Corporate Governance Assessment on the Top 100 Chinese Listed Companies.

Corporate governance of listed companies has become a focus in China capital market. Corporate governance being the most important organization structure and control mechanism of modern enterprises is directly affected by its external environment and internal mechanism. In view of the external environment, legal system, market system, monitoring capability, socio-economic system, cultural environmental etc. will all affect the effectiveness of corporate governance from different aspects. The effectiveness of corporate governance is very much related to government governance or the even broader public governance. In view of a companys internal environment, corporate governance involves the balance amongst the board of directors, the management, shareholders and other stakeholders. The core objective is to solve the agency issues of the companys internal and external parties by appropriately arranged policies, so that management can endeavor for the maximization of profits for shareholders and stakeholders. Well corporate governance not only can provide effective monitoring, but can also encourage enterprises to create wealth for the society to the uttermost, and become a pattern for enterprise citizen.Corporate governance, China, firm behaviour

Field Implementation of Concrete Strength Sensor to Determine Optimal Traffic Opening Time

In the fast-paced and time-sensitive fields of construction and concrete production, real-time monitoring of concrete strength is crucial. Traditional testing methods, such as hydraulic compression (ASTM C 39) and maturity methods (ASTM C 1074), are often laborious and challenging to implement on-site. Building on prior research (SPR 4210 and SPR 4513), we have advanced the electromechanical impedance (EMI) technique for in-situ concrete strength monitoring, crucial for determining safe traffic opening times. These projects have made significant strides in technology, including the development of an IoT-based hardware system for wireless data collection and a cloud-based platform for efficient data processing. A key innovation is the integration of machine learning tools, which not only enhance immediate strength predictions but also facilitate long-term projections vital for maintenance and asset management. To bring this technology to practical use, we collaborated with third-party manufacturers to set up a production line for the sensor and datalogger assembly. The system was extensively tested in various field scenarios, including pavements, patches, and bridge decks. Our refined signal processing algorithms, benchmarked against a mean absolute percentage error (MAPE) of 16%, which is comparable to the ASTM C39 interlaboratory variance of 14%, demonstrate reliable accuracy. Additionally, we have developed a comprehensive user manual to aid field engineers in deploying, connecting, and maintaining the sensing system, paving the way for broader implementation in real-world construction settings

Calibrating "Cheap Signals" in Peer Review without a Prior

Peer review lies at the core of the academic process, but even well-intentioned reviewers can still provide noisy ratings. While ranking papers by average ratings may reduce noise, varying noise levels and systematic biases stemming from ``cheap'' signals (e.g. author identity, proof length) can lead to unfairness. Detecting and correcting bias is challenging, as ratings are subjective and unverifiable. Unlike previous works relying on prior knowledge or historical data, we propose a one-shot noise calibration process without any prior information. We ask reviewers to predict others' scores and use these predictions for calibration. Assuming reviewers adjust their predictions according to the noise, we demonstrate that the calibrated score results in a more robust ranking compared to average ratings, even with varying noise levels and biases. In detail, we show that the error probability of the calibrated score approaches zero as the number of reviewers increases and is significantly lower compared to average ratings when the number of reviewers is small

Determining Optimal Traffic Opening Time Through Concrete Strength Monitoring: Wireless Sensing

Construction and concrete production are time-sensitive and fast-paced; as such, it is crucial to monitor the in-place strength development of concrete structures in real-time. Existing concrete strength testing methods, such as the traditional hydraulic compression method specified by ASTM C 39 and the maturity method specified by ASTM C 1074, are labor-intensive, time consuming, and difficult to implement in the field. INDOT’s previous research (SPR-4210) on the electromechanical impedance (EMI) technique has established its feasibility for monitoring in-situ concrete strength to determine the optimal traffic opening time. However, limitations of the data acquisition and communication systems have significantly hindered the technology’s adoption for practical applications. Furthermore, the packaging of piezoelectric sensor needs to be improved to enable robust performance and better signal quality. In this project, a wireless concrete sensor with a data transmission system was developed. It was comprised of an innovated EMI sensor and miniaturized datalogger with both wireless transmission and USB module. A cloud-based platform for data storage and computation was established, which provides the real time data visualization access to general users and data access to machine learning and data mining developers. Furthermore, field implementations were performed to prove the functionality of the innovated EMI sensor and wireless sensing system for real-time and in-place concrete strength monitoring. This project will benefit the DOTs in areas like construction, operation, and maintenance scheduling and asset management by delivering applicable concrete strength monitoring solutions

Multitask Learning with CTC and Segmental CRF for Speech Recognition

Segmental conditional random fields (SCRFs) and connectionist temporal classification (CTC) are two sequence labeling methods used for end-to-end training of speech recognition models. Both models define a transcription probability by marginalizing decisions about latent segmentation alternatives to derive a sequence probability: the former uses a globally normalized joint model of segment labels and durations, and the latter classifies each frame as either an output symbol or a "continuation" of the previous label. In this paper, we train a recognition model by optimizing an interpolation between the SCRF and CTC losses, where the same recurrent neural network (RNN) encoder is used for feature extraction for both outputs. We find that this multitask objective improves recognition accuracy when decoding with either the SCRF or CTC models. Additionally, we show that CTC can also be used to pretrain the RNN encoder, which improves the convergence rate when learning the joint model.Comment: 5 pages, 2 figures, camera ready version at Interspeech 201

The persistence of elliptic lower dimensional tori with prescribed frequency for Hamiltonian systems

In this paper we consider the persistence of lower dimensional tori of a class of analytic perturbed hamiltonian system, $$H=\langle \omega(\xi), I \rangle +\frac12 \Omega_0(u^2+v^2)+P(\theta,I,z,\bar{z};\xi)$$ and prove that if frequencies $(\omega_0,\Omega_0)$ satisfy some non-resonant conditions and the Brouwer degree of the frequency mapping $\omega(\xi)$ at $\omega_0$ is nonzero, then there exists an invariant lower dimensional invariant torus, whose frequencies are the small dilation of $\omega_0$