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We study bandit algorithms under data poisoning attacks in a bounded reward setting. We consider a strong attacker model in which the attacker can observe both the selected actions and their corresponding rewards and can contaminate the rewards with additive noise. We show that any bandit algorithm with regret $O(\log T)$ can be forced to suffer a regret $\Omega(T)$ with an expected amount of contamination $O(\log T)$. This amount of contamination is also necessary, as we prove that there exists an $O(\log T)$ regret bandit algorithm, specifically the classical UCB, that requires $\Omega(\log T)$ amount of contamination to suffer regret $\Omega(T)$. To combat such attacks, our second main contribution is to propose verification based mechanisms, which use limited verification to access a limited number of uncontaminated rewards. In particular, for the case of unlimited verifications, we show that with $O(\log T)$ expected number of verifications, a simple modified version of the ETC type bandit algorithm can restore the order optimal $O(\log T)$ regret irrespective of the amount of contamination used by the attacker. We also provide a UCB-like verification scheme, called Secure-UCB, that also enjoys full recovery from any attacks, also with $O(\log T)$ expected number of verifications. To derive a matching lower bound on the number of verifications, we prove that for any order-optimal bandit algorithm, this number of verifications $\Omega(\log T)$ is necessary to recover the order-optimal regret. On the other hand, when the number of verifications is bounded above by a budget $B$, we propose a novel algorithm, Secure-BARBAR, which provably achieves $O(\min\{C,T/\sqrt{B} \})$ regret with high probability against weak attackers where $C$ is the total amount of contamination by the attacker, which breaks the known $\Omega(C)$ lower bound of the non-verified setting if $C$ is large., Comment: Accepted to AAAI 2022

BackgroundAntioxidant micronutrients have a therapeutic potential for clinical treatment of obesity. NO research, however, has examined the connection between the complex level of dietary antioxidants and obesity.Materials and methodsWe mainly aimed to investigate the relationship between a combination of antioxidants and obesity using the database of the national health and nutrition examination survey (NHANES). This cross-sectional study contains a survey of 41,021 people (≥18 years) in total ranging from 2005 to 2018. Multivariate logistic and weighted quantile sum (WQS) regression were performed to investigate the associations between these antioxidants, both individually and collectively, and the prevalence of obesity. The restricted cubic spline (RCS) regression was also utilized to analyze the linearity of these associations.ResultsAccording to multivariate logistic models, we found that the levels of most antioxidants in the highest quartile were independently related to a lower prevalence of obesity, while a reverse result was observed in selenium (p for trend pConclusionOur study found that a high level of a complex of 11 dietary antioxidants is related to a lower prevalence of obesity and abdominal obesity, among this inverse associations iron and vitamin C have the greatest weight.

Introductionstrategy of periodic food restriction and fixed eating windows, could beneficially modify individuals by losing body weight, regulating glucose or lipid metabolism, reducing blood pressure, and modulating the immune system. Specific effects of IF and its mechanisms have not yet been assessed collectively. Thus, this systematic review aims to summarize and compare clinical trials that explored the immunomodulatory effects of IF.MethodsAfter screening, 28 studies were included in this systematic review.ResultsIn addition to weight loss, IF could benefit health subjects by strengthening their circadian rhythms, migrating immune cells, lower inflammatory factors, and enriching microbials. In addition of the anti-inflammatory effect by regulating macrophages, protection against oxidative stress with hormone secretion and oxidative-related gene expression plays a key beneficial role for the influence of IF on obese subjects.DiscussionPhysiological stress by surgery and pathophysiological disorders by endocrine diseases may be partly eased with IF. Moreover, IF might be used to treat anxiety and cognitive disorders with its cellular, metabolic and circadian mechanisms. Finally, the specific effects of IF and the mechanisms pertaining to immune system in these conditions require additional studies.

The limited electrical performance of microelectronic devices caused by low inter-particle connectivity and inferior printing quality is still the greatest hurdle to overcome for Aerosol jet printing (AJP) technology. Despite the incorporation of carbon nanotubes (CNTs) and specified solvents into functional inks can improve inter-particle connectivity and ink printability respectively, it is still challenging to consider multiple conflicting properties in mixture design simultaneously. This research proposes a novel hybrid multi-objective optimization method to determine the optimal functional ink composition to achieve low electrical resistivity and high printed line quality. In the proposed approach, silver ink, CNTs ink and ethanol are blended according to mixture design, and two response surface models (ReSMs) are developed based on the Analysis of Variance. Then a desirability function method is employed to identify a 2D optimal operating material window to balance the conflicting responses. Following that, the conflicting objectives are optimized in a more robust manner in the 3D mixture design space through the integration of a non-dominated sorting genetic algorithm III (NSGA-III) with the developed ReSMs and the corresponding statistical uncertainty. Experiments are conducted to validate the effectiveness of the proposed approach, which extends the methodology of designing materials with multi-component and multi-property in AJP technology. Published version This work was partly supported by the Major Projects of Natural Science Research in Universities of Anhui Province [grant number KJ2021ZD0137], Key Natural Science Project of Anhui Provincial Education Department [grant number KJ2021A1111] and partly by Doctoral Research Startup Project of Suzhou University [grant number 2021BSK023].

