Search

We incorporate unawareness into the delegation problem between a financial expert and an investor, and study their pre-delegation communication. The expert has superior awareness of the possible states of the world, and decides whether to reveal some of them to the investor. We find that the expert reveals all the possible states to the investor if the investor is initially aware of a large set of possible states, but reveals partially or nothing otherwise. An investor with a higher degree of unawareness tends to delegate a larger set of projects to the expert, giving rise to a higher incentive for the expert to keep her unaware.

The wall pressure fluctuations produced by a turbulent boundary layer (TBL) are the main excitation of structural vibrations and cabin noise in aircraft. To precisely estimate the structural vibrat...

This paper is the first to use the WeChat platform, one of the largest social networks, to conduct an online experiment of artificial investment games. We investigate how people’s forecasts about the financial market and investment decisions are shaped by whether they can observe others’ forecasts and whether they engage in public or private investment decisions. We find that with forecast sharing, subjects’ forecasts converge but in different directions across groups; consequently, forecast sharing does not lead to better forecasts nor more individually rational investment decisions. Whether or not subjects engage in public investment decisions does not significantly affect forecasts or investment.

Non-small-cell lung cancer (NSCLC) is a major cause of cancer-related death globally. Endothelial PAS domain-containing protein 1 (EPAS1) is a homolog of the hypoxia-inducible factor 1α and has been reported to confer tyrosine kinase inhibitor (TKI) resistance in NSCLC, but its role in peritoneal carcinomatosis of NSCLC is unknown. PC14HM, a high metastatic potential subline of NSCLC cell line PC14, was derived. Stable shRNA knockdown of EPAS1 was then established in PC14HM cells and subjected to assessment regarding the effects on proliferation and viability, xenograft tumor growth, metastatic potential, mesothelial–mesenchymal transition (MMT)-related characteristics and peritoneal carcinomatosis in a mouse model. EPAS1 expression was elevated in PC14HM cells. Knockdown of EPAS1 inhibited the proliferation and viability of PC14HM cells in vitro and suppressed tumorigenesis in vivo. In addition, the metastatic features and in vitro productions of MMT-inducing factors in PC14HM cells was also associated with EPAS1. More importantly, knockdown of EPAS1 drastically suppressed peritoneal carcinomatosis of PC14HM cells in vivo. EPAS1 promotes peritoneal carcinomatosis of NSCLC through enhancement of MMT and could therefore serve as a prognostic marker or a therapeutic target in treating NSCLC, particularly in patients with peritoneal carcinomatosis.

SUMMARYObesity is a major modifiable risk factor for pancreatic ductal adenocarcinoma (PDAC), yet how and when obesity contributes to PDAC progression is not well understood. Leveraging an autochthonous mouse model, we demonstrate a causal and reversible role for obesity in early PDAC progression, showing that obesity markedly enhances tumorigenesis, while genetic or dietary induction of weight loss intercepts cancer development. Bulk and single cell molecular analyses of human and murine samples define microenvironmental consequences of obesity that promote tumor development rather than new driver gene mutations. We observe increased inflammation and fibrosis and also provide evidence for significant pancreatic islet cell adaptation in obesity-associated tumors. Specifically, we identify aberrant islet beta cell expression of the peptide hormone cholecystokinin (CCK) in tumors as an adaptive response to obesity. Furthermore, beta cell CCK expression promotes oncogenicKras-driven pancreatic ductal tumorigenesis. Our studies argue that PDAC progression is driven by local obesity-associated changes in the tumor microenvironment – rather than systemic effects – and implicate endocrine-exocrine signaling beyond insulin in PDAC development. Furthermore, our demonstration that these obesity-associated adaptations are reversible supports the use of anti-obesity strategies to intercept PDAC early during progression.

A new attempt to measure the structure response generated by a turbulent boundary layer in a wind tunnel is proposed in this paper. To develop a scaling procedure that accommodates both the structural vibration and turbulent boundary-layer excitation for predicting the vibration response of a structure using a scaled model in a wind tunnel, a receptance method is used to extend the scaling procedure to a curved plate. Because the scaling equations are developed for a simply supported boundary and a clamped boundary, a modal analysis is used to confirm that the scaling equations are also applicable to a plate with unsupported boundary conditions. The characteristic parameters of the structural vibrations and the flow parameters for a full-scale object are both scaled. In addition, the scaling procedures consider the effects of the material used in the scaled model in predicting the vibrations of the full-scale structure. The scaling procedures are developed from theoretical methods, and numerical technique...

