Studies on Extended Cumulative Damage Models and Their Applications to Garbage Collections

This dissertation proposes several maintenance policies for extended cumulative damage models in reliability theory and their applications to garbage collection policies for a generational garbage collector in computer science. Using the tech-niques of cumulative processes, the expected costs per unit of time, i.e., expected cost rate models, are obtained, and optimal policies which minimize them are discussed analytically and computed numerically. An initial chapter gives introduction which is constructed by review of literatures and organization of dissertation. Extended cumulative damage models in theory and their optimizations are proposed in the following chapters: Chapter 2 proposes two basic preventive maintenance policies for a used system with an initial variable damage level. Chapter 3 considers three replacement policies that are com-bined additive with independent damages. Chapter 4 takes up three maintenance policies for an operating system which works at random times for jobs. Chapter 5 proposes a standard cumulative damage model in which the notion of “whichever occurs last” is applied, which is called maintenance last. As applications, two stochastic models based on the working schemes of a generational garbage collector are proposed in Chapter 6. In the end of dissertation, the results are summarized and future problems are given. The models proposed in Chapter 2-5 are derived from practical systems as introduced in every chapter and could be applied to them by suitable modifications and extensions. The theoretical methods proposed in Chapter 6 could provide some useful information to computer programmers to design more efficient collectors

Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and Explorations

Sound is one of the most informative and abundant modalities in the real world while being robust to sense without contacts by small and cheap sensors that can be placed on mobile devices. Although deep learning is capable of extracting information from multiple sensory inputs, there has been little use of sound for the control and learning of robotic actions. For unsupervised reinforcement learning, an agent is expected to actively collect experiences and jointly learn representations and policies in a self-supervised way. We build realistic robotic manipulation scenarios with physics-based sound simulation and propose the Intrinsic Sound Curiosity Module (ISCM). The ISCM provides feedback to a reinforcement learner to learn robust representations and to reward a more efficient exploration behavior. We perform experiments with sound enabled during pre-training and disabled during adaptation, and show that representations learned by ISCM outperform the ones by vision-only baselines and pre-trained policies can accelerate the learning process when applied to downstream tasks.Comment: Accepted at IROS 202

Internally Rewarded Reinforcement Learning

We study a class of reinforcement learning problems where the reward signals for policy learning are generated by a discriminator that is dependent on and jointly optimized with the policy. This interdependence between the policy and the discriminator leads to an unstable learning process because reward signals from an immature discriminator are noisy and impede policy learning, and conversely, an under-optimized policy impedes discriminator learning. We call this learning setting \textit{Internally Rewarded Reinforcement Learning} (IRRL) as the reward is not provided directly by the environment but \textit{internally} by the discriminator. In this paper, we formally formulate IRRL and present a class of problems that belong to IRRL. We theoretically derive and empirically analyze the effect of the reward function in IRRL and based on these analyses propose the clipped linear reward function. Experimental results show that the proposed reward function can consistently stabilize the training process by reducing the impact of reward noise, which leads to faster convergence and higher performance compared with baselines in diverse tasks.Comment: Accepted at ICML 2023. Project webpage at https://ir-rl.github.i

Effect of Spatholobus suberectus (Fabaceae) extract on second-degree burns in rats

Purpose: To evaluate the wound-healing effect of Spatholobus suberectus (Fabaceae) on seconddegree burns in a rat model.Methods: The animals were divided into normal, negative control, as well as 10 % Spatholobus suberectus (SS) (SS10), 20 % SS (SS20) and standard (STD) groups. Second-degree burns were inflicted by exposing a 3 × 3 cm sterile area of skin to boiling water for 10 min. The animals were treated topically twice daily for 2 weeks. Wound contraction (%) was measured after 2 weeks, while wound tissue  histopathology was assessed by hematoxylin & eosin and Masson’s trichrome staining. In addition, lipid peroxidation (malondialdehyde kit) and cytokine secretion (ELISA) were measured in liver and plasma, respectively.Results: The results of this study suggest that topical application of SS for 2 weeks significantly increases wound closure compared with the negative control. Moreover, treatment with SS significantly improved the pathological status of the wound throughout the protocol. There was also a significant decrease in malondialdehyde activity and increase in cytokine release in SS-treated rats compared with control rats.Conclusions: The results show that topical application of SS after inflicting second-degree burns in rats results in increased wound healing and decreased cytokine release and oxidative stress.Keyword: Spatholobus suberectus, Burns, Wound, Lipid peroxidation, Cytokine

Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic

Recent advancements in large language models have showcased their remarkable generalizability across various domains. However, their reasoning abilities still have significant room for improvement, especially when confronted with scenarios requiring multi-step reasoning. Although large language models possess extensive knowledge, their behavior, particularly in terms of reasoning, often fails to effectively utilize this knowledge to establish a coherent thinking paradigm. Generative language models sometimes show hallucinations as their reasoning procedures are unconstrained by logical principles. Aiming to improve the zero-shot chain-of-thought reasoning ability of large language models, we propose Logical Chain-of-Thought (LogiCoT), a neurosymbolic framework that leverages principles from symbolic logic to verify and revise the reasoning processes accordingly. Experimental evaluations conducted on language tasks in diverse domains, including arithmetic, commonsense, symbolic, causal inference, and social problems, demonstrate the efficacy of the enhanced reasoning paradigm by logic

Clathrin exchange during clathrin-mediated endocytosis

During clathrin-mediated endocytosis, clathrin-coated pits invaginate to form clathrin-coated vesicles (CVs). Since clathrin-coated pits are planar structures, whereas CVs are spherical, there must be a structural rearrangement of clathrin as invagination occurs. This could occur through simple addition of clathrin triskelions to the edges of growing clathrin-coated pits with very little exchange occurring between clathrin in the pits and free clathrin in the cytosol, or it could occur through large scale exchange of free and bound clathrin. In the present study, we investigated this question by studying clathrin exchange both in vitro and in vivo. We found that in vitro clathrin in CVs and clathrin baskets do not exchange with free clathrin even in the presence of Hsc70 and ATP where partial uncoating occurs. However, surprisingly FRAP studies on clathrin-coated pits labeled with green fluorescent protein–clathrin light chains in HeLa cells show that even when endocytosis is blocked by expression of a dynamin mutant or depletion of cholesterol from the membrane, replacement of photobleached clathrin in coated pits on the membrane occurs at almost the same rate and magnitude as when endocytosis is occurring. Furthermore, very little of this replacement is due to dissolution of old pits and reformation of new ones; rather, it is caused by a rapid ATP-dependent exchange of clathrin in the pits with free clathrin in the cytosol. On the other hand, consistent with the in vitro data both potassium depletion and hypertonic sucrose, which have been reported to transform clathrin-coated pits into clathrin cages just below the surface of the plasma membrane, not only block endocytosis but also block exchange of clathrin. Taken together, these data show that ATP-dependent exchange of free and bound clathrin is a fundamental property of clathrin-coated pits, but not clathrin baskets, and may be involved in a structural rearrangement of clathrin as clathrin-coated pits invaginate

Dissolved nutrient distributions in the Antarctic Cosmonaut Sea in austral summer 2021

