Multimode Code Research Articles

Safeguarding Oscillators and Qudits with Distributed Two-Mode Squeezing

Recent advancements in multi-mode Gottesman-Kitaev-Preskill (GKP) codes have shown great promise in enhancing the protection of both discrete and analog quantum information. This broadened range of protection brings opportunities beyond quantum computing to benefit quantum sensing by safeguarding squeezing — the essential resource in many quantum metrology protocols. However, the potential for quantum sensing to benefit quantum error correction has been less explored. In this work, we provide a unique example where techniques from quantum sensing can be applied to improve multi-mode GKP codes. Inspired by distributed quantum sensing, we propose the distributed two-mode squeezing (dtms) GKP codes that offer benefits in error correction with minimal active encoding operations. Indeed, the proposed codes rely on a single (active) two-mode squeezing element and an array of beamsplitters that effectively distributes continuous-variable correlations to many GKP ancillae, similar to continuous-variable distributed quantum sensing. Despite this simple construction, the code distance achievable with dtms-GKP qubit codes is comparable to previous results obtained through brute-force numerical search \cite{lin2023closest}. Moreover, these codes enable analog noise suppression beyond that of the best-known two-mode codes \cite{noh2020o2o} without requiring an additional squeezer. We also provide a simple two-stage decoder for the proposed codes, which appears near-optimal for the case of two modes and permits analytical evaluation.

Integrating human expertise & automated methods for a dynamic and multi-parametric evaluation of large language models’ feasibility in clinical decision-making

BackgroundRecent enhancements in Large Language Models (LLMs) such as ChatGPT have exponentially increased user adoption. These models are accessible on mobile devices and support multimodal interactions, including conversations, code generation, and patient image uploads, broadening their utility in providing healthcare professionals with real-time support for clinical decision-making. Nevertheless, many authors have highlighted serious risks that may arise from the adoption of LLMs, principally related to safety and alignment with ethical guidelines. ObjectiveTo address these challenges, we introduce a novel methodological approach designed to assess the specific feasibility of adopting LLMs within a healthcare area, with a focus on clinical nursing, evaluating their performance and thereby directing their choice. Emphasizing LLMs’ adherence to scientific advancements, this approach prioritizes safety and care personalization, according to the “Organization for Economic Co-operation and Development” frameworks for responsible AI. Moreover, its dynamic nature is designed to adapt to future evolutions of LLMs. MethodThrough integrating advanced multidisciplinary knowledge, including Nursing Informatics, and aided by a prospective literature review, seven key domains and specific evaluation items were identified as follows:1.State of the Art Alignment & Safety.2.Focus, Accuracy & Management of Prompt Ambiguity.3.Data Integrity, Data Security, Ethics & Sustainability, in accordance with OECD Recommendations for Responsible AI.4.Temporal Variability of Responses (Consistency)5.Adaptation to specific standardized terminology and Classifications for healthcare professionals.6.General Capabilities: Post User Feedback Self-Evolution Capability and Organization in Chapters.7.Ability to Drive Evolution in Healthcare.A Peer Review by experts in Nursing and AI was performed, ensuring scientific rigor and breadth of insights for an essential, reproducible, and coherent methodological approach. By means of a 7-point Likert scale, thresholds are defined in order to classify LLMs as “unusable”, “usable with high caution”, and “recommended” categories.Nine state of the art LLMs were evaluated using this methodology in clinical oncology nursing decision-making, producing preliminary results. Gemini Advanced, Anthropic Claude 3 and ChatGPT 4 achieved the minimum score of the State of the Art Alignment & Safety domain for classification as “recommended”, being also endorsed across all domains. LLAMA 3 70B and ChatGPT 3.5 were classified as “usable with high caution.” Others were classified as unusable in this domain. ConclusionThe identification of a recommended LLM for a specific healthcare area, combined with its critical, prudent, and integrative use, can support healthcare professionals in decision-making processes.

