VictorJuiz/thesis huggingface dataset:
Designing New Memory Systems for Next-Generation Data Centers

In recent years, there has been a trend towards greater use of DRAM in data center applications. In-memory key-value stores are being used to cache or replace disk-based databases, and memory-based big data frameworks are supplanting earlier disk-based frameworks. However, process technology improvements for DRAM have not kept pace and instead have stagnated in terms of cost and density. With next-generation memory technologies like STT-MRAM, PCRAM, and RRAM still far from commercial viability, improving memory utilization is the most potentially fruitful path towards reducing the cost of data center memory in the near term. A typical way to improve utilization of a resource in a data center is to disaggregate said resource, allowing it to be shared across multiple nodes. Disaggregating memory has generally been quite difficult because the latency of a round-trip across a typical data center network is much greater than the latency of a DRAM access. However, recent work on photonic interconnects promises to deliver data center networks with much lower latencies, making the concept of data center remote memory more feasible./nIn this work, we present the design of a DRAM caching remote memory system which divides a data center rack into compute-specialized and memory-specialized nodes. Each memory blade contains a fixed-function hardware controller that serves data from its large pool of DRAM to the compute blades through the rack network. Each compute blade contains a small local DRAM that is used as a cache for remote memory. This local DRAM is managed by a hardware controller which automatically refills the cache on misses by sending requests to remote memory. This system provides a global pool of memory that can be dynamically allocated among the compute blades and is transparent to software. We evaluated our system using microbenchmarks and realistic data center applications in cloud FPGA-based RTL simulations. Through these evaluations, we found that our DRAM caching system can serve data at lower latencies than earlier virtual memory-based remote memory systems and, with the aid of prefetching, can achieve performance comparable to local DRAM.

Advisor: Katz, Randy H
Rational design of random heteropolymers as synthetic terpene cyclase mimics

Chapter 1: A brief overview of enzyme-inspired materials is provided. It covers supramolecular systems, polymeric materials, de novo designed enzymes, and small molecule micelle catalysts./nChapter 2: Nano-reactor type random heteropolymers (RHPs) catalysis was studied. RHPs collapsed in nonpolar solvent, toluene, forming a hydrophilic interior. In polar microenvironments of RHPs, the rate of aminolysis reaction was enhanced although in toluene, there was no significant background reaction. RHP’s microenvironment was key to catalysis. The composition effects and additive effects were investigated./nChapter 3: New random heteropolymers (RHPs) were designed to mimic terpene cyclases. Squalene-hopene cyclase (SHC) was analyzed as a representative example of terpene cyclases. Its surface and active site composition as well as its sequence were examined. Analysis showed negatively charged aspartic acid 377 was incorporated in hydrophobic active site and could stabilize a carbenium intermediate. Also, the enzyme utilized cation-π interactions to stabilize carbocation intermediates. Thus, to imitate electrostatic stabilization and cation-π interactions, new RHPs were designed and studied. Statistical sequence study showed effective incorporation of negatively charged side chain in hydrophobic microenvironment in rationally designed RHP (S15)./nChapter 4: With rationally designed random heteropolymers (RHPs) in chapter 3, cyclization reaction of citronellal (CA) was tested. The newly designed RHPs were synthesized via RAFT polymerization. The polymer’s hydrophobic microenvironment was explored using solvatochromic study with pyrene. X-ray absorption near edge structure (XANES) technique was applied to distinguish the population of negatively charged monomers in hydrophobic environment. Then, the RHP-CA complex was studied with a variety of NMR techniques. Proton NMR showed the complexation was rapid and CA’s environment changed to more hydrophobic. Diffusion NMR demonstrated complexation of RHP with CA by showing the same diffusivity from CA and RHP. Nuclear Overhauser Effect Spectroscopy illustrated interesting RHP-CA interactions including possible electrostatic interactions and cation-π interactions. Those studies explained RHPs selective catalysis in water solution./nChapter 5: In Chapter 5, random heteropolymer (RHP)’s substrate scope was examined. Enzymes have substrate specificity due to its internal unique structures. However, enzyme’s substrate specificity could have a negative impact on industrial applications since their structures should be modified to use different substrates. In contrast, RHPs, as a more flexible system, can catalyzed different substrates when the reaction mechanism is similar. Thus, the rationally designed terpene cyclase mimic RHP was applied to another reaction which also proceeded carbenium intermediates. Here, RHPs’ versatility was studied by investigation of catalytic study of α-pinene oxide isomerization.

