Latest Discoveries

Title
Abstract
Author
Year
Dept.
Graphs Are Not Enough: Using Interactive Visual Analytics in Storage ResearchStorage researchers have always been interested in understanding the complex behavior of storage systems with the help of statistics, machine learning, and simple visualization techniques. However, when a system's behavior is affected by hundreds or even thousands of factors, existing approaches break down. Results are often difficult to interpret, and it can be challenging for humans to apply domain knowledge to a complex system. We propose to enhance storage system analysis by applying "interactive visual analytics," which can address the aforementioned limitations. We have devised a suitable Interactive Configuration Explorer (ICE), and conducted several case studies on a typical storage system, to demonstrate its benefits for storage system researchers and designers. We found that ICE makes it easy to explore a large parameter space, identify critical parameters, and quickly zero in on optimal parameter settings.Zhen Cao, Stony Brook University; Geoff Kuenning, Harvey Mudd College; Klaus Mueller, Anjul Tyagi, and Erez Zadok, Stony Brook University2019Computer Science
Automatically Solving Deduction Games via Symbolic Execution, Model Counting, and Entropy MaximizationWe present a technique for automatically solving deduction games in which a player makes repeated queries to a running implementation of the game and receives a game outcome, with the goal of discovering an unknown secret value. By making multiple queries, a player iteratively reduces the uncertainty about the secret until it is known. We show how to synthesize player queries using static program analysis, model-counting, and information theory. The system we describe automatically solves deduction games implemented in a Python-based game specification language.Mara Downing, Chris Thompson, Lucas Bang2019Computer Science
The Futility of Bias-Free Learning and SearchBuilding on the view of machine learning as search, we demonstrate the necessity of bias in learning, quantifying the role of bias (measured relative to a collection of possible datasets, or more generally, information resources) in increasing the probability of success. For a given degree of bias towards a fixed target, we show that the proportion of favorable information resources is strictly bounded from above. Furthermore, we demonstrate that bias is a conserved quantity, such that no algorithm can be favorably biased towards many distinct targets simultaneously. Thus bias encodes trade-offs. The probability of success for a task can also be measured geometrically, as the angle of agreement between what holds for the actual task and what is assumed by the algorithm, represented in its bias. Lastly, finding a favorably biasing distribution over a fixed set of information resources is provably difficult, unless the set of resources itself is already favorable with respect to the given task and algorithm.George D. Montanez, Jonathan Hayase, Julius Lauw, Dominique Macias, Akshay Trikha, Julia Vendemiatti2019Computer Science
Nonadiabatic Investigation of the Electronic Spectroscopy of trans-1,3-ButadieneLow-lying UV spectroscopy of trans-1,3-butadiene has been extensively studied by experimentalists and theorists. Though a host of techniques has been applied to understand its lowest electronic states, there are still important open questions. Among these are the positions of the two lowest valence excited states and the factors responsible for the spectral shape of the lowest allowed transitions. We present results from EOM-CC calculations in extended basis sets that are used to parametrize a three-electronic-state Koppel, Domcke, and Cederbaum (KDC) model. We test the sensitivity of the KDC model to a variety of parameters and address several outstanding questions regarding the spectrum. We find that the overall shape of the spectrum is determined primarily by the Franck–Condon envelope of the 11Bu state and that the princple impact of the doubly excited 21Ag state is to broaden the 11Bu peaks. There is only modest sensitivity to the relative position of these two states. We find that the lowest Rydberg state, the 11Bg state, has an unexpected impact on the third peak in the spectrum, and its effect is considerably more energy-dependent than that of the 21Ag state.Scott M. Rabidoux, Robert J. Cave, John F. Stanton2019Chemistry
Water exit dynamics of jumping archer fish: Integrating two-phase flow large-eddy simulation with experimental measurementsArcher fish jumping for prey capture are capable of achieving accelerations that can reach 12 times gravitational from a stationary start at the free surface. This behavior is associated with nontrivial production of hydrodynamic thrust. In this work, we numerically investigate the hydrodynamic and aerodynamic performance of a jumping smallscale archer fish (Toxotes microlepis) to elucidate the propulsive mechanisms that contribute to the rapid acceleration and the considerable jump accuracy. We conduct high-fidelity, two-phase flow, large-eddy simulation (LES) of an anatomically realistic archer fish using detailed jump kinematics in water, through the water/air interface, and in air. The complex fish body kinematics are reconstructed using high-speed imaging. The LES results during the water phase of the jump are compared with particle image velocimetry measurements of a live jumping archer fish, and excellent agreement is found. The numerical simulations further enable detailed analysis of the flow dynamics and elucidate for the first time the dynamics of the coherent vortical structures in both the water and air phases. In particular, the pectoral fins are shown to contribute to the initial spike in acceleration before water exit and to enhance the overall jumping performance of the fish.
