Arbitrary Space Research Articles

Harnessing heterogeneity in space with statistically guided meta-learning.

Spatial data are ubiquitous, massively collected, and widely used to support critical decision-making in many societal domains, including public health (e.g., COVID-19 pandemic control), agricultural crop monitoring, transportation, etc. While recent advances in machine learning and deep learning offer new promising ways to mine such rich datasets (e.g., satellite imagery, COVID statistics), spatial heterogeneity-an intrinsic characteristic embedded in spatial data-poses a major challenge as data distributions or generative processes often vary across space at different scales, with their spatial extents unknown. Recent studies (e.g., SVANN, spatial ensemble) targeting this difficult problem either require a known space-partitioning as the input, or can only support very limited number of partitions or classes (e.g., two) due to the decrease in training data size and the complexity of analysis. To address these limitations, we propose a model-agnostic framework to automatically transform a deep learning model into a spatial-heterogeneity-aware architecture, where the learning of arbitrary space partitionings is guided by a learning-engaged generalization of multivariate scan statistic and parameters are shared based on spatial relationships. Moreover, we propose a spatial moderator to generalize learned space partitionings to new test regions. Finally, we extend the framework by integrating meta-learning-based training strategies into both spatial transformation and moderation to enhance knowledge sharing and adaptation among different processes. Experiment results on real-world datasets show that the framework can effectively capture flexibly shaped heterogeneous footprints and substantially improve prediction performances.

Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts

How do we make sense of the input from our sensory organs, and put the perceived information into context of our past experiences? The hippocampal-entorhinal complex plays a major role in the organization of memory and thought. The formation of and navigation in cognitive maps of arbitrary mental spaces via place and grid cells can serve as a representation of memories and experiences and their relations to each other. The multi-scale successor representation is proposed to be the mathematical principle underlying place and grid cell computations. Here, we present a neural network, which learns a cognitive map of a semantic space based on 32 different animal species encoded as feature vectors. The neural network successfully learns the similarities between different animal species, and constructs a cognitive map of ‘animal space’ based on the principle of successor representations with an accuracy of around 30% which is near to the theoretical maximum regarding the fact that all animal species have more than one possible successor, i.e. nearest neighbor in feature space. Furthermore, a hierarchical structure, i.e. different scales of cognitive maps, can be modeled based on multi-scale successor representations. We find that, in fine-grained cognitive maps, the animal vectors are evenly distributed in feature space. In contrast, in coarse-grained maps, animal vectors are highly clustered according to their biological class, i.e. amphibians, mammals and insects. This could be a putative mechanism enabling the emergence of new, abstract semantic concepts. Finally, even completely new or incomplete input can be represented by interpolation of the representations from the cognitive map with remarkable high accuracy of up to 95%. We conclude that the successor representation can serve as a weighted pointer to past memories and experiences, and may therefore be a crucial building block to include prior knowledge, and to derive context knowledge from novel input. Thus, our model provides a new tool to complement contemporary deep learning approaches on the road towards artificial general intelligence.

Analytic solution on force analysis of tripod in arbitrary space

Tripod is a common support structure. The key to tripod design is to accurately analyze the force, select appropriate materials, and design the angle according to the size of the axial force. To study the stress condition and application performance of a tripod, this paper takes any tripod in space as the research object, through the combination of mathematics and mechanics, establishes a space rectangular coordinate system, solves the internal force of the tripod member, and verifies the correctness of the solution results. The evolution from arbitrary tripod structure to special tripod structure demonstrates the correctness of the reasoning method from general to special. An arbitrary space tripod is a common engineering application structure. Taking it as the research object, mechanical analysis has certain practical significance.

CORDIC-Based General Multiple Fading Generator for Wireless Channel Digital Twin

A wireless channel digital twin is a useful tool to evaluate the performance of a communication system at the physical or link level by generating the physical channel controllably. In this paper, a stochastic general fading channel model is proposed, which considered most of the channel fading types for various communication scenarios. By using the sum-of-frequency-modulation (SoFM) method, the phase discontinuity of the generated channel fading was well addressed. On this basis, a general and flexible generation architecture for channel fading was developed on a field programmable gate array (FPGA) platform. In this architecture, improved CORDIC-based hardware circuits for the trigonometric function, exponential function, and natural logarithm were designed and implemented, which improved the real-time performance of the system and the utilization rate of the hardware resources compared with the traditional LUT and CORDIC method. For a 16-bit fixed-point data bit width single-channel emulation, the hardware resource consumption was significantly reduced from 36.56% to 15.62% for the overall system by utilizing the compact time-division (TD) structure. Moreover, the classical CORDIC method brought an extra latency of 16 system clock cycles, while the latency caused by the improved CORDIC method was decreased by 62.5%. Finally, a generation scheme of a correlated Gaussian sequence was developed to introduce a controllable arbitrary space-time correlation for the channel generator with multiple channels. The output results of the developed generator were consistent with the theoretical results, which verified the correctness of both the generation method and hardware implementation. The proposed channel fading generator can be applied for the emulation of large-scale multiple-input, multiple-output (MIMO) channels under various dynamic communication scenarios.

