefit future applied RL work. We also describe the way we adapted our RL system to deal with these challenges, resulting in energy savings of approximately 9% and 13% respectively at the two live experiment sites., Comment: 27 pages, 11 figures

We study whether realistic 5G mm-wave cellular networks would cause harmful out-of-band interference to weather satellites sensing in the 23.8 GHz band. We estimate uplink and downlink interference from a single interferer and a network of interferers in New York City, using real 3D building data and realistic antenna patterns. We perform detailed ray-tracing propagation simulations, for locations of the MetOp-B weather satellite and its scanning orientations and ground interferer antenna orientations for representative urban cell sites. In addition to the ITU-R threshold of -136 dBm/200 MHz, we propose an alternative set of harmful interference thresholds directly related to the sensitivity of the satellite sensor. Our results show that the 3GPP power leakage limits are sufficient to ensure that interference from a single 5G device is not harmful if considering the ITU-R threshold, but not if the weather prediction software can tolerate only very low interference levels. Importantly, aggregate interference resulting in practice from a 5G network with realistic network densities is often harmful, even considering the least conservative ITU-R threshold. Overall, our comprehensive coexistence study thus strongly suggests that additional engineering and/or regulatory solutions will be necessary to protect weather satellite passive sensing from mm-wave cellular network interference.