Inhibition of the sodium-glucose co-transporter type 2 (SGLT2) has received growing acceptance as a novel, safe and effective means to improve glycemic control in patients with type 2 diabetes. Inhibition of SGLT2 lowers the renal glucose threshold and reduces plasma glucose by promoting glucose excretion in urine. Both animal studies and clinical trials in man suggest that SGLT2 inhibition has the potential to improve pancreatic β-cell function by reducing glucose toxicity. However, there is limited data exploring how reducing glucotoxicity via SGLT2 inhibition affects rates of β-cell proliferation and death throughout life in the context of insulin resistance and type 2 diabetes. SGLT2−/− mice were backcrossed to the db/db strain to produce littermate control db/db-SGLT2+/+ and experimental db/db-SGLT2−/− mice. Mice were euthanized at 5, 12 and 20 weeks of age to collect plasma for glucose, insulin, lipid and cytokine measures, and pancreata for histological analysis including determination of β-cell mass and rates of proliferation and death. SGLT2 deletion in db/db mice reduced plasma glucose as early as 5 weeks of age and continued throughout life without changes in plasma lipids or cytokines. Reduced plasma glucose levels occurred in parallel with an increase in the relative β-cell volume and reduced frequency of β-cell death, and no apparent change in rates of β-cell proliferation. These data add to a growing body of evidence demonstrating that improved glycemic control achieved through SGLT2 inhibition can preserve β-cell function and endogenous insulin secretion by reducing glucose toxicity and rates of β-cell death.

Background: Long noncoding RNAs (lncRNAs) are a new class of cancer regulators. Here, we aimed to investigate the diagnostic and therapeutic values of an lncRNA, differentiation antagonizing noncoding RNA (DANCR), in lung cancer. Methods: Real-time polymerase chain reaction was used to compare DANCR levels in normal and cancerous lung tissues as well as lung cancer cells. Lentiviral transduction was used to induce DANCR overexpression or silencing in vitro, followed by monitoring cell proliferation, colony formation, and changes in microRNA-216a (miR-216a) expression. DANCR-specific small hairpin RNA transduction was used to establish cells with stable DANCR knockdown, and silenced cells were used to initiate lung tumor xenografts, followed by monitoring tumor growth. Results: DANCR upregulation was seen in lung cancer, particularly in high-grade lung cancer tissues and aggressive cancer cells. Ectopic DANCR expression induced lung cancer cell proliferation and colony formation, whereas DANCR silencing induced opposing effects. The miR-216a level in cancer cells was negatively correlated with DANCR expression. The DANCR knockdown reduced the growth of tumor xenografts in vivo. Conclusion: DANCR upregulation is a potential indicator of aggressive lung cancer. Silencing of DANCR has great potential as a potent therapeutic strategy in lung cancer.

We incorporate unawareness into the delegation problem between a financial expert and an investor, and study their pre-delegation communication. The expert has superior awareness of the possible states of the world, and decides whether to reveal some of them to the investor. We find that the expert reveals all the possible states to the investor if the investor is initially aware of a large set of possible states, but only reveals partially or nothing otherwise. An investor of a higher degree of unawareness tends to delegate a larger set of projects to the expert, giving rise to a higher incentive for the expert to keep her unaware.

This article explores the relationship between the sent-down youth movement and economic development in rural China during the Cultural Revolution. It examines ways in which sent-down youth themselves initiated improvements in rural life, and more importantly, how local officials used both their presence to acquire equipment and technical training and their skills and education to promote rural industry. The sent-down youth offices established in the cities and the countryside inadvertently provided connections between remote rural counties and large urban centres that enabled the transfer of a significant quantity of material goods, ranging from electrical wires and broadcast cables to tractors and factory machinery. Ultimately, we show how individual sent-down youths, their families, and both urban and rural officials – none of whom had a role in determining government policies – identified and made use of resources that those policies unintentionally produced.