Hessian matrix non-decomposition theorem and its application to nonlinear filtering

In 1983, Roger Brockett gave his invited lecture at the International Congress of Mathematics about the classification of finite dimensional estimation algebra (FDEA), which plays a vitally important role in the classification of finite dimensional filters (FDFs). The Hessian matrix non-decomposition (HMND) theorem was one of the key steps for the complete classification of FDEAs with maximal rank of the arbitrary state space dimension (Yau, 2003; Yau and Hu, 2005). In (Yau et al., 1999), the HMND theorem for homogeneous polynomials of degree 4 was proven and at the same time, it is firstly proposed that there exists a HMND conjecture for the general polynomials of degree 2d with d≥2. In this paper, we consider the Hessian matrix of a nontrivial real homogeneous polynomial at degree 6. For the state space dimension n≤4, we prove such matrix cannot be decomposed in the form Q(x)Q(x)T, where Q(x) is a skew-symmetric matrix whose all elements are homogeneous polynomials of degree 2, and Q(x)T is the transpose of Q(x). The fundamental strategy we use in this paper is that we generalized the techniques proposed by Yau et al. (1999) for the case of degree 4 and derived identities of coefficient matrices for the case of degree 6. For the arbitrary state space dimension n, the HMND theorem is demonstrated under several conditions by certain basic linear algebra techniques. These results can be closely related to the classification of FDEAs with non-maximal rank in the filtering theory.

Quantum speed limits in arbitrary phase spaces

Quantum speed limits (QSLs) provide an upper bound for the speed of evolution of quantum states in any physical process. Based on the Stratonovich-Weyl correspondence, we derive a universal QSL bound in arbitrary phase spaces that is applicable for both continuous variable systems and finite-dimensional discrete quantum systems. This QSL bound allows the determination of speed limit bounds in specific phase spaces that are tighter than those in Wigner phase space or Hilbert space under the same metric, as illustrated by several typical examples, e.g., a single-mode free field and $N$-level quantum systems in phase spaces. This QSL bound also provides an experimentally realizable way to examine the speed limit in phase spaces relevant to applications in quantum information and quantum optics.

Global space-time Trefftz DG schemes for the time-dependent linear wave equation

In this paper we are concerned with Trefftz discretizations of the time-dependent linear wave equation in anisotropic media in arbitrary space dimensional domains $\Omega \subset \mathbb{R}^d~ (d\in \mathbb{N})$. We propose two variants of the Trefftz DG method, define novel plane wave basis functions based on rigorous choices of scaling transformations and coordinate transformations, and prove that the corresponding approximate solutions possess optimal-order error estimates with respect to the meshwidth $h$ and the condition number of the coefficient matrices, respectively. Besides, we propose the global Trefftz DG method combined with local DG methods to solve the time-dependent linear nonhomogeneous wave equation in anisotropic media. In particular, the error analysis holds for the (nonhomogeneous) Dirichlet, Neumann, and mixed boundary conditions from the original PDEs. Furthermore, a strategy to discretize the model in heterogeneous media is proposed. The numerical results verify the validity of the theoretical results, and show that the resulting approximate solutions possess high accuracy.

The simulated catalogue of optical transients and correlated hosts (SCOTCH)

ABSTRACT As we observe a rapidly growing number of astrophysical transients, we learn more about the diverse host galaxy environments in which they occur. Host galaxy information can be used to purify samples of cosmological Type Ia supernovae, uncover the progenitor systems of individual classes, and facilitate low-latency follow-up of rare and peculiar explosions. In this work, we develop a novel data-driven methodology to simulate the time-domain sky that includes detailed modelling of the probability density function for multiple transient classes conditioned on host galaxy magnitudes, colours, star formation rates, and masses. We have designed these simulations to optimize photometric classification and analysis in upcoming large synoptic surveys. We integrate host galaxy information into the snana simulation framework to construct the simulated catalogue of optical transients and correlated hosts (SCOTCH, a publicly available catalogue of 5-million idealized transient light curves in LSST passbands and their host galaxy properties over the redshift range 0 < z < 3. This catalogue includes supernovae, tidal disruption events, kilonovae, and active galactic nuclei. Each light curve consists of true top-of-the-galaxy magnitudes sampled with high (≲2 d) cadence. In conjunction with SCOTCH, we also release an associated set of tutorials and transient-specific libraries to enable simulations of arbitrary space- and ground-based surveys. Our methodology is being used to test critical science infrastructure in advance of surveys by the Vera C. Rubin Observatory and the Nancy G. Roman Space Telescope.