We consider a dynamic Bayesian persuasion setting where a single long-lived sender persuades a stream of ``short-lived'' agents (receivers) by sharing information about a payoff-relevant state. The state transitions are Markovian and the sender seeks to maximize the long-run average reward by committing to a (possibly history-dependent) signaling mechanism. While most previous studies of Markov persuasion consider exogenous agent beliefs that are independent of the chain, we study a more natural variant with endogenous agent beliefs that depend on the chain's realized history. A key challenge to analyze such settings is to model the agents' partial knowledge about the history information. We analyze a Markov persuasion process (MPP) under various information models that differ in the amount of information the receivers have about the history of the process. Specifically, we formulate a general partial-information model where each receiver observes the history with an $\ell$ period lag. Our technical contribution start with analyzing two benchmark models, i.e., the full-history information model and the no-history information model. We establish an ordering of the sender's payoff as a function of the informativeness of agent's information model (with no-history as the least informative), and develop efficient algorithms to compute optimal solutions for these two benchmarks. For general $\ell$, we present the technical challenges in finding an optimal signaling mechanism, where even determining the right dependency on the history becomes difficult. To bypass the difficulties, we use a robustness framework to design a "simple" \emph{history-independent} signaling mechanism that approximately achieves optimal payoff when $\ell$ is reasonably large., Comment: Minor revisions

Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution. However, how the creators' competition impacts user welfare and how the relevance-driven recommendation influences the dynamics in the long run are still largely unknown. This work provides theoretical insights into these research questions. We model the creators' competition under the assumptions that: 1) the platform employs an innocuous top-$K$ recommendation policy; 2) user decisions follow the Random Utility model; 3) content creators compete for user engagement and, without knowing their utility function in hindsight, apply arbitrary no-regret learning algorithms to update their strategies. We study the user welfare guarantee through the lens of Price of Anarchy and show that the fraction of user welfare loss due to creator competition is always upper bounded by a small constant depending on $K$ and randomness in user decisions; we also prove the tightness of this bound. Our result discloses an intrinsic merit of the myopic approach to the recommendation, i.e., relevance-driven matching performs reasonably well in the long run, as long as users' decisions involve randomness and the platform provides reasonably many alternatives to its users., Comment: Accepted as ICML2023 Oral

This paper provides a systematic study of the robust Stackelberg equilibrium (RSE), which naturally generalizes the widely adopted solution concept of the strong Stackelberg equilibrium (SSE). The RSE accounts for any possible up-to-$\delta$ suboptimal follower responses in Stackelberg games and is adopted to improve the robustness of the leader's strategy. While a few variants of robust Stackelberg equilibrium have been considered in previous literature, the RSE solution concept we consider is importantly different -- in some sense, it relaxes previously studied robust Stackelberg strategies and is applicable to much broader sources of uncertainties. We provide a thorough investigation of several fundamental properties of RSE, including its utility guarantees, algorithmics, and learnability. We first show that the RSE we defined always exists and thus is well-defined. Then we characterize how the leader's utility in RSE changes with the robustness level considered. On the algorithmic side, we show that, in sharp contrast to the tractability of computing an SSE, it is NP-hard to obtain a fully polynomial approximation scheme (FPTAS) for any constant robustness level. Nevertheless, we develop a quasi-polynomial approximation scheme (QPTAS) for RSE. Finally, we examine the learnability of the RSE in a natural learning scenario, where both players' utilities are not known in advance, and provide almost tight sample complexity results on learning the RSE. As a corollary of this result, we also obtain an algorithm for learning SSE, which strictly improves a key result of Bai et al. in terms of both utility guarantee and computational efficiency.

This paper extends the Isotonic Mechanism from the single-owner to multi-owner settings, in an effort to make it applicable to peer review where a paper often has multiple authors. Our approach starts by partitioning all submissions of a machine learning conference into disjoint blocks, each of which shares a common set of co-authors. We then employ the Isotonic Mechanism to elicit a ranking of the submissions from each author and to produce adjusted review scores that align with both the reported ranking and the original review scores. The generalized mechanism uses a weighted average of the adjusted scores on each block. We show that, under certain conditions, truth-telling by all authors is a Nash equilibrium for any valid partition of the overlapping ownership sets. However, we demonstrate that while the mechanism's performance in terms of estimation accuracy depends on the partition structure, optimizing this structure is computationally intractable in general. We develop a nearly linear-time greedy algorithm that provably finds a performant partition with appealing robust approximation guarantees. Extensive experiments on both synthetic data and real-world conference review data demonstrate the effectiveness of this generalized Isotonic Mechanism.

