Balancing Utility and Fairness in Submodular Maximization (Technical Report)

Submodular function maximization is central in numerous data science applications, including data summarization, influence maximization, and recommendation. In many of these problems, our goal is to find a solution that maximizes the \emph{average} of the utilities for all users, each measured by a monotone submodular function. When the population of users is composed of several demographic groups, another critical problem is whether the utility is fairly distributed across groups. In the context of submodular optimization, we seek to improve the welfare of the \emph{least well-off} group, i.e., to maximize the minimum utility for any group, to ensure fairness. Although the \emph{utility} and \emph{fairness} objectives are both desirable, they might contradict each other, and, to our knowledge, little attention has been paid to optimizing them jointly. In this paper, we propose a novel problem called \emph{Bicriteria Submodular Maximization} (BSM) to strike a balance between utility and fairness. Specifically, it requires finding a fixed-size solution to maximize the utility function, subject to the value of the fairness function not being below a threshold. Since BSM is inapproximable within any constant factor in general, we propose efficient data-dependent approximation algorithms for BSM by converting it into other submodular optimization problems and utilizing existing algorithms for the converted problems to obtain solutions to BSM. Using real-world and synthetic datasets, we showcase applications of our framework in three submodular maximization problems, namely maximum coverage, influence maximization, and facility location.Comment: 13 pages, 7 figures, under revie

Phase transitions and thermodynamic geometry of a Kerr-Newman black hole in a cavity

Being placed in a cavity is an effective way of reaching thermodynamic equilibrium for black holes. We investigate a Kerr-Newman black hole in a cavity as well as compare it with two reduced cases, i.e., a RN black hole in a cavity and a Kerr black hole in a cavity. We derive the quasi-local energy from the Hamiltonian, and construct the first law of thermodynamics accordingly. In a canonical ensemble, these black holes could undergo a van der Waals-like phase transition, which is very similar to that in AdS space. We further investigate the black holes' thermodynamic geometry, which is a powerful tool to diagnose microscopic interactions of a thermodynamic system. Our results show that in a cavity, although phase structures of these black holes are similar, their thermodynamic geometry show strong dissimilarities, implying that the microstructure of a black hole is sensitive to its states.Comment: 25 pages, 8 figure

Research on Experiential Marketing Strategy Based on the Sale of Baking Products

To understand the consumer is the prerequisite for the enterprise to enter the consumer market. With the market competition intensified, Traditional marketing strategy is difficult to achieve the expected goal of the enterprise. It needs new marketing theory to guide the market and satisfy the demand. In this paper, the sales of baking products are taken for an example in which 425 questionnaires analyze five models of emotion, culture, service, environment and personality in detail and the paper suppllies some corresponding suggestions & measure to tackle the problems as the lack of large-scale leading enterprises and Enterprise homogeneity

Denoising Diffusion Semantic Segmentation with Mask Prior Modeling

The evolution of semantic segmentation has long been dominated by learning more discriminative image representations for classifying each pixel. Despite the prominent advancements, the priors of segmentation masks themselves, e.g., geometric and semantic constraints, are still under-explored. In this paper, we propose to ameliorate the semantic segmentation quality of existing discriminative approaches with a mask prior modeled by a recently-developed denoising diffusion generative model. Beginning with a unified architecture that adapts diffusion models for mask prior modeling, we focus this work on a specific instantiation with discrete diffusion and identify a variety of key design choices for its successful application. Our exploratory analysis revealed several important findings, including: (1) a simple integration of diffusion models into semantic segmentation is not sufficient, and a poorly-designed diffusion process might lead to degradation in segmentation performance; (2) during the training, the object to which noise is added is more important than the type of noise; (3) during the inference, the strict diffusion denoising scheme may not be essential and can be relaxed to a simpler scheme that even works better. We evaluate the proposed prior modeling with several off-the-shelf segmentors, and our experimental results on ADE20K and Cityscapes demonstrate that our approach could achieve competitively quantitative performance and more appealing visual quality

Saikosaponin A Alleviates Symptoms of Attention Deficit Hyperactivity Disorder through Downregulation of DAT and Enhancing BDNF Expression in Spontaneous Hypertensive Rats

The disturbed dopamine availability and brain-derived neurotrophic factor (BDNF) expression are due in part to be associated with attention deficit hyperactivity disorder (ADHD). In this study, we investigated the therapeutical effect of saikosaponin a (SSa) isolated from Bupleurum Chinese DC, against spontaneously hypertensive rat (SHR) model of ADHD. Methylphenidate and SSa were orally administered for 3 weeks. Activity was assessed by open-field test and Morris water maze test. Dopamine (DA) and BDNF were determined in specific brain regions. The mRNA or protein expression of tyrosine hydroxylase (TH), dopamine transporter (DAT), and vesicles monoamine transporter (VMAT) was also studied. Both MPH and SSa reduced hyperactivity and improved the spatial learning memory deficit in SHRs. An increased DA concentration in the prefrontal cortex (PFC) and striatum was also observed after treating with the SSa. The increased DA concentration may partially be attributed to the decreased mRNA and protein expression of DAT in PFC while SSa exhibited no significant effects on the mRNA expression of TH and VMAT in PFC of SHRs. In addition, BDNF expression in SHRs was also increased after treating with SSa or MPH. The obtained result suggested that SSa may be a potential drug for treating ADHD

Chinese Social Media Reaction to the MERS-Cov and Avian Influenza A (H7N9) Outbreaks

Background: As internet and social media use have skyrocketed, epidemiologists have begun to use online data such as Google query data and Twitter trends to track the activity levels of influenza and other infectious diseases. In China, Weibo is an extremely popular microblogging site that is equivalent to Twitter. Capitalizing on the wealth of public opinion data contained in posts on Weibo, this study used Weibo as a measure of the Chinese people’s reactions to two different outbreaks: the 2012 Middle East Respiratory Syndrome Coronavirus (MERS-CoV) outbreak, and the 2013 outbreak of human infection of avian influenza A(H7N9) in China. Methods: Keyword searches were performed in Weibo data collected by The University of Hong Kong’s Weiboscope project. Baseline values were determined for each keyword and reaction values per million posts in the days after outbreak information was released to the public. Results: The results show that the Chinese people reacted significantly to both outbreaks online, where their social media reaction was two orders of magnitude stronger to the H7N9 influenza outbreak that happened in China than the MERS-CoV outbreak that was far away from China. Conclusions: These results demonstrate that social media could be a useful measure of public awareness and reaction to disease outbreak information released by health authorities

A versatile route to fabricate single atom catalysts with high chemoselectivity

Preparation of single atom catalysts (SACs) is of broad interest to materials scientists and chemists but remains a formidable challenge. Herein, we develop an efficient approach to synthesize SACs via a precursor-dilution strategy, in which metalloporphyrin (MTPP) with target metals are co-polymerized with diluents (tetraphenylporphyrin, TPP), followed by pyrolysis to N-doped porous carbon supported SACs (M1/N-C). Twenty-four different SACs, including noble metals and non-noble metals, are successfully prepared. In addition, the synthesis of a series of catalysts with different surface atom densities, bi-metallic sites, and metal aggregation states are achieved. This approach shows remarkable adjustability and generality, providing sufficient freedom to design catalysts at atomic-scale and explore the unique catalytic properties of SACs. As an example, we show that the prepared Pt1/N-C exhibits superior chemoselectivity and regioselectivity in hydrogenation. It only converts terminal alkynes to alkenes while keeping other reducible functional groups such as alkenyl, nitro group, and even internal alkyne intact