294 research outputs found
Temporal Interaction -- Bridging Time and Experience in Human-Computer Interaction
Traditional static user interfaces (UI) have given way to dynamic systems
that can intelligently adapt to and respond to users' changing needs. Temporal
interaction is an emerging field in human-computer interaction (HCI), which
refers to the study and design of UI that are capable of adapting and
responding to the user's changing behavioral and emotional states. By
comprehending and incorporating the temporal component of user interactions, it
focuses on developing dynamic and individualized user experiences. This idea
places a strong emphasis on the value of adjusting to user behavior and
emotions in order to create a more unique and interesting user experience. The
potential of temporal interaction to alter user interface design is highlighted
by this paper's examination of its capacity to adjust to user behavior and
react to emotional states. Designers can create interfaces that respond to the
changing demands, emotions, and behaviors of users by utilizing temporal
interactions. This produces interfaces that are not only highly functional but
also form an emotional connection with the users.Comment: 8 page
Depression and academic engagement among college students: the role of sense of security and psychological impact of COVID-19
BackgroundThe negative consequences of depression in college students have garnered global attention, especially in relation to academic achievement during the COVID-19 pandemic, which need critical assessment.AimThis study investigated whether a sense of security mediated the relationship between depression and academic engagement among college students during the pandemic and whether the moderating psychological impact of COVID-19 has a moderating effect on this relationship.MethodsIn this cross-sectional study, we recruited 466 college students from 30 provincial-level administrative regions in China via the Internet and used established scales to measure depression, academic engagement, a sense of security, and the psychological impact of COVID-19. The mediating and moderating effects were tested using the bootstrap method.ResultsDepression was found to negatively influence academic engagement, with a sense of security partially mediating this relationship. Moreover, the psychological impact of COVID-19 was shown to have a moderating effect on this mediating process.ConclusionThis study could aid in crafting pertinent strategies to mitigate the adverse effects of depression on learning amid unexpected public health crises and foster better mental health among college students
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Dynamic Network Characteristics of Power-electronics-based Power Systems
Power flow studies in traditional power systems aim to uncover the stationary relationship between voltage amplitude and phase and active and reactive powers; they are important for both stationary and dynamic power system analysis. With the increasing penetration of large-scale power electronics devices including renewable generations interfaced with converters, the power systems become gradually power-electronics-dominant and correspondingly their dynamical behavior changes substantially. Due to the fast dynamics of converters, such as AC current controller, the quasi-stationary state approximation, which has been widely used in power systems, is no longer appropriate and should be reexamined. In this paper, for a better description of network characteristics, we develop a novel concept of dynamic power flow and uncover an explicit dynamic relation between the instantaneous powers and the voltage vectors. This mathematical relation has been well verified by simulations on transient analysis of a small power-electronics-based power system, and a small-signal frequency-domain stability analysis of a voltage source converter connected to an infinitely strong bus. These results demonstrate the applicability of the proposed method and shed an improved light on our understanding of power-electronics-dominant power systems, whose dynamical nature remains obscure
High Sensitivity Tunable Radio Frequency Sensors
Highly sensitive and tunable RF sensors that provide detection and analysis of single cells and particles are provided. The tunable RF sensors are configured as tunable interferometers, wherein cells or particles to be analyzed are passed through a channel, such as a microfluidic channel, across waveguides corresponding to reference and test branches of the interferometers. A network analyzer coupled to the interferometers can be configured to measure a plurality of scattering parameters, such as transmission scattering coefficients (S.sub.21) of the reference and test branches, to evaluate characteristics of cells passing through the channel. A plurality of tunable interferometers may be employed, each interferometer operating in different frequency bands such that information obtain from the plurality of interferometers may be combined to provide further information
Automatic Model Selection with Large Language Models for Reasoning
Chain-of-Thought (CoT) and Program-Aided Language Models (PAL) represent two
distinct reasoning methods, each with its own strengths. CoT employs natural
language, offering flexibility and interpretability, while PAL utilizes
programming language, yielding more structured and rigorous logic. We introduce
a model selection method to combine the best of both worlds by employing a
large language model (LLM) to dynamically select between them. Our theoretical
analysis underscores the feasibility of this method, which is further
corroborated by empirical results. Our proposed method demonstrates significant
performance improvements across eight reasoning datasets with Codex, ChatGPT,
and GPT-4. Additionally, our method is complementary to self-consistency; when
integrated, it can further enhance performance while significantly reducing
computation costs. Moreover, we achieve new state-of-the-art results on GSM8K
and SVAMP, with respective accuracies of 96.8% and 93.7%. Our code, data and
prompts are available at https://github.com/XuZhao0/Model-Selection-ReasoningComment: EMNLP 2023 Finding
Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding
We endow Large Language Models (LLMs) with fine-grained self-evaluation to
refine multi-step reasoning inference. We propose an effective prompting
approach that integrates self-evaluation guidance through stochastic beam
search. Our approach explores the reasoning search space using a
well-calibrated automatic criterion. This enables an efficient search to
produce higher-quality final predictions. With the self-evaluation guided
stochastic beam search, we also balance the quality-diversity trade-off in the
generation of reasoning chains. This allows our approach to adapt well with
majority voting and surpass the corresponding Codex-backboned baselines by
, , and on the GSM8K, AQuA, and StrategyQA benchmarks,
respectively, in few-shot accuracy. Analysis of our decompositional reasoning
finds it pinpoints logic failures and leads to higher consistency and
robustness. Our code is publicly available at
https://github.com/YuxiXie/SelfEval-Guided-Decoding.Comment: Our code is publicly available at
https://github.com/YuxiXie/SelfEval-Guided-Decodin
Heart failure with midrange ejection fraction: Prior left ventricular ejection fraction and prognosis
Evidence-based guidelines for heart failure management depend mainly on current left ventricular ejection fraction (LVEF). However, fewer studies have examined the impact of prior LVEF. Patients may enter the heart failure with midrange ejection fraction (HFmrEF) category when heart failure with preserved ejection fraction (HFpEF) deteriorates or heart failure with reduced ejection fraction (HFrEF) improves. In this study, we examined the association between change in LVEF and adverse outcomes. HFmrEF patients with at least two or more echocardiograms 3 months apart at the First Affiliated Hospital of Dalian Medical University between September 1, 2015 and November 30, 2019 were identified. According to the prior LVEF, the subjects were divided into improved group (prior LVEF < 40%), stable group (prior LVEF between 40 and 50%), and deteriorated group (prior LVEF ≥ 50%). The primary outcomes were cardiovascular death, all-cause mortality, hospitalization for worsening heart failure, and composite event of all-cause mortality or all-cause hospitalization. A total of 1,168 HFmrEF patients (67.04% male, mean age 63.60 ± 12.18 years) were included. The percentages of improved, stable, and deteriorated group were 310 (26.54%), 334 (28.60%), and 524 (44.86%), respectively. After a period of follow-up, 208 patients (17.81%) died and 500 patients met the composite endpoint. The rates of all-cause mortality were 35 (11.29%), 55 (16.47%), and 118 (22.52%), and the composite outcome was 102 (32.90%), 145 (43.41%), and 253 (48.28%) for the improved, stable, and deteriorated groups, respectively. Cox regression analysis showed that the deterioration group had higher risk of cardiovascular death (HR: 1.707, 95% CI: 1.064-2.739, = 0.027), all-cause death (HR 1.948, 95% CI 1.335-2.840, = 0.001), and composite outcome (HR 1.379, 95% CI 1.096-1.736, = 0.006) compared to the improvement group. The association still remained significant after fully adjusted for both all-cause mortality (HR = 1.899, 95% CI 1.247-2.893, = 0.003) and composite outcome (HR: 1.324, 95% CI: 1.020-1.718, = 0.035). HFmrEF patients are heterogeneous with three different subsets identified, each with different outcomes. Strategies for managing HFmrEF should include previously measured LVEF to allow stratification based on direction changes in LVEF to better optimize treatment. [Abstract copyright: Copyright © 2021 Zhang, Sun, Zhang, Chen, Zhang, He, Song, Tse and Liu.
InstructCoder: Empowering Language Models for Code Editing
Code editing encompasses a variety of pragmatic tasks that developers deal
with daily. Despite its relevance and practical usefulness, automatic code
editing remains an underexplored area in the evolution of deep learning models,
partly due to data scarcity. In this work, we explore the use of large language
models (LLMs) to edit code based on user instructions, covering a broad range
of implicit tasks such as comment insertion, code optimization, and code
refactoring. To facilitate this, we introduce InstructCoder, the first dataset
designed to adapt LLMs for general-purpose code editing, containing
highdiversity code-editing tasks. It consists of over 114,000
instruction-input-output triplets and covers multiple distinct code editing
scenarios. The dataset is systematically expanded through an iterative process
that commences with code editing data sourced from GitHub commits as seed
tasks. Seed and generated tasks are used subsequently to prompt ChatGPT for
more task data. Our experiments demonstrate that open-source LLMs fine-tuned on
InstructCoder can edit code correctly based on users' instructions most of the
time, exhibiting unprecedented code-editing performance levels. Such results
suggest that proficient instruction-finetuning can lead to significant
amelioration in code editing abilities. The dataset and the source code are
available at https://github.com/qishenghu/CodeInstruct
UGC: Unified GAN Compression for Efficient Image-to-Image Translation
Recent years have witnessed the prevailing progress of Generative Adversarial
Networks (GANs) in image-to-image translation. However, the success of these
GAN models hinges on ponderous computational costs and labor-expensive training
data. Current efficient GAN learning techniques often fall into two orthogonal
aspects: i) model slimming via reduced calculation costs;
ii)data/label-efficient learning with fewer training data/labels. To combine
the best of both worlds, we propose a new learning paradigm, Unified GAN
Compression (UGC), with a unified optimization objective to seamlessly prompt
the synergy of model-efficient and label-efficient learning. UGC sets up
semi-supervised-driven network architecture search and adaptive online
semi-supervised distillation stages sequentially, which formulates a
heterogeneous mutual learning scheme to obtain an architecture-flexible,
label-efficient, and performance-excellent model
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