Ali Khosronejad, Leah Mendelson, Alexandra H. Techet, Seokkoo Kang, Dionysios Angelidis, Fotis Sotiropoulos2020Engineering
A Trypanosoma brucei ORFeome-based Gain-of-Function Library reveals novel genes associated with melarsoprol resistanceTrypanosoma brucei is an early branching protozoan that causes Human and Animal African Trypanosomiasis. Forward genetics approaches are powerful tools for uncovering novel aspects of Trypanosomatid biology, pathogenesis, and therapeutic approaches against trypanosomiasis. Here we have generated a T. brucei ORFeome consisting of over 90% of the targeted genome and used it to make an inducible Gain-of-Function library for broadly applicable forward genetic screening. Using a critical drug of last resort, melarsoprol, we conducted a proof of principle genetic screen. Hits arising from this screen support the significance of trypanothione, a key player in redox metabolism, as a target of melarsoprol and implicate novel proteins of the flagellum and mitochondria in drug resistance. This study has produced two powerful new genetic tools for kinetoplastida research, which are expected to promote major advances in kinetoplastida biology and therapeutic development in the years to come.M. Carter, H.S. Kim, S. Gomez, S. Gritz, S. Larson, D. Schultz, G.A. Hovel-Miner2019Biology
Identification of clinically approved small molecules that inhibit growth and promote surface remodeling in the African trypanosomeTrypanosoma brucei are unicellular parasites endemic to Sub-Saharan Africa that cause fatal disease in humans and animals. Infection with these parasites is caused by the bite of the tsetse fly vector, and parasites living extracellularly in the blood of infected animals evade the host immune system through antigenic variation. Existing drugs for Human and Animal African Trypanosomiasis are difficult to administer and can have serious side effects. Resistance to some drugs is also increasing, creating an urgent need for alternative trypanosomiasis therapeutics. In addition to identifying drugs that inhibit trypanosome growth, we wish to identify small molecules that can induce bloodstream form parasites to differentiate into forms adapted for the insect vector. These insect stage parasites do not vary proteins on their cell surface, making them vulnerable to the host immune system. To identify drugs that trigger differentiation of the parasite from bloodstream to insect stages, we engineered bloodstream reporter parasites that express Green Fluorescent Protein (GFP) following induction of the invariant insect-stage specific procyclin transcript. Using these bloodstream reporter strains in combination with high-throughput flow cytometry, we screened a library of 1,585 U.S. or foreign-approved drugs and identified eflornithine, spironolactone, and phenothiazine as small molecules that induce transcription of procylin. Both eflornithine and spironolactone also affect transcript levels for a subset of differentiation associated genes. We further identified 154 compounds that inhibit trypanosome growth. As all of these compounds have already undergone testing for human toxicity, they represent good candidates for repurposing as trypanosome therapeutics. Finally, this study is proof of principle that fluorescent reporters are a useful tool for small molecule or genetic screens aimed at identifying molecules or processes that initiate remodeling of the parasite surface during life cycle stage transitions.Madison Elle Walsh, Eleanor Mary Naudzius, Savanah Jessica Diaz, Theodore William Wismar, Mikhail Martchenko, Danae Schulz2019Biology
MAD-TN: A Tool for Measuring Fluency in Human-Robot CollaborationFluency is an important metric in Human-Robot Interaction (HRI) that describes the coordination with which humans and robots collaborate on a task. Fluency is inherently linked to the timing of the task, making temporal constraint networks a promising way to model and measure fluency. We show that the Multi-Agent Daisy Temporal Network (MAD-TN) formulation, which expands on an existing concept of daisy-structured networks, is both an effective model of human-robot collaboration and a natural way to measure a number of existing fluency metrics. The MAD-TN model highlights new metrics that we hypothesize will strongly correlate with human teammates' perception of fluency.James C. Boerkoel Jr., Gretchen Rice, Seth Isaacson2019Computer Science
