Search

Justice is an ancient concept that originated from commodity exchange and has been continuously critiqued and discussed in philosophy, ethics, and law to identify potential pathways for societies and institutions. Since the 1960s, justice research led by sociologists and political scientists has identified spatial injustices, attributing them to inherent flaws in economic systems, social order, and legal frameworks. Subsequently, the emphasis on spatial aspects, proposed by Henri Lefebvre's "right to the city" and the spatiality of social justice explored by Marxist geographers, inspired scholars such as Pirie, Soja, and Iveson. These scholars have continuously refined the theoretical framework of spatial justice, prioritizing real-world spatial practices. The essence of spatial justice can be understood through a series of dichotomies: it is both a theoretical critique and a practical methodology, passively influenced by social systems yet spatially proactive, static yet dynamic, and both process and outcome. The essential questions are "seeing spatial justice" and "seeking spatial justice", which involve "describing the justice level of social spatial outcomes" and "exploring the spatial causes and formation processes of injustice." Additionally, the two main research directions are "territorial distribution justice" and "local discrimination." Spatial justice does not imply absolute equality in distribution processes or outcomes; rather, it recognizes differences in human needs that lead to unequal distributions. The principles of spatial justice include both equality and differentiation. Scholars generally agree that the principle of equality should meet basic needs, such as transportation infrastructure, primary education, and healthcare services, which ensure minimum living standards. Based on differentiated needs--determined by factors such as region, identity, environment, contributions, and merits--non-essential material goods, social resources, and environmental assets can be allocated unequally. However, the concept of human needs is inherently subjective and is closely related to age, education, perception, and experience. Evaluating the reasonableness of these needs is complex and challenging. Assessing spatial justice from the perspective of human needs requires addressing two key issues: defining the scope of basic and non-basic needs and establishing assessment standards or indices for multidimensional needs. Although researchers such as Davies and Daniels have long argued that distribution should consider actual social needs rather than just population size, current empirical analyses, such as accessibility and availability studies, remain limited to measuring the ratio of resources to population size. Few studies have explored the characteristics and variances in needs related to residents, service levels, quality, and pricing, remaining distant from real, multidimensional, and differentiated human needs. By summarizing existing theories and methods, this study proposes three areas for future research: (1) research on territorial distribution justice should explore actual demand content beyond population size and establish relevant evaluation indicators; (2) urban spatial practices should adopt justice standards from a human-centric perspective to mitigate unjust socio-spatial outcomes; and (3) the forms and methods of achieving justice should be explored at a global regional scale. [ABSTRACT FROM AUTHOR]

In this paper, we first study the maximally commutative set in prime dimensional systems, which is a set of generalized Pauli matrices, and it can be used to detect the local discrimination of generalized Bell states. We give a simple characterization of prime dimensional maximally commutative sets, that is, a subset of a set of generalized Bell states, whose second subscript is a multiple of the first subscript. Furthermore, some sets of generalized Bell states which can be locally distinguishable by one-way local operation and classical communication (LOCC) are constructed by using the structural characteristics of prime dimensional maximally commutative sets. Based on these distinguishable generalized Bell states, we propose a (t, n)-threshold quantum secret sharing scheme. Compared with the existing quantum secret sharing scheme, it can be found that there are enough distinguishable states to encode classical information in our scheme, the dealer only needs to send entangled particles once to make the participants get their secret share, which makes the secret sharing process more efficient than the existing schemes. Finally, we prove that this protocol is secure under dishonest participant attack, interception-and-resend attack and entangle-and-measure attack. [ABSTRACT FROM AUTHOR]

Justice is an ancient concept that originated from commodity exchange and has been continuously critiqued and discussed in philosophy, ethics, and law to identify potential pathways for societies and institutions. Since the 1960s, justice research led by sociologists and political scientists has identified spatial injustices, attributing them to inherent flaws in economic systems, social order, and legal frameworks. Subsequently, the emphasis on spatial aspects, proposed by Henri Lefebvre's "right to the city" and the spatiality of social justice explored by Marxist geographers, inspired scholars such as Pirie, Soja, and Iveson. These scholars have continuously refined the theoretical framework of spatial justice, prioritizing real-world spatial practices. The essence of spatial justice can be understood through a series of dichotomies: it is both a theoretical critique and a practical methodology, passively influenced by social systems yet spatially proactive, static yet dynamic, and both process and outcome. The essential questions are "seeing spatial justice" and "seeking spatial justice", which involve "describing the justice level of social spatial outcomes" and "exploring the spatial causes and formation processes of injustice." Additionally, the two main research directions are "territorial distribution justice" and "local discrimination." Spatial justice does not imply absolute equality in distribution processes or outcomes; rather, it recognizes differences in human needs that lead to unequal distributions. The principles of spatial justice include both equality and differentiation. Scholars generally agree that the principle of equality should meet basic needs, such as transportation infrastructure, primary education, and healthcare services, which ensure minimum living standards. Based on differentiated needs—determined by factors such as region, identity, environment, contributions, and merits—non-essential material goods, social resources, and environmental assets can be allocated unequally. However, the concept of human needs is inherently subjective and is closely related to age, education, perception, and experience. Evaluating the reasonableness of these needs is complex and challenging. Assessing spatial justice from the perspective of human needs requires addressing two key issues: defining the scope of basic and non-basic needs and establishing assessment standards or indices for multidimensional needs. Although researchers such as Davies and Daniels have long argued that distribution should consider actual social needs rather than just population size, current empirical analyses, such as accessibility and availability studies, remain limited to measuring the ratio of resources to population size. Few studies have explored the characteristics and variances in needs related to residents, service levels, quality, and pricing, remaining distant from real, multidimensional, and differentiated human needs. By summarizing existing theories and methods, this study proposes three areas for future research: (1) research on territorial distribution justice should explore actual demand content beyond population size and establish relevant evaluation indicators; (2) urban spatial practices should adopt justice standards from a human-centric perspective to mitigate unjust socio-spatial outcomes; and (3) the forms and methods of achieving justice should be explored at a global regional scale.

