Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click prediction, conversions have different types in nature, and each type may be associated with different decisive factors. In this paper, we formulate conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together. These models share feature representations, but have their specific parameters, providing the benefit of information-sharing across all tasks. We then propose Multi-Task Field-weighted Factorization Machine (MT-FwFM) to solve these tasks jointly. Our experiment results show that, compared with two state-of-the-art models, MT-FwFM improve the AUC by 0.74% and 0.84% on two conversion types, and the weighted AUC across all conversion types is also improved by 0.50%.Comment: SIGKD

Do green economy stocks matter for the carbon and energy markets? Evidence of connectedness effects and hedging strategies

Purpose: We phrase our analysis around the connectedness effects and portfolio allocation in the “Carbon-Energy-Green economy” system. Design/methodology/approach: This paper utilizes the TVP-VAR method provided by Antonakakis et al. (2020) and Chatziantoniou et al. (2021), and portfolio back-testing models, including bivariate portfolios and multivariate portfolios. Findings: Firstly, the connectedness within the “Carbon-Energy-Green economy” system is strong, and is mainly driven by short-term (weekly) connectedness. Notably, the COVID-19 pandemic leads to a vertical increase in the connectedness of this system. Secondly, in the “Carbon-Energy-Green economy” system, most of the sectors in the green economy stocks tend to be the transmitters of shocks to other markets (particularly the energy efficiency sector), while the carbon and energy markets are always the recipients of shocks from other markets (particularly the crude oil market). Thirdly, Green economy sector stocks have satisfactory hedging effects on the market risk of carbon and energy assets. Interestingly, hedging risks in relatively “dirty” assets requires more green economy stocks than in relatively “clean” assets. Finally, the results indicate that portfolios that include green economy stocks significantly outperform portfolios that do not contain green economy stocks, further demonstrating the crucial role of green economy stocks in this system. Originality/value: Understanding the interactions and portfolio allocation in the “Carbon-Energy-Green economy” system, especially identifying the role of the green economy performance in this system, is important for investors and policymakers

Energy barrier at the N719-dye/CsSnI3 interface for photogenerated holes in dye-sensitized solar cells

This report is to address the question if black γ-polymorph of cesium tin tri-iodide (B-γ-CsSnI3) can be used as a solid-state hole-transport material in the conventional DSSCs with the N719 dye to replace the liquid electrolyte as reported by I. Chung et al. on Nature 485, 486, (2012). Here we demonstrate rigorously that B-γ-CsSnI3 is not energetically possible to collect photogenerated holes because of the large energy barrier at the interface of N719/B-γ-CsSnI3. Therefore, it cannot serve as a hole-transporter for the conventional DSSCs although it is a good hole-conducting material. A solution-based method was employed to synthesize the B-γ-CsSnI3 polycrystalline thin-films used for this work. These thin-films were then characterized by X-ray diffraction, Hall measurements, optical reflection, and photoluminescence (PL). Particularly, spatially resolved PL intensity images were taken after B-γ-CsSnI3 was incorporated in the DSSC structure to insure the material integrity. The means of ultraviolet photoemission spectroscopy (UPS) was used to reveal why B-γ-CsSnI3 could not act as the substitute of liquid electrolyte in the conventional DSSCs. For the completeness, other two related compounds, one is the yellow polymorph of CsSnI3 and other is Cs2SnI6 with tetravalent tin instead of double-valent tin in CsSnI3 were also investigated by UPS

Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs

As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide efficient model serving. However, it is challenging to develop efficient tensor programs for deep learning operators due to the high complexity of modern accelerators and the rapidly growing number of operators. Deep learning compilers, such as Apache TVM, adopt declarative scheduling primitives to lower the bar of developing tensor programs. However, we show that this approach is insufficient to cover state-of-the-art tensor program optimizations. In this paper, we propose to embed the scheduling process into tensor programs and use dedicated mappings, called task mappings, to define the computation assignment and ordering. This new approach greatly enriches the expressible optimizations by allowing developers to manipulate tensor programs at a much finer granularity. We call the proposed method the task-mapping programming paradigm. In addition, we propose a new post-scheduling fusion optimization that allows developers to focus on scheduling every single operator and automates the fusion after scheduling. It greatly reduces the engineering efforts for operator fusion. Our proposed paradigm also constructs an efficient hardware-centric schedule space, which is agnostic to the program input size and greatly reduces the tuning time. With the proposed paradigm, we implement a deep learning compiler Hidet. Extensive experiments on modern convolution and transformer models show that Hidet outperforms state-of-the-art DNN inference framework, ONNX Runtime, and compiler, TVM equipped with scheduler AutoTVM and Ansor, by up to 1.48x (1.22x on average). It also reduces the tuning time by 20x and 11x compared with AutoTVM and Ansor, respectively. We open-sourced hidet at https://www.github.com/hidet-org/hidet.Comment: 15 pages, 22 figures, 1 tabl

Influences of mental accounting on consumption decisions: asymmetric effect of a scarcity mindset