Dynamic Control of Probabilistic Simple Temporal NetworksThe controllability of a temporal network is defined as an agent's ability to navigate around the uncertainty in its schedule and is well-studied for certain networks of temporal constraints. However, many interesting real-world problems can be better represented as Probabilistic Simple Temporal Networks (PSTNs) in which the uncertain durations are represented using potentially-unbounded probability density functions. This can make it inherently impossible to control for all eventualities. In this paper, we propose two new dynamic con-trollability algorithms that attempt to maximize the likelihood of successfully executing a schedule within a PSTN. The first approach, which we call MIN-LOSS DC, finds a dynamic scheduling strategy that minimizes loss of control by using a conflict-directed search to decide where to sacrifice the control in a way that optimizes overall success. The second approach , which we call MAX-GAIN DC, works in the other direction: it finds a dynamically controllable schedule and then attempts to progressively strengthen it by capturing additional uncertainty. Our approaches are the first known that work by finding maximally dynamically controllable schedules. We empirically compare our approaches against two existing PSTN offline dispatch approaches and one online approach and show that our MIN-LOSS DC algorithm outper-forms the others in terms of maximizing execution success while maintaining competitive runtimes.James C. Boerkoel Jr., Lindsay Popowski, Michael Gao2020Computer Science
DREAM: An Algorithm for Mitigating the Overhead of Robust ReschedulingGenerating and executing temporal plans is difficult in uncertain environments. The current state-of-the-art algorithm for probabilistic temporal networks maintains a high success rate by rescheduling frequently as uncertain events are resolved, but this approach involves substantial resource overhead due to computing and communicating new schedules between agents. Aggressive rescheduling could thus reduce overall mission duration or success in situations where agents have limited energy or computing power, and may not be feasible when communication is limited. In this paper, we propose new approaches for heuristically deciding when rescheduling is most efficacious. We propose two compatible approaches, Allowable Risk and Sufficient Change, that can be employed in combination to compromise between the computation rate, the communication rate, and success rate for new schedules. We show empirically that both approaches allow us to gracefully trade success rate for lower computation and/or communication as compared to the state-of-the-art dynamic scheduling algorithm.Jordan R. Abrahams, David A. Chu, Grace Diehl, Marina Knittel, Judy Lin, Liam Lloyd, James C. Boerkoel Jr., Jeremy Frank2019Computer Science
Measuring and Optimizing Durability against Scheduling DisturbancesFlexibility is a useful and common metric for measuring the amount of slack in a Simple Temporal Network (STN) solution space. We extend this concept to specific schedules within an STN's solution space, developing a related notion of durability that captures an individual schedule's ability to withstand disturbances and still remain valid. We identify practical sources of scheduling disturbances that motivate the need for durable schedules, and create a geometrically-inspired empirical model that enables testing a given schedule's ability to withstand these disturbances. We develop a number of durability metrics and use these to characterize and compute specific schedules that we expect to have high durability. Using our model of disturbances, we show that our durability metrics strongly predict a schedule's resilience to practical scheduling disturbances. We also demonstrate that the schedules we identify as having high durability are up to three times more resilient to disturbances than an arbitrarily chosen schedule is.Joon Young Lee, Vivaswat Ojha, James C. Boerkoel Jr.2019Computer Science
‘Make this adult mess make sense again’: the psychic lives of gentrification’s childrenThis paper advocates for increased scholarly curiosity about the painful and hopeful psychic agency of children and youth in critiquing neoliberal urban gentrification and imagining alternative forms of city life. It performs a geographically and theoretically informed reading of American director Ira Sachs’ 2016 film Little Men, a story about a brief but intense childhood friendship that is ended by an eviction. Drawing on the gentrification and psychoanalytic geography literatures, I turn to psychoanalyst D.W. Winnicott’s key clinical observations regarding ‘antisocial’ children and youth. Bringing these tools into dialogue with Little Men, I consider the revealing differences between the film’s shooting script and the final cut, as well as the film’s reception. Little Men and its child protagonists, I argue, should inspire more fine-grained attention to gentrification’s psychic dimensions, which both animate the process and open it to contestation.David K. Seitz2019Humanities, Social Sciences, and the Arts
