CHINESE COPYRIGHT LAW, PEER PRODUCTION AND THE PARTICIPATORY MEDIA AGE: AN OLD REGIME IN A NEW WORLD

In 2005, a funny flash song, "I Don't Want to Say I'm a Chicken", spread over the Internet (hereafter referred to as the Chicken Song Case). People were sharing it among friends, downloading it and using it as a mobile phone ring tone, and singing the song on KTV. The flash song is the lament of a chicken that was happy to be a source of eggs and meat, but is now facing extermination because of the threat of bird flu. Although the lyrics of the "Chicken Song" are creative and humorous, the melody of the song is lifted entirely from a famous Chinese song, "I Don't Want to Say", written by Li Haiying. As a result Li has sued the wireless content provider Kongzhong.com where the "Chicken Song" first appeared, for copyright infringement. Li believes he is owed an apology, 2 million Yuan in compensation, court costs and 50000 Yuan for mental suffering. In 2006, a video spoof of a big-budget film created by a Chinese blogger triggered a hot debate among Chinese legal academics on copyright law. Hue Ge in his short video titled, "The Bloodbath That Began with a Steamed Bun", mocks much more than Chen Kaige's movie, "The Promise" (hereafter referred to as the Steamed Bun Case). The video pokes fun at the premise of the movie in which a hungry girl lies to a boy and steals his steamed bun. The boy grows up hating the world and becomes a cold-blooded killer. Chen was so infuriated by the "Steamed Bun" that he threw stones at Hu and threatened to seek litigation against him

Inhibition of AR Restores the Sensitivity to Fulvestrant in ER-Positive Breast Cancer Cells

Endocrine therapy has been widely used in treating estrogen receptor (ER)-positive breast cancer which accounts for nearly 75% of breast cancer. Though endocrine therapy has shown great potency, acquired resistance occurs. Fulvestrant, the first selective ER down-regulator (SERD), was confirmed to completely suppress ERα and notably efficient. However, it has been observed that some ER-positive breast cancer would eventually develop unresponsiveness and acquired resistance to it, resulting in poor outcome. Several mechanisms have been proposed to be involved in antiestrogen resistance, such as activated pathways and altered expression of microRNAs. Of note, it is postulated androgen receptor (AR) which is often observed in most primary and metastatic breast cancer, might be a crucial protein associated with the efficacy of Fulvestrant, due to its common co-expression and intricate crosstalk with ER. In this study, we demonstrate that treatment suppressing ER would shift tumors from ER dependence to AR dependence, resulting in resistance to Fulvestrant and tumor growth. A blockade of AR could increase the sensitivity to Fulvestrant, and dual inhibition of AR and ER would be more effective than either drug alone, which might provide an insight into choosing optimal therapy for patients with AR-expressing ER-positive breast cancer. Furthermore, activated AR could also upregulate its downstream factor SOX9 to promote cell migration and proliferation

DNet: distributional network for distributional individualized treatment effects

There is a growing interest in developing methods to estimate individualized treatment effects (ITEs) for various real-world applications, such as e-commerce and public health. This paper presents a novel architecture, called DNet, to infer distributional ITEs. DNet can learn the entire outcome distribution for each treatment, whereas most existing methods primarily focus on the conditional average treatment effect and ignore the conditional variance around its expectation. Additionally, our method excels in settings with heavy-tailed outcomes and outperforms state-of-the-art methods in extensive experiments on benchmark and real-world datasets. DNet has also been successfully deployed in a widely used mobile app with millions of daily active users

Copyright law, digital content and the internet in the Asia-Pacific

This e-book provides an insight into the key issues facing copyright law and digital content policy in a networked information world, based on papers presented at the First International Forum on the Content Industry and Intellectual Property. Published by Sydney University Press, this e-book provides an insight into the key issues facing copyright law and digital content policy in a networked information world, based on papers presented at the First International Forum on the Content Industry and Intellectual Property. The book features chapters from a wide range of experts in their respective fields from across the Asia-Pacific region, including Australia, the People\u27s Republic of China, Hong Kong, Indonesia and Singapore. Some of the areas examined include the new digital environment, digital content policy, the networked information economy, copyright law and new media. The book provides a timely and scholarly appraisal of the legal and policy considerations facing anyone trying to regulate, sponsor or utilise the vast array of new media and content platforms now available

