Sequential Memory with Temporal Predictive Coding

Forming accurate memory of sequential stimuli is a fundamental function of biological agents. However, the computational mechanism underlying sequential memory in the brain remains unclear. Inspired by neuroscience theories and recent successes in applying predictive coding (PC) to \emph{static} memory tasks, in this work we propose a novel PC-based model for \emph{sequential} memory, called \emph{temporal predictive coding} (tPC). We show that our tPC models can memorize and retrieve sequential inputs accurately with a biologically plausible neural implementation. Importantly, our analytical study reveals that tPC can be viewed as a classical Asymmetric Hopfield Network (AHN) with an implicit statistical whitening process, which leads to more stable performance in sequential memory tasks of structured inputs. Moreover, we find that tPC exhibits properties consistent with behavioral observations and theories in neuroscience, thereby strengthening its biological relevance. Our work establishes a possible computational mechanism underlying sequential memory in the brain that can also be theoretically interpreted using existing memory model frameworks.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Robust Unsupervised Cross-Lingual Word Embedding using Domain Flow Interpolation

This paper investigates an unsupervised approach towards deriving a universal, cross-lingual word embedding space, where words with similar semantics from different languages are close to one another. Previous adversarial approaches have shown promising results in inducing cross-lingual word embedding without parallel data. However, the training stage shows instability for distant language pairs. Instead of mapping the source language space directly to the target language space, we propose to make use of a sequence of intermediate spaces for smooth bridging. Each intermediate space may be conceived as a pseudo-language space and is introduced via simple linear interpolation. This approach is modeled after domain flow in computer vision, but with a modified objective function. Experiments on intrinsic Bilingual Dictionary Induction tasks show that the proposed approach can improve the robustness of adversarial models with comparable and even better precision. Further experiments on the downstream task of Cross-Lingual Natural Language Inference show that the proposed model achieves significant performance improvement for distant language pairs in downstream tasks compared to state-of-the-art adversarial and non-adversarial models

PERSONALIZING HOSPITAL-BASED STROKE EDUCATION: DESIGNING A NOVEL RECOVERY APP TO PREPARE STROKE PATIENTS FOR THE TRANSITION HOME

Stroke is a leading cause of adult disability in the United States. It is a complex disease for which timely, intervention and treatment has the highest yield. Individualized patient education is crucial for optimizing recovery, preventing recurrence, and improving patient outcomes. Current hospital-based education is largely paper-based and suboptimal for meeting the varied, yet precise educational needs of the stroke patients and their care partners (carers). To augment the current education program, the multidisciplinary stroke team at the Johns Hopkins Comprehensive Stroke Center is building a mobile app that will provide individualized, interactive, and accessible education to prepare hospitalized stroke patients and their carers for the transition from hospital to home. This thesis project lays groundwork for the development of the app by designing its overall structure and navigation, and by prototyping specific paths demonstrating key features and functions of the app. User-centered methodology was implemented to focus each stage of app design on the fulfillment of unmet learning needs of stroke patients and carers in acute hospital care and after hospital discharge. Key identified needs were synthesized into four enabling objectives and the underlying information architecture that informed the scope and structure of the app. Digital prototypes of seven key tasks that translated the abstract groundwork into concrete visuals were developed in an iterative and collaborative process with stakeholders. The core features designed were (i) personalization of daily educational content, (ii) actionable recovery goal-setting, (iii) progress tracking, and (iv) improved two-way communication between patient and care team. Corresponding information architecture, interactive digital prototypes, and a model for progressive personalization were constructed. Together, these contributions provide the foundation for development of the first iteration of the app, and serve as valuable communication tools for continued collaboration and planning between stakeholders. The user-centered methodology imparted structure and strategy to the design process, while iteration enabled adaptability to new insights. Frequent usability testing, inquiry, and collaboration with stakeholders were essential to design refinement. The continued use of these methods during app development will maximize usability and efficacy of this novel personalized educational resource for early stroke recovery

Computational design process modeling

In the conceptual design phase, relatively simple equations and functions (or compiled code) are used to describe the aircraft and to perform trade-off studies. The latter require an optimal execution sequence in order to reduce computational cost and design time, respectively. The focus of this paper is the dynamic derivation of the optimal computational plan for each study so that the designer could focus on designing the aircraft rather than managing the process flow. Two methodologies, the Design Structure Matrix (DSM) and the Incidence Matrix are used for the computational process modeling. The incidence matrix describes the relationship between variables and equations/models. The DSM has been used to express the dependency relationships between the models and also, after manipulation, to produce the solution process. The designer specifies the independent (known) variables first. Then the variable flow is modeled using the Incidence Matrix Method (IMM). It determines how data flows through the models, and also identifies any strongly connected components (SCCs). The second step is to rearrange all equations/models hierarchically in order to reduce the feedback loops in each of the identified SCCs. This is achieved by the application of a genetic-based algorithm. Subsequently all SCCs and noncoupled models are assembled into a macro model which forms a global DSM. The global DSM is further rearranged to obtain an upper triangular matrix which defines the final model execution sequence. A simple aircraft sizing example is presented to illustrate the proposed method and algorithm. Advantages of the method include improved efficiency and the ability to deal with both algebraic and numerical models as well as with multiple outputs per model

Distribution and dynamics of water in the blended pastes unraveled by thermoporometry and dielectric properties

Water distribution in hardened paste and its dynamics determine many properties related to durability. Moisture distribution was determined by thermoporometry combined with vacuum drying. Dynamics of confined water were measured by broadband dielectric spectroscopy. Water in pores <2.4 nm cannot form tetrahedral ice structure due to geometrical constraints. The volume of unfrozen water (in interlayer and gel pores) decreases after the drying at all relative humidity levels. An evident coarsening of gel pores occurs with drying between 75 % and 55 % RH. 35 % fly ash and slag have limited effects on relaxation processes of silanol hydroxyl groups and interlayer water. However, they slow down the dynamics of water in small gel pores, thereby enhancing interactions between water and the solid interface. This study clarifies the microstructural changes during the drying and reveals the sensitivity of water dynamics to the chemical environment in C-S-H of blended pastes