María Acuña. Poesía descalza. Granada: Valparaíso Ediciones, 2020. (Barefoot Poetry)In 2007, the Congress of Deputies in Spain approved the Law of Historical Memory. The law sought to redress wrongs experienced by victims on both sides of the Spanish Civil War (1936-1939) and condemned the Franco regime. Since then, exhumation of the repressed past has witnessed a burgeoning. Restoration ought to include not just notables like Federico García Lorca but also the unsung voices that played a quiet yet relevant role. Within this context, I have edited the unpublished poetry of María Acuña (1928-1994). Poesía descalza is the first volume to come out. The first woman to wear pants in her village, to smoke in public, among the few to give birth out of wedlock when the Church sanctioned the harshening dictatorship, she cuts a striking figure across the somber Francoist years. My research draws on her poetry to reconstruct the postwar female experience. Women’s creativity under Franco, regional lives too often neglected, must be acknowledged and written into Spain’s intellectual history.Isabel Balseiro2020Humanities, Social Sciences, and the Arts
The Bias-Expressivity Trade-offLearning algorithms need bias to generalize and perform better than random guessing. We examine the flexibility (expressivity) of biased algorithms. An expressive algorithm can adapt to changing training data, altering its outcome based on changes in its input. We measure expressivity by using an information-theoretic notion of entropy on algorithm outcome distributions, demonstrating a trade-off between bias and expressivity. To the degree an algorithm is biased is the degree to which it can outperform uniform random sampling, but is also the degree to which is becomes inflexible. We derive bounds relating bias to expressivity, proving the necessary trade-offs inherent in trying to create strongly performing yet flexible algorithms.George D. Montañez, Julius Lauw, Dominique Macias, Akshay Trikha, Julia Vendemiatti2019Computer Science
Decomposable Probability-of-Success Metrics in Algorithmic SearchPrevious studies have used a specific success metric within an algorithmic search framework to prove machine learning impossibility results. However, this specific success metric prevents us from applying these results on other forms of machine learning, e.g. transfer learning. We define decomposable metrics as a category of success metrics for search problems which can be expressed as a linear operation on a probability distribution to solve this issue. Using an arbitrary decomposable metric to measure the success of a search, we demonstrate theorems which bound success in various ways, generalizing several existing results in the literature.George D. Montañez, Tyler Sam, Jake Williams, Abel Tadesse, Huey Sun2020Computer Science
The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm CapacityAlgorithm performance in supervised learning is a combination of memorization, generalization, and luck. By estimating how much information an algorithm can memorize from a dataset, we can set a lower bound on the amount of performance due to other factors such as generalization and luck. With this goal in mind, we introduce the Labeling Distribution Matrix (LDM) as a tool for estimating the capacity of learning algorithms. The method attempts to characterize the diversity of possible outputs by an algorithm for different training datasets, using this to measure algorithm flexibility and responsiveness to data. We test the method on several supervised learning algorithms, and find that while the results are not conclusive, the LDM does allow us to gain potentially valuable insight into the prediction behavior of algorithms. We also introduce the Label Recorder as an additional tool for estimating algorithm capacity, with more promising initial results.George D. Montañez, Pedro Sandoval Segura, Julius Lauw, Daniel Bashir, Kinjal Shah, Sonia Sehra, Dominique Macias2019Computer Science