Best proximity results for p-proximal contractions on topological spaces

In this article, we investigate some sufficient conditions for the existence and uniqueness of best proximity points for the topological p-proximal contractions and p-proximal contractive mappings on arbitrary topological spaces. Moreover, our results are illustrated by a few numerical examples and they generalize some known results in the literature.

Calibrated simplex-mapping classification.

We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confidence level for each of their predictions. In essence, the training of our classifier proceeds in two steps. In a first step, the training data is represented in a latent space whose geometry is induced by a regular (n - 1)-dimensional simplex, n being the number of classes. We design this representation in such a way that it well reflects the feature space distances of the datapoints to their own- and foreign-class neighbors. In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data. With this latent-space representation, our calibrated classifier is readily defined. We rigorously establish its core theoretical properties and benchmark its prediction and calibration properties by means of various synthetic and real-world data sets from different application domains.

T-duality of D-brane versus O-plane actions

It is known that, in the static gauge, the world-volume and the transverse Kaluza-Klein (KK) reductions of the O-plane effective actions on a circle satisfy the T-duality constraint for arbitrary base space background. In this paper we show that due to the presence of the second fundamental form in the D-brane couplings at order α′ and higher, the T-duality is satisfied only for a subclass of the couplings for arbitrary base space background. They are m = 0 couplings where m is the number of overset{sim }{B} -field (without derivative on it). For m > 0 couplings, the base space metric must be block-diagonal and the momentum U(1) vector field in the transverse reduction must be zero. However, the derivatives of the metric and the vector field are arbitrary.Using the assumption that the effective actions at the critical dimension are background independent, we then show that the T-duality constraint for the couplings at order α′ and for m = 0, fixes completely both bulk and boundary actions. These couplings indicate that the propagators of the massless open string fields receive α′-correction. We have also imposed the T-duality constraint on m = 1, 2, 3, 4 couplings. Because of the above restriction on the base space background in these cases, however, the T-duality can only partially fix the couplings for m > 0. This study shows that the Dirac-Born-Infeld (DBI) factor appears in both bulk and boundary actions at order α′.

Reciprocal Safety Velocity Cones for Decentralized Collision Avoidance in Multi-Agent Systems

In this paper, we solve the inter-agent collision avoidance problem in an arbitrary n-dimensional Euclidean space using reciprocal safety velocity cones (RSVCs). We propose a decentralized feedback control strategy that guarantees simultaneously asymptotic stabilization to a reference and collision avoidance. Our algorithm is purely decentralized in the sense that each agent uses only local information about its neighbouring agents. Moreover, the proposed solution can be implemented using only inter-agent bearing measurements. Therefore, the algorithm is a sensor-based control strategy which is practically implementable using a wide range of sensors such as vision systems and range scanners. Simulation results in a two dimensional environment cluttered with agents shows that the number of possible deadlocks is marginal and decrease with the decrease in the clutteredness of the workspace.

Informed Circular Fields for Global Reactive Obstacle Avoidance of Robotic Manipulators

In this paper a global reactive motion planning framework for robotic manipulators in complex dynamic environments is presented. In particular, the circular field predictions (CFP) planner from Becker et al. (2021) is extended to ensure obstacle avoidance of the whole structure of a robotic manipulator. Towards this end, a motion planning framework is developed that leverages global information about promising avoidance directions from arbitrary configuration space motion planners, resulting in improved global trajectories while reactively avoiding dynamic obstacles and decreasing the required computational power. The resulting motion planning framework is tested in multiple simulations with complex and dynamic obstacles and demonstrates great potential compared to existing motion planning approaches.

Kazhdan constants and isomorphic graph pairs

Let G be a finite group, and let Γ be a subset of G. The Kazhdan constant of the pair (G,Γ) is defined to bethe maximum distance we can guarantee that an arbitrary unitvector in an arbitrary nontrivial irreducible unitary representation space of G can be moved by some element of Γ. The Kazhdanconstant relates to the expansion properties of the Cayley graph generated by G and Γ, and has been much studied in this context. Different pairs (G1,Γ1) and (G2,Γ2) may give rise to isomorphic Cayley graphs. In this paper, we investigate the question: To whatextent is the Kazhdan constant a graph invariant? In other words, if the pairs yield isomorphic Cayley graphs, must the corresponding Kazhdan constants be equal? In our main theorem, we constructan infinite family of such pairs where the Kazhdan constants areunequal. Other relevant results are presented as well.