Mixed selectivity in monkey anterior intraparietal area during visual and motor processes

Classical studies suggest that the anterior intraparietal area (AIP) contributes to the encoding of specific information such as objects and actions of self and others, through a variety of neuronal classes, such as canonical, motor and mirror neurons. However, these studies typically focused on a single variable, leaving it unclear whether distinct sets of AIP neurons encode a single or multiple sources of information and how multimodal coding emerges. Here, we chronically recorded monkey AIP neurons in a variety of tasks and conditions classically employed in separate experiments. Most cells exhibited mixed selectivity for observed objects, executed actions, and observed actions, enhanced when this information came from the monkey’s peripersonal working space. In contrast with the classical view, our findings indicate that multimodal coding emerges in AIP from partially-mixed selectivity of individual neurons for a variety of information relevant for planning actions directed to both physical objects and other subjects.

Learning a holistic and comprehensive code representation for code summarization

Code summarization is the task of describing the function of code snippets in natural language, which benefits program comprehension and boosts software productivity. Despite lots of effort made by previous studies, existing models are not comprehensive enough to represent code, and yet the literature does not consider to model source code with API usage from a holistic perspective. To this end, this paper proposes a novel multi-modal code summarization approach called HCCS (Learning a Holistic and Comprehensive code representation for Code Summarization). We first design a neural network based on the graph attention mechanism to encode API Context Graph (ACG), which highlights holistic information of source code. Then, a multi-modal framework with a tree encoder for Abstract Syntax Tree (AST) and a code encoder for code tokens is incorporated to learn a more comprehensive code representation. Afterwards, we propose a fusing layer to integrate the encodings, which are then passed to a joint-decoder to generate summaries. The experimental results show that HCCS achieves better performance than the state of the arts (i.e., HCCS scores 9.5% higher in terms of BLEU metric and 11.46% in terms of BERTScore). As a result, HCCS is an effective approach to generate high-quality summaries.

Autistic traits are associated with enhanced working memory capacity for abstract visual stimuli

We tested whether the association between autistic traits and enhanced performance in visual-perceptual tasks extends to visual working memory capacity. We predicted that any positive effect of autistic traits on visual working memory performance would be greatest during domain-specific tasks, in which visual resources must be relied upon. We used a visual ‘matrix’ task, involving recall of black-and-white chequered patterns which increased in size, to establish participants' capacity (span). We assessed 144 young adults' (M = 22.0 years, SD = 2.5) performance on abstract, ‘low semantic’ versus ‘high semantic’ task versions. The latter offered multimodal coding due to the availability of long-term memory resources that could supplement visual working memory. Participants also completed measures of autistic traits and trait anxiety. Autistic traits, especially Attention to Detail, Attention Switching, and Communication, positively predicted visual working memory capacity, specifically in the low semantic task, which relies on visual working memory resources. Autistic traits are therefore associated with enhanced processing and recall of visual information. The benefit is removed, however, when multimodal coding may be incorporated, emphasising the visual nature of the benefit. Strengths in focused attention to detail therefore appear to benefit domain-specific visual working memory task performance.

Processes and Manifestations of Digital Resilience: Video and Textual Insights From Sexual and Gender Minority Youth

Minority stressors harm sexual and gender minority youth (SGMY). This may be mitigated by promotive and protective factors and processes that manifest resilient coping. SGMY increasingly interact with information communication technologies (ICTs) to meet psychological needs, yet research often problematizes youths’ ICT use, inhibiting understanding about ICTs’ potential resilience-enhancing utilities. This study analyzes text and video responses of 609 SGMY aged 14 to 29 residing in Canada or the United States to an open-ended survey question about the benefits of using ICTs. Constructivist grounded theory integrating multimodal coding was used to analyze the data, producing a framework of digital resilience—digital processes and actions that generate positive growth—with four themes: Regulating Emotions and Curating Microsystems; Learning and Integrating; Advocating and Leading; and Cultivating Relationships and Communities of Care. Implications for clinical practice, survey innovation, and application of findings in fostering affirming digital microsystems for SGMY are discussed.