Emerging evidence indicates B-cell activating factor (BAFF, Tnfsf13b) to be an important cytokine for antitumor immunity. In this study, we generated a BAFF-overexpressing B16.F10 melanoma cell model and found that BAFF-expressing tumors grow more slowly in vivo than control tumors. The tumor microenvironment (TME) of BAFF-overexpressing tumors had decreased myeloid infiltrates with lower PD-L1 expression. Monocyte depletion and anti-PD-L1 antibody treatment confirmed the functional importance of monocytes for the phenotype of BAFF-mediated tumor growth delay. RNA sequencing analysis confirmed that monocytes isolated from BAFF-overexpressing tumors were characterized by a less exhaustive phenotype and were enriched for in genes involved in activating adaptive immune responses and NF-κB signaling. Evaluation of patients with late-stage metastatic melanoma treated with inhibitors of the PD-1/PD-L1 axis demonstrated a stratification of patients with high and low BAFF plasma levels. Patients with high BAFF levels experienced lower responses to anti-PD-1 immunotherapies. In summary, these results show that BAFF, through its effect on tumor-infiltrating monocytes, not only impacts primary tumor growth but can serve as a biomarker to predict response to anti-PD-1 immunotherapy in advanced disease. Significance: The BAFF cytokine regulates monocytes in the melanoma microenvironment to suppress tumor growth, highlighting the importance of BAFF in antitumor immunity.

Soil salinization is one of the most important abiotic stresses adversely affecting plant growth and yield. In this study, we cloned and characterized a putative salt tolerance gene encoding a bHLH (basic helix-loop-helix) transcription factor, MdbHLH106L (MdbHLH106-like), in the genome of apple (Malus domestica Borkh.). The expression level and promoter activity of MdbHLH106L are increased under salt stress. Overexpressing MdbHLH106L in ‘Orin’ calli can promote the expression level of MdNHX1 (Na+/H+ exchanger 1). Further analysis showed that MdbHLH106L is able to bind the MdNHX1 promoter. Moreover, we also confirmed the interaction between MdbHLH106L and MdZAT10. In conclusion, MdbHLH106L participates in the salt-stress signaling pathway and may mediate salt tolerance by activating the MdNHX1 promoter and interacting with MdZAT10. Our findings may serve as a theoretical basis for further investigating molecular mechanisms of stress responses and for enriching genetic resources of stress-tolerant apple cultivars.

Apple (Malus domestica Borkh.) is an important fruit tree species worldwide. Apple fruits are favored by consumers because of their antioxidative, anti-inflammatory, and antitumor effects as well as their protective effects against cardiovascular diseases and other health benefits. There is considerable interest in red-fleshed apple fruits among breeders because of their high flavonoid and anthocyanin contents. However, the flavonoids extracted from red-fleshed apple fruits must still be functionally characterized, especially regarding their protective effects against certain pathologies. In this study, the flavonoid components and contents in the extracts prepared from red-fleshed apple cultivar "Meihong" were determined. Additionally, the in vitro antioxidant activities and protective effects of the extracts against CCl4 -induced acute liver injury were investigated. The red-fleshed apple flesh flavonoid extract (RAFF) exhibited strong in vitro antioxidant activities. Compared with the model control mice treated with CCl4 , the mice pretreated with high (800 mg/kg·bw), middle (400 mg/kg·bw), and low (200 mg/kg·bw) RAFF doses had significantly lower CCl4 -induced serum aspartate aminotransferase, alanine aminotransferase, and alkaline phosphatase activities. Moreover, the RAFF pretreatment also significantly decreased the liver malondialdehyde activity and prevented the CCl4 -induced decrease in liver superoxide dismutase, glutathione peroxidase, catalase, and reduced glutathione levels. Furthermore, a histopathological examination revealed that RAFF inhibited the inflammatory cell infiltration and cell boundary loss caused by CCl4 in the liver. Thus, RAFF is a natural antioxidant with significant antioxidative activities and liver protective effects. The results of this study may be relevant for enhancing the application of the red-fleshed apple fruit extract as a food additive. PRACTICAL APPLICATION: We took the self-selected red-fleshed apple cultivar "Meihong" as the unique research material, and the active ingredients of its flavonoid extract, in vitro antioxidant activity and hepatoprotective effect were analyzed. It is of great significance to promote the development of the red-fleshed apple industry, and also provides an important reference for the development of natural antioxidants.

Summary Methylation at the MdMYB1 promoter in apple sports has been reported as a regulator of the anthocyanin pathway, but little is known about how the locus is recognized by the methylation machinery to regulate anthocyanin accumulation. In this study, we analysed three differently coloured ‘Fuji’ apples and found that differences in the transcript levels of MdMYB1, which encodes a key regulator of anthocyanin biosynthesis, control the anthocyanin content (and therefore colour) in fruit skin. The CHH methylation levels in the MR3 region (−1246 to −780) of the MdMYB1 promoter were found to be negatively correlated with MdMYB1 expression. Thus, they were ideal materials to study DNA methylation in apple sports. The protein of RNA‐directed DNA methylation (RdDM) pathway responsible for CHH methylation, MdAGO4, was found to interact with the MdMYB1 promoter. MdAGO4s can interact with MdRDM1 and MdDRM2s to form an effector complex, fulfilling CHH methylation. When MdAGO4s and MdDRM2s were overexpressed in apple calli and Arabidopsis mutants, those proteins increase the CHH methylation of AGO4‐binding sites. In electrophoretic mobility shift assays, MdAGO4s were found to specifically bind to sequence containing ATATCAGA. Knockdown of MdNRPE1 did not affect the binding of MdAGO4s to the c3 region of the MdMYB1 promoter in 35S::AGO4 calli. Taken together, our data show that the MdMYB1 locus is methylated through binding of MdAGO4s to the MdMYB1 promoter to regulate anthocyanin biosynthesis by the RdDM pathway.