Ad Hoc Quantum Network Routing Protocol based on Quantum Teleportation

Abstract-In this paper, a quantum communication routing protocol is designed for quantum ad hoc network. This protocol is on-demand routing based on EPR numbers shared by adjacent nodes, concerning that it is a limited source. When quantum channel is established, quantum states from one quantum device can be teleport to another even when they do not share EPR pairs wirelessly. Part of information transferred by classic channel can be dealt with using simple logics. In this way, the goal of safety communication between source and destination is realized, improving the weakness of ad hoc network such as Eavesdropping and Active attacks. In terms of time complexity, the mechanism transports a quantum bit in time almost the same as the quantum teleportation does regardless of the number of hops between the source and destination. Index Terms-EPR pair, quantum route, quantum entanglement, ad hoc network, quantum teleportation

DFlow: Efficient Dataflow-based Invocation Workflow Execution for Function-as-a-Service

The Serverless Computing is becoming increasingly popular due to its ease of use and fine-grained billing. These features make it appealing for stateful application or serverless workflow. However, current serverless workflow systems utilize a controlflow-based invocation pattern to invoke functions. In this execution pattern, the function invocation depends on the state of the function. A function can only begin executing once all its precursor functions have completed. As a result, this pattern may potentially lead to longer end-to-end execution time. We design and implement the DFlow, a novel dataflow-based serverless workflow system that achieves high performance for serverless workflow. DFlow introduces a distributed scheduler (DScheduler) by using the dataflow-based invocation pattern to invoke functions. In this pattern, the function invocation depends on the data dependency between functions. The function can start to execute even its precursor functions are still running. DFlow further features a distributed store (DStore) that utilizes effective fine-grained optimization techniques to eliminate function interaction, thereby enabling efficient data exchange. With the support of DScheduler and DStore, DFlow can achieving an average improvement of 60% over CFlow, 40% over FaaSFlow, 25% over FaasFlowRedis, and 40% over KNIX on 99%-ile latency respectively. Further, it can improve network bandwidth utilization by 2x-4x over CFlow and 1.5x-3x over FaaSFlow, FaaSFlowRedis and KNIX, respectively. DFlow effectively reduces the cold startup latency, achieving an average improvement of 5.6x over CFlow and 1.1x over FaaSFlowComment: 22 pages, 13 figure

SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction

With the growing model size, deep neural networks (DNN) are increasingly trained over massive GPU accelerators, which demands a proper parallelization plan that transforms a DNN model into fine-grained tasks and then schedules them to GPUs for execution. Due to the large search space, the contemporary parallelization plan generators often rely on empirical rules that couple transformation and scheduling, and fall short in exploring more flexible schedules that yield better memory usage and compute efficiency. This tension can be exacerbated by the emerging models with increasing complexity in their structure and model size. SuperScaler is a system that facilitates the design and generation of highly flexible parallelization plans. It formulates the plan design and generation into three sequential phases explicitly: model transformation, space-time scheduling, and data dependency preserving. Such a principled approach decouples multiple seemingly intertwined factors and enables the composition of highly flexible parallelization plans. As a result, SuperScaler can not only generate empirical parallelization plans, but also construct new plans that achieve up to 3.5X speedup compared to state-of-the-art solutions like DeepSpeed, Megatron and Alpa, for emerging DNN models like Swin-Transformer and AlphaFold2, as well as well-optimized models like GPT-3

KI-basierte Detektion von Gebäuden mittels Deep Learning und amtlichen Geodaten zur Baufallerkundung

