cssi/SciSciGPT-SciSciNet huggingface dataset:
Dissemination and stakeholder engagement practices among dissemination & implementation scientists: Results from an online survey.

Introduction There has been an increasing focus on disseminating research findings, but less about practices specific to disseminating and engaging non-researchers. The present project sought to describe dissemination practices and engagement of stakeholders among dissemination & implementation (D&I) scientists. Methods Methods to disseminate to and engage non-research stakeholders were assessed using an online survey sent to a broad, diverse sample of D&I scientists. Results Surveys were received from 210 participants. The majority of respondents were from university or research settings in the United States. (69%) or Canada (13%), representing a mix of clinical (28%) and community settings (34%). 26% had received formal training in D&I. Respondents indicated routinely engaging in a variety of dissemination-related activities, with academic journal publications (88%), conference presentations (86%), and reports to funders (74%) being the most frequent. Journal publication was identified as the most impactful on respondents’ careers (94%), but face-to-face meetings with stakeholders were rated as most impactful on practice or policy (40%). Stakeholder involvement in research was common, with clinical and community-based researchers engaging stakeholder groups in broadly similar ways, but with critical differences noted between researchers with greater seniority, those with more D&I training, those based in the United States vs. Canada, and those in community vs. clinical research settings. Conclusions There have been increases in stakeholder engagement, but few other practices since the 2012 survey, and some differences across subgroups. Methods to engage different stakeholders deserve more in-depth investigation. D&I researchers report substantial misalignment of incentives and behaviors related to dissemination to non-research audiences.
Long non-coding RNA GAS5 acts as proliferation “brakes” in CD133+ cells responsible for tumor recurrence

Presence of quiescent, therapy evasive population often described as cancer stem cells (CSC) or tumor initiating cells (TIC) is often attributed to extreme metastasis and tumor recurrence. This population is typically enriched in a tumor as a result of microenvironment or chemotherapy induced stress. The TIC population adapts to this stress by turning on cell cycle arrest programs that is a "fail-safe" mechanism to prevent expansion of malignant cells to prevent further injury. Upon removal of the "stress" conditions, these cells restart their cell cycle and regain their proliferative nature thereby resulting in tumor relapse. Growth Arrest Specific 5 (GAS5) is a long-non-coding RNA that plays a vital role in this process. In pancreatic cancer, CD133+ population is a typical representation of the TIC population that is responsible for tumor relapse. In this study, we show for the first time that emergence of CD133+ population coincides with upregulation of GAS5, that reprograms the cell cycle to slow proliferation by inhibiting GR mediated cell cycle control. The CD133+ population further routed metabolites like glucose to shunt pathways like pentose phosphate pathway, that were predominantly biosynthetic in spite of being quiescent in nature but did not use it immediately for nucleic acid synthesis. Upon inhibiting GAS5, these cells were released from their growth arrest and restarted the nucleic acid synthesis and proliferation. Our study thus showed that GAS5 acts as a molecular switch for regulating quiescence and growth arrest in CD133+ population, that is responsible for aggressive biology of pancreatic tumors.
How Many Impulses Redux

A central problem in orbit transfer optimization is to determine the number, time, direction, and magnitude of velocity impulses that minimize the total impulse. This problem was posed in 1967 by T. N. Edelbaum, and while notable advances have been made, a rigorous means to answer Edelbaum’s question for multiple-revolution maneuvers has remained elusive for over five decades. We revisit Edelbaum’s question by taking a bottom-up approach to generate a minimum-fuel switching surface. Sweeping through time profiles of the minimum-fuel switching function for increasing admissible thrust magnitude, and in the high-thrust limit, we find that the continuous thrust switching surface reveals the N-impulse solution. It is also shown that a fundamental minimum-thrust solution plays a pivotal role in our process to determine the optimal minimum-fuel maneuver for all thrust levels. Remarkably, we find that the answer to Edelbaum’s question is not generally unique, but is frequently a set of equal-Δv extremals. We further find, when Edelbaum’s question is refined to seek the number of finite-duration thrust arcs for a specific rocket engine, that a unique extremal is usually found. Numerical results demonstrate the ideas and their utility for several interplanetary and Earth-bound optimal transfers that consist of up to eleven impulses or, for finite thrust, short thrust arcs. Another significant contribution of the paper can be viewed as a unification in astrodynamics where the connection between impulsive and continuous-thrust trajectories are demonstrated through the notion of optimal switching surfaces.
Multi-agent Reinforcement Learning for Networked System Control