The transformation of sugars is a sustainable method of production of high value-added chemicals and fuels. Traditional procedures used H2SO4 for sugar dehydration, followed by hydrogenation with f...

Many real-world strategic games involve interactions between multiple players. We study a hierarchical multi-player game structure, where players with asymmetric roles can be separated into leaders and followers, a setting often referred to as Stackelberg game or leader-follower game. In particular, we focus on a Stackelberg game scenario where there are multiple leaders and a single follower, called the Multi-Leader-Single-Follower (MLSF) game. We propose a novel asymmetric equilibrium concept for the MLSF game called Correlated Stackelberg Equilibrium (CSE). We design online learning algorithms that enable the players to interact in a distributed manner, and prove that it can achieve no-external Stackelberg-regret learning. This further translates to the convergence to approximate CSE via a reduction from no-external regret to no-swap regret. At the core of our works, we solve the intricate problem of how to learn equilibrium in leader-follower games with noisy bandit feedback by balancing exploration and exploitation in different learning structures.

In the principal-agent problem formulated in [Myerson 1982], agents have private information (type) and make private decisions (action), both of which are unobservable to the principal. Myerson pointed out an elegant solution that relies on the revelation principle, which states that without loss of generality optimal coordination mechanisms of this problem can be assumed to be truthful and direct. Consequently, the problem can be solved by a linear program when the support sets of the action and type spaces are finite. In this paper, we extend Myerson's results to the setting where the principal's action space might be infinite and subject to additional design constraints. This generalized principal-agent model unifies several important design problems -- including contract design, information design, and Bayesian Stackelberg games -- and encompasses them as special cases. We present a revelation principle for this general model, based on which a polynomial-time algorithm is derived for computing the optimal coordination mechanism. This algorithm not only implies new efficient algorithms simultaneously for all the aforementioned special cases but also significantly simplifies previous approaches in the literature.

Sugar content and cold tolerance are important apple (Malus × domestica) characteristics targeted by breeding programs. Here, a vacuolar membrane sucrose transporter, MdSWEET16, was cloned and characterized. A phylogenetic tree analysis found that MdSWEET16 was on the same polygenetic branch as Arabidopsis thaliana SWEET16 and SWEET17. MdSWEET16 was located on chromosome 2 and consists of six exons and five introns. The recombinant protein was obtained by prokaryotic induction, and the amino acid sequence and transmembrane domain were analyzed by bioinformatics. The encoded ~ 30-kDa protein has seven transmembrane domains and is localized on the tonoplasts of ‘Orin’ callus protoplasts. The expression of MdSWEET16 changed in response to sucrose and low temperature in ‘Orin’ calli. In addition, we also analyzed the expression level of MdSWEET16 during different fruit developmental stages using qRT-PCR. MdSWEET16 was highly expressed in young fruit, and its expression level during fruit development was significantly negatively correlated with sucrose content as assessed by a quantitative fluorescence analysis. Overexpression of MdSWEET16 in ‘Orin’ calli could reduce their sucrose content but increased their cold tolerance compared with MdSWEET16 RNA interference calli, which indicated that MdSWEET16 is involved in the sucrose transport and cold tolerance of apple. MdSWEET16’s expression level was significantly correlated with the sucrose content and low temperature, and was induced by sucrose and low temperatures. Overexpression of MdSWEET16 increased ‘Orin’ calli cold tolerance. The prokaryotic expression of its recombinant protein can be used to further study the function of the MdSWEET16 protein in apple.

The thermal processing parameters is very important to the hot rolling and forging process for producing grain refinement in lightweight high-manganese and aluminum steels. In this work, the high temperature deformation behaviors of a low-density steel of Fe30Mn11Al1C alloyed with 0.1Nb and 0.1V were studied by isothermal hot compression tests at temperatures of 850–1150 °C and strain rates between 0.01 s−1 and 10 s−1. It was found that the flow stress constitutive model could be effectively established by the Arrhenius based hyperbolic sine equation with an activation energy of about 389.1 kJ/mol. The thermal processing maps were developed based on the dynamic material model at different strains. It’s shown that the safe region for high temperatures in a very broad range of both deformation temperature and deformation strain and only a small unstable high deformation region, located at low temperatures lower than 950 °C. The deformation microstructures were found to be fully recrystallized microstructure in the safe deformation region and the grain size decreases along with decreasing temperature and increasing strain rate. Whereas the deformation microstructures is composed by grain refinement-recrystallized grains and a small fraction of non-recrystallized microstructure in the unstable deformation region, indicating that the deformation behaviors controlled by continuous dynamic recrystallization. The Hall Petch relationship between microhardness and the grain size of the high temperature deformed materials indicates that high strength low-density steel could be developed by a relative low temperature deformation and high strain rate.

