State Representation Research Articles

Demonstratives as attention tools: Evidence of mentalistic representations within language

Linguistic communication is an intrinsically social activity that enables us to share thoughts across minds. Many complex social uses of language can be captured by domain-general representations of other minds (i.e., mentalistic representations) that externally modulate linguistic meaning through Gricean reasoning. However, here we show that representations of others' attention are embedded within language itself. Across ten languages, we show that demonstratives-basic grammatical words (e.g., "this"/"that") which are evolutionarily ancient, learned early in life, and documented in all known languages-are intrinsic attention tools. Beyond their spatial meanings, demonstratives encode both joint attention and the direction in which the listener must turn to establish it. Crucially, the frequency of the spatial and attentional uses of demonstratives varies across languages, suggesting that both spatial and mentalistic representations are part of their conventional meaning. Using computational modeling, we show that mentalistic representations of others' attention are internally encoded in demonstratives, with their effect further boosted by Gricean reasoning. Yet, speakers are largely unaware of this, incorrectly reporting that they primarily capture spatial representations. Our findings show that representations of other people's cognitive states (namely, their attention) are embedded in language and suggest that the most basic building blocks of the linguistic system crucially rely on social cognition.

Enhancing deep reinforcement learning for scale flexibility in real-time strategy games

Real-time strategy (RTS) games present a unique challenge for AI agents due to the combination of several fundamental AI problems. While Deep Reinforcement Learning (DRL) has shown promise in the development of autonomous agents for the genre, existing architectures often struggle with games featuring maps of varying dimensions. This limitation hinders the agent’s ability to generalize its learned strategies across different scenarios. This paper proposes a novel approach that overcomes this problem by incorporating Spatial Pyramid Pooling (SPP) within a DRL framework. We leverage the GridNet architecture’s encoder–decoder structure and integrate an SPP layer into the critic network of the Proximal Policy Optimization (PPO) algorithm. This SPP layer dynamically generates a standardized representation of the game state, regardless of the initial observation size. This allows the agent to effectively adapt its decision-making process to any map configuration. Our evaluations demonstrate that the proposed method significantly enhances the model’s flexibility and efficiency in training agents for various RTS game scenarios, albeit with some discernible limitations when applied to very small maps. This approach paves the way for more robust and adaptable AI agents capable of excelling in sequential decision problems with variable-size observations.

A note on Majorana representation of quantum states

A note on Majorana representation of quantum states

Sequential action-induced invariant representation for reinforcement learning

How to accurately learn task-relevant state representations from high-dimensional observations with visual distractions is a realistic and challenging problem in visual reinforcement learning. Recently, unsupervised representation learning methods based on bisimulation metrics, contrast, prediction, and reconstruction have shown the ability for task-relevant information extraction. However, due to the lack of appropriate mechanisms for the extraction of task information in the prediction, contrast, and reconstruction-related approaches and the limitations of bisimulation-related methods in domains with sparse rewards, it is still difficult for these methods to be effectively extended to environments with distractions. To alleviate these problems, in the paper, the action sequences, which contain task-intensive signals, are incorporated into representation learning. Specifically, we propose a Sequential Action–induced invariant Representation (SAR) method, which decouples the controlled part (i.e., task-relevant information) and the uncontrolled part (i.e., task-irrelevant information) in noisy observations through sequential actions, thereby extracting effective representations related to decision tasks. To achieve it, the characteristic function of the action sequence’s probability distribution is modeled to specifically optimize the state encoder. We conduct extensive experiments on the distracting DeepMind Control suite while achieving the best performance over strong baselines. We also demonstrate the effectiveness of our method at disregarding task-irrelevant information by applying SAR to real-world CARLA-based autonomous driving with natural distractions. Finally, we provide the analysis results of generalization drawn from the generalization decay and t-SNE visualization. Code and demo videos are available at https://github.com/DMU-XMU/SAR.git.

Embracing Multiheterogeneity and Privacy Security Simultaneously: A Dynamic Privacy-Aware Federated Reinforcement Learning Approach.

