A Folkman Linear Family

For graphs $F$ and $G$, let $F\to (G,G)$ signify that any red/blue edge coloring of $F$ contains a monochromatic $G$. Define Folkman number $f(G;p)$ to be the smallest order of a graph $F$ such that $F\to (G,G)$ and $\omega(F) \le p$. It is shown that $f(G;p)\le cn$ for graphs $G$ of order $n$ with $\Delta(G)\le \Delta$, where $\Delta\ge 3$, $c=c(\Delta)$ and $p=p(\Delta)$ are positive constants.Comment: 11 page

Towards personalized cancer management: diagnosis and (immuno)therapy

This thesis endeavors to tackle the intricate challenges posed by cancer, emphasizing early detection, modulation of the tumor immune microenvironment (TIME), and overcoming targeted therapy resistance. Chapter 1 serves as the comprehensive introduction to the thesis. Early detection is crucial for effective treatment initiation, especially in esophageal squamous cell carcinoma (ESCC), where challenges arise from symptom absence and high screening costs. Chapter 2 introduces a diagnostic biomarker test utilizing salivary extracellular vesicles, demonstrating high sensitivity and specificity. This non-invasive screening tool is cost-effective and reliable. In Chapter 3A, the focus shifts to reprogramming the immune landscape in ESCC with low-dose metformin, revealing its potential to alter TIME by enhancing anti-tumoral immune cell infiltration and reducing tumor-promoting elements. Chapter 3B delves deeper into metformin's intricate immuno-metabolic effects, stressing the need for further research to understand mechanisms and optimize its combination with immunotherapy. Chapter 4 uncovers the role of V-domain Ig suppressor of T cell activation (VISTA) in macrophage biology, demonstrating its impact on anti-tumor immunity by steering macrophages toward an anti-inflammatory phenotype while enhancing phagocytosis. Chapter 5 presents a novel approach to combat trastuzumab resistance in ERBB2-overexpressing breast cancer using small activating RNA (saRNA) encapsulated in trastuzumab-retargeted nanoparticles. Chapter 6 provides a summary of the work and perspectives, and Chapter 7 contains a Dutch summary. Collectively, the thesis underscores the potential of biomarker discovery, immune modulation, and innovative therapeutic strategies to reshape cancer management, offering new avenues for early detection and personalized interventions in the global fight against cancer

Optimizing Average-Maximum TTR Trade-off for Cognitive Radio Rendezvous

In cognitive radio (CR) networks, "TTR", a.k.a. time-to-rendezvous, is one of the most important metrics for evaluating the performance of a channel hopping (CH) rendezvous protocol, and it characterizes the rendezvous delay when two CRs perform channel hopping. There exists a trade-off of optimizing the average or maximum TTR in the CH rendezvous protocol design. On one hand, the random CH protocol leads to the best "average" TTR without ensuring a finite "maximum" TTR (two CRs may never rendezvous in the worst case), or a high rendezvous diversity (multiple rendezvous channels). On the other hand, many sequence-based CH protocols ensure a finite maximum TTR (upper bound of TTR) and a high rendezvous diversity, while they inevitably yield a larger average TTR. In this paper, we strike a balance in the average-maximum TTR trade-off for CR rendezvous by leveraging the advantages of both random and sequence-based CH protocols. Inspired by the neighbor discovery problem, we establish a design framework of creating a wake-up schedule whereby every CR follows the sequence-based (or random) CH protocol in the awake (or asleep) mode. Analytical and simulation results show that the hybrid CH protocols under this framework are able to achieve a greatly improved average TTR as well as a low upper-bound of TTR, without sacrificing the rendezvous diversity.Comment: Accepted by IEEE International Conference on Communications (ICC 2015, http://icc2015.ieee-icc.org/

I2SRM: Intra- and Inter-Sample Relationship Modeling for Multimodal Information Extraction