This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to stabilize the training of each local agent. Further, we propose a new differentiable communication protocol, called NeurComm, to reduce information loss and non-stationarity in NMARL. Based on experiments in realistic NMARL scenarios of adaptive traffic signal control and cooperative adaptive cruise control, an appropriate spatial discount factor effectively enhances the learning curves of non-communicative MARL algorithms, while NeurComm outperforms existing communication protocols in both learning efficiency and control performance.
Actin protrusions push at apical junctions to maintain E-cadherin adhesion

Cadherin-mediated cell-cell adhesion is actin-dependent, but the precise role of actin in maintaining cell-cell adhesion is not fully understood. Actin polymerization-dependent protrusive activity is required to push distally separated cells close enough to initiate contact. Whether protrusive activity is required to maintain adhesion in confluent sheets of epithelial cells is not known. By electron microscopy as well as live cell imaging, we have identified a population of protruding actin microspikes that operate continuously near apical junctions of polarized Madin-Darby canine kidney (MDCK) cells. Live imaging shows that microspikes containing E-cadherin extend into gaps between E-cadherin clusters on neighboring cells, while reformation of cadherin clusters across the cell-cell boundary correlates with microspike withdrawal. We identify Arp2/3, EVL, and CRMP-1 as 3 actin assembly factors necessary for microspike formation. Depleting these factors from cells using RNA interference (RNAi) results in myosin II-dependent unzipping of cadherin adhesive bonds. Therefore, actin polymerization-dependent protrusive activity operates continuously at cadherin cell-cell junctions to keep them shut and to prevent myosin II-dependent contractility from tearing cadherin adhesive contacts apart.
Combination Antibiotic Delivery in PMMA Provides Sustained Broad-Spectrum Antimicrobial Activity and Allows for Postimplantation Refilling

Antibiotics are commonly added to poly(methyl methacrylate) (PMMA) by surgeons to locally treat infections such as in bone cement for joint replacement surgeries, as well as implantable antimicrobial "beads". However, this strategy is of limited value in high-risk patients where infections can be recurrent or chronic and otherwise hard to treat. Also, when only one drug is incorporated and applied toward polymicrobial infections (multiple bacterial species), there is a high risk that bacteria can develop antibiotic resistance. To combat these limitations, we developed a combination antibiotic PMMA composite system composed of rifampicin-filled β-cyclodextrin (β-CD) microparticles added into PMMA filled with a second drug. Different formulations were evaluated through zone of inhibition, drug activity, antibiotic release, and refilling, as well as mechanical studies. Our combination antibiotic PMMA composite system achieved up to an 8-fold increase in the duration of antimicrobial activity in comparison to clinically used antibiotic-filled PMMA. Inclusion of CD microparticles also allowed for refilling of additional antibiotics after simulated implantation, resulting in additional windows of therapeutic efficacy. Mechanical testing showed that our tested formulations did have a small, but significant decrease in mechanical properties when compared to unmodified controls. While further studies are needed to determine whether the tested formulations are still suitable for load-bearing applications (e.g., bone cement), our composites are certainly amenable for a variety of nonload-bearing applications (e.g., antimicrobial "beads" and temporary spacer in two-stage arthroscopic revisions).
Inequality, Information Failures, and Air Pollution

Abstract Research spanning several disciplines has repeatedly documented disproportionate pollution exposure in low-income communities and communities of color. Among the various proposed causes of this pattern, those that have received the most attention are income inequality, discrimination, and firm costs (of inputs and regulatory compliance). We argue that an additional channel – information – is likely to play an important role in generating disparities in pollution exposure. We present multiple reasons for a tendency to underestimate pollution burdens. Using a model of housing choice, we then derive conditions under which “hidden” pollution leads to an inequality — even when all households face the same lack of information. This inequality arises when households sort according to known pollution and other disamenities, which we show are positively correlated with hidden pollution. To help bridge the gap between environmental justice and economics, we discuss the relationship between hidden information and three different distributional measures: exposure to pollution; exposure to hidden pollution; and welfare loss due to hidden pollution.
Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety

The decision and planning system for autonomous driving in urban environments is hard to design. Most current methods manually design the driving policy, which can be expensive to develop and maintain at scale. Instead, with imitation learning we only need to collect data and the computer will learn and improve the driving policy automatically. However, existing imitation learning methods for autonomous driving are hardly performing well for complex urban scenarios. Moreover, the safety is not guaranteed when we use a deep neural network policy. In this paper, we proposed a framework to learn the driving policy in urban scenarios efficiently given offline connected driving data, with a safety controller incorporated to guarantee safety at test time. The experiments show that our method can achieve high performance in realistic simulations of urban driving scenarios.
IVOA: Introspective Vision for Obstacle Avoidance

Vision, as an inexpensive yet information rich sensor, is commonly used for perception on autonomous mobile robots. Unfortunately, accurate vision-based perception requires a number of assumptions about the environment to hold – some examples of such assumptions, depending on the perception algorithm at hand, include purely lambertian surfaces, texture-rich scenes, absence of aliasing features, and refractive surfaces. In this paper, we present an approach for introspective vision for obstacle avoidance (IVOA) – by leveraging a supervisory sensor that is occasionally available, we detect failures of stereo vision-based perception from divergence in plans generated by vision and the supervisory sensor. By projecting the 3D coordinates where the plans agree and disagree onto the images used for vision-based perception, IVOA generates a training set of reliable and unreliable image patches for perception. We then use this training dataset to learn a model of which image patches are likely to cause failures of the vision-based perception algorithm. Using this model, IVOA is then able to predict whether the relevant image patches in the observed images are likely to cause failures due to vision (both false positives and false negatives). We empirically demonstrate with extensive real-world data from both indoor and outdoor environments, the ability of IVOA to accurately predict the failures of two distinct vision algorithms.
Distributed and Energy Efficient Self-Organization for On–Off Wireless Sensor Networks

In this paper, we utilize clustering to achieve energy efficiency for the on–off wireless sensor network, whose member nodes alternate between active and inactive states. In the proposed Distributed and Energy Efficient Self Organization (DEESO) scheme, the head election is adjusted adaptively to the remaining battery levels of local active nodes, which is a completely distributed approach compared to LEACH that relying on other routing schemes to access global information. Furthermore, we apply the Adaptive Channel Assignment (ACA) to address the on-off topology changes. Simulation results show that DEESO delivers 184% amount of data to the base station as LEACH for the same amount of energy consumption and the effective network lifetime is extended by around 50%.
Level-1 phylogenetic networks and their balanced minimum evolution polytopes

Balanced minimum evolution is a distance-based criterion for the reconstruction of phylogenetic trees. Several algorithms exist to find the optimal tree with respect to this criterion. One approach is to minimize a certain linear functional over an appropriate polytope. Here we present polytopes that allow a similar linear programming approach to finding phylogenetic networks. We investigate a two-parameter family of polytopes that arise from phylogenetic networks, and which specialize to the Balanced Minimum Evolution polytopes as well as the Symmetric Travelling Salesman polytopes. We show that the vertices correspond to certain level-1 phylogenetic networks, and that there are facets or faces for every split. We also describe lower bound faces and a family of faces for every dimension.
Elimination of galanin synthesis in noradrenergic neurons reduces galanin in select brain areas and promotes active coping behaviors

Accumulating evidence indicates that disruption of galanin signaling is associated with neuropsychiatric disease, but the precise functions of this neuropeptide remain largely unresolved due to lack of tools for experimentally disrupting its transmission in a cell type-specific manner. To examine the function of galanin in the noradrenergic system, we generated and crossed two novel knock-in mouse lines to create animals lacking galanin specifically in noradrenergic neurons (GalcKO-Dbh). We observed reduced levels of galanin peptide in pons, hippocampus, and prefrontal cortex of GalcKO-Dbh mice, indicating that noradrenergic neurons are a significant source of galanin to those brain regions, while midbrain and hypothalamic galanin levels were comparable to littermate controls. In these same brain regions, we observed no change in levels of norepinephrine or its major metabolite at baseline or after an acute stressor, suggesting that loss of galanin does not affect noradrenergic synthesis or turnover. GalcKO-Dbh mice had normal performance in tests of depression, learning, and motor-related behavior, but had an altered response in some anxiety-related tasks. Specifically, GalcKO-Dbh mice showed increased marble and shock probe burying and had a reduced latency to eat in a novel environment, indicative of a more proactive coping strategy. Together, these findings indicate that noradrenergic neurons provide a significant source of galanin to discrete brain areas, and noradrenergic-specific galanin opposes adaptive coping responses.
Secure Traffic Lights: Replay Attack Detection for Model-based Smart Traffic Controllers