The WRKY transcription factors play key roles in plant growth and abiotic stress responses; however, the molecular mechanisms behind their involvement in anthocyanin biosynthesis are still unclear. In our study, we identified a Leu zipper motif and a WRKY domain in MdWRKY40 protein. Phylogenetic tree analysis showed that MdWRKY40, AtWRKY18 and AtWRKY40 were on the same evolutionary branch and were Group IIa WRKY proteins. Yeast two-hybrid and bimolecular fluorescence complementation assays showed that MdWRKY40 could interact with itself to form homodimers. Overexpressing MdMYB111 in red-fleshed callus inhibited the expression of MdANS and decreased the anthocyanin content. EMSA assay showed that MdMYB111 and MdWRKY40 could bind the MRE and the W box, respectively, in the MdANS promoter. Overexpressing MdWRKY40 in red-fleshed callus did not affect the expression of MdANS or the anthocyanin content. However, overexpressing MdWRKY40 in callus overexpressing MdMYB111 weakened the inhibitory effect of MdMYB111 on anthocyanin biosynthesis. Knocking out the Leu zipper motif of MdWRKY40 (LLSMdWRKY40) prevented its self-interaction, and knocking out C-x5-C sequence of MdWRKY40 (LCSMdWRKY40) prevented it from binding to W box. It did not weaken the inhibitory effect of MdMYB111 on anthocyanin biosynthesis when overexpressing LCSMdWRKY40 or LLSMdWRKY40 in callus overexpressing MdMYB111. Thus, MdMYB111 and MdWRKY40 may play important roles in the anthocyanin biosynthetic pathway. MdWRKY40 interacts with itself to form homodimers by the Leu zipper motif at the N-terminal, and it binds two W boxes distantly separated in the MdANS promoter in the presence of C-x5-C sequence. MdMYB111 binds the MRE in the looped region induced by MdWRKY40. In addition, it weakens the inhibitory effect of MdMYB111 on expression of MdANS and anthocyanin biosynthesis when overexpressing MdWRKY40 in callus overexpressing MdMYB111.

Light is an important environmental factor affecting plant growth and development. Additionally, HY5 is a central factor that coordinates light signal transduction and regulates the expression of flower color-related genes. However, there are few reports describing the co-regulation of apple fruit coloration by MdHY5 and MYB transcription factors. In this study, we detected a light-inducible gene, MdMYBDL1, which encodes a MYB-like domain and is homologous to AtMYBD in Arabidopsis thaliana. Moreover, we observed that MdHY5 binds to the G-box element of the MdMYBDL1 promoter to upregulate expression. The overexpression of MdMYBDL1 enhanced anthocyanin accumulation in apple calli and inhibited the expression of MdMYB16 and its homolog, MdMYB308. Furthermore, MdMYB16 can form a dimer with MdMYB308 and functions as a negative regulator of anthocyanin biosynthesis. Interestingly, MdMYB16 and MdMYB308 promoter activities were inhibited by MdMYBDL1 and MdHY5. These findings imply that MdHY5 responds to light signals and functions upstream of different types of MYB transcription factors, ultimately regulating anthocyanin accumulation in apples.

Malus sieversii and its red-fleshed variant (M. sieversii f. niedzwetzkyana) can provide important resources for producing cider. This not only helps to protect wild germplasm resources, but also extends the capabilities of the apple industry. So the present study aimed to determine the changes in nutritional quality during the brewing process to obtain the optimal fermentation time. The samples were taken periodically and we use HPLC, HPCE, GC–MS, ICP-MS and other methods to determine the nutritional components of red-fleshed cider. The results showed that the contents of 24 polyphenols, mineral elements and organic acids were in dynamic change during fermentation and types and contents of 16 kinds of amino acids were decreased. At a fermentation temperature of 25–27 °C and using a standard yeast inoculation, the optimal fermentation time of red-fleshed cider being 8 days, for the transformation of sugars to alcohols can be done thoroughly and retain more nutrients. The fermentation process for red-fleshed cider consisted mainly of a number of different complex microbial and biochemical reactions.

In many plants, anthocyanin biosynthesis is affected by environmental conditions. Ultraviolet-B (UV-B) radiation promotes anthocyanin accumulation and fruit coloration in apple skin, whereas high temperature suppresses these processes. In this study, we characterized a B-box transcription factor, MdCOL4, from 'Fuji' apple, and identified its role in anthocyanin biosynthesis by overexpressing its encoding gene in apple red callus. The expression of MdCOL4 was reduced by UV-B, but promoted by high temperature. We explored the regulatory relationship between heat shock transcription factors (HSFs) and MdCOL4, and found that MdHSF3b and MdHSF4a directly bound to the heat shock element cis-element of the MdCOL4 promoter. MdCOL4 interacted with MdHY5 to synergistically inhibit the expression of MdMYB1, and MdCOL4 directly bound to the promoters of MdANS and MdUFGT, which encode genes in the anthocyanin biosynthetic pathway, to suppress their expression. Our findings shed light on the molecular mechanism by which MdCOL4 suppresses anthocyanin accumulation in apple skin under UV-B and high temperature.