Cascaded Photon Confinement‐Mediated Orthogonal RGB‐Switchable NaErF4‐Cored Upconversion Nanoarchitectures for Logicalized Information Encryption and Multimodal Luminescent Anti‐Counterfeiting

AbstractOrthogonal upconversion nanoparticles (UCNPs) have received increasing research attention due to their unique optical performance. However, the establishment of the orthogonal UCNPs emitting primary red/green/blue (RGB) UCL remains a great challenge. Herein, RGB‐switchable NaErF4‐cored core–multishell UCNPs (RGB‐UCNPs) are constructed through the coordinative implementation of outside‐in cascaded photon harvesting and interfacial energy‐transfer (IET) inhibition. By modulating several constructional factors, the power‐density‐independent red, green, and blue luminescence in high color purity is readily available in the elaborated RGB‐UCNPs under 1550, 808, and 980 nm excitations, respectively. Notably, full‐color emissions, involving red, yellow, green, cyan, blue, magenta and white, are dynamically implemented in this single architecture through manipulating the excitation power of the combined 1550/808/980 nm lasers. These emission profiles of RGB‐UCNPs make them promising in broad photonic applications. As a proof of concept, the viability of the logicalized information encryption strategy and smartphone APP‐assisted multimodal luminescent QR code anti‐counterfeiting technique is demonstrated, which are both established based on directly useable RGB‐UCNPs with assistance of a self‐built combined laser system. This work not only opens up a new avenue to gain RGB‐switchable UCL and flexible full‐color output in a single nanostructure, but also provides enlightenment for developing the application scenarios of multicolor‐tunable UCNPs.

Quantum dense coding network using multimode squeezed states of light

We present a framework of a multimode dense coding network with multiple senders and a single receiver using continuous variable systems. The protocol is scalable to arbitrary numbers of modes with the encoding being displacements while the decoding involves homodyne measurements of the modes after they are combined in a pairwise manner by a sequence of beam splitters, thereby exhibiting its potentiality to implement in laboratories with currently available resources. We compute the closed form expression of the dense coding capacity for the cases of two and three senders that involve sharing of three- and four-mode states respectively. The dense coding capacity is calculated with the constraint of fixed average energy transmission when the modes of the sender are transferred to the receiver after the encoding operation. In both the cases, we demonstrate the quantum advantage of the protocol using paradigmatic classes of three- and four-mode states. The quantum advantage increases with the increase in the amount of energy that is allowed to be transmitted from the senders to the receiver.

Cross-Modal Discrimination Hashing Retrieval Using Variable Length

Fast cross-modal retrieval technology based on hash coding has become a hot topic for the rich multimodal data (text, image, audio, etc.), especially security and privacy challenges in the Internet of Things and mobile edge computing. However, most methods based on hash coding are only mapped to the common hash coding space, and it relaxes the two value constraints of hash coding. Therefore, the learning of the multimodal hash coding may not be sufficient and effective to express the original multimodal data and cause the hash encoding category to be less discriminatory. For the sake of solving these problems, this paper proposes a method of mapping each modal data to the optimal length of hash coding space, respectively, and then the hash encoding of each modal data is solved by the discrete cross-modal hash algorithm of two value constraints. Finally, the similarity of multimodal data is compared in the potential space. The experimental results of the cross-model retrieval based on variable hash coding are better than that of the relative comparison methods in the WIKI data set, NUS-WIDE data set, as well as MIRFlickr data set, and the method we proposed is proved to be feasible and effective.