Minimal Complexity Requirements for Proteins and OtherHow complex do proteins (and other multi-part recognition systems) need to be? Using an informationtheoretic framework, we characterize the information costs of recognition tasks and the information capacity of combinatorial recognition systems, to determine minimum complexity requirements for systems performing such tasks. Reducing the recognition task to a finite set of binary constraints, we determine the sizes of minimal equivalent constraint sets using a form of distinguishability, and show how the representation of constraint sets as binary circuits or decision trees also results in minimum constraint set size requirements. We upper-bound the number of configurations a recognition system can distinguish between as a function of the number of parts it contains, which we use to determine the minimum number of parts needed to accomplish a given recognition task. Lastly, we apply our framework to DNA-binding proteins and derive estimates for the minimum number of amino acids needed to accomplish binding tasks of a given complexity.George D. Montañez, Laina Sanders, Howard Deshong2020Computer Science
Virtue as a framework for the design and use of artificial intelligencePractitioners are seeking information and ethical guidance related to the design and utilization of artificial intelligence (AI) across a variety of applications. Developments at Google illustrate the potential good of AI as well as potential concerns. In this article, we use Google to create a context that demonstrates the relevance of virtue for the ethical design and use of AI. We describe a set of ethical challenges encountered by Google and introduce virtue as a framework for ethical decision making that can be applied broadly to numerous organizations. We also examine support for virtue in ethical decision making as well as its power in attracting and retaining the employees who develop AI and the customers who use it. To conclude, we apply the virtue framework to Google’s AI challenges and offer suggestions for its use in other organizations.George D. Montañez, Mitchell J. Neubert2020Computer Science
Synthesis of Tris-Heterocycles via a Cascade IMCR/Aza Diels-Alder + CuAAC Strategy6-Triazolylmethyl-pyrrolo[3,4-b]pyridin-5-one tris-heterocycles were synthesized in 43–57% overall yields. The two-stage synthesis involved a cascade process (Ugi-3CR/aza Diels-Alder/N-acylation/aromatization) followed by a copper-assisted alkyne-azide [3+2] cycloaddition (CuAAC). This efficient and convergent strategy proceeded via complex terminal alkynes functionalized with a fused bis-heterocycle at the α-position. The final products are ideal candidates for SAR studies as they possess two privileged scaffolds in medicinal chemistry: 4-substituted or 1,4-substituted 1H-1,2,3-triazoles and pyrrolo[3,4-b]pyridin-5-ones.David A. Vosburg, Manuel A. Rentería-Gómez, Alejandro Islas-Jácome, Shrikant G. Pharande, Rocío Gámez-Montaño2019Chemistry
Aqueous Dearomatization/Diels−Alder Cascade to a Grandifloracin PrecursorA green laboratory experiment has been developed in which students perform an aqueous oxidation/cycloaddition reaction to convert salicyl alcohol into a pentacyclic diepoxydione that is readily isolated by filtration. Drawing on their knowledge of periodate-mediated 1,2-diol cleavage, students propose a mechanism for the oxidation of salicyl alcohol (which is not a 1,2-diol) and the structure of the transient product (prior to a spontaneous Diels−Alder dimerization). Students then characterize salicyl alcohol and their diepoxide product by mass spectrometry, IR spectroscopy, and H, C, and twodimensional NMR spectroscopy. The only organic solvents used are small amounts for IR and NMR spectroscopy.David A. Vosburg, Emily A. Shimizu, Brett Cory, Johnson Hoang, Giovanni G. Castro, Michael E. Jung2019Chemistry
Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space
Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (−)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening.David A. Vosburg, et al. 2018Chemistry
Divergent Diels–Alder Reactions in the Biosynthesis and Synthesis of Endiandric-Type Tetracycles: A Computational StudyEndiandric acids and related polyketide natural products arise from polyene precursors and occur naturally as fused and bridged tetracycles. In some cases, the intramolecular Diels–Alder reactions that produce fused and bridged tetracycles result from a diene tether that may act as either a 4π or 2π component in the cycloaddition. To examine the preference for fused or bridged products, we applied density functional theory (using the M06-2X and B3LYP functionals) to reactants with various substituents for both fused and bridged transition states. Fused products were generally preferred except when disfavored by extreme steric hindrance (e.g., a tert-butyl group). These computational results are consistent with experimental data and suggest the existence of as-yet undiscovered natural products.David A. Vosburg, Kareesa J. Kron, Mikaela KosichRobert J. Cave2018Chemistry