Oesophageal cancer (OC) is one of the most fatal malignancies in the world, and chemoresistance restricts the therapeutic outcome of OC. Long noncoding RNA (lncRNA) was reported to play roles in multiple cancer types. Yet, the function of lncRNA in chemoresistance of OC has not been reported. A lncRNA gene, PCAT-1, showed higher expression in OC tissues, especially higher in secondary OC compared with normal mucosa tissues. Overexpression of PCAT-1 increased the proliferation rate and growth of OC cells. Inhibition of PCAT-1 decreased proliferation and growth of OC cells, and increased cisplatin chemosensitivity. In a mouse OC xenograft model, PCAT-1 inhibition repressed OC growth in vivo. Therefore, PCAT-1 may potentially serve as a therapeutic target for treating OC. PCAT-1 promotes development of OC and represses the chemoresistance of OC to cisplatin, and silencing of PCAT-1 may be a therapeutic strategy for treating OC.

Inequality between private and public patients in Australia has been an ongoing concern due to its two tiered insurance system. This paper investigates the variations in hospital length of stay for hip replacements using Victorian Admitted Episodes Dataset from 2003/2004 to 2014/2015, employing a Bayesian hierarchical random coefficient model with trend allowing for structural break. We find systematic differences in the length of stay between public and private hospitals, after observable patient complexity is controlled. This suggests shorter stay in public hospitals due to pressure from Activity-based funding scheme, and longer stay in private system due to potential moral hazard. Our counterfactual analysis shows that public patients stay 1.4 days shorter than private in 2014, which leads to the 'quicker but sicker' concern that is commonly voiced by the public. We also identify widespread variations among individual hospitals. Sources for such variation warrant closer investigation by policy makers.

We study a retail financial market with naive investors who are unaware of possible financial fraud. In our model, firms strategically choose whether to offer normal or fraudulent products to possibly unaware investors. Having new firms in the market makes offering normal products less profitable and thus discourages firms from behaving honestly. In a leader-follower environment, an honest firm may sell a normal product to sophisticated investors, while a dishonest firm targets only naive investors. By disclosing information about financial fraud, the honest firm can steal market share from the dishonest firm, but doing so may induce the dishonest firm to deviate and compete for the normal-product market, which limits the honest firm's incentive to disclose information. Policy instruments, such as increasing legal punishment, implementing public education programs, and lowering the interest rate ceiling, may also trigger the honest firm to strategically shroud information. As a consequence, these policies cannot ensure an improvement in investor welfare.

The paper studies how common codes of artificial language in communication are developed in the laboratory. We find that codes emerging from an environment with more variable spatial positions tend to use a limited set of symbols to represent positions, whereas codes emerging from an environment with more variable geometric shapes tend to discriminate among shapes. The paper also experimentally shows that "language" affects the way its "speakers" share theview about a novel figure.

The paper studies how common codes of artificial language in communication are developed in the laboratory. We find that codes emerging from an environment with more variable spatial positions tend to use a limited set of symbols to represent positions, whereas codes emerging from an environment with more variable geometric shapes tend to discriminate among shapes. The paper also experimentally shows that “language” affects the way its “speakers” share the view about a novel figure. Accepted version

In the presence of limited liability, the incentive compatibility constraints in financial contracting models with asymmetric information are weaker than assumed in much of the literature. In this paper, we identify the problem that has been overlooked in the literature and provide a rigorous proof of optimality of debt contracts under fairly general assumptions.

This paper characterizes the optimal supply-chain contract from the perspective of contract theory. While most of the supply-chain literature takes the contractual form as given and investigates its implications and uses in various contexts, our approach does not impose any functional form restriction in the contract space. We show that when the retailer faces limited liability and privately observes demand, very generally, the optimal arrangement can be implemented by the familiar buy-back contract. Under this contract, the retailer makes a fixed payment and sells unsold inventory back to the supplier at a price equal to the retailer's salvage value. Even though the returns can be inefficient from the supply chain perspective, they serve as a screening tool that differentiates different types of retailers ex post. When the demand is observable to both parties, the optimal contract resembles the classical consignment arrangement with revenue sharing. On the other hand, in the absence of limited liability, the optimal contract features the franchise fee contract, according to which the retailer pays a fixed transfer regardless of the actual units sold. Our analysis therefore provides a holistic justification of several practical contracts in supply chains.