Design and Simulation Studies of a Continuously Tunable Sub-THz Gyrotron Using Magnetic and Thermal Tuning Schemes

In this article, the frequency tunability of a sub-Terahertz (THz) gyrotron has been investigated using both magnetic and thermal tuning schemes. A triode-type magnetron injection gun (MIG) has been designed using an electron optics tool, which predicted an annular electron beam with a < 4% velocity spread. Furthermore, the cavity has been modeled to operate in the TE11,2 mode at 527 GHz for dynamic nuclear polarization-nuclear magnetic resonance (DNP/NMR) applications. The transient behavior of the present gyrotron has been studied using a time-dependent self-consistent multimode code. A maximum continuous wave (CW) power of ~14 W was obtained at 527.20 GHz with 16.75 kV, 90 mA, and 1.85 pitch of e-beam. The tunable bandwidth has been obtained as ~0.31 GHz (527.20–527.51 GHz) by using a magnetic tuning scheme and ~0.31 GHz (526.90–527.20 GHz) by using the thermal tuning scheme by taking advantage of gyrotron sensitivity on cavity structural parameters and cavity shape.

Encoding Qubits in Multimode Grid States

Encoding logical quantum information in harmonic oscillator modes is a promising and hardware-efficient approach to the realization of a quantum computer. In this work, we propose to encode logical qubits in grid states of an ensemble of harmonic oscillator modes. We first discuss general results about these multimode bosonic codes; how to design them, how to practically implement them in different experimental platforms and how lattice symmetries can be leveraged to perform logical non-Clifford operations. We then introduce in detail two two-mode grid codes based on the hypercubic and D4 lattices, respectively, showing how to perform a universal set of logical operations. We demonstrate numerically that multimode grid codes have, compared to their single-mode counterpart, increased robustness against propagation of errors from ancillas used for error correction. Finally, we highlight some interesting links between multidimensional lattices and single-mode grid codes concatenated with qubit codes.

Efficient Semi-Supervised Multimodal Hashing With Importance Differentiation Regression.

Multi-modal hashing learns compact binary hash codes by collaborating heterogeneous multi-modal features at both the model training and online retrieval stages to support large-scale multimedia retrieval. Previous multi-modal hashing methods mainly focus on supervised and unsupervised hashing. The performance of supervised hashing largely relies on the number of labeled data, which is practically expensive to obtain. Unsupervised hashing methods cannot effectively capture the semantic correlations of multi-modal data without any labels for supervision. In this paper, we propose an Efficient Semi-supervised Multi-modal Hashing with Importance Differentiation Regression (ESMH-IDR) model, which can alleviate the existing problems by learning from both labeled and unlabeled data. Specifically, in this paper, we develop an efficient semi-supervised multi-modal hash code learning module. It learns the hash codes for labeled data in an efficient asymmetric way, and simultaneously performs nonlinear regression using the same projection matrix as the labeled samples to preserve the intrinsic data structure of unlabeled data. Besides, different from existing methods, we propose an importance differentiation regression strategy to learn hash functions by specially considering the different importance of hash codes learned from the labeled and unlabeled samples. Finally, we develop an efficient discrete optimization method guaranteed with convergence to iteratively solve the hash optimization problem. Experiments on several public multimedia retrieval datasets demonstrate the superiority of our proposed method on both retrieval effectiveness and efficiency. Our source codes and testing datasets can be obtained at https://github.com/ChaoqunZheng/ESMH.

Multimodal Representation Learning for Place Recognition Using Deep Hebbian Predictive Coding.