Advisor: Xu, Ting
Efficient robotic manipulation skill learning

In this thesis, we study how to efficiently learn robotic manipulation skills from various perspectives. We start by reviewing the current trend of applying learning-based methods in the domain of robotics; despite their great potentials, these methods still have limits on sample efficiency and the ability to generalize across domains. We aim to tackle these issues from roughly two aspects: introducing suitable policy priors into the learning process and robotic specific representations that are effective and generalizable./nWe first introduce an effective algorithm called residual reinforcement learning, which essentially regularizes policy learning in function space by superimposing its action with the control signal from a ``prior controller", the variance during policy learning is reduced at the cost of injecting certain amount of bias which can be quantified. We validate our method on a variety of contact-rich fine-grained robotic manipulation tasks. On the other perspective, we study how robot learning could benefit from an effective representation. We introduce an action space representation for reinforcement learning algorithms: an operational space force controller. When a RL agent is combined with this operational force controller, it tends to learn substantially faster and perform better than other action space in several high-precision assembly tasks. We then propose a representation that explicitly models gripper features in grasping tasks, this allows us to collect data entirely in simulation and transfer to real-world without any fine-tuning nor loss of performance. We conduct large-scale real world experiments on a variety of objects and environments to validate this method. On a similar fashion, we extend a method called domain randomization by explicitly modelling robot rigid transformations in the task of pose estimation, which again trains entirely on synthetic data and follows a zero-shot transfer to real world. Results show that our method could achieve significantly better accuracy. Finally, we conclude that injecting prior information in suitable forms such as control bias, state representation, simulation model, etc., will dramatically increase the performance in a broad range of hard robotic problems; however these inductive bias will make our solution less general. We point future directions as to come up with principled way to design and inject such prior information to learning systems.

Advisor: Abbeel, Pieter
Agogino, Alice
Voltage Imaging with a Near-Infrared Phosphine Oxide Rhodamine Voltage Reporter

Light can be used to provide an optical readout of relative changes in membrane potential in a minimally invasive manner with high spatiotemporal resolution via the use of fluorescent voltage reporters. These reporters localize to cell membranes and are composed of three primary elements: a fluorophore, a molecular wire, and an electron rich aniline donor. The donor modulates the fluorescence of the reporter in response to changes in the membrane potential of a cell through a photo induced electron transfer (PeT) process. The existing color palette of these reporters is limited to the visible range of light (< 700 nm). Near-infrared (NIR) fluorescent dyes are favorable since the use of NIR light avoids cellular autofluorescence, is less damaging than visible light, and can be used simultaneously with other tools in the visible spectrum such as calcium indicators and optogenetic actuators. To take advantage of these properties, we synthesized and characterized four NIR voltage reporters using a phosphine oxide rhodamine fluorophore with a maximum absorbance at 704 nm and peak emission at 723 nm. These NIR phosphine oxide rhodamine voltage reporters, poRhoVRs, exhibit fractional changes in fluorescence ranging from 13-43% per 100 mV with good signal to noise ratios. We have successfully used poRhoVR to monitor spontaneous neuronal activity in cultured neurons as well as activity evoked through field stimulation or via optogenetic activation. We have also used poRhoVR in mouse retina in conjunction with GCaMP6f, to monitor pharmacologically induced changes in activity, while offering greater spatial resolution than possible with multi-electrode arrays. These poRhoVR dyes can easily be modified to be genetically targetable by appending chemical handles for proteins such as HaloTag.

Advisor: Miller, Evan W
The Role of SIRT2 in Metabolic Regulation During Aging and Overnutrition

Aging was once thought to be an inevitable process of deterioration. However, with significant advance in aging research in recent decades, it is more widely accepted that the aging process is under regulatory control and can be slowed or even reversed, at least to some extent, by different interventions to extend the lifespan or healthspan. Aging is associated with a wide range of human diseases. Understanding how aging-associated conditions are regulated is of great importance and holds promise to help extend the lifespan or healthspan. In addition, it is essential for our body to have the ability to sense and respond to fluctuations in environmental nutrient levels. Dysregulated nutrient sensing plays a role in some known pathological conditions. Aging and overnutrition can both contribute to chronic low-grade inflammation, which has been shown to contribute to many diseases, such as obesity, diabetes, Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and cancer. The aim of this dissertation work was to gain molecular insights into the metabolic regulation during aging and overnutrition. Silent information regulator 2 (Sir2) proteins, or sirtuins, are highly conserved nicotinamide adenine dinucleotide (NAD+) dependent protein deacetylases. They have been to play a significant role in cellular metabolic homeostasis regulation. There are seven sirtuins in mammals and located in different subcellular areas. SIRT2, a cytosolic deacetylase, has been reported to suppress inflammation in multiple inflammation-inducing mouse models. Aberrant inflammasome activations are believed to be involved in various inflammatory disorders. However, the molecular mechanisms explaining how inflammasomes activation is regulated under different conditions remain elusive. Thus, we set out to investigate if SIRT2 suppress chronic inflammation through regulating the NOD-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome directly or indirectly. We found that SIRT2 can specifically deacetylate NLRP3 to suppress the assembly and activation of NLRP3 inflammasome. Using cell-based systems and aging mouse models, we demonstrated that SIRT2 and NLRP3 deacetylation prevent, and can be targeted to reverse, aging-associated chronic inflammation and insulin resistance. These results suggest the dysregulation of the acetylation switch of the NLRP3 inflammasome as an origin of aging-associated chronic inflammation and highlights the reversibility of aging-associated chronic inflammation and insulin resistance. Our study not only broadens the understanding of the molecular mechanism about how SIRT2-NLRP3 axis is involve in the metabolic regulation during aging and overnutrition, but also provides important insights into the interface of immunity and metabolism. Besides, it may inspire novel therapeutical strategies to deal with aging-associated conditions and diseases.