With growing demand for privacy-preserving reinforcement learning (RL) applications, federated RL (FRL) has emerged as a potential solution. However, existing FRL methods struggle with multiple sources of heterogeneity, while lacking robust privacy guarantees. In this study, we propose DPA-FedRL, the dynamic privacy-aware FRL framework, to simultaneously mitigate both issues. First, we innovatively put forward the concept of "multiheterogeneity" and embed the environmental heterogeneity into agents' state representations. Next, to ensure privacy during model aggregation, we incorporate a differentially private mechanism in form of Gaussian noise and modify its global sensitivity, tailored to suit FRL's unique characteristics. Encouragingly, our approach dynamically allocates privacy budget based on heterogeneity levels, which strikes a balance between privacy and utility. From the theoretical perspective, we give rigorous convergence, privacy, and sensitivity guarantees for our proposed method. Through extensive experiments on diverse datasets, we demonstrate that DPA-FedRL surpasses state-of-the-art approaches (PPO-DP-SGD, PAvg, and QAvg) in some highly heterogeneous environments. Notably, our novel privacy attack simulations enable quantitative privacy assessment, validating that DPA-FedRL offers over 1.359 × stronger protection than baselines.

Holographic Gaussian Boson Sampling with Matrix Product States on 3D cQED Processors.

We introduce quantum circuits for simulations of multimode state vectors on 3D circuit quantum electrodynamics (cQED) processors using matrix product state representations. The circuits are demonstrated as applied to simulations of molecular docking based on holographic Gaussian boson sampling (GBS), as illustrated for the binding of a thiol-containing aryl sulfonamide ligand to the tumor necrosis factor-α converting enzyme receptor. We show that cQED devices with a modest number of modes could be employed to simulate multimode systems by repurposing working modes through measurement and reinitialization. We anticipate that a wide range of GBS applications could be implemented on compact 3D cQED processors analogously using the holographic approach. Simulations on qubit-based quantum computers could be implemented analogously using circuits that represent continuous variables in terms of truncated expansions of Fock states.

Inductive biases of neural network modularity in spatial navigation.

The brain may have evolved a modular architecture for daily tasks, with circuits featuring functionally specialized modules that match the task structure. We hypothesize that this architecture enables better learning and generalization than architectures with less specialized modules. To test this, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the modular agent, with an architecture that segregates computations of state representation, value, and action into specialized modules, achieved better learning and generalization. Its learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to recursive Bayesian estimation. This agent's behavior also resembles macaques' behavior more closely. Our results shed light on the possible rationale for the brain's modularity and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.

A model linking emotional dysregulation in neurodivergent people to the proprioceptive impact of joint hypermobility.

Emotional feelings are putatively ascribed to central representation of bodily states in the context of expectation and uncertainty in both internal state and external world. Neurodivergent people are more likely to experience co-occurring mental health challenges, although mechanistic insights underpinning this association are scarce. We therefore undertook a study to test whether imprecise processing of proprioceptive error signals may underlie the connection between neurodivergence and emotional dysregulation. In a cohort of people with complex chronic conditions, including chronic pain/fatigue, and complex trauma, and in a comparison group, we assessed presence of neurodivergence, variant connective tissue manifested through joint hypermobility, and emotional dysregulation. We present a data-informed conceptual model showing that variant connective tissue determines whether proprioceptive surprise is linked with emotional dysregulation in neurodivergent individuals. We suggest that future research in this area may have important clinical implications for the interaction of mental and physical wellbeing in neurodivergent people. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.

An approximate equivalence for the GNS representation of the Haar state of $$SU_{q}(2)$$

An approximate equivalence for the GNS representation of the Haar state of $$SU_{q}(2)$$

Quantum acoustics unravels Planckian resistivity

Strange metals exhibit universal linear-in-temperature resistivity described by a Planckian scattering rate, the origin of which remains elusive. By employing an approach inspired by quantum optics, we arrive at the coherent state representation of lattice vibrations: quantum acoustics. Utilizing this nonperturbative framework, we demonstrate that lattice vibrations could serve as active drivers in the Planckian resistivity phenomenon, challenging prevailing theories. By treating charge carriers as quantum wave packets negotiating the dynamic acoustic field, we find that a competition ensues between localization and delocalization giving rise to the previously conjectured universal quantum bound of diffusion, [Formula: see text], independent of temperature or any other material parameters. This leads to the enigmatic T-linear resistivity over hundreds of degrees, except at very low temperatures. Quantum diffusion also explains why strange metals have much higher electrical resistivity than typical metals. Our work elucidates the critical role of phonons in Planckian resistivity from a unique perspective and reconsiders their significance in the transport properties of strange metals.

