catena-Poly[[[aqua­silver(I)]-μ-1,1′-(butane-1,4-di­yl)di-1H-imidazole-κ2 N 3:N 3′] hemi(biphenyl-4,4′-dicarboxyl­ate) 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-di­yl)di-1H-imidazole (BBI) ligands and one water O atom in a distorted T-shaped coordination geometry. The biphenyl-4,4′-dicarboxyl­ate (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 supra­molecular architecture by O—H⋯O hydrogen-bonding inter­actions between water mol­ecules and carboxyl­ate O atoms of the BPDC dianions

Poly[[aqua­(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 carboxyl­ate O atoms from three 2-nitro-5-sulfonatobenzoate ligands and one water O atom in a distorted octa­hedral geometry. The bipy solvent mol­ecule 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 supra­molecular 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.

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

Observation of pi/2 modes in an acoustic Floquet system

Topological phases of matter have remained an active area of research in the last few decades. Periodic driving is known to be a powerful tool for enriching such exotic phases, which leads to various phenomena with no static analogs. One such phenomenon is the emergence of the elusive $pi/2$ modes, i.e., a type of topological boundary state pinned at a quarter of the driving frequency. The latter may lead to the formation of Floquet parafermions in the presence of interaction, which is known to support more computational power than Majorana particles. In this work, we experimentally verify the signature of $\pi/2$ modes in an acoustic waveguide array, which is designed to simulate a square-root periodically driven Su-Schrieffer-Heeger model. This is accomplished by confirming the $4T$-periodicity ($T$ being the driving period) profile of an initial-boundary excitation, which we also show theoretically to be the smoking gun evidence of $\pi/2$ modes. Our findings are expected to motivate further studies of $\pi/2$ modes in quantum systems for potential technological applications.Comment: 6 pages, 3 figure. Comments are welcom