225 research outputs found
catena-Poly[[[aquaÂsilver(I)]-μ-1,1′-(butane-1,4-diÂyl)di-1H-imidazole-κ2 N 3:N 3′] hemi(biphenyl-4,4′-dicarboxylÂate) dihydrate]
In the title compound, {[Ag(C10H14N4)(H2O)](C14H8O4)0.5·2H2O}n, the AgI ion is three-coordinated by two N atoms from two independent 1,1′-(butane-1,4-diÂyl)di-1H-imidazole (BBI) ligands and one water O atom in a distorted T-shaped coordination geometry. The biphenyl-4,4′-dicarboxylÂate (BPDC) dianions do not coordinate to AgI ions but act as counter-ions. The AgI ions are linked by BBI ligands, forming a zigzag chain. These chains are linked into a two-dimensional supraÂmolecular architecture by O—H⋯O hydrogen-bonding interÂactions between water molÂecules and carboxylÂate O atoms of the BPDC dianions
Poly[[aquaÂ(μ-4,4′-bipyridine-κ2 N:N′)(μ3-2-nitro-5-sulfonatobenzoato-κ3 O 1:O 1′:O 5)copper(II)] 4,4′-bipyridine hemisolvate]
In the title compound, [Cu(C7H3NO7S)(C10H8N2)(H2O)]·0.5C10H8N2, the CuII atom is six-coordinated by two N atoms from two different bipyridine (bipy) ligands, one sulfonate O atom and two carboxylÂate O atoms from three 2-nitro-5-sulfonatobenzoate ligands and one water O atom in a distorted octaÂhedral geometry. The bipy solvent molÂecule lies on an inversion center. The CuII atoms are linked by the bipy ligands, forming one-dimensional chains, which are connected by the 2-nitro-5-sulfonatobenzoate ligands into a two-dimensional layer-like network. The two-dimensional structure is extended by O—H⋯O and O—H⋯N hydrogen bonds into a three-dimensional supraÂmolecular network
Entrepreneurship and Growth: Evidence from China
This paper examines the impact of entrepreneurship on economic growth by using a panel data set of 29 provinces in China over 20 years. Two indicators of entrepreneurship are defined and introduced into the traditional growth regression framework that is estimated using the system generalized method of moments. We also use the ratio of staff and workers of state-owned enterprises and per capita sown land area as the instrumental variables to identify the causal effect of entrepreneurship on economic growth. Our results suggest that entrepreneurship has a significant positive effect on economic growth and this finding is robust even after we control for other demographic and institutional variables. Our study provides some evidence that may be used as a basis for evaluating the effect of China’s policy on private business which has been increasingly relaxed since the late 1970s.
A Comprehensive Overview on the Generalized Anxiety Disorder – Etiology and Treatment
Generalized Anxiety Disorder (GAD) is a mental disorder that affects people across the lifespan. The study of GAD has evolved over time, with advancements in research methodologies and treatments. This paper provides an examination of the current understanding of GAD, including etiological factors and evidence-based treatments. Advances in neuroimaging technologies have contributed to a greater understanding of the neurological underpinnings of GAD, while psychotherapeutic interventions have emerged as effective treatment strategies. Moreover, GAD is associated with traits such as avoidance of perceived harm, neuroticism levels, and introversion preference. Studies have also explored the etiology of GAD from a genetic perspective. Future research should focus on validating the efficacy of treatments and exploring novel therapeutic combinations. Additionally, investigating the role of early life events, societal stressors, and cognitive biases in the development of GAD may provide insights into improving management and treatment strategies. This study provides further insights into the etiology of GAD and its treatment
Towards the Law of Capacity Gap in Distilling Language Models
Language model (LM) distillation is a trending area that aims to distil the
knowledge resided in a large teacher LM to a small student one. While various
methods have been proposed to push the distillation to its limits, it is still
a pain distilling LMs when a large capacity gap is exhibited between the
teacher and the student LMs. The pain is mainly resulted by the curse of
capacity gap, which describes that a larger teacher LM cannot always lead to a
better student LM than one distilled from a smaller teacher LM due to the
affect of capacity gap increment. That is, there is likely an optimal point
yielding the best student LM along the scaling course of the teacher LM. Even
worse, the curse of capacity gap can be only partly yet not fully lifted as
indicated in previous studies.
However, the tale is not ever one-sided. Although a larger teacher LM has
better performance than a smaller teacher LM, it is much more
resource-demanding especially in the context of recent large LMs (LLMs).