Advisor: Chen, Danica
The effect of short-term subsidies on future demand for potable water in rural Bihar, India: A randomized controlled trial

Short-term subsidies are a commonly used policy lever in low- and middle-income countries to increase the immediate adoption of single use health products. However, reduced prices could be counterproductive towards demand for products requiring repeat purchase, inducing anchoring effects in which recipients reference the initial discounted price, undervaluing products on future offer. Alternatively, short-term subsidies might allow for positive learning to dominate, leading to higher future valuation, purchase and use. In this experiment, subsidy offers were randomized for a novel potable water delivery service to 526 households in rural Bihar to examine whether discounts led to price anchoring or positive learning. Treatment households received subsidized delivery offers for 30 days, while controls were offered market price for the same period. One month after subsidies expired, treatment households were 13.1 percentage points more likely than controls to purchase water delivery at the market price (95% CI: 6.3 to 19.9). Furthermore, treatment households purchased 86.9% more liters of water per week than controls (95% CI: 25.9 to 148.0). Results clearly show that, when paired with social marketing and weekly reminders, short-term subsidies can positively influence both the probability of future demand and increase the total quantity of water purchased.

Advisor: Dow, William H
Electromagnetic Energy Harvester for Sensors on Overhead Power Distribution Lines

The electric grid began its service over 100 years ago and accumulated many safety concerns/nregarding its infrastructure and nearby environment. The wireless sensor network has been/ndeployed to monitor the condition of both the grid infrastructure and environment, and/nnon-interrupted power sources with minimal maintenance has been an engineering challenge./nHere, design of an inexpensive and durable electromagnetic energy harvester coupled with/nthe magnetic field from the power line conductors for powering the wireless sensor network is/ndocumented. A prototype of the energy harvester is first built to evaluate the device’s output/npower by placing it against the power line conductor which carries an AC current of 30 A./nThe power output is measured up to around 120 milli-watts with the use of a magnetic flux/nguide made of electrical steels. This result shows that the electromagnetic energy harvester/nis promising for most sensor applications whose power consumptions are usually from several/nmicro-watts to a few milli-watts./nA simulation model is developed to calculate the output power based on critical parameters/nof a specific harvester configuration for an optimized energy harvester system. This model/nincludes the saturation e↵ect in the magnetic material as it has significant impact on the/nharvester’s power output observed from the experimental results. Circuit analysis with/nconsideration of nonlinear saturation e↵ects is conducted using a current transformer model,/nand it provides good approximations to the experimental measurements. Furthermore, a/nfinite element model is also developed to improve the accuracy of the circuit model, and it/nis used to optimize the energy harvester designs. Several parameters of the energy harvester/nare investigated in di↵erent configurations to maximize the output power, and up to 0.4/nwatts is obtained with three pieces of flux guides when the AC current in the power line/nconductor is 30 A.

Advisor: Lin, Liwei
Physiology and Physiological Covariation in Close Relationships in Schizophrenia

Critical, intrusive family environments are a predictor of relapse and poor functional outcome in schizophrenia. Reactivity in the autonomic nervous system (ANS) has been proposed as a potential mechanism underlying this link, but little research has examined ANS physiological reactivity in schizophrenia in the context of family interactions. Further, physiological covariation – physiological interdependence between individuals – predicts important relationship and mental health outcomes, yet no work has examined physiological covariation between people with schizophrenia and their family members. The current study investigated physiological reactivity and physiological covariation during a conflict conversation between young adults with schizophrenia (n = 20) and without schizophrenia (n = 29) and their parents. Respiratory sinus arrhythmia (RSA) and inter-beat interval (IBI) were recorded during a 10-minute conversation about an area of conflict in the participants’ relationship. Participants rated relationship qualities and affect, and participants with schizophrenia were rated on symptom severity. Results indicated that young adults with schizophrenia reported higher negative affect after a conflict conversation and had lower average RSA relative to baseline during the conflict conversation compared to young adults without schizophrenia. IBI and RSA covariation were associated with lower parental caring, with the effect of caring on RSA covariation driven by the control group. Within the schizophrenia group, weaker RSA covariation was related to higher negative symptoms. Together, these findings provide novel support for the importance of ANS, particularly PNS, reactivity and covariation in family relationships in people with and without schizophrenia.

