Balance between information gain and reversibility in weak measurement

We derive a tight bound between the quality of estimating a quantum state by measurement and the success probability of undoing the measurement in arbitrary dimensional systems, which completely describes the tradeoff relation between the information gain and reversibility. In this formulation, it is clearly shown that the information extracted from a weak measurement is erased through the reversing process. Our result broadens the information-theoretic perspective on quantum measurement as well as provides a standard tool to characterize weak measurements and reversals.Comment: 5 pages, final versio

Regulation of Life Span by \u3cem\u3eDAF-16\u3c/em\u3e/Forkhead Transcription Factor in \u3cem\u3eCaenorhabditis elegans\u3c/em\u3e: A Dissertation

The insulin/IGF-1 signaling pathway plays a pivotal role in life span regulation in diverse organisms. In Caenorhabditis elegans, a PI 3-kinase signaling cascade downstream of DAF-2, an ortholog of the mammalian insulin and insulin-like growth factor-1 (IGF-1) receptor, negatively regulates DAF-16/forkhead transcription factor. DAF-16 then regulates a wide variety of genes involved in longevity, stress response, metabolism and development. DAF-16 also receives signals from other pathways regulating life span and development. However, the precise mechanism by which DAF-16 directs multiple functions is poorly understood. First, in Chapter II, we demonstrate that JNK is a novel positive regulator of DAF-16 in both life span regulation and stress resistance. Our genetic analysis suggests that the JNK pathway acts in parallel with the insulin-like signaling pathway to regulate life span and both pathways converge onto DAF-16. We also show that JNK-1 directly interacts with and phosphorylates DAF-16. Moreover, in response to heat stress, JNK-1 promotes the translocation of DAF-16 into thc nucleus. Our findings define a novel interaction between the stress response pathway (JNK) and the master regulator of life span (DAF-16), and provide a mechanism by which JNK regulates longevity and stress resistance. Next, in Chapter III, we focus on the downstream targets of DAF-16. Here, we used a modified chromatin immunoprecipitation (ChIP) method to identify direct target promoters of DAF-16. We cloned 103 target sequences containing consensus DAF-16 binding sites and randomly selected 33 targets for further analysis. The expression of majority of these genes is regulated in a DAF-16-dependent manner. Moreover, inactivation of more than 50% of these genes significantly altered DAF-16-dependent functions such as longevity, fat storage and dauer diapause. Our results show that the ChIP-based cloning strategy leads to greater enrichment of DAF-16 target genes, compared to previous studies using DNA micro array or bioinformatics. We also demonstrate that DAF-16 is recruited to multiple promoters to coordinate regulation of its downstream target genes. In summary, we identified the JNK signaling pathway as a novel input into DAF-16 to adapt animals to the environmental stresses. We also revealed a large number of novel outputs of DAF-16. Taken together, these studies provide insight into the complex regulation by DAF-16 to control diverse biological functions and eventually broaden our understanding of aging

Depolymerization and decolorization of chitosan by ozone treatment

Currently, depolymerization and decolorization of chitosan are achieved by chemical or enzymatic methods which are time consuming and expensive. Ozone has been shown to be able to degrade macromolecules and remove pigments due to its high oxidation potential. In this study, the effects of ozone treatment on depolymerization and decolorization of chitosan were investigated. Crawfish chitosan was ozonated in water and acetic acid solution for 0, 5, 10, 15, and 20 minutes at room temperature with 12wt% gas. For the determination of viscosity–average Molecular weight of chitosan, an ubbelohde viscometer was used to measure the intrinsic viscosity, and the Mark-Houwink equation was used to calculate molecular weight. Color of ozone-treated chitosan was analyzed using a Minolta spectrophotometer. The degree of deacetylation was determined by a colloid titration method. Molecular weight of ozone-treated chitosan in acetic acid solution decreased appreciably as the ozone treatment time increased. Ozonation for 20 minutes reduced the molecular weight of the chitosan by 92% (104 KDa) compared to the untreated chitosan (1333 KDa) with a decrease in viscosity of the chitosan solution. Ozonation for 5 min markedly increased the whiteness of chitosan; however, further ozonation resulted in development of yellowness. In case of the ozonation in water, there were no significant differences of the molecular weight and color between ozone-treated chitosans. However, results showed that ozone treatment of chitosan in both water and acetic acid solution was not effective in removing acetyl groups (deacetylation) in chitosan molecules. This study showed that ozone can be used to modify molecular weight and remove pigments of chitosan without chemical use in a shorter time with less cost

Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning

Many robotic tasks are composed of a lot of temporally correlated sub-tasks in a highly complex environment. It is important to discover situational intentions and proper actions by deliberating on temporal abstractions to solve problems effectively. To understand the intention separated from changing task dynamics, we extend an empowerment-based regularization technique to situations with multiple tasks based on the framework of a generative adversarial network. Under the multitask environments with unknown dynamics, we focus on learning a reward and policy from the unlabeled expert examples. In this study, we define situational empowerment as the maximum of mutual information representing how an action conditioned on both a certain state and sub-task affects the future. Our proposed method derives the variational lower bound of the situational mutual information to optimize it. We simultaneously learn the transferable multi-task reward function and policy by adding an induced term to the objective function. By doing so, the multi-task reward function helps to learn a robust policy for environmental change. We validate the advantages of our approach on multi-task learning and multi-task transfer learning. We demonstrate our proposed method has the robustness of both randomness and changing task dynamics. Finally, we prove that our method has significantly better performance and data efficiency than existing imitation learning methods on various benchmarks.Comment: Accepted in ICRA 202