Rapid urbanization calls for smart traffic management solutions that incorporate sensors, distributed traffic controllers and V2X communication technologies to provide fine-grained traffic control to mitigate congestion. As in many other cyber-physical systems, smart traffic management systems typically lack security measures. This allows numerous opportunities for adversarial entities to craft attacks on the sensor networks, wireless data sharing and/or the distributed traffic controllers. We show that such vulnerabilities can be exploited to disrupt mobility in a large urban area and cause unsafe conditions for drivers and the pedestrians on the roads. Specifically, in this paper, we look into vulnerabilities in model-based traffic controllers and show that, even with state-of-the-art attack detectors in place, false-data injection can be used to hamper mobility. We demonstrate a replay attack by modeling an isolated intersection in VISSIM, a popular traffic simulator and also discuss countermeasures to thwart such attacks.
Division of labour promotes the spread of information in colony emigrations by the ant Temnothorax rugatulus

The fitness of group-living animals often depends on how well members share information needed for collective decision-making. Theoretical studies have shown that collective choices can emerge in a...
Testing hypotheses about the microbiome using the linear decomposition model (LDM).

MOTIVATION Methods for analyzing microbiome data generally fall into one of two groups: tests of the global hypothesis of any microbiome effect, which do not provide any information on the contribution of individual operational taxonomic units (OTUs); and tests for individual OTUs, which do not typically provide a global test of microbiome effect. Without a unified approach, the findings of a global test may be hard to resolve with the findings at the individual OTU level. Further, many tests of individual OTU effects do not preserve the false discovery rate (FDR). RESULTS We introduce the linear decomposition model (LDM), that provides a single analysis path that includes global tests of any effect of the microbiome, tests of the effects of individual OTUs while accounting for multiple testing by controlling the FDR, and a connection to distance-based ordination. The LDM accommodates both continuous and discrete variables (e.g. clinical outcomes, environmental factors) as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based P-values that can control for sample correlation. The LDM can also be applied to transformed data, and an 'omnibus' test can easily combine results from analyses conducted on different transformation scales. We also provide a new implementation of PERMANOVA based on our approach. For global testing, our simulations indicate the LDM provided correct type I error and can have comparable power to existing distance-based methods. For testing individual OTUs, our simulations indicate the LDM controlled the FDR well. In contrast, DESeq2 often had inflated FDR; MetagenomeSeq generally had the lowest sensitivity. The flexibility of the LDM for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. We also show that our implementation of PERMANOVA can outperform existing implementations. AVAILABILITY AND IMPLEMENTATION The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows. CONTACT yijuan.hu@emory.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Predicting variation in endowment effect magnitudes

Abstract Hundreds of studies demonstrate human cognitive biases that are both inconsistent with “rational” decision-making and puzzlingly patterned. One such bias, the “endowment effect” (also known as “reluctance to trade”), occurs when people instantly value an item they have just acquired at a much higher price than the maximum they would have paid to acquire it. This bias impedes a vast range of real-world transactions, making it important to understand. Prior studies have documented items that do or do not generate endowment effects, and have noted that the effects vary in magnitude. But none has predicted any of the substantial between-item variation in those magnitudes across a large and novel set of items. Working from evolutionary theory, we derived six factors that predicted 52% of the between-item variation in magnitudes for a novel set of 24 items. These results deepen understanding of both the causes of and patterns in endowment effects. More broadly, they suggest that many other cognitive biases may be similarly approached, and potentially linked by a common theoretical framework.
A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation over High Mountain Asia

The accurate representation of the local-scale variability of precipitation plays an important role in understanding the hydrological cycle and land-atmosphere interactions in the High Mountain Asi...
Definite determinantal representations via orthostochastic matrices

