100 research outputs found
Microstructure and mechanical properties of large size as-cast Ti-43Al-9V-0.2Y (at.%) alloy ingot from brim to centre
A Ti-43Al-9V-0.2Y (at.%) alloy ingot with the size of Ф160×400mm was prepared by vacuum arc remelting (VAR). The microstructure of the as-cast Ti-43Al-9V-0.2Y alloy was composed of B2/α₂/γ lamellar colonies and massive B2 and γ phases which were distributed along the boundaries of these lamellar colonies in the form of equiaxed grains. Based on the grain size variation along the radius direction of the ingot, the ingot could be divided into four ring regions from brim to centre. It has been understood that the grain size variation between these four regions was due to the interplay of the effects of the cooling rate and the yttrium content on solidified microstructures in these regions. Mechanical testing of the samples cut from these four regions showed that there existed a clear correlation between the yield strength and the average grain sizes of the four ring regions, which approximately conformed to a Hall-Petch relationship
Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems
Recent academic research has extensively examined algorithmic collusion
resulting from the utilization of artificial intelligence (AI)-based dynamic
pricing algorithms. Nevertheless, e-commerce platforms employ recommendation
algorithms to allocate exposure to various products, and this important aspect
has been largely overlooked in previous studies on algorithmic collusion. Our
study bridges this important gap in the literature and examines how
recommendation algorithms can determine the competitive or collusive dynamics
of AI-based pricing algorithms. Specifically, two commonly deployed
recommendation algorithms are examined: (i) a recommender system that aims to
maximize the sellers' total profit (profit-based recommender system) and (ii) a
recommender system that aims to maximize the demand for products sold on the
platform (demand-based recommender system). We construct a repeated game
framework that incorporates both pricing algorithms adopted by sellers and the
platform's recommender system. Subsequently, we conduct experiments to observe
price dynamics and ascertain the final equilibrium. Experimental results reveal
that a profit-based recommender system intensifies algorithmic collusion among
sellers due to its congruence with sellers' profit-maximizing objectives.
Conversely, a demand-based recommender system fosters price competition among
sellers and results in a lower price, owing to its misalignment with sellers'
goals. Extended analyses suggest the robustness of our findings in various
market scenarios. Overall, we highlight the importance of platforms'
recommender systems in delineating the competitive structure of the digital
marketplace, providing important insights for market participants and
corresponding policymakers.Comment: 33 pages, 5 figures, 4 table
What To Do (and Not to Do) with Causal Panel Analysis under Parallel Trends: Lessons from A Large Reanalysis Study
Two-way fixed effects (TWFE) models are ubiquitous in causal panel analysis
in political science. However, recent methodological discussions challenge
their validity in the presence of heterogeneous treatment effects (HTE) and
violations of the parallel trends assumption (PTA). This burgeoning literature
has introduced multiple estimators and diagnostics, leading to confusion among
empirical researchers on two fronts: the reliability of existing results based
on TWFE models and the current best practices. To address these concerns, we
examined, replicated, and reanalyzed 37 articles from three leading political
science journals that employed observational panel data with binary treatments.
Using six newly introduced HTE-robust estimators, we find that although
precision may be affected, the core conclusions derived from TWFE estimates
largely remain unchanged. PTA violations and insufficient statistical power,
however, continue to be significant obstacles to credible inferences. Based on
these findings, we offer recommendations for improving practice in empirical
research
A Close Look at Spatial Modeling: From Attention to Convolution
Vision Transformers have shown great promise recently for many vision tasks
due to the insightful architecture design and attention mechanism. By
revisiting the self-attention responses in Transformers, we empirically observe
two interesting issues. First, Vision Transformers present a queryirrelevant
behavior at deep layers, where the attention maps exhibit nearly consistent
contexts in global scope, regardless of the query patch position (also
head-irrelevant). Second, the attention maps are intrinsically sparse, few
tokens dominate the attention weights; introducing the knowledge from ConvNets
would largely smooth the attention and enhance the performance. Motivated by
above observations, we generalize self-attention formulation to abstract a
queryirrelevant global context directly and further integrate the global
context into convolutions. The resulting model, a Fully Convolutional Vision
Transformer (i.e., FCViT), purely consists of convolutional layers and firmly
inherits the merits of both attention mechanism and convolutions, including
dynamic property, weight sharing, and short- and long-range feature modeling,
etc. Experimental results demonstrate the effectiveness of FCViT. With less
than 14M parameters, our FCViT-S12 outperforms related work ResT-Lite by 3.7%
top1 accuracy on ImageNet-1K. When scaling FCViT to larger models, we still
perform better than previous state-of-the-art ConvNeXt with even fewer
parameters. FCViT-based models also demonstrate promising transferability to
downstream tasks, like object detection, instance segmentation, and semantic
segmentation. Codes and models are made available at:
https://github.com/ma-xu/FCViT
CB-Conformer: Contextual biasing Conformer for biased word recognition
Due to the mismatch between the source and target domains, how to better
utilize the biased word information to improve the performance of the automatic
speech recognition model in the target domain becomes a hot research topic.
Previous approaches either decode with a fixed external language model or
introduce a sizeable biasing module, which leads to poor adaptability and slow
inference. In this work, we propose CB-Conformer to improve biased word
recognition by introducing the Contextual Biasing Module and the Self-Adaptive
Language Model to vanilla Conformer. The Contextual Biasing Module combines
audio fragments and contextual information, with only 0.2% model parameters of
the original Conformer. The Self-Adaptive Language Model modifies the internal
weights of biased words based on their recall and precision, resulting in a
greater focus on biased words and more successful integration with the
automatic speech recognition model than the standard fixed language model. In
addition, we construct and release an open-source Mandarin biased-word dataset
based on WenetSpeech. Experiments indicate that our proposed method brings a
15.34% character error rate reduction, a 14.13% biased word recall increase,
and a 6.80% biased word F1-score increase compared with the base Conformer
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