Recently, Zhang et al. raised two classes of orthogonal product states which cannot be exactly discriminated by local operations and classical communication (LOCC) in the quantum system of (2k + 1) ⊗ 2l and (3k + i) ⊗ (3l + j) (Sci. Rep. 6, 28864, 2016). However, it remains a meaningful question that what entanglement resources are necessary and/or sufficient for this task to be possible with LOCC. In this paper, we present a method of using only an auxiliary 2 ⊗ 2 maximally entangled state to complete the local discrimination. Specifically, by utilizing a two-qubit maximally entangled state as an ancillary resource, we propose a concrete protocol respectively to locally identify orthogonal product states in 5 ⊗ 6 and 5 ⊗ 5. Then we generalize the distinguishing method respectively to the states in the quantum system of (2k + 1) ⊗ 2l and (3k + i) ⊗ (3l + j). These results can make us have a better understanding of the relationship between quantum entanglement and quantum nonlocality. [ABSTRACT FROM AUTHOR]

Quantum nonlocality without entanglement is a fantastic phenomenon in quantum theory. This kind of quantum nonlocality is based on the task of local discrimination of quantum states. Recently, Bandyopadhyay and Halder (2021 Phys. Rev. A 104 L050201) studied the problem: is there any set of orthogonal states which can be locally distinguishable, but under some orthogonality preserving local measurement, each outcome will lead to a locally indistinguishable set. The set with such property is called to have hidden nonlocality. Moreover, if such phenomenon can not arise from discarding subsystems which is termed as local irredundancy, we call it genuine hidden nonlocality. There, they presented several sets of entangled states with genuine hidden nonlocality. However, they doubted the existence of a set without entanglement but with genuine hidden nonlocality. In this paper, we eliminate this doubt by constructing a series of sets without entanglement but whose nonlocality can be genuinely activated. We derive a method to tackle with the local irredundancy problem which is a key tricky for the systems whose local dimensions are composite numbers. Unexpectedly, the constructions of genuine hidden nonlocal sets without entanglement seems to be easier than that with entanglement. Therefore, from this perspective, this kind of nonlocality is rather different from the Bell nonlocality.

In this work, we study the local distinguishability of maximally entangled states (MESs). In particular, we are concerned with whether any fixed number of MESs can be locally distinguishable for sufficiently large dimensions. Fan and Tian et al. have already obtained two satisfactory results for the generalized Bell states (GBSs) and the qudit lattice states when applied to prime or prime power dimensions. We construct a general twist-teleportation scheme for any orthonormal basis with MESs that is inspired by the method used in [Phys. Rev. A 70, 022304 (2004)]. Using this teleportation scheme, we obtain a sufficient and necessary condition for one-way distinguishable sets of MESs, which include the GBSs and the qudit lattice states as special cases. Moreover, we present a generalized version of the results in [Phys. Rev. A 92, 042320 (2015)] for the arbitrary dimensional case. [ABSTRACT FROM AUTHOR]

• Form a weighted 0-norm non-negative matrix tri-factorization with global and local discriminant regularizers to simultaneously considers feature weights, data noise, local manifolds, and global scatter in co-clustering. • Optimize the formed objective function to solve the proposed co-clustering problem by an alternate update rule, whose convergence is proved in theory. • Verify our algorithm's duality, robustness, effectiveness, and competitiveness through comprehensive experiments on synthetic, corrupted and real datasets. In the past few decades, the clustering problem has made considerable progress, and co-clustering algorithms have attracted more attention. Compared with one-side clustering, co-clustering not only groups samples according to the distribution of features but also groups features according to the distribution of samples at the same time. This duality helps to explore the structural information of data, such as genes and texts. In this paper, a new co-clustering algorithm is proposed to simultaneously consider feature weights, data noise, local manifolds, and global scatter, named robust weighted co-clustering with global and local discrimination. Furthermore, an alternate update rule is put forward to optimize objective, theoretically proven to converge. Then, the algorithm's duality, robustness, and effectiveness have been verified on synthetic, corrupted, and real datasets, respectively. The runtime and parameter sensitivity of the algorithm are also analyzed. Finally, sufficient experiments clarify the competitiveness of our algorithm compared to other ones. [ABSTRACT FROM AUTHOR]

Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g. individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e. specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible and powerful strategy.

