59 research outputs found
Impacts of industrial heterogeneity and technical innovation on the relationship between environmental performance and financial performance
In this paper, we investigate the relationship between environmental performance (EP) and financial performance (FP) from the perspective of technical innovation in Chinese industrial sectors. We also consider industrial heterogeneity and take temporal variations of the link into account. We collect the required data from different Chinese statistical yearbooks from 2004 to 2015. We use an aggregated index of environmental pollutants as a proxy for EP and return on assets as a proxy for FP, and we employ research and development expenditure to capture technical innovation. The empirical results indicate that industrial heterogeneity exists and the EPâFP link varies in different industrial groups. There is no evidence that the EPâFP link becomes more positive and more significant over time. Furthermore, the mediation effect of technical innovation and environmental pressures can jointly affect the link. Finally, technical innovation partially mediates the EPâFP link but only in Chinese light-polluting sectors not in heavy-polluting sectors. The mediating role of technical innovation has a great impact on shaping the EPâFP link. When technical innovation partially mediates the focal link, apart from the indirect link, the direct EPâFP link is likely to be positive. If not, the direct EPâFP link is likely to be negative
Exploring the relationships between different types of environmental regulations and environmental performance : evidence from China
The literature on the relationship between environmental regulations (ERs) and environmental performance (EP) of firms has largely ignored consideration of different kinds of ERs and the potential non-linear relationship between ERs and EP. This study uses the literature to differentiate three types of ERs (command-and-control regulations, market-based regulations and informal regulations) and further investigates (i) the linear links between different types of ERs and EP, and (ii) the potential non-linear relationships. The results provide support that the links between ERs and EP are not linear for command-and-control regulations and market-based regulations but non-linear and positive. For informal regulations, both the linear and non-linear relationships are not significant. We further test the impacts of time lag effects. Command-and-control regulations have impacts on EP both in current and the preceding years, whereas market-based regulations only affect EP in current year rather than in the preceding years. It takes 2 years to see the effects of informal regulations on EP
Muddled Loyalty: A Study of Islamic Centers in Boston Area
Thesis advisor: Peter SkerryThis thesis is a further study of Peter Skerryâs 2011 article, âthe Muslim-American Muddle,â in which he argues that not only non-Muslim Americans are worrying about Muslimsâ loyalty issue due to the fear of radical Islamism and terrorism, but also Muslims are confused. My basic argument is that Muslims are still suffering from their muddled loyalty. It is not because they are disloyal but because, in light of Grodzins, their organizations guide them in different directions which are not always en route to national loyalty as non-Muslims expect. Inspired by Morton Grodzinsâs theory on social structure and national loyalty in liberal democracies and James Q. Wilsonâs insightful study on political organizations, this research has sought to understand the Muslim muddle with an in-depth inquiry and examination on one of the most common and important Islamic organizationsâIslamic centers and mosques with an ethnographical method. The evidence of this thesis was collected between April 2016 and December 2017. In fact, I almost visited every mosque in Massachusetts. However, I was not always lucky to build strong connections with many centers for various reasons. In this thesis, I only select those mosques that I had visited more than three times. And I try my best to interview as many leaders as possible. I also manage to keep a geographical and sectarian balance in my sample. I hope to cover all types of mosques in Boston area. My findings are interesting, though of course often confusing and may contradicting with each other but I am duty-bound to report them even if it may had negative impact on the generalization power of my argument. I find that Islamic centers have different goals and offer different incentives to overcome collective actions problems. Both solidarity and political engagement are valued by Islamic centers in general, but individual organizations have different preferences which are results of divergent immigrant experiences. So the organizational aspect of Muslims community is fragmented. However, the increasing external political pressure in the post 9/11 period did not overcome the problem but aggravated it by simply empowering purposive mosques like ISBCC in public sphere.Thesis (MA) â Boston College, 2018.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Political Science
Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning
In representation learning, a disentangled representation is highly desirable
as it encodes generative factors of data in a separable and compact pattern.
Researchers have advocated leveraging disentangled representations to complete
downstream tasks with encouraging empirical evidence. This paper further
investigates the necessity of disentangled representation in downstream
applications. Specifically, we show that dimension-wise disentangled
representations are unnecessary on a fundamental downstream task, abstract
visual reasoning. We provide extensive empirical evidence against the necessity
of disentanglement, covering multiple datasets, representation learning
methods, and downstream network architectures. Furthermore, our findings
suggest that the informativeness of representations is a better indicator of
downstream performance than disentanglement. Finally, the positive correlation
between informativeness and disentanglement explains the claimed usefulness of
disentangled representations in previous works. The source code is available at
https://github.com/Richard-coder-Nai/disentanglement-lib-necessity.git.Comment: Accepted to AAAI-202
Optical ReLU-like Activation Function Based on a Semiconductor Laser with Optical Injection
Artificial neural networks usually consist of successive linear
multiply-accumulate operations and nonlinear activation functions. However,
most optical neural networks only achieve the linear operation in the optical
domain, while the optical implementation of activation function remains
challenging. Here we present an optical ReLU-like activation function based on
a semiconductor laser subject to the optical injection in experiment. The
ReLU-like function is achieved in a broad regime above the Hopf bifurcation of
the injection-locking diagram. In particular, the slope of the activation
function is reconfigurable by tuning the frequency difference between the
master laser and the slave laser
Opening the Black Box: The Impacts of Environmental Regulations on Technological Innovation.
Environmental regulations (ERs) that can stimulate technological innovation (TI) are the key to enabling a win-win strategy that benefits both economic development and environmental protection. This study seeks to analyze the impacts of ERs on TI. Previous literature highlighted that the black box of TI can be decomposed into technology investment and technology transformation, but empirical studies on such a decomposition have largely been ignored. Moreover, a detailed discussion of the links between ERs and the decomposed components of TI has not been conducted in developing countries such as China. Our study attempts to address these research gaps by (i) decomposing TI using a novel data envelopment analysis (DEA) procedure and further analyzing the impacts of ERs on the decomposed components of TI and (ii) applying this novel methodology to Chinese context. Accordingly, this study is conducted in two stages. First, a novel application of the slack-based measure Network DEA model is developed to uncover the black box of TI using Chinese data in order to estimate the overall efficiency of technological innovation (TIE) and to decompose it into the efficiency of technology investment (TVE) and the efficiency of technology transformation (TTE). Second, a random effect Tobit model is applied to (i) investigate both the linear and nonlinear impacts of ERs on TIE in all sectors and (ii) examine whether the impacts of ERs on TVE and TTE in different subprocesses are heterogeneous or not. Our results have showed the benefits of decomposing TI: while technology transformation in China closely follows the trend of TI, the trend of technology investment is somewhat different. The estimation results further indicate that the impacts of ERs on TIE are nonlinear. Besides, ERs have heterogeneous impacts on the decomposed components of TI. The impacts of ERs on TVE are nonlinear, whereas the impacts of ERs on TTE are statistically insignificant
Diagnosis after Zooming in: A Multi-label Classification Model by Imitating Doctor Reading Habits to Diagnose Brain Diseases
International audiencePurpose: Computed tomography (CT) has the advantages of being low cost and noninvasive and is a primary diagnostic method for brain diseases. However, it is a challenge for junior radiologists to diagnose CT images accurately and comprehensively. It is necessary to build a system that can help doctors diagnose and provide an explanation of the predictions. Despite the success of deep learning algorithms in the field of medical image analysis, the task of brain disease classification still faces challenges: Researchers lack attention to complex manual labeling requirements and the incompleteness of prediction explanations. More importantly, most studies only measure the performance of the algorithm, but do not measure the effectiveness of the algorithm in the actual diagnosis of doctors. Methods: In this paper, we propose a model called DrCT2 that can detect brain diseases without using image-level labels and provide a more comprehensive explanation at both the slice and sequence levels. This model achieves reliable performance by imitating human expert reading habits: targeted scaling of primary images from the full slice scans and observation of suspicious lesions for diagnosis. We evaluated our model on two open-access data sets: CQ500 and the RSNA Intracranial Hemorrhage Detection Challenge. In addition, we defined three tasks to comprehensively evaluate model interpretability by measuring whether the algorithm can select key images with lesions. To verify the algorithm from the perspective of practical application, three junior radiologists were invited to participate in the experiments, comparing the effects before and after human-computer cooperation in different aspects. Results: The method achieved F1-scores of 0.9370 on CQ500 and 0.8700 on the RSNA data set. The results show that our model has good interpretability under the premise of good performance. Human radiologist evaluation experiments have proven that our model can effectively improve the accuracy of the diagnosis and improve efficiency. Conclusions: We proposed a model that can simultaneously detect multiple brain diseases.The report generated by the model can assist doctors in avoiding missed diagnoses, and it has good clinical application value
Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction
We introduce Omni-LOS, a neural computational imaging method for conducting
holistic shape reconstruction (HSR) of complex objects utilizing a
Single-Photon Avalanche Diode (SPAD)-based time-of-flight sensor. As
illustrated in Fig. 1, our method enables new capabilities to reconstruct
near- surrounding geometry of an object from a single scan spot. In
such a scenario, traditional line-of-sight (LOS) imaging methods only see the
front part of the object and typically fail to recover the occluded back
regions. Inspired by recent advances of non-line-of-sight (NLOS) imaging
techniques which have demonstrated great power to reconstruct occluded objects,
Omni-LOS marries LOS and NLOS together, leveraging their complementary
advantages to jointly recover the holistic shape of the object from a single
scan position. The core of our method is to put the object nearby diffuse walls
and augment the LOS scan in the front view with the NLOS scans from the
surrounding walls, which serve as virtual ``mirrors'' to trap lights toward the
object. Instead of separately recovering the LOS and NLOS signals, we adopt an
implicit neural network to represent the object, analogous to NeRF and NeTF.
While transients are measured along straight rays in LOS but over the spherical
wavefronts in NLOS, we derive differentiable ray propagation models to
simultaneously model both types of transient measurements so that the NLOS
reconstruction also takes into account the direct LOS measurements and vice
versa. We further develop a proof-of-concept Omni-LOS hardware prototype for
real-world validation. Comprehensive experiments on various wall settings
demonstrate that Omni-LOS successfully resolves shape ambiguities caused by
occlusions, achieves high-fidelity 3D scan quality, and manages to recover
objects of various scales and complexity
The role of tripartite motif-containing 28 in cancer progression and its therapeutic potentials
Tripartite motif-containing 28 (TRIM28) belongs to tripartite motif (TRIM) family. TRIM28 not only binds and degrades its downstream target, but also acts as a transcription co-factor to inhibit gene expression. More and more studies have shown that TRIM28 plays a vital role in tumor genesis and progression. Here, we reviewed the role of TRIM28 in tumor proliferation, migration, invasion and cell death. Moreover, we also summarized the important role of TRIM28 in tumor stemness sustainability and immune regulation. Because of the importance of TRIM28 in tumors, TIRM28 may be a candidate target for anti-tumor therapy and play an important role in tumor diagnosis and treatment in the future
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