Pseudo Mask Augmented Object Detection

In this work, we present a novel and effective framework to facilitate object detection with the instance-level segmentation information that is only supervised by bounding box annotation. Starting from the joint object detection and instance segmentation network, we propose to recursively estimate the pseudo ground-truth object masks from the instance-level object segmentation network training, and then enhance the detection network with top-down segmentation feedbacks. The pseudo ground truth mask and network parameters are optimized alternatively to mutually benefit each other. To obtain the promising pseudo masks in each iteration, we embed a graphical inference that incorporates the low-level image appearance consistency and the bounding box annotations to refine the segmentation masks predicted by the segmentation network. Our approach progressively improves the object detection performance by incorporating the detailed pixel-wise information learned from the weakly-supervised segmentation network. Extensive evaluation on the detection task in PASCAL VOC 2007 and 2012 [12] verifies that the proposed approach is effective

Binding of Au3+ Ions by Polyadenine DNA

Interactions between DNA and metal ions are important for many applications such as metal sensing, therapeutics and nanotechnology. Many studies have already been published on these topics. DNA can bind metal ions via its phosphate backbone and the nucleobases. The phosphate backbone likes to bind hard metal ions, whereas the bases tend to coordinate with softer metal ions. Gold in particular is one metal that has many interactions with DNA. Gold nanoparticles are very stable and DNA-functionalized nanoparticles have been used for biosensing, drug delivery and the design of smart materials. However, many of these studies have been carried out on gold surfaces where gold has an oxidation state of zero. The study of the interactions between gold ions (Au3+) and DNA has been limited. Au3+ is a highly soft metal ion and it may have strong interactions with DNA bases. The goal of this thesis is to study the interactions between gold ions and DNA. Fluorescein (FAM) labelled 15-mer homopolymer DNA (FAM-A15, C15, G15, and T15) were first incubated with a source of Au3+ ions (HAuCl4). The reaction products were characterized by denaturing gel electrophoresis and kinetic studies were done using fluorescence quenching assays. In the electrophoresis studies, little to no products were observed with T15 and C15 DNA. A smearing of the gel electrophoresis bands along with some fluorescence quenching was observed with both A15 and G15 DNA suggesting stable complex formation that survived the denaturing gel electrophoresis conditions. A15 DNA showed complete reaction, while G15 showed around 80% reaction yield after 1 hour. Since poly-A DNA showed the highest activity for binding to Au3+, studies of the formation of the polyadenine – Au3+ complex were then conducted as a function of pH and salt. Complex formation was favored at a lower pH (pH 4) and would not form under higher pH (pH 8) conditions. Salt was found to be required for Au3+ to react with DNA. Four different salts (NaF, NaCl, NaBr, and NaI) were tested to see the effects on binding kinetics. The addition of NaF and NaI did not allow the formation of the Au3+-poly-A complex, but NaCl and NaBr allowed the formation of this complex. Therefore, a moderate affinity ligand such as Cl- and Br- favored the reaction. A random 24mer DNA was then compared to A15 for binding to Au3+ and other metal ions. Both DNA only showed evidence of complex formation with Au3+ and not with other metals including Hg2+ and Pb2+. Fluorescence quenching assays of A15 and Au3+ were done as a function of concentration of different salts. First order kinetic rate constants were found to be 0.60 mins-1, 0.91 mins-1, 1.5 mins-1, and 1.9 mins-1 for buffers with no salt, 100 mM NaBr, 100 mM NaCl, and 10 mM NaBr respectively. This reaction was also found to be reversible as the fluorescence could be recovered using potassium cyanide (KCN) or glutathione (GSH). When added post complex formation, KCN could recover almost all the fluorescence while GSH could recover around 60%. The effects of Au3+ ions on the catalytic activity of the 17E zinc dependent DNAzyme were also studied using gel electrophoresis. Under normal conditions, 17E cleaves an RNA substrate with a cleavage percent of 71%, however with concentrations of 25 mM Au3+ and above, the cleavage of the substrate was completely inhibited. The Au3+ was found to be binding to the DNAzyme-substrate duplex in a similar fashion to A15

Impact Analysis of Seismic Events On Integrated Electricity and Natural Gas Systems

Seismic events can cause devastating impacts on both overground and underground energy system infrastructure. This paper proposes a methodology to evaluate the impact of seismic events on the security of integrated electricity and gas system, mainly focusing on pipelines leakage and connection loss of electricity transmission lines. A stochastic model is used to formulate the damage level based on earthquake severity. The seismic impact on the integrated system is classified according to the levels of pipe leak and electricity line failure. Load curtailment due to limited generation capacity and overloaded transmission lines is thereafter quantified. Seismic intensity is generated randomly based on Monte Carlo simulation so that a certain seismic intensity can be related to relevant load curtailment. An integrated energy system with a 30-busbar electricity system and a 6-node natural gas network is used to demonstrate the effectiveness of the proposed method. The results clearly illustrate damage consequences under seismic events in terms of both probability and severity levels. This work can inform resilience enhancement scheme design based on the vulnerability performance and impact of both systems

Dynamic Causal Disentanglement Model for Dialogue Emotion Detection

Emotion detection is a critical technology extensively employed in diverse fields. While the incorporation of commonsense knowledge has proven beneficial for existing emotion detection methods, dialogue-based emotion detection encounters numerous difficulties and challenges due to human agency and the variability of dialogue content.In dialogues, human emotions tend to accumulate in bursts. However, they are often implicitly expressed. This implies that many genuine emotions remain concealed within a plethora of unrelated words and dialogues.In this paper, we propose a Dynamic Causal Disentanglement Model based on hidden variable separation, which is founded on the separation of hidden variables. This model effectively decomposes the content of dialogues and investigates the temporal accumulation of emotions, thereby enabling more precise emotion recognition. First, we introduce a novel Causal Directed Acyclic Graph (DAG) to establish the correlation between hidden emotional information and other observed elements. Subsequently, our approach utilizes pre-extracted personal attributes and utterance topics as guiding factors for the distribution of hidden variables, aiming to separate irrelevant ones. Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables, enabling the accumulation of emotion-related information throughout the conversation. To guide this disentanglement process, we leverage the ChatGPT-4.0 and LSTM networks to extract utterance topics and personal attributes as observed information.Finally, we test our approach on two popular datasets in dialogue emotion detection and relevant experimental results verified the model's superiority