LLM-Powered Conversational Voice Assistants: Interaction Patterns, Opportunities, Challenges, and Design Guidelines

Conventional Voice Assistants (VAs) rely on traditional language models to discern user intent and respond to their queries, leading to interactions that often lack a broader contextual understanding, an area in which Large Language Models (LLMs) excel. However, current LLMs are largely designed for text-based interactions, thus making it unclear how user interactions will evolve if their modality is changed to voice. In this work, we investigate whether LLMs can enrich VA interactions via an exploratory study with participants (N=20) using a ChatGPT-powered VA for three scenarios (medical self-diagnosis, creative planning, and debate) with varied constraints, stakes, and objectivity. We observe that LLM-powered VA elicits richer interaction patterns that vary across tasks, showing its versatility. Notably, LLMs absorb the majority of VA intent recognition failures. We additionally discuss the potential of harnessing LLMs for more resilient and fluid user-VA interactions and provide design guidelines for tailoring LLMs for voice assistance

Solvent-Dictated Sodium Sulfur Redox Reactions: Investigation of Carbonate and Ether Electrolytes

Sulfur-based cathode chemistries are essential for the development of high energy density alkali-ion batteries. Here, we elucidate the redox kinetics of sulfur confined on carbon nanotubes, comparing its performance in ether-based and carbonate-based electrolytes at room temperature. The solvent is found to play a key role for the electrochemical reactivity of the sulfur cathode in sodium–sulfur (Na–S) batteries. Ether-based electrolytes contribute to a more complete reduction of sulfur and enable a higher electrochemical reversibility. On the other hand, an irreversible solution-phase reaction is observed in carbonate solvents. This study clearly reveals the solvent-dependent Na–S reaction pathways in room temperature Na–S batteries and provides an insight into realizing their high energy potential, via electrolyte formulation design

Highly Concentrated KTFSI : Glyme Electrolytes for K/Bilayered‐V₂O₅ Batteries

Highly concentrated glyme‐based electrolytes are friendly to a series of negative electrodes for potassium‐based batteries, including potassium metal. However, their compatibility with positive electrodes has been rarely explored. In this work, the influence of the molar fraction of potassium bis(trifluoromethanesulfonyl)imide dissolved in glyme on the cycling ability of K/bilayered‐V2O5 batteries has been investigated. At high salt concentration, the interaction between K+ ions with the glyme is strengthened, leading to a limited number of free glyme molecules. Therefore, the anodic decomposition of the electrolyte solvent, as well as the dissolution of the Al current collectors, is effectively suppressed, resulting in the improved cycling ability of the K/bilayered‐V2O5 cells. In these cells, the positive electrode active material exhibits reversible capacities of 93 and 57 mAh g−1 at specific current densities of 50 and 1000 mA g−1, respectively. After 200 charge‐discharge cycles at 500 mA g−1, the cell retains 94 % of the initial capacity. The promising rate performance and capacity retention demonstrate the importance of proper electrolyte engineering for the K/bilayered‐V2O5 batteries, and the good compatibility of highly concentrated glyme‐based electrolytes with positive electrode materials for potassium batteries

Highly concentrated KTFSI: Glyme electrolytes for K/bilayered-V2O5 batteries

Highly concentrated glyme-based electrolytes are friendly to a series of negative electrodes for potassium-based batteries, including potassium metal. However, their compatibility with positive electrodes has been rarely explored. In this work, the influence of the molar fraction of potassium bis(trifluoromethanesulfonyl)imide dissolved in glyme on the cycling ability of K/bilayered-V2O5 batteries has been investigated. At high salt concentration, the interaction between K+ ions with the glyme is strengthened, leading to a limited number of free glyme molecules. Therefore, the anodic decomposition of the electrolyte solvent, as well as the dissolution of the Al current collectors, is effectively suppressed, resulting in the improved cycling ability of the K/bilayered-V2O5 cells. In these cells, the positive electrode active material exhibits reversible capacities of 93 and 57 mAh g−1 at specific current densities of 50 and 1000 mA g−1, respectively. After 200 charge-discharge cycles at 500 mA g−1, the cell retains 94 % of the initial capacity. The promising rate performance and capacity retention demonstrate the importance of proper electrolyte engineering for the K/bilayered-V2O5 batteries, and the good compatibility of highly concentrated glyme-based electrolytes with positive electrode materials for potassium batteries. © 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA

"Mango Mango, How to Let The Lettuce Dry Without A Spinner?'': Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cooking Partner

The rapid advancement of the Large Language Model (LLM) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, particularly due to their extensive flexibility. However, users' real-world experiences interacting with these assistants remain unexplored. In this research, we chose cooking, a complex daily task, as a scenario to investigate people's successful and unsatisfactory experiences while receiving assistance from an LLM-based CA, Mango Mango. We discovered that participants value the system's ability to provide extensive information beyond the recipe, offer customized instructions based on context, and assist them in dynamically planning the task. However, they expect the system to be more adaptive to oral conversation and provide more suggestive responses to keep users actively involved. Recognizing that users began treating our LLM-CA as a personal assistant or even a partner rather than just a recipe-reading tool, we propose several design considerations for future development.Comment: Under submission to CHI202

RACE: An Efficient Redundancy-aware Accelerator for Dynamic Graph Neural Network

Dynamic Graph Neural Network (DGNN) has recently attracted a significant amount of research attention from various domains, because most real-world graphs are inherently dynamic. Despite many research efforts, for DGNN, existing hardware/software solutions still suffer significantly from redundant computation and memory access overhead, because they need to irregularly access and recompute all graph data of each graph snapshot. To address these issues, we propose an efficient redundancy-aware accelerator, RACE, which enables energy-efficient execution of DGNN models. Specifically, we propose a redundancy-aware incremental execution approach into the accelerator design for DGNN to instantly achieve the output features of the latest graph snapshot by correctly and incrementally refining the output features of the previous graph snapshot and also enable regular accesses of vertices\u27 input features. Through traversing the graph on the fly, RACE identifies the vertices that are not affected by graph updates between successive snapshots to reuse these vertices\u27 states (i.e., their output features) of the previous snapshot for the processing of the latest snapshot. The vertices affected by graph updates are also tracked to incrementally recompute their new states using their neighbors\u27 input features of the latest snapshot for correctness. In this way, the processing and accessing of many graph data that are not affected by graph updates can be correctly eliminated, enabling smaller redundant computation and memory access overhead. Besides, the input features, which are accessed more frequently, are dynamically identified according to graph topology and are preferentially resident in the on-chip memory for less off-chip communications. Experimental results show that RACE achieves on average 1139× and 84.7× speedups for DGNN inference, with average 2242× and 234.2× energy savings, in comparison with the state-of-the-art software DGNN running on Intel Xeon CPU and NVIDIA A100 GPU, respectively. Moreover, for DGNN inference, RACE obtains on average 13.1×, 11.7×, 10.4×, and 7.9× speedup and 14.8×, 12.9×, 11.5×, and 8.9× energy savings over the state-of-the-art Graph Neural Network accelerators, i.e., AWB-GCN, GCNAX, ReGNN, and I-GCN, respectively

Comparison of Nasopharyngeal MR, 18 F-FDG PET/CT, and 18 F-FDG PET/MR for Local Detection of Natural Killer/T-Cell Lymphoma, Nasal Type.

Objectives The present study aims to compare the diagnostic efficacy of MR, 18F-FDG PET/CT, and 18F-FDG PET/MR for the local detection of early-stage extranodal natural killer/T-cell lymphoma, nasal type (ENKTL). Patients and Methods Thirty-six patients with histologically proven early-stage ENKTL were enrolled from a phase 2 study (Cohort A). Eight nasopharyngeal anatomical regions from each patient were imaged using 18F-FDG PET/CT and MR. A further nine patients were prospectively enrolled from a multicenter, phase 3 study; these patients underwent 18F-FDG PET/CT and PET/MR after a single 18F-FDG injection (Cohort B). Region-based sensitivity and specificity were calculated. The standardized uptake values (SUV) obtained from PET/CT and PET/MR were compared, and the relationship between the SUV and apparent diffusion coefficients (ADC) of PET/MR were analyzed. Results In Cohort A, of the 288 anatomic regions, 86 demonstrated lymphoma involvement. All lesions were detected by 18F-FDG PET/CT, while only 70 were detected by MR. 18F-FDG PET/CT exhibited a higher sensitivity than MR (100% vs. 81.4%, χ2 = 17.641, P < 0.001) for local detection of malignancies. The specificity of 18F-FDG PET/CT and MR were 98.5 and 97.5%, respectively (χ2 = 0.510, P = 0.475). The accuracy of 18F-FDG PET/CT was 99.0% and the accuracy of MR was 92.7% (χ2 = 14.087, P < 0.001). In Cohort B, 72 anatomical regions were analyzed. PET/CT and PET/MR have a sensitivity of 100% and a specificity of 92.5%. The two methods were consistent (κ = 0.833, P < 0.001). There was a significant correlation between PET/MR SUVmax and PET/CT SUVmax (r = 0.711, P < 0.001), and SUVmean (r = 0.685, P < 0.001). No correlation was observed between the SUV and the ADC. Conclusion In early-stage ENKTL, nasopharyngeal MR showed a lower sensitivity and a similar specificity when compared with 18F-FDG PET/CT. PET/MR showed similar performance compared with PET/CT