Zusammenfassung: Es wird ein neuartiger Ansatz vorgestellt, der auf einer Erkennung von Gebäuden und Gebäudeveränderungen aus hochaufgelösten Luftbildern anhand von Künstlicher Intelligenz (KI) beruht. Die zum Trainieren des KI-Systems notwendigen Datenbestände sind die Gebäudeumrisse aus der amtlichen Digitalen Flurkarte (DFK) und das lagerichtige Digitale Orthophoto (TrueDOP). Die semantische Detektion der Gebäude und Gebäudeveränderungen erfolgt über optische Aufnahmen und Oberflächenmodelle, wie dem normalisierten Oberflächenmodell (nDOM) und einem aus der Differenz von zwei Zeitepochen abgeleiteten Oberflächenmodell (tDOM). Am Beispiel der Baufallerkundung werden die Ergebnisse einer aktuellen Forschungskooperation zwischen der Bayerischen Vermessungsverwaltung (BVV) und der Technischen Universität München (TUM) aufgezeigt und bestehenden Verfahrenslösungen gegenübergestellt. Die vorgestellte KI-basierte Verfahrenslösung ist grundsätzlich auf alle Vermessungsverwaltungen der Länder bundesweit übertragbar. Summary: A novel approach is introduced which is based on the detection of buildings and building changes from high-resolution aerial images using artificial intelligence (AI). The data sets necessary for training the AI system are the building outlines from the official Digital Cadastre Map (DFK) and Digital Orthophotos without building lean (TrueDOP). The semantic detection of the buildings and building changes is carried out based on optical images and digital surface models, such as the normalized digital surface model (nDOM) and a Temporal Digital Surface Model (tDOM) derived from the difference of two time epochs. The results of the current cooperation between the Bavarian Agency for Digitisation, High-Speed Internet and Surveying (BVV) and the Technical University of Munich (TUM) on the detection of buildings and building changes are presented and compared with results derived from existing approaches. The presented AI-based solution is basically transferable to all surveying administrations of the federal states nationwide

[68Ga]Ga-DOTA-FAPI-04 PET/MR in patients with acute myocardial infarction: potential role of predicting left ventricular remodeling.

PURPOSE To assess predictive value of 68Ga-labeled fibroblast activation protein inhibitor-04 ([68Ga]Ga-DOTA-FAPI-04) PET/MR for late left ventricular (LV) remodeling in patients with ST-segment elevated myocardial infarction (STEMI). METHODS Twenty-six patients with STEMI were included in the study. [68Ga]Ga-DOTA-FAPI-04 PET/MR was performed at baseline and at average 12 months after STEMI. LV remodeling was defined as >10% increase in LV end-systolic volume (LVESV) from baseline to 12 months. RESULTS The LV remodeling group demonstrated higher [68Ga]Ga-DOTA-FAPI-04 uptake volume (UV) at baseline than the non-LV remodeling group (p < 0.001). [68Ga]Ga-DOTA-FAPI-04 UV at baseline was a significant predictor (OR = 1.048, p = 0.011) for LV remodeling at 12 months after STEMI. Compared to clinical information, MR imaging and cardiac function parameters at baseline, [68Ga]Ga-DOTA-FAPI-04 UV demonstrated better predictive ability (AUC = 0.938, p < 0.001) for late LV remodeling, with sensitivity of 100.0% and specificity of 81.3%. CONCLUSIONS [68Ga]Ga-DOTA-FAPI-04 PET/MR is an effective tool to non-invasively quantify myocardial fibroblasts activation, and baseline [68Ga]Ga-DOTA-FAPI-04 UV may have potential predictive value for late LV remodeling

Uric acid predicts recovery of left ventricular function and adverse events in heart failure with reduced ejection fraction: Potential mechanistic insight from network analyses

Background and Aims: Heart failure with reduced ejection fraction (HFrEF) still carries a high risk for a sustained decrease in left ventricular ejection fraction (LVEF) even with the optimal medical therapy. Currently, there is no effective tool to stratify these patients according to their recovery potential. We tested the hypothesis that uric acid (UA) could predict recovery of LVEF and prognosis of HFrEF patients and attempted to explore mechanistic relationship between hyperuricemia and HFrEF. Methods: HFrEF patients with hyperuricemia were selected from the National Inpatient Sample (NIS) 2016-2018 database and our Xianyang prospective cohort study. Demographics, cardiac risk factors, and cardiovascular events were identified. Network-based analysis was utilized to examine the relationship between recovery of LVEF and hyperuricemia, and we further elucidated the underlying mechanisms for the impact of hyperuricemia on HFrEF. Results: After adjusting confounding factors by propensity score matching, hyperuricemia was a determinant of HFrEF [OR 1.247 (1.172-1.328); Conclusion: Lower baseline UA value predicted the LVEF recovery and less long-term adverse events in HFrEF patients. Our results provide new insights into underlying mechanistic relationship between hyperuricemia and HFrEF