Optron: Better Medical Image Registration via Optimizing in the Loop

Previously, in the field of image registration, there are mainly two paradigms, the traditional optimization-based methods, and the deep-learning-based methods. We designed a robust training architecture that is simple and generalizable. We present Optron, a general training architecture incorporating the idea of optimizing-in-the-loop. By iteratively optimizing the prediction result of a deep learning model through a plug-and-play optimizer module in the training loop, Optron introduces pseudo ground truth to an unsupervised training process. This pseudo supervision provides more direct guidance towards model training compared with unsupervised methods. Utilizing this advantage, Optron can consistently improve the models' performance and convergence speed. We evaluated our method on various combinations of models and datasets, and we have achieved state-of-the-art performance on the IXI dataset, improving the previous state-of-the-art method TransMorph by a significant margin of +1.6% DSC. Moreover, Optron also consistently achieved positive results with other models and datasets. It increases the validation DSC on IXI for VoxelMorph and ViT-V-Net by +2.3% and +2.2% respectively, demonstrating our method's generalizability. Our implementation is publicly available at https://github.com/miraclefactory/optronComment: 10 pages, 5 figures, 4 table

Automatically learning topics and difficulty levels of problems in online judge systems

Online Judge (OJ) systems have been widely used in many areas, including programming, mathematical problems solving, and job interviews. Unlike other online learning systems, such as Massive Open Online Course, most OJ systems are designed for self-directed learning without the intervention of teachers. Also, in most OJ systems, problems are simply listed in volumes and there is no clear organization of them by topics or difficulty levels. As such, problems in the same volume are mixed in terms of topics or difficulty levels. By analyzing large-scale users’ learning traces, we observe that there are two major learning modes (or patterns). Users either practice problems in a sequential manner from the same volume regardless of their topics or they attempt problems about the same topic, which may spread across multiple volumes. Our observation is consistent with the findings in classic educational psychology. Based on our observation, we propose a novel two-mode Markov topic model to automatically detect the topics of online problems by jointly characterizing the two learning modes. For further predicting the difficulty level of online problems, we propose a competition-based expertise model using the learned topic information. Extensive experiments on three large OJ datasets have demonstrated the effectiveness of our approach in three different tasks, including skill topic extraction, expertise competition prediction and problem recommendation

4-[2-(Hydrogen phosphonato)-2-hydroxy-2-phosphonoethyl]pyridinium

The title compound, C7H11NO7P2, exists as a zwitterion in which the positive charge resides on the protonated pyridyl N atom and the negative charge on one of the two phosphate groups. In the crystal, adjacent molcules are linked by O—H⋯O and N—H⋯O hydrogen bonds into a three-dimensional network

Prognostic impact of H3K27me3 expression on locoregional progression after chemoradiotherapy in esophageal squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Trimethylation of lysine 27 on histone H3 (H3K27me3) by enhancer of zeste homolog 2 (EZH2) is an epigenetic mark that mediates gene silencing. EZH2 is overexpressed and correlates with poor prognosis in many cancers. However, the clinical implication of H3K27me3 in human malignancies has not been well established. We wished to ascertain whether a correlation exists between the expression of H3K27me3 and clinical outcome in a group of patients with esophageal squamous cell carcinoma (ESCC) treated with definitive chemoradiotherapy (CRT).</p> <p>Methods</p> <p>The method of immunohistochemistry (IHC) was utilized to examine the protein expression of H3K27me3 in 98 pretreatment biopsy specimens of ESCC and in 30 samples of normal esophageal mucosa. The clinical/prognostic significance of H3K27me3 expression was statistically analyzed.</p> <p>Results</p> <p>The expression frequency and expression levels of H3K27me3 were significantly higher in ESCCs than in normal tissues. There was a positive correlation between H3K27me3 expression and WHO grade (<it>P </it>= 0.016), tumor size (<it>P </it>= 0.019), T status (<it>P </it>= 0.024), locoregional progression (<it>P </it>= 0.009) and EZH2 expression (<it>P </it>= 0.036). High H3K27me3 expression was associated with poor locoregional progression-free survival (LPFS) (<it>P </it>= 0.010) in ESCC. Further analysis demonstrated that H3K27me3 could stratify patient outcome in T2-3 (<it>P </it>= 0.048), N0 (<it>P </it>= 0.005) and M0 (<it>P </it>= 0.018) stages as well as in CRT effective group (<it>P </it>= 0.022).</p> <p>Conclusions</p> <p>Our data suggests that H3K27me3 expression examined by IHC might be useful for stratifying LPFS for different subsets of ESCC patients treated with definitive CRT.</p

Optimization with a Genetic Algorithm for Multilayer Electromagnetic Wave Absorption Cement Mortar Filled with Expended Perlite

Abstract: Due to the complexity of the design of multilayer electromagnetic (EM) wave absorbing materials, it is difficult to establish the relationship between material parameters (type and filling ratios) and EM properties using traditional trial and error methods. Based on the measured EM parameters within a few materials and Boltzmann mixing theory, a database of EM parameters was thereafter built up. In this study, the genetic algorithm (GA) was used to design the multilayer wave-absorbing cement mortar. In order to verify this method, a multilayer mortar was fabricated and measured. The simulated and measured results are well consistent, which convincingly verifies computer-aided design. In addition, the optimized result expresses that the first layer as a matching layer guides EM waves into the interior of the material, while the other layers as absorption layers attenuate EM waves. The multilayer material may not meet the impedance gradient principle but still exhibits better EM wave absorption performance. The reflection loss (RL) of all optimized three layer sample is below –6.89 dB in the full frequency band and the minimum RL is –26.21 dB. This composite absorbing material and the GA method provide more design ideas for the design of future cement-based wave-absorbing materials and save a lot of time and material cost

AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems

Recently, there has been an emergence of employing LLM-powered agents as believable human proxies, based on their remarkable decision-making capability. However, existing studies mainly focus on simulating human dialogue. Human non-verbal behaviors, such as item clicking in recommender systems, although implicitly exhibiting user preferences and could enhance the modeling of users, have not been deeply explored. The main reasons lie in the gap between language modeling and behavior modeling, as well as the incomprehension of LLMs about user-item relations. To address this issue, we propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimizes both kinds of agents together. Specifically, at each time step, we first prompt the user and item agents to interact autonomously. Then, based on the disparities between the agents' decisions and real-world interaction records, user and item agents are prompted to reflect on and adjust the misleading simulations collaboratively, thereby modeling their two-sided relations. The optimized agents can also propagate their preferences to other agents in subsequent interactions, implicitly capturing the collaborative filtering idea. Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions. The results show that these agents can demonstrate personalized behaviors akin to those of real-world individuals, sparking the development of next-generation user behavior simulation