To address the demand for detection of point / dim targets in complex environments, a cryogenic long-wave infrared detection system was designed. In order to improve the target detection capability, the system adopts high-order aspheric surfaces to reduce the number of optical lenses and improve the system transmittance. At the same time, it corrects on-axis / off-axis aberrations and advanced aberrations to improve the imaging quality of the system. In order to effectively reduce the background radiation and improve the system signal-to-noise ratio, the system adopts cryogenic optical technology. Through the scheme design and calculation analysis of the active refrigeration unit, the requirements of the overall and optical technical indicators are met. The outline of the aircraft image obtained by the field experiment is clear and distinguishable, which meets the requirements of target detection. The system has a good application prospect in the field of infrared imaging in early warning systems.

The transition from vegetative to reproductive growth is the most dramatic phase change in plants. To better understand the molecular regulation of floral transition and flower development in red-fleshed apple (Malus sieversii f. niedzwetzkyana), we isolated and characterized a floral MADS-box gene, MdAGL24-like, which shares sequence similarity with AGAMOUS-LIKE 24 (AGL24) from other species. Spatial expression analysis showed that MdAGL24-like dynamically expressed in flowers, followed by roots and fruits. Subcellular localization analysis indicated that, like other transcript factors, MdAGL24-like was localized in the nucleus. Protein interaction analysis showed that MdAGL24-like could interact with MdSOC1 and MdAP1 in vivo and in vitro. MdAGL24-like and MdSOC1 could increase each other’s expression by binding the CArG motifs in their promoters. Unlike MdSOC1, MdAGL24-like might indirectly promote the expression of MdLFY by upregulating the expression of MdSOC1. Ectopic expression of MdAGL24-like in wild-type Arabidopsis induced early flowering like the phenotypes induced by other AGL24 genes. Similar to AGL24 in Arabidopsis, MdAGL24-like could rescue the late-flowering phenotype of the agl24 mutant to some extent. These results help clarify the molecular mechanism underlying flowering and provide a means of shortening the juvenile period in red-fleshed apples and other fruit trees.

Cold stress has always been a major abiotic factor affecting the yield and quality of temperate fruit crops. Ethylene plays a critical regulatory role in the cold stress response, but the underlying molecular mechanisms remain elusive. Here, we revealed that ethylene positively modulates apple responses to cold stress. Treatment with 1-aminocyclopropane-1-carboxylate (an ethylene precursor) and aminoethoxyvinylglycine (an ethylene biosynthesis inhibitor) respectively increased and decreased the cold tolerance of apple seedlings. Consistent with the positive effects of ethylene on cold stress responses, a low-temperature treatment rapidly induced ethylene release and the expression of MdERF1B, which encodes an ethylene signaling activator, in apple seedlings. Overexpression of MdERF1B significantly increased the cold tolerance of apple plant materials (seedlings and calli) and Arabidopsis thaliana seedlings. A quantitative real-time PCR analysis indicated that MdERF1B upregulates the expression of the cold-responsive gene MdCBF1 in apple seedlings. Moreover, MdCIbHLH1, which functions upstream of CBF-dependent pathways, enhanced the binding of MdERF1B to target gene promoters as well as the consequent transcriptional activation. The stability of MdERF1B-MdCIbHLH1 was affected by cold stress and ethylene. Furthermore, MdERF1B interacted with the promoters of two genes critical for ethylene biosynthesis, MdACO1 and MdERF3. The resulting upregulated expression of these genes promoted ethylene production. However, the downregulated MdCIbHLH1 expression in MdERF1B-overexpressing apple calli significantly inhibited ethylene production. These findings imply that MdERF1B-MdCIbHLH1 is a potential regulatory module that integrates the cold and ethylene signaling pathways in apple.

Additional file 4: S2 Fig. Calibration curves in ICC (A), ECC (B), and GBC (C). Red: 1-year calibration curves; blue: 3-year calibration curves; green: 5-year calibration curves.