Weakly symmetric functions on spaces of Lebesgue integrable functions

In this work, we present the notion of a weakly symmetric function. We show that the subset of all weakly symmetric elements of an arbitrary vector space of functions is a vector space itself. Moreover, the subset of all weakly symmetric elements of some algebra of functions is an algebra. Also we consider weakly symmetric functions on the complex Banach space $L_p[0,1]$ of all Lebesgue measurable complex-valued functions on $[0,1]$ for which the $p$th power of the absolute value is Lebesgue integrable. We show that every continuous linear functional on $L_p[0,1],$ where $p\in (1,+\infty),$ can be approximated by weakly symmetric continuous linear functionals.

Компактность линейных операторов на квазибанаховых пространствах голоморфных функций

We state conditions under which some classical operators acting from abstract quasi-Banach spaces of functions holomorphic in a plain domain into a weighted space of the same functions with sup-norm are compact. It is obtained abstract criteria for the compactness of a linear operator on an arbitrary quasi-Banach space which are stated in terms of delta-functions and formulate their realizations for both classical and generalized Fock spaces. The above results are applied to the weighted composition operator. It is established some conditions for the compactness of this operator which are given in terms of norms of delta-functions in the corresponding dual spaces. These results are essential generalizations of the known Zorboska’s ones. Namely, we significantly extended the class of weighted spaces of holomorphic functions with uniform norms for which one can state some conditions for the compactness of an arbitrary linear operator or the weighted composition operator.

Uniqueness of best proximity pairs and rigidity of semimetric spaces

For arbitrary semimetric space \((X, d)\) and disjoint proximinal subsets \(A\), \(B\) of \(X\) we define the proximinal graph as a bipartite graph with parts \(A\) and \(B\) whose edges \(\{a, b\}\) satisfy the equality \(d(a, b) = {\text {dist}}(A, B)\). We characterize the semimetric spaces whose proximinal graphs have at most one edge and the semimetric spaces whose proximinal graphs have the vertices with degree at most \(1\) only. This allows us to describe the necessary and sufficient conditions for uniqueness of the best proximity pairs and best approximations.

Lipschitz Bernoulli Utility Functions

We obtain several variants of the classic von Neumann–Morgenstern expected utility theorem with and without the completeness axiom in which the derived Bernoulli utility functions are Lipschitz. The prize space in these results is an arbitrary separable metric space, and the utility functions are allowed to be unbounded. The main ingredient of our results is a novel (behavioral) axiom on the underlying preference relations, which is satisfied by virtually all stochastic orders. The proof of the main representation theorem is built on the fact that the dual of the Kantorovich–Rubinstein space is (isometrically isomorphic to) the Banach space of Lipschitz functions that vanish at a fixed point. An application to the theory of nonexpected utility is also provided.

Shaped beam from multiple beams using planar phased arrays

ABSTRACT This paper proposes a synthesis and optimization procedure to generate user-specific shaped and multiple beams in any arbitrary 3D space. The synthesis method generates independently controllable multiple beams over different azimuth and elevation angles with variable peak powers. These multiple beams can form any user-defined shaped pattern with an optimum number of beams, their power, and placement in the angles using the proposed direct synthesis method. Different evolutionary algorithms are employed to optimize the beam peak parameters to mitigate the ripple, side lobe level, and beam spread. The synthesis method is validated on a fully functional integrated 5G mmWave phased array for various shaped beam patterns. Measured sector, isoflex beams between , , and dual beams with unequal powers are presented and compared with the shaped beam synthesis method.

Poles of finite-dimensional representations of Yangians

Let $\mathfrak{g}$ be a finite-dimensional simple Lie algebra over $\mathbb{C}$, and let $Y_{\hbar}(\mathfrak{g})$ be the Yangian of $\mathfrak{g}$. In this paper, we study the sets of poles of the rational currents defining the action of $Y_{\hbar}(\mathfrak{g})$ on an arbitrary finite-dimensional vector space $V$. Using a weak, rational version of Frenkel and Hernandez' Baxter polynomiality, we obtain a uniform description of these sets in terms of the Drinfeld polynomials encoding the composition factors of $V$ and the inverse of the $q$-Cartan matrix of $\mathfrak{g}$. We then apply this description to obtain a concrete set of sufficient conditions for the cyclicity and simplicity of the tensor product of any two irreducible representations, and to classify the finite-dimensional irreducible representations of the Yangian double.