Multimodal information extraction is attracting research attention nowadays, which requires aggregating representations from different modalities. In this paper, we present the Intra- and Inter-Sample Relationship Modeling (I2SRM) method for this task, which contains two modules. Firstly, the intra-sample relationship modeling module operates on a single sample and aims to learn effective representations. Embeddings from textual and visual modalities are shifted to bridge the modality gap caused by distinct pre-trained language and image models. Secondly, the inter-sample relationship modeling module considers relationships among multiple samples and focuses on capturing the interactions. An AttnMixup strategy is proposed, which not only enables collaboration among samples but also augments data to improve generalization. We conduct extensive experiments on the multimodal named entity recognition datasets Twitter-2015 and Twitter-2017, and the multimodal relation extraction dataset MNRE. Our proposed method I2SRM achieves competitive results, 77.12% F1-score on Twitter-2015, 88.40% F1-score on Twitter-2017, and 84.12% F1-score on MNRE

Asymmetric Polynomial Loss For Multi-Label Classification

Various tasks are reformulated as multi-label classification problems, in which the binary cross-entropy (BCE) loss is frequently utilized for optimizing well-designed models. However, the vanilla BCE loss cannot be tailored for diverse tasks, resulting in a suboptimal performance for different models. Besides, the imbalance between redundant negative samples and rare positive samples could degrade the model performance. In this paper, we propose an effective Asymmetric Polynomial Loss (APL) to mitigate the above issues. Specifically, we first perform Taylor expansion on BCE loss. Then we ameliorate the coefficients of polynomial functions. We further employ the asymmetric focusing mechanism to decouple the gradient contribution from the negative and positive samples. Moreover, we validate that the polynomial coefficients can recalibrate the asymmetric focusing hyperparameters. Experiments on relation extraction, text classification, and image classification show that our APL loss can consistently improve performance without extra training burden.Comment: ICASSP 202

Fluid-present Anatexis of Neoarchean Tonalite and Amphibolite in the Western Shandong Province

Metatonalite and amphibolite from the Taishan region of the Western Shandong Province in the North China Craton record c. 2.60 Ga fluid-present partial melting via the breakdown of biotite, plagioclase and quartz to produce peritectic hornblende and anatectic melt. Eight paired leucosome–melanosome samples from metatonalite and three paired samples from amphibolite were investigated to evaluate the composition of the melt. Hornblende, biotite and plagioclase in the leucosomes and hornblende and plagioclase in melanosomes from both rock types have similar compositions. Two leucosome samples from the metatonalite were influenced by the removal of heavy rare earth element-rich hornblende and the accumulation of plagioclase. The other leucosomes are interpreted to represent near initial melt compositions with a minor component of peritectic hornblende and are enriched in Si, Na and Sr and depleted in K, Ca, Ba and Rb relative to melanosomes. The enrichment of Na in the melt is inconsistent with experimental results of fluid-present melting of tonalite, but is broadly consistent with the experimental results of fluid-fluxed melting of amphibolite. The absence of K-feldspar in both rock types is a critical control on the composition of anatectic melt and initial melt compositions were probably similar for both rock types. Leucosomes inherited rare earth element patterns from their sources, which suggests that some trace element diagrams used to infer tectonic settings and depths of melting are not appropriate for reworked components of Archean grey gneisses. Whole-rock δ18O suggest that the fluids responsible for inducing local melting were derived from the intrusion and crystallization of ~2.60 Ga tonalites and trondhjemites in the Taishan region. One amphibolite sample has a relatively low δ18O suggestive of interaction with meteoric water or seawater possibly related to crustal extension and asthenosphere upwelling at ~2.60 Ga. Fluid-present partial melting reworked 2.75–2.60 Ga tonalites and amphibolites, generating ~2.60 Ga sodium-rich components of grey gneisses in the Western Shandong Province

Fast Crack Detection Using Convolutional Neural Network

To improve the efficiency and reduce the labour cost of the renovation process, this study presents a lightweight Convolutional Neural Network (CNN)-based architecture to extract crack-like features, such as cracks and joints. Moreover, Transfer Learning (TF) method was used to save training time while offering comparable prediction results. For three different objectives: 1) Detection of the concrete cracks; 2) Detection of natural stone cracks; 3) Differentiation between joints and cracks in natural stone; We built a natural stone dataset with joints and cracks information as complementary for the concrete benchmark dataset. As the results show, our model is demonstrated as an effective tool for industry use