A ubiquitous learning problem in today's digital market is, during repeated interactions between a seller and a buyer, how a seller can gradually learn optimal pricing decisions based on the buyer's past purchase responses. A fundamental challenge of learning in such a strategic setup is that the buyer will naturally have incentives to manipulate his responses in order to induce more favorable learning outcomes for him. To understand the limits of the seller's learning when facing such a strategic and possibly manipulative buyer, we study a natural yet powerful buyer manipulation strategy. That is, before the pricing game starts, the buyer simply commits to "imitate" a different value function by pretending to always react optimally according to this imitative value function. We fully characterize the optimal imitative value function that the buyer should imitate as well as the resultant seller revenue and buyer surplus under this optimal buyer manipulation. Our characterizations reveal many useful insights about what happens at equilibrium. For example, a seller with concave production cost will obtain essentially 0 revenue at equilibrium whereas the revenue for a seller with convex production cost is the Bregman divergence of her cost function between no production and certain production. Finally, and importantly, we show that a more powerful class of pricing schemes does not necessarily increase, in fact, may be harmful to, the seller's revenue. Our results not only lead to an effective prescriptive way for buyers to manipulate learning algorithms but also shed lights on the limits of what a seller can really achieve when pricing in the dark., Comment: Neural Information Processing Systems (NeurIPS 2021)

Additional file 7: S5 Fig. Kaplan-Meier survival curves of patients receiving curative and non-curative surgery, respectively. A-B: survival curves of patients who underwent curative surgery. C-D: survival curves of patients who underwent non-curative surgery.

Additional file 2: S2 Table. Correlations between INR and Other Clinicopathological Characteristics.

Additional file 3: S1 Fig. Calibration curves and decisive curve analysis of the nomogram in the training cohort. A-C: 1-, 3- and 5-year calibration curves. D-F: 1-, 3- and 5-year decisive curve analyses.

Diffusion source identification on networks is a problem of fundamental importance in a broad class of applications, including rumor controlling and virus identification. Though this problem has received significant recent attention, most studies have focused only on very restrictive settings and lack theoretical guarantees for more realistic networks. We introduce a statistical framework for the study of diffusion source identification and develop a confidence set inference approach inspired by hypothesis testing. Our method efficiently produces a small subset of nodes, which provably covers the source node with any pre-specified confidence level without restrictive assumptions on network structures. Moreover, we propose multiple Monte Carlo strategies for the inference procedure based on network topology and the probabilistic properties that significantly improve the scalability. To our knowledge, this is the first diffusion source identification method with a practically useful theoretical guarantee on general networks. We demonstrate our approach via extensive synthetic experiments on well-known random network models and a mobility network between cities concerning the COVID-19 spreading.

Additional file 5: S3 Fig. Time-dependent area under receiver operating characteristic curves in ICC (A), ECC (B), and GBC (C).

Additional file 5: S3 Fig. Time-dependent area under receiver operating characteristic curves in ICC (A), ECC (B), and GBC (C).

We study a natural competitive-information-design variant for the Pandora's Box problem (Weitzman 1979), where each box is associated with a strategic information sender who can design what information about the box's prize value to be revealed to the agent when the agent inspects the box. This variant with strategic boxes is motivated by a wide range of real-world economic applications for Pandora's Box. The main contributions of this article are two-fold: (1) we study informational properties of Pandora's Box by showing an intrinsic connection between informativeness of any box's value distribution and the utility order of the search agent; and (2) we fully characterize the pure symmetric equilibrium for the boxes' competitive information revelation, which reveals various insights regarding information competition and the resultant agent utility at equilibrium., Comment: 40 pages, 2 figures. Earlier version appeared in SODA'23

Additional file 1

Additional file 1: Table S1. Univariable and multivariable analysis of progression-free survival. Table S2. Univariable and multivariable analysis of time-to-progression. Table S3. Univariable and multivariable analysis of overall survival.

Additional file 4: S2 Fig. Calibration curves in ICC (A), ECC (B), and GBC (C). Red: 1-year calibration curves; blue: 3-year calibration curves; green: 5-year calibration curves.

Additional file 6: S4 Fig. Decisive curve analysis in ICC (A-C), ECC (D-F), and GBC (G-I).

Additional file 3: S1 Fig. Calibration curves and decisive curve analysis of the nomogram in the training cohort. A-C: 1-, 3- and 5-year calibration curves. D-F: 1-, 3- and 5-year decisive curve analyses.

Additional file 6: S4 Fig. Decisive curve analysis in ICC (A-C), ECC (D-F), and GBC (G-I).

Additional file 1: S1 Table. Correlations between FBG and Other Clinicopathological Characteristics.