Abstract Definite Determinantal polynomials play a crucial role in semidefinite programming problems. Helton and Vinnikov proved that real zero (RZ) bivariate polynomials are definite determinantals. Indeed, in general, it is a difficult problem to decide whether a given polynomial is definite determinantal, and if it is, it is of paramount interest to determine a definite determinantal representation of that polynomial. We provide a necessary and sufficient condition for the existence of definite determinantal representation of a bivariate polynomial by identifying its coefficients as scalar products of two vectors where the scalar products are defined by orthostochastic matrices. This alternative condition enables us to develop a method to compute a monic symmetric/Hermitian determinantal representations for a bivariate polynomial of degree d. In addition, we propose a computational relaxation to the determinantal problem which turns into a problem of expressing the vector of coefficients of the given polynomial as convex combinations of some specified points.
An Analysis of Computer Operations Under Running Time Priority Disciplines

Running-time priority disciplines for sequencing computer operations are those which discriminate among programs on the basis of the amount of service (running-time) they require. Discrimination is explicit in the models analyzed by Kesten and Runnenburg [1] and Miller and Schrage [2] in which the priority rule is shortest-job-first. However, in the models of particular interest in this paper, the discrimination is necessarily implicit since it is assumed that the running-times of arriving programs are not known in advance. We shall now describe in detail the queuing models to be analyzed in the following sections. Our initial description will use conventional queuing terminology; subsequently, the correspondences between this terminology and the terminology of our particular application will be established.
Chapter 14 – Hatchobaru Power Station, Oita Prefecture, Kyushu, Japan

Publisher Summary The islands of Japan lie directly along a section of the Pacific Ring of Fire. There are abundant geothermal resources having a wide range of temperatures and applications. The enjoyment of thermal spas is a well-known part of Japanese culture. Extensive surveys were begun in 1918 to identify promising areas for geothermal development and the following year saw the successful drilling of a steam well—the Tsurumi well—in the Bozu-Jigoku district of the famous hot spring city of Beppu on the island of Kyushu. The Hatchobaru geothermal resource is a high-temperature system that is currently host to three power units having a total installed capacity of 112 MW. It has experienced difficulty in avoiding interference between its reinjection and production wells owing to the close coupling of the wells along the fluid-controlling faults. A new model that does just that predicts a 4% annual decline in power output, amounting to about 20 MW over five years, without any new wells. However, the simulation indicated that by drilling one new production well per year aimed at the NE4 fault the power could be stepped back up to full capacity in five years. It is predicted that the cooling of the reservoir will nevertheless continue unabated in the interim periods between the new well additions.
The sea-level fingerprints of ice-sheet collapse during interglacial periods

Abstract Studies of sea level during previous interglacials provide insight into the stability of polar ice sheets in the face of global climate change. Commonly, these studies correct ancient sea-level highstands for the contaminating effect of isostatic adjustment associated with past ice age cycles, and interpret the residuals as being equivalent to the peak eustatic sea level associated with excess melting, relative to present day, of ancient polar ice sheets. However, the collapse of polar ice sheets produces a distinct geometry, or fingerprint, of sea-level change, which must be accounted for to accurately infer peak eustatic sea level from site-specific residual highstands. To explore this issue, we compute fingerprints associated with the collapse of the Greenland Ice Sheet, West Antarctic Ice Sheet, and marine sectors of the East Antarctic Ice Sheet in order to isolate regions that would have been subject to greater-than-eustatic sea-level change for all three cases. These fingerprints are more robust than those associated with modern melting events, when applied to infer eustatic sea level, because: (1) a significant collapse of polar ice sheets reduces the sensitivity of the computed fingerprints to uncertainties in the geometry of the melt regions; and (2) the sea-level signal associated with the collapse will dominate the signal from steric effects. We evaluate these fingerprints at a suite of sites where sea-level records from interglacial marine isotopes stages (MIS) 5e and 11 have been obtained. Using these results, we demonstrate that previously discrepant estimates of peak eustatic sea level during MIS5e based on sea-level markers in Australia and the Seychelles are brought into closer accord.
THE THREE-BODY PROBLEM