Dissolved nutrients are essential to marine productivity and ecosystem structures in the Southern Ocean. The spatial distributions of dissolved nutrients in the Cosmonaut Sea were studied during the 37th Chinese National Antarctic Research Expedition in 2021. The relative standard deviations of the nitrate (NO3-N), nitrite (NO2-N), ammonium (NH4-N), phosphate (PO4-P), and silicate (SiO3-Si) concentrations found in duplicate samples (n=2) were 1.01%, 9.04%, 6.45%, 0.94%, and 0.67%, respectively. The mean NO3-N, NO2-N, NH4-N, PO4-P, and SiO3-Si concentrations in the mixed layer were 26.41±4.13, 0.15±0.09, 0.51±0.22, 1.73±0.23, and 41.48±6.94 μmol·L−1, respectively, and were higher than the relevant limitation concentrations. The concentrations were generally bounded horizontally by the Southern Boundary (SB) of the Antarctic Circumpolar Current, the NO3-N, NO2-N, NH4-N, and PO4-P concentrations being higher northeast than southwest of the SB but the SiO3-Si concentrations being higher southwest than northeast, indicating that the SB dominates nutrient distributions in the mixed layer. The NO3-N, NH4-N, and PO4-P concentrations gradually increased moving vertically down from the mixed layer to 200 m deep and then remained at 33.73±3.51, 0.26±0.13, and 2.28±0.10 μmol·L−1, respectively, to the bottom. The SiO3-Si concentration increased as depth increased and reached a maximum in the bottom layer. The NO2-N concentration decreased rapidly as depth increased and was ~0 μmol·L−1 at >150 m deep. Circumpolar Deep Water upwelling may cause high nutrient concentrations in shallower layers up to the 100 m layer between 62.5°S and 64°S

Cryogenic quasi-static embedded DRAM for energy-efficient compute-in-memory applications

Compute-in-memory (CIM) presents an attractive approach for energy-efficient computing in data-intensive applications. However, the development of suitable memory designs to achieve high-performance CIM remains a challenging task. Here, we propose a cryogenic quasi-static embedded DRAM to address the logic-memory mismatch of CIM. Guided by the re-calibrated cryogenic device model, the designed four-transistor bit-cell achieves full-swing data storage, low power consumption, and extended retention time at cryogenic temperatures. Combined with the adoption of cryogenic write bitline biasing technique and readout circuitry optimization, our 4Kb cryogenic eDRAM chip demonstrates a 1.37$\times$10$^6$ times improvement in retention time, while achieving a 75 times improvement in retention variability, compared to room-temperature operation. Moreover, it also achieves outstanding power performance with a retention power of 112 fW and a dynamic power of 108 $\mu$W at 4.2 K, which can be further decreased by 7.1% and 13.6% using the dynamic voltage scaling technique. This work reveals the great potential of cryogenic CMOS for high-density data storage and lays a solid foundation for energy-efficient CIM implementations

SOT-MRAM-Enabled Probabilistic Binary Neural Networks for Noise-Tolerant and Fast Training

We report the use of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) to implement a probabilistic binary neural network (PBNN) for resource-saving applications. The in-plane magnetized SOT (i-SOT) MRAM not only enables field-free magnetization switching with high endurance (> 10^11), but also hosts multiple stable probabilistic states with a low device-to-device variation (< 6.35%). Accordingly, the proposed PBNN outperforms other neural networks by achieving an 18* increase in training speed, while maintaining an accuracy above 97% under the write and read noise perturbations. Furthermore, by applying the binarization process with an additional SOT-MRAM dummy module, we demonstrate an on-chip MNIST inference performance close to the ideal baseline using our SOT-PBNN hardware

TMIE defines pore and gating properties of the mechanotransduction channel of mammalian cochlear hair cells

TMC1 and TMC2 (TMC1/2) have been proposed to form the pore of the mechanotransduction channel of cochlear hair cells. Here, we show that TMC1/2 cannot form mechanotransduction channels in cochlear hair cells without TMIE. TMIE binds to TMC1/2, and a TMIE mutation that perturbs TMC1/2 binding abolishes mechanotransduction. N-terminal TMIE deletions affect the response of the mechanotransduction channel to mechanical force. Similar to mechanically gated TREK channels, the C-terminal cytoplasmic TMIE domain contains charged amino acids that mediate binding to phospholipids, including PIP2. TMIE point mutations in the C terminus that are linked to deafness disrupt phospholipid binding, sensitize the channel to PIP2 depletion from hair cells, and alter the channel's unitary conductance and ion selectivity. We conclude that TMIE is a subunit of the cochlear mechanotransduction channel and that channel function is regulated by a phospholipid-sensing domain in TMIE with similarity to those in other mechanically gated ion channels