Consequently, instead of sticking to lifting the curse, leaving the curse as is
should be arguably fine. Even better, in this paper, we reveal that the optimal
capacity gap is almost consistent across different student scales and
architectures, fortunately turning the curse into the law of capacity gap. The
law later guides us to distil a 3B student LM (termed MiniMA) from a 7B teacher
LM (adapted LLaMA2-7B). MiniMA is demonstrated to yield a new
compute-performance pareto frontier among existing 3B LMs on commonly used
benchmarks, and its instruction-tuned version (termed MiniChat) outperforms a
wide range of 3B competitors in GPT4 evaluation and could even compete with
several 7B chat models.Comment: 22 pages, 8 figures, 12 tables, work in progress. Code and
checkpoints are available at https://github.com/GeneZC/MiniM
Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models
Large Language Models (LLMs) demonstrate remarkable performance on a variety
of natural language understanding (NLU) tasks, primarily due to their
in-context learning ability. This ability could be applied to building babylike
models, i.e. models at small scales, improving training efficiency. In this
paper, we propose a "CoThought" pipeline, which efficiently trains smaller
"baby" language models (BabyLMs) by leveraging the Chain of Thought prompting
of LLMs. Our pipeline restructures a dataset of less than 100M in size using
GPT-3.5-turbo, transforming it into task-oriented, human-readable texts that
are comparable to the school texts for language learners. The BabyLM is then
pretrained on this restructured dataset in a RoBERTa fashion. In evaluations
across 4 benchmarks, our BabyLM outperforms the vanilla RoBERTa in 10
linguistic, NLU, and question-answering tasks by more than 3 points, showing a
superior ability to extract contextual information. These results suggest that
compact LMs pretrained on small, LLM-restructured data can better understand
tasks and achieve improved performance.Comment: CoNLL 2023 BabyLM Challeng
OpenFE: Automated Feature Generation beyond Expert-level Performance
The goal of automated feature generation is to liberate machine learning
experts from the laborious task of manual feature generation, which is crucial
for improving the learning performance of tabular data. The major challenge in
automated feature generation is to efficiently and accurately identify useful
features from a vast pool of candidate features. In this paper, we present
OpenFE, an automated feature generation tool that provides competitive results
against machine learning experts. OpenFE achieves efficiency and accuracy with
two components: 1) a novel feature boosting method for accurately estimating
the incremental performance of candidate features. 2) a feature-scoring
framework for retrieving effective features from a large number of candidates
through successive featurewise halving and feature importance attribution.
Extensive experiments on seven benchmark datasets show that OpenFE outperforms
existing baseline methods. We further evaluate OpenFE in two famous Kaggle
competitions with thousands of data science teams participating. In one of the
competitions, features generated by OpenFE with a simple baseline model can
beat 99.3\% data science teams. In addition to the empirical results, we
provide a theoretical perspective to show that feature generation is beneficial
in a simple yet representative setting. The code is available at
https://github.com/ZhangTP1996/OpenFE.Comment: 23 pages, 3 figure
GoferBot: A Visual Guided Human-Robot Collaborative Assembly System
The current transformation towards smart manufacturing has led to a growing
demand for human-robot collaboration (HRC) in the manufacturing process.
Perceiving and understanding the human co-worker's behaviour introduces
challenges for collaborative robots to efficiently and effectively perform
tasks in unstructured and dynamic environments. Integrating recent data-driven
machine vision capabilities into HRC systems is a logical next step in
addressing these challenges. However, in these cases, off-the-shelf components
struggle due to generalisation limitations. Real-world evaluation is required
in order to fully appreciate the maturity and robustness of these approaches.
Furthermore, understanding the pure-vision aspects is a crucial first step
before combining multiple modalities in order to understand the limitations. In
this paper, we propose GoferBot, a novel vision-based semantic HRC system for a
real-world assembly task. It is composed of a visual servoing module that
reaches and grasps assembly parts in an unstructured multi-instance and dynamic
environment, an action recognition module that performs human action prediction
for implicit communication, and a visual handover module that uses the
perceptual understanding of human behaviour to produce an intuitive and
efficient collaborative assembly experience. GoferBot is a novel assembly
system that seamlessly integrates all sub-modules by utilising implicit
semantic information purely from visual perception
Experimental Study on the Influence of Slickwater on Shale Permeability
There are two diametrically opposite views of the influence of slickwater on shale permeability among scholars at home and abroad. We used the shale outcrops rock samples from the Lower Silurian Longmaxi Formation in Sichuan Basin. The permeability of these dry samples before and after immersion in different solution systems were tested by pulse attenuation method. The experimental results show that the impregnation of different slickwater components and standard salt solution can promote the increase of the permeability of shale samples. The stress sensitivity of shale samples after liquid immersion is medium weak to weak. The sample stress sensitivity is weak after soaked by the synergist solution and Drag reducing agent solution, and the sensitivity of the sample stress is medium weak after immersed by the standard saline solution, defoamer solution and antiswelling solution; The Ki/K0 of the shale sample after liquid immersion on σi/σ0 is consistent with the exponential stress sensitive evaluation model. With the increase of soaking time, the increase of sample permeability increases first and then decreases
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