Online social network has become a popular way for users to express themselves, connect and share information with each other. However, in online social networks, the connections between different users are all in binary status, which neglects the relationship strengths between them. Meanwhile, the relationship strength between different users is activity field specific. In different activity fields, such as traveling, shopping, and sport, the relationship strengths between the same users may vary significantly. Therefore, in this paper we propose a general framework to measure the relationship strengths between different users, taking consideration not only the user's profile information but also the interaction activities and the activity fields. We conduct the experiments on Facebook dataset and the results show that the proposed framework is promising and can be used to improve the performances of various applications.

Video recommendation is an important tool to help people access interesting videos. In this paper, we propose a universal scheme to integrate rich information for personalized video recommendation. Our approach regards video recommendation as a ranking task. First, it generates multiple ranking lists by exploring different information sources. In particular, one novel source user's relationship strength is inferred through the online social network and applied to recommend videos. Second, based on multiple ranking lists, a multi-task rank aggregation approach is proposed to integrate these ranking lists to generate a final result for video recommendation. It is shown that our scheme is flexible that can easily incorporate other methods by adding their generated ranking lists into our multi-task rank aggregation approach. We conduct experiments on a large dataset with 76 users and more than 11,000 videos. The experimental results demonstrate the feasibility and effectiveness of our approach.

The properties of a time-of-flight scintillator bar, EJ-200, wrapped with different reflective materials, were studied using 800MeV/c electron beam at the test beam of IHEP in China. It is 230 cm long with a cross-section of 5 x 6 cm 2, viewed from both ends by R5924 photomultiplier tubes. The results show that the time resolution and the attenuation length depend on the reflective materials. (c) 2005 Elsevier B.V. All rights reserved.

With internet delivery of video content surging to an un-precedented level, video recommendation has become an important approach for helping people access interesting videos. In this paper, we propose a novel approach to integrate viewing history for personalized video recommendation. For a given user, our approach calculates a recommendation score for each video candidate, that composes of two parts: the interest degree of this video by the user's friends, and the taste similarities between the user and his friends. We measure the interest degree of each video by considering its textual, visual and popularity information. Meanwhile, we construct tag set for each user based on his/her viewing history to estimate the taste similarities between different users. The final recommended videos are ranked according to the accumulated recommendation scores from different recommenders. We conduct experiments with 45 users and more than 11, 000 videos, and the results demonstrate the feasibility and effectiveness of our approach.

This paper examines the interaction between an informed seller and an uninformed buyer, facing the possibly inappropriate status quo product. The buyer can invest cognitive resources before the purchasing decision. In the one-shot interaction, it is impossible for the seller to always sell the appropriate product. If the extent of mis-selling (the probability that the status quo product is not appropriate) is low, the seller announces that nothing of the status quo product will go wrong. Otherwise, he randomizes between truth-telling and mis-selling when the status quo product is not appropriate. The transaction cost of the buyer's pre-purchase deliberation increases with the extent of mis-selling as the extent of mis-selling is small and decreases thereafter. Finally, reputation with a "tip" mechanism or competition between sellers may restore the efficient outcome where this transaction cost vanishes.

Video recommendation is a hot research topic to help people access interesting videos. The existing video recommendation approaches include CBF, CF and HF. However, these approaches treat the relationships between all users as equal and neglect an important fact that the acquaintances or friends may be a more reliable source than strangers to recommend interesting videos. Thus, in this paper we propose a novel approach to improve the accuracy of video recommendation. For a given user, our approach calculates a recommendation score for each video candidate that composes of two parts: the interest degree of this video by the user's friends, and the relationship strengths between the user and his friends. The final recommended videos are ranked according to the accumulated recommendation scores from different recommenders. We conducted experiments with 45 participants and the results demonstrated the feasibility and effectiveness of our approach.