The effect of size-scale on the kinematics of elastic energy releaseElastically-driven motion has been used as a strategy to achieve high speeds in small organisms and engineered micro-robotic devices. We examine the size-scaling relations determining the limit of elastic energy release from elastomer bands that efficiently cycle mechanical energy with minimal loss. The maximum center-of-mass velocity of the elastomer bands was found to be size-scale independent, while smaller bands demonstrated larger accelerations and shorter durations of elastic energy release. Scaling relationships determined from these measurements are consistent with the performance of small organisms and engineered devices which utilize elastic elements to power motion.Mark Ilton, S. M. Cox, Thijs Egelmeers, Gregory P. Sutton,d S. N. Patek, Alfred J. Crosby2019Physics
Beyond power amplification: Latch-mediated spring actuation is an emerging framework for the study of diverse elastic systemsRapid biological movements, such as the extraordinary strikes of
mantis shrimp and accelerations of jumping insects, have captivated
generations of scientists and engineers. These organisms store
energy in elastic structures (e.g. springs) and then rapidly release it
using latches, such that movement is driven by the rapid conversion
of stored elastic to kinetic energy using springs, with the dynamics of
this conversion mediated by latches. Initially drawn to these systems
by an interest in the muscle power limits of small jumping insects,
biologists established the idea of power amplification, which refers
both to a measurement technique and to a conceptual framework
defined by the mechanical power output of a system exceeding
muscle limits. However, the field of fast elastically driven movements
has expanded to encompass diverse biological and synthetic
systems that do not have muscles – such as the surface tension
catapults of fungal spores and launches of plant seeds. Furthermore,
while latches have been recognized as an essential part of many
elastic systems, their role in mediating the storage and release of
elastic energy from the spring is only now being elucidated. Here, we
critically examine the metrics and concepts of power amplification and
encourage a framework centered on latch-mediated spring actuation
(LaMSA). We emphasize approaches and metrics of LaMSA systems
that will forge a pathway toward a principled, interdisciplinary field.
M. Ilton, S. J. Longo, S. M. Cox, E. Azizi, J. P. Olberding, R. St Pierre, S. N. Patek2019Physics
Why do Large Animals Never Actuate Their Jumps with Latch-Mediated Springs? Because They can Jump Higher Without Them As animals get smaller, their ability to generate usable work from muscle contraction is decreased by the muscle’s force–velocity properties, thereby reducing their effective jump height. Very small animals use a spring-actuated system, which prevents velocity effects from reducing available energy. Since force–velocity properties reduce the usable work in even larger animals, why don’t larger animals use spring-actuated jumping systems as well? We will show that muscle length–tension properties limit spring-actuated systems to generating a maximum one-third of the possible work that a muscle could produce—greatly restricting the jumping height of spring-actuated jumpers. Thus a spring-actuated jumping animal has a jumping height that is one-third of the maximum possible jump height achievable were 100% of the possible muscle work available. Larger animals, which could theoretically use all of the available muscle energy, have a maximum jumping height that asymptotically approaches a value that is about three times higher than that of spring-actuated jumpers. Furthermore, a size related “crossover point” is evident for these two jumping mechanisms: animals smaller than this point can jump higher with a spring-actuated mechanism, while animals larger than this point can jump higher with a muscle-actuated mechanism. We demonstrate how this limit on energy storage is a consequence of the interaction between length–tension properties of muscles and spring stiffness. We indicate where this crossover point occurs based on modeling and then use jumping data from the literature to validate that larger jumping animals generate greater jump heights with muscle-actuated systems than spring-actuated systems.Gregory P Sutton, Elizabeth Mendoza, Emanuel Azizi, Sarah J Longo, Jeffrey P Olberding, Mark Ilton, Sheila N Patek2019Physics