Publisher Summary This chapter focuses on the three-body problem and discusses three sources of the binding energy discrepancy: (1) relativistic corrections, (2) off-shell dependence, and/or (3) three-body force. The relativistic corrections have received minimal attention because they are ambiguous. The question of off-shell dependence or dependence on the form of the potential is more interesting and active. The chapter also discusses ways of approximating the dynamics with separable interaction so as to make detailed calculations possible in a wide range of systems. It discusses the development of a considerable numerical prowess and sophistication through the widespread use of high-speed computers. These developments are means to improved understanding of the basic physics of the three-body system. The chapter further focuses on the three-body scattering and break-up problem, three-body forces, three-nucleon bound state problem, three-body decay, and other aspects of the system in which the nucleons lose their asymptotic identity.
A forcing axiom for a non-special Aronszajn tree

Abstract Suppose that T ⁎ is an ω 1 -Aronszajn tree with no stationary antichain. We introduce a forcing axiom PFA ( T ⁎ ) for proper forcings which preserve these properties of T ⁎ . We prove that PFA ( T ⁎ ) implies many of the strong consequences of PFA , such as the failure of very weak club guessing, that all of the cardinal characteristics of the continuum are greater than ω 1 , and the P-ideal dichotomy. On the other hand, PFA ( T ⁎ ) implies some of the consequences of diamond principles, such as the existence of Knaster forcings which are not stationarily Knaster.
Chapter 6 – Applications Related to Ordinary and Partial Differential Equations

Chapter 6 discusses several of Mathematica's functions that relate to ordinary and partial differential equations. The examples used to illustrate the various commands are similar to examples routinely done in a one or two-semester differential equations course. (For more detailed discussions regarding Mathematica and differential equations see references like Abell and Braselton's Differential Equations with Mathematica, [2].)
Spectral norm of a symmetric tensor and its computation

We show that the spectral norm of a $d$-mode real or complex symmetric tensor in $n$ variables can be computed by finding the fixed points of the corresponding polynomial map. For a generic complex symmetric tensor the number of fixed points is finite, and we give upper and lower bounds for the number of fixed points. For $n=2$ we show that these fixed points are the roots of a corresponding univariate polynomial of degree at most $(d-1)^2+1$, except certain cases, which are completely analyzed. In particular, for $n=2$ the spectral norm of $d$-symmetric tensor is polynomially computable in $d$ with a given relative precision. For a fixed $n>2$ we show that the spectral norm of a $d$-mode symmetric tensor is polynomially computable in $d$ with a given relative precision with respect to the Hilbert-Schmidt norm of the tensor. These results show that the geometric measure of entanglement of $d$-mode symmetric qunits on $\mathbb{C}^n$ are polynomially computable for a fixed $n$.
Markov Blanket Ranking using Kernel-based Conditional Dependence Measures

Developing feature selection algorithms that move beyond a pure correlational to a more causal analysis of observational data is an important problem in the sciences. Several algorithms attempt to do so by discovering the Markov blanket of a target, but they all contain a forward selection step which variables must pass in order to be included in the conditioning set. As a result, these algorithms may not consider all possible conditional multivariate combinations. We improve on this limitation by proposing a backward elimination method that uses a kernel-based conditional dependence measure to identify the Markov blanket in a fully multivariate fashion. The algorithm is easy to implement and compares favorably to other methods on synthetic and real datasets.
PERSEUS: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models

Deep learning models are increasingly used for end-user applications, supporting both novel features such as facial recognition, and traditional features, e.g. web search. To accommodate high inference throughput, it is common to host a single pre-trained Convolutional Neural Network (CNN) in dedicated cloud-based servers with hardware accelerators such as Graphics Processing Units (GPUs). However, GPUs can be orders of magnitude more expensive than traditional Central Processing Unit (CPU) servers. These resources could also be under-utilized facing dynamic workloads, which may result in inflated serving costs. One potential way to alleviate this problem is by allowing hosted models to share the underlying resources, which we refer to as multi-tenant inference serving. One of the key challenges is maximizing the resource efficiency for multi-tenant serving given hardware with diverse characteristics, models with unique response time Service Level Agreement (SLA), and dynamic inference workloads. In this paper, we present PERSEUS, a measurement framework that provides the basis for understanding the performance and cost trade-offs of multi-tenant model serving. We implemented PERSEUS in Python atop a popular cloud inference server called Nvidia TensorRT Inference Server. Leveraging PERSEUS, we evaluated the inference throughput and cost for serving various models and demonstrated that multi-tenant model serving led to up to 12% cost reduction.
Thermal models for solar hard X-ray bursts