Recognising familiar places is a competence required in many engineering applications that interact with the real world such as robot navigation. Combining information from different sensory sources promotes robustness and accuracy of place recognition. However, mismatch in data registration, dimensionality, and timing between modalities remain challenging problems in multisensory place recognition. Spurious data generated by sensor drop-out in multisensory environments is particularly problematic and often resolved through adhoc and brittle solutions. An effective approach to these problems is demonstrated by animals as they gracefully move through the world. Therefore, we take a neuro-ethological approach by adopting self-supervised representation learning based on a neuroscientific model of visual cortex known as predictive coding. We demonstrate how this parsimonious network algorithm which is trained using a local learning rule can be extended to combine visual and tactile sensory cues from a biomimetic robot as it naturally explores a visually aliased environment. The place recognition performance obtained using joint latent representations generated by the network is significantly better than contemporary representation learning techniques. Further, we see evidence of improved robustness at place recognition in face of unimodal sensor drop-out. The proposed multimodal deep predictive coding algorithm presented is also linearly extensible to accommodate more than two sensory modalities, thereby providing an intriguing example of the value of neuro-biologically plausible representation learning for multimodal navigation.

Trade-Offs on Number and Phase Shift Resilience in Bosonic Quantum Codes

Quantum codes typically rely on large numbers of degrees of freedom to achieve low error rates. However each additional degree of freedom introduces a new set of error mechanisms. Hence minimizing the degrees of freedom that a quantum code utilizes is helpful. One quantum error correction solution is to encode quantum information into one or more bosonic modes. We revisit rotation-invariant bosonic codes, which are supported on Fock states that are gapped by an integer g apart, and the gap g imparts number shift resilience to these codes. Intuitively, since phase operators and number shift operators do not commute, one expects a trade-off between resilience to number-shift and rotation errors. Here, we obtain results pertaining to the non-existence of approximate quantum error correcting g-gapped single-mode bosonic codes with respect to Gaussian dephasing errors. We show that by using arbitrarily many modes, g-gapped multi-mode codes can yield good approximate quantum error correction codes for any finite magnitude of Gaussian dephasing and amplitude damping errors.

RF behavior of a 35 GHz conventional cavity gyrotron using multimode analysis and PIC simulation

ABSTRACT In this paper, the RF behavior of a 35 GHz, TE 04 mode cylindrical cavity gyrotron is investigated using the nonlinear multimode theory and PIC code “CST Particle Studio”. A code is developed to realize the beam-wave interaction mechanism using time-dependent multimode theory. The cold cavity behavior is observed for the cavity operation in the designed TE 04 mode and 35 GHz frequency. The magnetic field profile is kept uniform along the length of the interaction structure. The output power is estimated for the designed mode as well as all nearby competing modes using the multimode analysis and PIC simulation. This yields the output power more than 200 kW with 33% efficiency in the designed TE 04 mode, whereas the smaller output power in the nearby competing modes. Power sustainability is also observed for the stable operation of the device. The effects of beam velocity spread and detuning of magnetic field on its performance are also observed.

Peek-a-who? Exploring the dynamics of early communication with an interactive partner swap paradigm and state space grid visualization

Effective caregiver-infant communication occurs when interactive partners successfully coordinate multiple modalities (e.g., body movements, affect, eye gaze). The complex interplay of multiple modalities during caregiver-infant interactions is difficult to capture, which has made a comprehensive, evidence-based understanding of caregiver-infant communication difficult to achieve. We present a novel methodological approach to address this challenge by combining an Interactive Partner Swap (IPS) paradigm with a longitudinal design, detailed multimodal coding, and data visualization via state space grids (SSGs). We demonstrate the utility of our approach by presenting three sets of SSGs which reveal both dyadic flexibility and stability in caregiver-infant peek-a-boo interactions across three levels: micro (moment-to-moment), meso (interactive context), and macro (infant development). By using SSGs to explore the patterns that hold and others that differ systematically across interactive partner and infant development, our novel approach promises to offer critical first steps to creating a more detailed understanding of the dynamics of early multimodal communication.

Low-Temperature-Induced Controllable Transversal Shell Growth of NaLnF4 Nanocrystals.