Data snapshots of the access and participation of ‘women’ academics in UK universities: Questioning continued gendered, racialised and geopolitical inequalities

AbstractReplete with espoused discourses of equality, diversity and inclusion within public bodies, is the UK, wherein lauded initiatives reward its universities’ commitments to increasing the access and positioning of ‘women’ in higher education. This paper contributes a critical quantitative analysis of the state of representation and participation of academic staff within these universities generally, and the majority‐female discipline of education particularly. Education is important because it has a direct relation to social change and ethicality. It may maintain or reproduce the status quo; however, exercising its transformative potential is essential for the success of various international frameworks aiming to address global inequality, including most recently the Sustainable Development Goals. Sensitised by QuantCrit principles, a descriptive statistical exploration was undertaken of the staff composition and employment conditions captured within the administrative datasets reported on academic staff by all the public universities in the devolved nations of England, Northern Ireland, Scotland, and Wales from 2015 to 2020. The findings of this study confirmed: (i) the continuation of gendered inequalities across the academic hierarchy, particularly as the pyramid narrows to the assigned intellectual leadership position of ‘professor’; (ii) racialised, gendered inequalities in access to employment, and in positioning once employed; and (iii) more adverse conditions where gendered, racialised and geopolitical inequalities intersect, most extremely for Black African female academics. The study demonstrates that the centring of ‘race’ and consideration of nationality are required to challenge coloniality, and to bring to the fore the differential impacts of systems of discrimination within this globally influential sector.

A note on Majorana representation of quantum states

A note on Majorana representation of quantum states

Towards optimal control: the probabilistic route in quantum systems

There is a fundamental limit to what is knowable about atomic and molecular scale systems. This fuzziness is not always due to the act of measurement. Other contributing factors include system parameter uncertainty, functional uncertainty that originates from input functions, and sensors noises to mention a few. This indeterminism has led to major challenges in the development of accurate control methods for atomic scale systems. To address the probabilistic and uncertain nature of these systems, this work proposes a novel control framework that considers the representation of the quantum system states and the quantification of its physical properties following a probabilistic approach. Our framework is fully probabilistic. It uses the Shannon relative entropy from information theory to design optimal randomised controllers that can achieve a desired outcome of an atomic scale system. Two experiments are carried out to illustrate the applicability and effectiveness of the proposed approach.

Entropy variances of pure coherent states in the diffusion channel

Abstract Using the operator correspondence of the real and fictious modes in the thermo entangled state representation, we solve the quantum master equation describing the diffusion channel and obtain the Kraus operator-sum representation of its analytical solution. we find that the pure coherent states evolve into the new mixed thermal superposed states in the diffusion channel. Also, we investigate the statistical properties of the initial coherent states and their entropy evolutions in the diffusion channel, and find that the entropy evolutions are only related to the decay time and without the amplitudes of the initial coherent states.

A Transformation-Aggregation Framework for State Representation of Autonomous Driving Systems

A Transformation-Aggregation Framework for State Representation of Autonomous Driving Systems

ANALYSIS OF THE CONCLUSION OF THE PLENUM OF THE CONSTITUTIONAL COURT IN RELATION TO THE VIOLATION OF THE CONSTITUTION BY THE PRESIDENT OF GEORGIA AND INTERNATIONAL PRACTICE

From 2013 until the present, the dispute on the separation of competencies between the leaders of the state government has been ongoing in political life, as well as on an international scale in the field of foreign relations. The field of foreign relations is of national importance and is directly related to the state’s image and foreign policy. The conclusion of the international treaties, as well as the appointment of the ambassadors and representation of the state, has become the subject of dispute at various times. State representation by the President in foreign relations has led to a Constitutional Reference submitted by the Parliament of Georgia to the Constitutional Court of Georgia, alleging a violation of the Constitution by the President for the first time in the country’s history. As a result, according to the conclusion of the Constitutional Court of Georgia dated October 16, 2023, President Ms. Zurabishvili violated the Constitution of Georgia by exercising representation powers without the consent of the Government. Based on the above, this thesis will analyze the meaning of the representation power in foreign relations, the President’s constitutional status, and the President’s role in foreign relations, acting as the socalled “neutral arbitrator”. This thesis will include a discussion on the roles of the Parliament and the Constitutional Court of Georgia in the political-legal process of Presidential impeachment proceedings. It will also examine the conclusions and decisions of Constitutional Courts in various states regarding alleged constitutional violations by Presidents and disputes over competence.

