Teddy Eddie

A poem about Teddy Bear whose name is Eddie

Excavation in the Sky: Historical Inference in Astronomy

The philosophy of historical sciences investigates their distinct objects of study, epistemic challenges, and methodological solutions. Rethinking astronomy in this light offers a contribution. First, the methodology of historical sciences adds to a more adequate description of how astronomers study and utilize token events. Second, astronomy faces a typical difficulty in identifying traces of some past events and has developed a delicate solution. This enriches the idea of trace and suggests a methodology that relies on iterations between data-driven approaches and theory-driven approaches, together with the cross-validation between multiple relevant historical events or datasets

Utjecaj dodatka ekstrakta maslačka, saharoze i starter kulture na viskoznost, sposobnost zadržavanja vode i pH jogurta

Dandelion extract is a traditional Chinese medicine and contains significant nutritional value. The aim of this study was to research the optimum fermentation conditions for dandelion addition to plain yogurt using a single factor experiments and orthogonal experiment. The results of the present study demonstrated that the addition of dandelion extract affected the viscosity, water-holding capacity and pH of yogurt. Optimized conditions for dandelion addition to plain yogurt based on viscosity, incubation time, pH and sensory score were 10 % sucrose, 0.3 % of the starter cultures, incubation time of 6.5 hours and 3 % dandelion extract. A new kind of dandelion yogurt with high viscosity, good water-holding capacity and good taste was prepared in this study.Ekstrakt maslačka je tradicionalni kineski lijek, a također ima i značajnu nutritivnu vrijednost. Cilj ovog istraživanja bio je ispitati optimalne uvjete fermentacije za dodavanje maslačka u jogurt korištenjem pojedinih faktorskih eksperimenata i ortogonalnog eksperimenta. Rezultati ove studije pokazali su da dodavanje ekstrakta maslačaka utječe na viskoznost, sposobnost zadržavanja vode i pH jogurta. Optimalni uvjeti za dodavanje maslačka u jogurt na temelju viskoznosti, vremena inkubacije, pH i senzorskog rezultata bili su 10 % saharoze, 0,3 % starter kulture, vrijeme inkubacije od 6,5 sati i dodatak 3 % ekstrakta maslačka. U ovoj studiji pripremljena je nova vrsta jogurta s dodatkom maslačka karakteriziranog visokom viskoznošću, dobrom sposobnošću zadržavanja vode i dobrim okusom

Who Makes the Choice? Artificial Neural Networks in Science and Non-Uniqueness

Machine learning techniques have become an essential part of many scientific inquiries, promoting novel discoveries. Here we distinguish between the output-oriented approach which regards neural networks as black boxes, and the feature-oriented approach which seeks to reveal and scientifically adopt the features captured by the network for the purpose of exploration and novelty. Focusing on the latter, we point at an issue of non-uniqueness when choosing between three types of features – mathematical, diagnostic, and real-world features. Scientists make choices among numerous features and rationalize their choices with background assumptions, but when aiming at exploration in an immature domain, rationalization neither justifies the choice nor guarantees that the chosen features are real. As a result, we propose that machine-captured features for this purpose should not be used as full-fledged evidence, but scientists should focus on the instrumental value of these features, such as refining existing descriptions or methods and inspiring future directions of research. We also suggest promoting the transparency of feature selection rationale and the plurality of choices

Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing popularity of taxi requesting services such as Uber and Didi Chuxing (in China), we are able to collect large-scale taxi demand data continuously. How to utilize such big data to improve the demand prediction is an interesting and critical real-world problem. Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations. Recent advances in deep learning have shown superior performance on traditionally challenging tasks such as image classification by learning the complex features and correlations from large-scale data. This breakthrough has inspired researchers to explore deep learning techniques on traffic prediction problems. However, existing methods on traffic prediction have only considered spatial relation (e.g., using CNN) or temporal relation (e.g., using LSTM) independently. We propose a Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations. Specifically, our proposed model consists of three views: temporal view (modeling correlations between future demand values with near time points via LSTM), spatial view (modeling local spatial correlation via local CNN), and semantic view (modeling correlations among regions sharing similar temporal patterns). Experiments on large-scale real taxi demand data demonstrate effectiveness of our approach over state-of-the-art methods.Comment: AAAI 2018 pape

On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems

Reinforcement learning serves as a potent tool for modeling dynamic user interests within recommender systems, garnering increasing research attention of late. However, a significant drawback persists: its poor data efficiency, stemming from its interactive nature. The training of reinforcement learning-based recommender systems demands expensive online interactions to amass adequate trajectories, essential for agents to learn user preferences. This inefficiency renders reinforcement learning-based recommender systems a formidable undertaking, necessitating the exploration of potential solutions. Recent strides in offline reinforcement learning present a new perspective. Offline reinforcement learning empowers agents to glean insights from offline datasets and deploy learned policies in online settings. Given that recommender systems possess extensive offline datasets, the framework of offline reinforcement learning aligns seamlessly. Despite being a burgeoning field, works centered on recommender systems utilizing offline reinforcement learning remain limited. This survey aims to introduce and delve into offline reinforcement learning within recommender systems, offering an inclusive review of existing literature in this domain. Furthermore, we strive to underscore prevalent challenges, opportunities, and future pathways, poised to propel research in this evolving field.Comment: under revie