Inhibition of Notch1 Signaling Alleviates Endotoxin-Induced Inflammation Through Modulating Retinal Microglia Polarization

Microglial cells are resident immune cells and play an important role in various cerebral and retinal inflammatory diseases. Notch1 signaling is involved in the microglia polarization and the control of cerebral inflammatory reactions. However, its role in endotoxin-induced uveitis (EIU) remains unknown. This study aimed to investigate the role of Notch1 signaling on retinal microglia polarization and inflammation in the cultured retinal microglial cells and EIU rat model. We found that Notch1 signaling blockade with N-[N-(3, 5-difluorophenacetyl)-1-alany1-S-phenyglycine t-butyl ester (DAPT) shifted retinal microglia phenotype from pro-inflammatory M1 phenotype (COX2+ and iNOS+) to anti-inflammatory M2 phenotype (Arg-1+) and reduced the release of pro-inflammatory cytokines both in vivo and in vitro. Moreover, DAPT treatment contributed to prevent retinal ganglion cells from apoptosis, reduce the intraocular infiltrating cells, and attenuate the impairment of retinal function. Taken together, these results suggest that inhibition of Notch1 signaling could alleviate the inflammatory response in EIU rat mainly through regulating the polarization of retinal microglia. Therefore, Notch1 signaling might be a promising therapeutic target in the treatment of ocular inflammatory diseases

Characterizing the Structural Pattern Predicting Medication Response in Herpes Zoster Patients Using Multivoxel Pattern Analysis

Herpes zoster (HZ) can cause a blistering skin rash with severe neuropathic pain. Pharmacotherapy is the most common treatment for HZ patients. However, most patients are usually the elderly or those that are immunocompromised, and thus often suffer from side effects or easily get intractable post-herpetic neuralgia (PHN) if medication fails. It is challenging for clinicians to tailor treatment to patients, due to the lack of prognosis information on the neurological pathogenesis that underlies HZ. In the current study, we aimed at characterizing the brain structural pattern of HZ before treatment with medication that could help predict medication responses. High-resolution structural magnetic resonance imaging (MRI) scans of 14 right-handed HZ patients (aged 61.0 ± 7.0, 8 males) with poor response and 15 (aged 62.6 ± 8.3, 5 males) age- (p = 0.58), gender-matched (p = 0.20) patients responding well, were acquired and analyzed. Multivoxel pattern analysis (MVPA) with a searchlight algorithm and support vector machine (SVM), was applied to identify the spatial pattern of the gray matter (GM) volume, with high predicting accuracy. The predictive regions, with an accuracy higher than 79%, were located within the cerebellum, posterior insular cortex (pIC), middle and orbital frontal lobes (mFC and OFC), anterior and middle cingulum (ACC and MCC), precuneus (PCu) and cuneus. Among these regions, mFC, pIC and MCC displayed significant increases of GM volumes in patients with poor response, compared to those with a good response. The combination of sMRI and MVPA might be a useful tool to explore the neuroanatomical imaging biomarkers of HZ-related pain associated with medication responses

Classification of temporal lobe epilepsy based on neuropsychological tests and exploration of its underlying neurobiology

ObjectiveTo assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests.MethodsTwenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain alterations and neuropsychological tests.ResultsWe found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the Right-Left Orientation Test difference was related to the superior temporal and the banks of the superior temporal sulcus (bankssts). The Conditional Association Learning Test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the Component Verbal Fluency Test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups.ConclusionThese results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE