4,318 research outputs found
A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding
Zero-shot dialogue understanding aims to enable dialogue to track the user's
needs without any training data, which has gained increasing attention. In this
work, we investigate the understanding ability of ChatGPT for zero-shot
dialogue understanding tasks including spoken language understanding (SLU) and
dialogue state tracking (DST). Experimental results on four popular benchmarks
reveal the great potential of ChatGPT for zero-shot dialogue understanding. In
addition, extensive analysis shows that ChatGPT benefits from the multi-turn
interactive prompt in the DST task but struggles to perform slot filling for
SLU. Finally, we summarize several unexpected behaviors of ChatGPT in dialogue
understanding tasks, hoping to provide some insights for future research on
building zero-shot dialogue understanding systems with Large Language Models
(LLMs).Comment: Technical Repor
Improvements to enhance robustness of third-order scale-independent WENO-Z schemes
Although there are many improvements to WENO3-Z that target the achievement
of optimal order in the occurrence of the first-order critical point (CP1),
they mainly address resolution performance, while the robustness of schemes is
of less concern and lacks understanding accordingly. In light of our analysis
considering the occurrence of critical points within grid intervals, we
theoretically prove that it is impossible for a scale-independent scheme that
has the stencil of WENO3-Z to fulfill the above order achievement, and current
scale-dependent improvements barely fulfill the job when CP1 occurs at the
middle of the grid cell. In order to achieve scale-independent improvements, we
devise new smoothness indicators that increase the error order from 2 to 4 when
CP1 occurs and perform more stably. Meanwhile, we construct a new global
smoothness indicator that increases the error order from 4 to 5 similarly,
through which new nonlinear weights with regard to WENO3-Z are derived and new
scale-independents improvements, namely WENO-ZES2 and -ZES3, are acquired.
Through 1D scalar and Euler tests, as well as 2D computations, in comparison
with typical scale-dependent improvement, the following performances of the
proposed schemes are demonstrated: The schemes can achieve third-order accuracy
at CP1 no matter its location in the stencil, indicate high resolution in
resolving flow subtleties, and manifest strong robustness in hypersonic
simulations (e.g., the accomplishment of computations on hypersonic
half-cylinder flow with Mach numbers reaching 16 and 19, respectively, as well
as essentially non-oscillatory solutions of inviscid sharp double cone flow at
M=9.59), which contrasts the comparative WENO3-Z improvement
New Therapeutic Approaches for the Treatment of Rheumatoid Arthritis may Rise from the Cholinergic Anti-Inflammatory Pathway and Antinociceptive Pathway
Due to the complex etiology of rheumatoid arthritis (RA), it is difficult to be completely cured at the current stage although many approaches have been applied in clinics, especially the wide application of nonsteroidal anti-inflammatory drugs (NSAIDs) and disease-modifying antirheumatic drugs (DMARDs). New drug discovery and development via the recently discovered cholinergic anti-inflammatory and antinociceptive pathways should be promising. Based on the above, the nicotinic acetylcholine receptor agonists maintain the potential for the treatment of RA. Therefore, new therapeutic approaches may rise from these two newly discovered pathways. More preclinical experiments and clinical trials are required to confirm our viewpoint
1,3-Dibenzyloxy-5-(bromomethyl)benzene
In the title compound, C21H19BrO2, the dihedral angles between the central benzene ring and the two peripheral rings are 50.28 (5) and 69.75 (2)°. The O—CH2 bonds lie in the plane of the central ring and adopt a syn–anti conformation
Microstructure and texture evolutions in FeCrAl cladding tube during pilger processing
The microstructure of FeCrAl cladding tubes depends on the fabricating process history. In this study, the microstructural characteristics of wrought FeCrAl alloys during industrial pilger processing into thin-walled tubes were investigated. The hot extruded tube showed ∼100 μm equiaxed grains with weak α∗-fiber in {h11}<1/h12> texture, while pilger rolling process change the microstructure to fragmented and elongated grains along the rolling direction. The pilgered textures could be predicted with the VPSC model. The inter-pass annealing at 800–850 \ub0C for 1 h results in recovery and recrystallization of the ferric matrix and restoration of ductility. The final finished tube shows fine recrystallized grains (∼11 μm) with dominant γ-fiber in three dimensions. Pilger rolling enhanced α-fiber while annealing reduced α-fiber and enhanced γ-fiber. Microstructural evolution in the Laves precipitates followed the sequence of faceted needle-like → spherical → faceted ellipsoidal. Thermomechanical processing resulted in cladding tubes with an area fraction of ∼5% and a number density of 5
7 10−11 m−2 in Laves precipitates, which is half that of the first-pilgered tube. Laves precipitates pin the grain boundaries to control the microstructure and prevent grain coarsening
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Graph learning plays a pivotal role and has gained significant attention in
various application scenarios, from social network analysis to recommendation
systems, for its effectiveness in modeling complex data relations represented
by graph structural data. In reality, the real-world graph data typically show
dynamics over time, with changing node attributes and edge structure, leading
to the severe graph data distribution shift issue. This issue is compounded by
the diverse and complex nature of distribution shifts, which can significantly
impact the performance of graph learning methods in degraded generalization and
adaptation capabilities, posing a substantial challenge to their effectiveness.
In this survey, we provide a comprehensive review and summary of the latest
approaches, strategies, and insights that address distribution shifts within
the context of graph learning. Concretely, according to the observability of
distributions in the inference stage and the availability of sufficient
supervision information in the training stage, we categorize existing graph
learning methods into several essential scenarios, including graph domain
adaptation learning, graph out-of-distribution learning, and graph continual
learning. For each scenario, a detailed taxonomy is proposed, with specific
descriptions and discussions of existing progress made in distribution-shifted
graph learning. Additionally, we discuss the potential applications and future
directions for graph learning under distribution shifts with a systematic
analysis of the current state in this field. The survey is positioned to
provide general guidance for the development of effective graph learning
algorithms in handling graph distribution shifts, and to stimulate future
research and advancements in this area
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