Advisor: Kring, Ann M
Model-based Deep Reinforcement Learning for Robotic Systems

Deep learning has shown promising results in robotics, but we are still far from having intelligent systems that can operate in the unstructured settings of the real world, where disturbances, variations, and unobserved factors lead to a dynamic environment. The premise of the work in this thesis is that model-based deep RL provides an efficient and effective framework for making sense of the world, thus allowing for reasoning and adaptation capabilities that are necessary for successful operation in the dynamic settings of the world./nWe first build up a model-based deep RL framework and demonstrate that it can indeed allow for efficient skill acquisition, as well as the ability to repurpose models to solve a variety of tasks. We then scale up these approaches to enable locomotion with a 6-DoF legged robot on varying terrains in the real world, as well as dexterous manipulation with a 24-DoF anthropomorphic hand in the real world. Next, we focus on the inevitable mismatch between an agent's training conditions and the test conditions in which it may actually be deployed, thus illuminating the need for adaptive systems. Inspired by the ability of humans and animals to adapt quickly in the face of unexpected changes, we present a meta-learning algorithm within this model-based RL framework to enable online adaptation of large, high-capacity models using only small amounts of data from the new task. We demonstrate these fast adaptation capabilities in both simulation and the real-world, with experiments such as a 6-legged robot adapting online to an unexpected payload or suddenly losing a leg. We then further extend the capabilities of our robotic systems by enabling the agents to reason directly from raw image observations. Bridging the benefits of representation learning techniques with the adaptation capabilities of meta-RL, we present a unified framework for effective meta-RL from images. With robotic arms in the real world that learn peg insertion and ethernet cable insertion to varying targets, we show the fast acquisition of new skills, directly from raw image observations in the real world. Finally, we conclude by discussing the key limitations of our existing approaches and present promising directions for future work in the area of model-based deep RL for robotic systems.

Advisor: Fearing, Ronald S
Levine, Sergey
Structure-Function Studies in Prokaryotic Photosynthesis and CO2-Concentrating Mechanisms

Photosynthesis encompasses the set of reactions that convert light into stored chemical energy. Photosynthetic organisms are the foundation of life, as they catalyze the assimilation of inorganic carbon (Ci) into the biosphere and are the ultimate source of nutrition on Earth./nCyanobacteria are an ancient group of oxygenic photosynthetic prokaryotes and remain important primary producers today. The chloroplasts of all land plants can trace their ancestry to cyanobacteria. In many ways, such as the light reactions of photosynthesis that capture solar energy, the protein machinery of cyanobacteria is a representative, simplified form of that found in chloroplasts. In other ways, the cyanobacterial machinery is more complex as in the dark reactions that ultimately store solar energy as chemical energy by fixing CO2. To overcome inherent limitations in the principal CO2-fixing enzyme rubisco, cyanobacteria possess an elaborate integrated physiological strategy to increase the local CO2 concentration near rubisco and facilitate on-pathway catalysis. This physiological strategy, known as a CO2-concentrating mechanism (CCM), relies on energy-coupled Ci-transporters and encapsulation of rubisco in proteinaceous organelles known as carboxysomes. While some species of land plants possess CCMs, none resemble nor are nearly as productive as those found in cyanobacteria. Therefore, cyanobacteria are an ideal platform to study the basic workings of photosynthetic machinery and potentially provide insight on engineering strategies that may improve photosynthetic productivity in land plants of societal interest./nHere, we investigated the structure and function of protein complexes from cyanobacteria and other prokaryotes to elucidate their roles in photosynthesis and CCMs. First, we determined the structure of the NAD(P)H dehydrogenase-like complex, a key component in the light reactions, and develop putative models for its catalytic mechanism. Next, we discovered a new type of Ci-transporter we named the DABs accumulate bicarbonate (DAB) complex and characterize the complex through a variety of genetic, biochemical, and biophysical approaches. Finally, we determined the structure of simplified carboxysome shells that provide insight into the architecturally principles and assembly of native shell components.

Advisor: Savage, David F