Joint Embedding Learning of Educational Knowledge Graphs

As an efficient model for knowledge organization, the knowledge graph has been widely adopted in several fields, e.g., biomedicine, sociology, and education. And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks. In general, knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph. And conventional embedding learning models rely on structural relationships among entities and relations. However, in educational knowledge graphs, structural relationships are not the focus. Instead, rich literals of the graphs are more valuable. In this paper, we focus on this problem and propose a novel model for embedding learning of educational knowledge graphs. Our model considers both structural and literal information and jointly learns embedding representations. Three experimental graphs were constructed based on an educational knowledge graph which has been applied in real-world teaching. We conducted two experiments on the three graphs and other common benchmark graphs. The experimental results proved the effectiveness of our model and its superiority over other baselines when processing educational knowledge graphs

Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation

Deep reinforcement learning (DRL) has been proven its efficiency in capturing users' dynamic interests in recent literature. However, training a DRL agent is challenging, because of the sparse environment in recommender systems (RS), DRL agents could spend times either exploring informative user-item interaction trajectories or using existing trajectories for policy learning. It is also known as the exploration and exploitation trade-off which affects the recommendation performance significantly when the environment is sparse. It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems. As a step to address this issue, We design a novel intrinsically ,otivated reinforcement learning method to increase the capability of exploring informative interaction trajectories in the sparse environment, which are further enriched via a counterfactual augmentation strategy for more efficient exploitation. The extensive experiments on six offline datasets and three online simulation platforms demonstrate the superiority of our model to a set of existing state-of-the-art methods

Bactericidal synergism between phage endolysin Ply2660 and cathelicidin LL-37 against vancomycin-resistant Enterococcus faecalis biofilms

Antibiotic resistance and the ability to form biofilms of Enterococcus faecalis have compromised the choice of therapeutic options, which triggered the search for new therapeutic strategies, such as the use of phage endolysins and antimicrobial peptides. However, few studies have addressed the synergistic relationship between these two promising options. Here, we investigated the combination of the phage endolysin Ply2660 and the antimicrobial peptide LL-37 to target drug-resistant biofilm-producing E. faecalis. In vitro bactericidal assays were used to demonstrate the efficacy of the Ply2660–LL-37 combination against E. faecalis. Larger reductions in viable cell counts were observed when Ply2660 and LL-37 were applied together than after individual treatment with either substance. Transmission electron microscopy revealed that the Ply2660–LL-37 combination could lead to severe cell lysis of E. faecalis. The mode of action of the Ply2660–LL-37 combination against E. faecalis was that Ply2660 degrades cell wall peptidoglycan, and subsequently, LL-37 destroys the cytoplasmic membrane. Furthermore, Ply2660 and LL-37 act synergistically to inhibit the biofilm formation of E. faecalis. The Ply2660–LL-37 combination also showed a synergistic effect for the treatment of established biofilm, as biofilm killing with this combination was superior to each substance alone. In a murine peritoneal septicemia model, the Ply2660–LL-37 combination distinctly suppressed the dissemination of E. faecalis isolates and attenuated organ injury, being more effective than each treatment alone. Altogether, our findings indicate that the combination of a phage endolysin and an antimicrobial peptide may be a potential antimicrobial strategy for combating E. faecalis

Preparation of Chitosan Microflower and Factors Affecting Its Morphology

Chitosan (CS) was dissolved with the aid of ultrasound and hydrogen peroxide treatment. Then, sodium tripolyphosphate (TPP) was introduced as a crosslinker into CS solution from bottom to top. Finally, chitosan microflower (CSMF) was obtained by collecting the resulting precipitate and freeze-drying it. CSMF was characterized and the factors affecting its formation were studied. The results showed that the size of CSMF was 1–2 μm in diameter. The Fourier transform infrared (FTIR) spectrum of CSMF showed a vibration peak of phosphate group at 532 cm-1. The crystal form of CS changed from semi-crystalline structure to hydrated polycrystalline structure after conversion into CSMF. X-ray photoelectron spectroscopy (XPS) showed that CSMF produced C-N＋ bond, and thermogravimetric analysis (TGA) showed that the thermal stability of CSMF was slightly lower than that of CS. Also, it was found that pretreatment method, ultrasonic time, CS solution temperature and CS/TPP ratio (m/m) but not ultrasound power or hydrogen peroxide addition could affect the flower-shaped structure of CSMF. Furthermore, it was inferred that the formation mechanism of CSMF was related to that fact that after the degradation of CS into short- or long-chain CS within a certain molecular mass range, relatively longer and shorter degraded CS chains were crosslinked by TPP to respectively form the pedestal of the microflower structure and nanosheets which were self-assembled on the substate through the interaction between the –NH3+ and phosphate ions in the structure of CS