The celebrated Bayesian persuasion model considers strategic communication between an informed agent (the sender) and uninformed decision makers (the receivers). The current rapidly-growing literature assumes a dichotomy: either the sender is powerful enough to communicate separately with each receiver (a.k.a. private persuasion), or she cannot communicate separately at all (a.k.a. public persuasion). We propose a model that smoothly interpolates between the two, by introducing a natural multi-channel communication structure in which each receiver observes a subset of the sender���s communication channels. This captures, e.g., receivers on a network, where information spillover is almost inevitable. Our main result is a complete characterization specifying when one communication structure is better for the sender than another, in the sense of yielding higher optimal expected utility universally over all prior distributions and utility functions. The characterization is based on a simple pairwise relation among receivers - one receiver information-dominates another if he observes at least the same channels. We prove that a communication structure M��� is (weakly) better than M��� if and only if every information-dominating pair of receivers in M��� is also such in M���. This result holds in the most general model of Bayesian persuasion in which receivers may have externalities - that is, the receivers' actions affect each other. The proof is cryptographic-inspired and it has a close conceptual connection to secret sharing protocols. As a surprising consequence of the main result, the sender can implement private Bayesian persuasion (which is the best communication structure for the sender) for k receivers using only O(log k) communication channels, rather than k channels in the naive implementation. We provide an implementation that matches the information-theoretical lower bound on the number of channels - not only asymptotically, but exactly. Moreover, the main result immediately implies some results of [Kerman and Tenev, 2021] on persuading receivers arranged in a network such that each receiver observes both the signals sent to him and to his neighbours in the network. We further provide an additive FPTAS for an optimal sender���s signaling scheme when the number of states of nature is constant, the sender has an additive utility function and the graph of the information-dominating pairs of receivers is a directed forest. We focus on a constant number of states, as even for the special case of public persuasion and additive sender���s utility, it was shown by [Shaddin Dughmi and Haifeng Xu, 2017] that one can achieve neither an additive PTAS nor a polynomial-time constant-factor optimal sender���s utility approximation (unless P=NP). We leave for future research studying exact tractability of forest communication structures, as well as generalizing our result to more families of sender���s utility functions and communication structures. Finally, we prove that finding an optimal signaling scheme under multi-channel persuasion is computationally hard for a general family of sender���s utility functions - separable supermajority functions, which are specified by choosing a partition of the set of receivers and summing supermajority functions corresponding to different elements of the partition, multiplied by some non-negative constants. Note that one can easily deduce from [Emir Kamenica and Matthew Gentzkow, 2011] and [Itai Arieli and Yakov Babichenko, 2019] that finding an optimal signaling scheme for such utility functions is computationally tractable for both public and private persuasion. This difference illustrates both the conceptual and the computational hardness of general multi-channel persuasion., LIPIcs, Vol. 215, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022), pages 11:1-11:2

We study the problem of incentivizing exploration for myopic users in linear bandits, where the users tend to exploit arm with the highest predicted reward instead of exploring. In order to maximize the long-term reward, the system offers compensation to incentivize the users to pull the exploratory arms, with the goal of balancing the trade-off among exploitation, exploration and compensation. We consider a new and practically motivated setting where the context features observed by the user are more informative than those used by the system, e.g., features based on users' private information are not accessible by the system. We propose a new method to incentivize exploration under such information gap, and prove that the method achieves both sublinear regret and sublinear compensation. We theoretical and empirically analyze the added compensation due to the information gap, compared with the case that the system has access to the same context features as the user, i.e., without information gap. We also provide a compensation lower bound of our problem.

Most algorithmic studies on multi-agent information design so far have focused on the restricted situation with no inter-agent externalities; a few exceptions investigated truly strategic games such as zero-sum games and second-price auctions but have all focused only on optimal public signaling. This paper initiates the algorithmic information design of both \emph{public} and \emph{private} signaling in a fundamental class of games with negative externalities, i.e., singleton congestion games, with wide application in today's digital economy, machine scheduling, routing, etc. For both public and private signaling, we show that the optimal information design can be efficiently computed when the number of resources is a constant. To our knowledge, this is the first set of efficient \emph{exact} algorithms for information design in succinctly representable many-player games. Our results hinge on novel techniques such as developing certain "reduced forms" to compactly characterize equilibria in public signaling or to represent players' marginal beliefs in private signaling. When there are many resources, we show computational intractability results. To overcome the issue of multiple equilibria, here we introduce a new notion of equilibrium-\emph{oblivious} hardness, which rules out any possibility of computing a good signaling scheme, irrespective of the equilibrium selection rule., Comment: Accepted to EC'22

Additional file 7: S5 Fig. Kaplan-Meier survival curves of patients receiving curative and non-curative surgery, respectively. A-B: survival curves of patients who underwent curative surgery. C-D: survival curves of patients who underwent non-curative surgery.

Additional file 2: S2 Table. Correlations between INR and Other Clinicopathological Characteristics.

Dominated actions are natural (and perhaps the simplest possible) multi-agent generalizations of sub-optimal actions as in standard single-agent decision making. Thus similar to standard bandit learning, a basic learning question in multi-agent systems is whether agents can learn to efficiently eliminate all dominated actions in an unknown game if they can only observe noisy bandit feedback about the payoff of their played actions. Surprisingly, despite a seemingly simple task, we show a quite negative result; that is, standard no regret algorithms -- including the entire family of Dual Averaging algorithms -- provably take exponentially many rounds to eliminate all dominated actions. Moreover, algorithms with the stronger no swap regret also suffer similar exponential inefficiency. To overcome these barriers, we develop a new algorithm that adjusts Exp3 with Diminishing Historical rewards (termed Exp3-DH); Exp3-DH gradually forgets history at carefully tailored rates. We prove that when all agents run Exp3-DH (a.k.a., self-play in multi-agent learning), all dominated actions can be iteratively eliminated within polynomially many rounds. Our experimental results further demonstrate the efficiency of Exp3-DH, and that state-of-the-art bandit algorithms, even those developed specifically for learning in games, fail to eliminate all dominated actions efficiently.