A scarcity mindset is considered to impact consumer behaviors. Our research aimed to examine the moderating effect of the scarcity mindset on the relationship between mental accounting and hedonic (vs. utilitarian) consumption. We conducted an online experimental design (mental accounting: windfall gains vs. hard-earning gains; consumption: hedonic products vs. utilitarian products) and verified our hypotheses in two distinct samples: a student sample and an adult sample. Our results showed that consumers who received windfall gains tended to use it for hedonic consumption rather than utilitarian consumption. Intriguingly, such an effect was insignificant under a high level of a scarcity mindset but significant under a low level of the scarcity mindset. Moreover, consumers who received hard-earning gains tended to spend the money on utilitarian (vs. hedonic) consumption. However, we did not detect the impact of the scarcity mindset on such effects. Our research suggested an asymmetric effect of the scarcity mindset on hedonic (vs. utilitarian) consumption under two different mental accounts. It highlights the important role of the scarcity mindset in consumer behaviors, which leaves avenues for future research to understand marketing promotion strategies for distinct products

Alarming contagion effects: the dangerous ripple effect of extreme price spillovers across crude oil, carbon emission allowance, and agriculture futures markets

The inherent financial interconnections between crude oil prices, carbon emission allowances, and agriculture commodity futures warrant a thorough investigation as fossil energy consumption, carbon emissions, and agriculture plants are three critical components of global environmental protection. This paper aims to quantify not only the normal (mean quantile) static and dynamic spillover effects among them in both time and frequency domains but also the more critical extreme spillovers that occur across various time horizons. Additionally, we explore the vital role of carbon futures in hedging risk and enhancing the performance of oil and agricultural portfolios. Empirical results indicate that, under extreme market situations, the total spillovers among oil, carbon, and agriculture commodity futures are much larger than those under normal conditions. Furthermore, soybean and corn are generally the most potent information transmitters over other futures in the time domain, while carbon emission allowance futures act as an obvious spillover receiver at both normal and extreme market conditions across various time frequencies. Both the total spillover and the net spillover are centred at a short-term frequency (i.e., one to four weeks). Finally, we find that carbon futures can contribute to improving the hedge effectiveness and performance of oil and agricultural portfolios. These findings have valuable implications for policymakers, relevant producers/consumers, as well as futures investors

Cryptocurrency uncertainty and volatility forecasting of precious metal futures markets

Several common properties shared by cryptocurrencies and precious metals, such as safe haven, hedge and diversification for risk assets, have been widely discussed since Bitcoin was created in 2008. However, no studies have explored whether cryptocurrency market uncertainties can help to explain and forecast volatilities in precious metal markets. By using the GARCH-MIDAS model incorporating cryptocurrency policy and price uncertainty, as well as several other commonly used uncertainty measures, this paper compares the in-sample impacts and out-of-sample predictive abilities of these uncertainties on volatility forecasts of COMEX gold and silver futures markets. The in-sample results demonstrate the significant impacts of cryptocurrency uncertainty on the volatilities of precious metal futures markets, and the out-of-sample evidence further confirms the superior predictive power of cryptocurrency uncertainty on volatility forecasting of the precious metal market. Our conclusions are robust through various model evaluation approaches based not only on predicting errors but also on forecasting directions across different forecasting time horizons

Aberrant Calcium Signaling in Astrocytes Inhibits Neuronal Excitability in a Human Down Syndrome Stem Cell Model.

Down syndrome (DS) is a genetic disorder that causes cognitive impairment. The staggering effects associated with an extra copy of human chromosome 21 (HSA21) complicates mechanistic understanding of DS pathophysiology. We examined the neuron-astrocyte interplay in a fully recapitulated HSA21 trisomy cellular model differentiated from DS-patient-derived induced pluripotent stem cells (iPSCs). By combining calcium imaging with genetic approaches, we discovered the functional defects of DS astroglia and their effects on neuronal excitability. Compared with control isogenic astroglia, DS astroglia exhibited more-frequent spontaneous calcium fluctuations, which reduced the excitability of co-cultured neurons. Furthermore, suppressed neuronal activity could be rescued by abolishing astrocytic spontaneous calcium activity either chemically by blocking adenosine-mediated signaling or genetically by knockdown of inositol triphosphate (IP3) receptors or S100B, a calcium binding protein coded on HSA21. Our results suggest a mechanism by which DS alters the function of astrocytes, which subsequently disturbs neuronal excitability

Quantum spin probe of single charge dynamics

Electronic defects in semiconductors form the basis for many emerging quantum technologies. Understanding defect spin and charge dynamics in solid state platforms is crucial to developing these building blocks, but many defect centers are difficult to access at the single-particle level due to the lack of sensitive readout techniques. A method for probing optically inactive spin defects would reveal semiconductor physics at the atomic scale and advance the study of new quantum systems. We exploit the intrinsic correlation between the charge and spin states of defect centers to measure defect charge populations and dynamics through the steady-state spin population, read-out at the single-defect level with a nearby optically active qubit. We directly measure ionization and charge relaxation of single dark defects in diamond, effects we do not have access to with traditional coherence-based quantum sensing. These spin resonance-based methods generalize to other solid state defect systems in relevant materials.Comment: 8 pages, 4 figure