The SimCell™ overcomes many limitations seen in traditional systems for fed-batch process development. In this study, we demonstrated that SimCell is a robust automated tool for rapid platform process development. A platform feed and fed-batch process parameters were identified for three recombinant CHO cell lines using three consecutive SimCell experiments: Feasibility, Feed Screening and Feed Strategy Optimization. The microfluidic miniature bioreactor system was first calibrated and baseline performance was set up for each cell line. Seven chemically-defined (CD) feeds were designed based on spent media analysis and were screened in the SimCell using 378 microbioreactors. The optimal feed improved growth substantially and increased recombinant protein titers by 2 fold compared to batch cultures for all three cell lines. SimCell fed-batch process parameters associated with the feed, such as temperature shift, pH, feed schedule and volume, were optimized using 420 microbioreactors in a Design-of-Experiment (DoE) layout. Similarities and differences in fed-batch parameters were seen across the three cell lines. Recombinant protein titers were improved by 3–4 fold compared with batch cultures. For one cell line, recombinant protein titers were doubled compared to the original control process. Statistical analysis was performed to identify the optimal production parameters for each cell line.

Community Question Answer (cQA) archives contain rich sources of knowledge on extensive topics, in which the quality of the submitted answer is uneven, ranging from excellent detailed answers to completely unrelated content. We propose a framework to generate complete, relevant, and trustful answer summaries. The framework discusses answer summarization in terms of maximum coverage problem with knapsack constraint on conceptual level. Global inference algorithm is employed to extract sentences according to the saliency scores of concepts. The saliency score of each concept is assigned through a two-layer graph-based random walk model incorporating the user social features and text content from answers. The experiments are implemented on a data set from Yahoo! Answer. The results show that our method generates satisfying summaries and is superior to the state-of-the-art approaches in performance.

Financial time series streams are watched closely by millions of traders. What exactly do they look for and how can we help them do it faster? Physicists study the time series emerging from their sensors. The same question holds for them. Musicians produce time series. Consumers may want to compare them. This tutorial presents techniques and case studies for four problems:1. Finding sliding window correlations in financial, physical, and other applications.2. Discovering bursts in large sensor data of gamma rays.3. Matching hums to recorded music, even when people don't hum well.4. Maintaining and manipulating time-ordered data in a database setting.This tutorial draws mostly from the book High Performance Discovery in Time Series: techniques and case studies, Springer-Verlag 2004. You can find the power point slides for this tutorial at http://cs.nyu.edu/cs/faculty/shasha/papers/sigmod04.ppt.The tutorial is aimed at researchers in streams, data mining, and scientific computing. Its applications should interest anyone who works with scientists or financial "quants." The emphasis will be on recent results and open problems. This is a ripe area for further advance.

Autonomous robot architectures typically fall within two camps, top-down symbolic AI systems, and physically grounded bottom-up reactive systems. In this paper, we present a novel behavior fusion based architecture for the control of an intelligent powered wheelchair, which takes advantage of the modularity presented by the reactive bottom-up design, the inference mechanism of symbolic AI systems, and the flexibility of the layered architecture. Thus, this control architecture of wheelchair can realize the multi-level information fusion and facilitates task and information sharing and trading between man and machine. In addition, the basic behaviors and their fusion algorithm are also described.

In this paper a novel approach of self-localization for an intelligent wheelchair is presented. Different from previous approaches we adopt a two-stage matching strategy to position control of an intelligent wheelchair, which is necessary if the current location is not known due to re-initialisation or an accident. Based on the landmark tree developed here, we combine a method similar to image-retrieval and a Bayes law based matching algorithm to locate the wheelchair in multiple resolutions, which is related to the requirement of task-completion. A new path-planning algorithm is introduced, which is originated from our landmark tree and is easy to use.

We study information disclosure in a vertically differentiated duopoly with unaware consumers. Sellers have private information about the adverse effect of their products, while consumers are unaware of existence of such adverse effect unless they are informed by the sellers. We show that information remains hidden only if information disclosure reduces the size of the market. In such a case, whether information is disclosed depends on the intensity of competition, the difference of the adverse effect between the two sellers, and the timing at which pricing and information disclosure decisions are made.