Highly controllable anisotropic shell growth is essential for further engineering the function and properties of lanthanide-doped luminescence nanocrystals, especially in some of the advanced applications such as multi-mode bioimaging, security coding and three-dimensional (3D) display. However, the understanding of the transversal shell growth mechanism is still limited today, because the shell growth direction is impacted by multiple complex factors, such as the anisotropy of surface ligand-binding energy, anisotropic core–shell lattice mismatch, the size of cores and varied shell crystalline stability. Herein, we report a highly controlled transversal shell growth method for hexagonal sodium rare-earth tetrafluoride (β-NaLnF4) nanocrystals. Exploiting the relationship between reaction temperature and shell growth direction, we found that the shell growth direction could be tuned from longitudinal to transversal by decreasing the reaction temperature from 310 °C to 280 °C. In addition to the reaction temperature, we also discussed the roles of other factors in the transversal shell growth of nanocrystals. A suitable core size and a relative lower shell precursor concentration could promote transversal shell growth, although different shell hosts played a minor role in changing the shell growth direction.

Конструирование пространственных образов: от когнитивной лингвистики к когнитивной поэтике (на материале романа Милорада Павича «Drugo telo»)

Статья посвящена проблеме конструирования пространственных образов в романе Милорада Павича «Drugo telo». Выявлено, что механизм ментального конструирования пространственных образов основан на принципе дополнительности вербальных и невербальных языков, то есть на принципе мультимодального кодирования информации. За вербальным текстом стоят визуальные, аудиальные, тактильные и иные образы. Достоверность ментальных пространственных образов обеспечивается детальностью воспоминаний, на которые опирается читатель в ходе интерпретации текста. Реалистичность восприятия текста предуготована тем, что за словесными индексами в памяти читателя хранятся иконические невербальные картины.

Engaging the Senses in Qualitative Research via Multimodal Coding: Triangulating Transcript, Audio, and Video Data in a Study With Sexual and Gender Minority Youth

The variety of formats in which qualitative data may be collected have been explored within the methodological literature. Yet, the multiple options for coding these data formats have not been comprehensively detailed. While transcript analysis is widely used across disciplines, it may have limitations—particularly for research involving marginalized populations. This paper presents a multimodal coding approach as a methodological innovation for triangulating three data formats (transcript, audio, and video), detailed through the application of this analytic approach during a qualitative study exploring media engagement with sexual and gender minority youth (SGMY). Nineteen semi-structured interviews with SGMY were filmed and transcribed. Nine independent coders then utilized the innovative multimodal approach to code the three data formats using a constructivist grounded theory framework. Some codes were similar across modalities, such as those related to safety issues and finding identity and community through media. Others differed between modalities, such as those related to participant affect, perceived contradictions, discrepancies between verbal statements and body language, level of comfort and engagement, and distress when discussing traumatic experiences. Video coding captured the broadest range of emotions and experiences from marginalized youth, while transcripts provided the most straightforward form of data for coding. Multimodal coding may be applicable across qualitative approaches to enrich analyses and account for potential biases, thereby enhancing analytical lenses in qualitative inquiry. Methodological strategies for coding and integrating data types are discussed.

NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans

Comprehensively resolving neuronal identities in whole-brain images is a major challenge. We achieve this in C.elegans by engineering a multicolor transgene called NeuroPAL (a neuronal polychromatic atlas of landmarks). NeuroPAL worms share a stereotypical multicolor fluorescence map for the entire hermaphrodite nervous system that resolves all neuronal identities. Neurons labeled with NeuroPAL do not exhibit fluorescence in the green, cyan, or yellow emission channels, allowing the transgene to be used with numerous reporters of gene expression or neuronal dynamics. We showcase three applications that leverage NeuroPAL for nervous-system-wide neuronal identification. First, we determine the brainwide expression patterns of all metabotropic receptors for acetylcholine, GABA, and glutamate, completing a map of this communication network. Second, we uncover changes in cell fate caused by transcription factor mutations. Third, we record brainwide activity in response to attractive and repulsive chemosensory cues, characterizing multimodal coding for these stimuli.