Gaussian process learning of nonlinear dynamics

One of the pivotal tasks in scientific machine learning is to represent underlying dynamical systems from time series data. Many methods for such dynamics learning explicitly require the derivatives of state data, which are not directly available and can be approximated conventionally by finite differences. However, the discrete approximations of time derivatives may result in poor estimations when state data are scarce and/or corrupted by noise, thus compromising the predictiveness of the learned dynamical models. To overcome this technical hurdle, we propose a new method that learns nonlinear dynamics through a Bayesian inference of characterizing model parameters. This method leverages a Gaussian process representation of states, and constructs a likelihood function using the correlation between state data and their derivatives, yet prevents explicit evaluations of time derivatives. Through a Bayesian scheme, a probabilistic estimate of the model parameters is given by the posterior distribution, and thus a quantification is facilitated for uncertainties from noisy state data and the learning process. Specifically, we will discuss the applicability of the proposed method to several typical scenarios for dynamical systems: identification and estimation with an affine parametrization, nonlinear parametric approximation without prior knowledge, and general parameter estimation for a given dynamical system.

Ntegrating Quantum-Inspired Principles into Financial Data Science: Optimizing Reservoir Computing Models

This research explores the integration of quantum-inspired principles into financial data science with a focus on optimizing Reservoir Computing (RC) models. This approach aims to achieve a more nuanced representation of market states, improving accuracy in predicting stock price movements and capturing complex interdependencies among financial assets. Utilizing historical data from Yahoo Finance, implement and optimize RC models enhanced by Quantum State Model (QSM) and Entangled Asset Model (EAM) techniques. The results demonstrate the potential benefits and challenges of applying these quantum-inspired methods to financial data analysis. Keywords: Computational Intelligence, Financial Data Science, Quantum Computing, Quantum Machine Learning (QML), Reservoir Computing

Accuracy in imagining

Recent treatments of imagination have increasingly treated imagining as a skill. Insofar as imaginative accuracy is one of the factors that underwrites this skill, it is important to understand what it means to say that an imagining is accurate. This paper takes up that task. The discussion proceeds in four parts. First, I address two worries that may naturally arise about the coherence of the notion of imaginative accuracy. Second, with those worries addressed, I turn to an exploration of what is meant by imaginative accuracy. My discussion relies on two key points: first, that accuracy is best understood in terms of aim; and second, that imaginings aim at the representation of fictional states of affairs. I call this line of thought the fictionality approach. Third, I look more closely at six different types of imaginings in an effort to develop and clarify the fictionality approach. Finally, I turn to what I call the calibration objection. Given the nature of imagination, there seems to be no way to calibrate one’s judgments of imaginative accuracy. After showing how much of the force of the calibration objection can be defused, I offer some brief concluding remarks.

Accurate and gate-efficient quantum Ansätze for electronic states without adaptive optimization

The ability of quantum computers to overcome the exponential memory scaling of many-body problems is expected to transform quantum chemistry. Quantum algorithms require accurate representations of electronic states on a quantum device, but current approximations struggle to combine chemical accuracy and gate efficiency while preserving physical symmetries, and rely on measurement-intensive adaptive methods that tailor the wave function to each molecule. In this contribution, we present a symmetry-preserving and gate-efficient that provides chemically accurate molecular energies with a well-defined circuit structure. Our approach exploits local qubit connectivity, orbital optimization, and connections with generalized valence bond theory to maximize the accuracy that is obtained with shallow quantum circuits. Numerical simulations for molecules with weak and strong electron correlation, including benzene, water, and the singlet-triplet gap in tetramethyleneethane, demonstrate that chemically accurate energies are achieved with as much as 84% fewer two-qubit gates compared to state-of-the-art adaptive techniques. Published by the American Physical Society 2024