Thermal models for hard X-ray bursts consisting of a one-dimensional flux tube whose central electrons are heated to about 400 million K are examined. It is found that the evolution of a thermal X-ray source is a sensitive function of the electron-ion thermal coupling and the state of the plasma into which the source expands. When this coupling is weak, the heated electrons separate into a region of high temperature of about 400 million K and a region of lower temperature of about 100 million K, a process which leads to a power-law X-ray spectrum. In the case of strong coupling there is only one dominant temperature, about 200 million K, and the X-ray spectrum resembles a true thermal spectrum.
To Warn or Not to Warn: Online Signaling in Audit Games

Routine operational use of sensitive data is often governed by law and regulation. For instance, in the medical domain, there are various statues at the state and federal level that dictate who is permitted to work with patients’ records and under what conditions. To screen for potential privacy breaches, logging systems are usually deployed to trigger alerts whenever a suspicious access is detected. However, such mechanisms are often inefficient because 1) the vast majority of triggered alerts are false positives, 2) small budgets make it unlikely that a real attack will be detected, and 3) attackers can behave strategically, such that traditional auditing mechanisms cannot easily catch them. To improve efficiency, information systems may invoke signaling, so that whenever a suspicious access request occurs, the system can, in real time, warn the user that the access may be audited. Then, at the close of a finite period, a selected subset of suspicious accesses are audited. This gives rise to an online problem in which one needs to determine 1) whether a warning should be triggered and 2) the likelihood that the data request event will be audited. In this paper, we formalize this auditing problem as a Signaling Audit Game (SAG), in which we model the interactions between an auditor and an attacker in the context of signaling and the usability cost is represented as a factor of the auditor’s payoff. We study the properties of its Stackelberg equilibria and develop a scalable approach to compute its solution. We show that a strategic presentation of warnings adds value in that SAGs realize significantly higher utility for the auditor than systems without signaling. We perform a series of experiments with 10 million real access events, containing over 26K alerts, from a large academic medical center to illustrate the value of the proposed auditing model and the consistency of its advantages over existing baseline methods.
LIT: Light-Field Inference of Transparency for Refractive Object Localization

Translucency is prevalent in everyday scenes. As such, perception of transparent objects is essential for robots to perform manipulation. Compared with texture-rich or texture-less Lambertian objects, transparency induces significant uncertainty on object appearances. Ambiguity can be due to changes in lighting, viewpoint, and backgrounds, each of which brings challenges to existing object pose estimation algorithms. In this work, we propose LIT, a two-stage method for transparent object pose estimation using light-field sensing and photorealistic rendering. LIT employs multiple filters specific to light-field imagery in deep networks to capture transparent material properties, with robust depth and pose estimators based on generative sampling. Along with the LIT algorithm, we introduce the light-field transparent object dataset ProLIT for the tasks of recognition, localization and pose estimation. With respect to this ProLIT dataset, we demonstrate that LIT can outperform both state-of-the-art end-to-end pose estimation methods and a generative pose estimator on transparent objects. The link of supplementary material can be found at: https://sites.google.com/umich.edu/prolit.
Impact of adolescent obesity on middle-age health of women given data MAR.

We analyze adolescent BMI and middle-age systolic blood pressure (SBP) repeatedly measured on women enrolled in the Fels Longitudinal Study (FLS) between 1929 and 2010 to address three questions: Do adolescent-specific growth rates in body mass index (BMI) and menarche affect middle-age SBP? Do they moderate the aging effect on middle-age SBP? Have the effects changed over historical time? To address the questions, we propose analyzing a growth curve model (GCM) that controls for age, birth-year cohort, and historical time. However, several complications in the data make the GCM analysis nonstandard. First, the person-specific adolescent BMI and middle-age SBP trajectories are unobservable. Second, missing data are substantial on BMI, SBP, and menarche. Finally, modeling the latent trajectories for BMI and SBP, repeatedly measured on two distinct sets of unbalanced time points, are computationally intensive. We adopt a bivariate GCM for BMI and SBP with correlated random coefficients. To efficiently handle missing values of BMI, SBP, and menarche assumed missing at random, we estimate their joint distribution by maximum likelihood via the EM algorithm where the correlated random coefficients and menarche are multivariate normal. The estimated distribution will be transformed to the desired GCM for SBP that includes the random coefficients of BMI and menarche as covariates. We demonstrate unbiased estimation by simulation. We find that adolescent growth rates in BMI and menarche are positively associated with, and moderate, the aging effect on SBP in middle age, controlling for age, cohort, and historical time, but the effect sizes are at most modest. The aging effect is significant on SBP, controlling for cohort and historical time, but not vice versa.
Sample Amplification: Increasing Dataset Size even when Learning is Impossible