It has been shown that for two different multipartite unitary operations U and U , when tr( U U ) = 0, they can always be perfectly distinguished by local operations and classical communication in the single-run scenario. However, how to find the detailed scheme to complete the local discrimination is still a fascinating problem. In this paper, aiming at some U and U acting on the bipartite and tripartite space respectively, especially for U U locally unitary equivalent to the high dimensional X-type hermitian unitary matrix V with tr V = 0, we put forward the explicit local distinguishing schemes in the single-run scenario. [ABSTRACT FROM AUTHOR]

In the study of local discrimination for multipartite unitary operations, Duan et al. (Phys Rev Lett 100(2):020503, ) exhibited an ingenious expression: Any two different unitary operations $$U_1$$ and $$U_2$$ are perfectly distinguishable by local operations and classical communication in the single-run scenario if and only if 0 is in the local numerical range of $$U_1^\dag U_2$$ . However, how to determine when 0 is in the local numerical range remains unclear. So it is generally hard to decide the local discrimination of nonlocal unitary operations with a single run. In this paper, for two-qubit diagonal unitary matrices V and their local unitary equivalent matrices, we present a necessary and sufficient condition for determining whether the local numerical range is a convex set or not. The result can be used to easily judge the locally perfect distinguishability of any two unitary operations $$U_1$$ and $$U_2$$ satisfying $$U_1^\dag U_2=V$$ . Moreover, we design the corresponding protocol of local discrimination. Meanwhile, an interesting phenomenon is discovered: Under certain conditions with a single run, $$U_1$$ and $$U_2$$ such that $$U_1^\dag U_2=V$$ are locally distinguishable with certainty if and only if they are perfectly distinguishable by global operations. [ABSTRACT FROM AUTHOR]

In this paper, we treat a local discrimination problem in the framework of asymmetric hypothesis testing. We choose a known bipartite pure state $ | \Psi \rangle $ as an alternative hypothesis and the completely mixed state as a null hypothesis. As a result, we analytically derive an optimal type-2 error and an optimal positive operator valued measure (POVM) for one-way local operations and classical communication (LOCC) POVM and separable POVM. For two-way LOCC POVM, we study a family of simple three-step LOCC protocols, and show that the best protocol in this family has strictly better performance than any one-way LOCC protocol in low-dimensional systems when there may exist differences between two-way LOCC POVM and one-way LOCC POVM. [ABSTRACT FROM AUTHOR]

Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g., individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e., specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible, and powerful strategy. [ABSTRACT FROM AUTHOR]

This paper presents a particle filter-based visual tracking method with online feature selection mechanism. In color-based particle filter algorithm the weights of particles do not always represent the importance correctly, this may cause that the object tracking based on particle filter converge to a local region of the object. In our proposed visual tracking method, the Bhattacharyya distance and the local discrimination between the object and background are used to define the weights of the particles, which can solve the existing local convergence problem. Experiments demonstrates that the proposed method can work well not only in single object tracking processes but also in multiple similar objects tracking processes. [ABSTRACT FROM AUTHOR]

In this paper we present a self-organizing neural network paradigm that is able to discriminate information locally using a strategy for information coding and processing inspired in recent findings in living neural systems. The proposed neural network uses: 1) neural signatures to identify each unit in the network; 2) local discrimination of input information during the processing; and 3) a multicoding mechanism for information propagation regarding the who and the what of the information. The local discrimination implies a distinct processing as a function of the neural signature recognition and a local transient memory. In the context of artificial neural networks none of these mechanisms has been analyzed in detail, and our goal is to demonstrate that they can be used to efficiently solve some specific problems. To illustrate the proposed paradigm, we apply it to the problem of multidimensional sorting, which can take advantage of the local information discrimination. In particular, we compare the results of this new approach with traditional methods to solve jigsaw puzzles and we analyze the situations where the new paradigm improves the performance. [ABSTRACT FROM AUTHOR]

Abstract: It is shown that two copies are enough to distinguish a complete basis of maximally entangled states in canonical form by constructing an explicit protocol. In particular, in such a protocol, no auxiliary system is needed and the symmetrical roles of certain indexes of the canonical form are embodied properly. [Copyright &y& Elsevier]