Given data drawn from an unknown distribution, $D$, to what extent is it possible to ``amplify'' this dataset and output an even larger set of samples that appear to have been drawn from $D$? We formalize this question as follows: an $(n,m)$ $\text{amplification procedure}$ takes as input $n$ independent draws from an unknown distribution $D$, and outputs a set of $m > n$ ``samples''. An amplification procedure is valid if no algorithm can distinguish the set of $m$ samples produced by the amplifier from a set of $m$ independent draws from $D$, with probability greater than $2/3$. Perhaps surprisingly, in many settings, a valid amplification procedure exists, even when the size of the input dataset, $n$, is significantly less than what would be necessary to learn $D$ to non-trivial accuracy. Specifically we consider two fundamental settings: the case where $D$ is an arbitrary discrete distribution supported on $\le k$ elements, and the case where $D$ is a $d$-dimensional Gaussian with unknown mean, and fixed covariance. In the first case, we show that an $\left(n, n + \Theta(\frac{n}{\sqrt{k}})\right)$ amplifier exists. In particular, given $n=O(\sqrt{k})$ samples from $D$, one can output a set of $m=n+1$ datapoints, whose total variation distance from the distribution of $m$ i.i.d. draws from $D$ is a small constant, despite the fact that one would need quadratically more data, $n=\Theta(k)$, to learn $D$ up to small constant total variation distance. In the Gaussian case, we show that an $\left(n,n+\Theta(\frac{n}{\sqrt{d}} )\right)$ amplifier exists, even though learning the distribution to small constant total variation distance requires $\Theta(d)$ samples. In both the discrete and Gaussian settings, we show that these results are tight, to constant factors. Beyond these results, we formalize a number of curious directions for future research along this vein.
Efficiently learning structured distributions from untrusted batches

We study the problem, introduced by Qiao and Valiant, of learning from untrusted batches. Here, we assume m users, all of whom have samples from some underlying distribution over 1, …, n. Each user sends a batch of k i.i.d. samples from this distribution; however an є-fraction of users are untrustworthy and can send adversarially chosen responses. The goal of the algorithm is to learn in total variation distance. When k = 1 this is the standard robust univariate density estimation setting and it is well-understood that (є) error is unavoidable. Suprisingly, Qiao and Valiant gave an estimator which improves upon this rate when k is large. Unfortunately, their algorithms run in time which is exponential in either n or k. We first give a sequence of polynomial time algorithms whose estimation error approaches the information-theoretically optimal bound for this problem. Our approach is based on recent algorithms derived from the sum-of-squares hierarchy, in the context of high-dimensional robust estimation. We show that algorithms for learning from untrusted batches can also be cast in this framework, but by working with a more complicated set of test functions. It turns out that this abstraction is quite powerful, and can be generalized to incorporate additional problem specific constraints. Our second and main result is to show that this technology can be leveraged to build in prior knowledge about the shape of the distribution. Crucially, this allows us to reduce the sample complexity of learning from untrusted batches to polylogarithmic in n for most natural classes of distributions, which is important in many applications. To do so, we demonstrate that these sum-of-squares algorithms for robust mean estimation can be made to handle complex combinatorial constraints (e.g. those arising from VC theory), which may be of independent technical interest.
Harnessing the linguistic signal to predict scalar inferences

Pragmatic inferences often subtly depend on the presence or absence of linguistic features. For example, the presence of a partitive construction (of the) increases the strength of a so-called scalar inference: listeners perceive the inference that Chris did not eat all of the cookies to be stronger after hearing “Chris ate some of the cookies” than after hearing the same utterance without a partitive, “Chris ate some cookies”. In this work, we explore to what extent neural network sentence encoders can learn to predict the strength of scalar inferences. We first show that an LSTM-based sentence encoder trained on an English dataset of human inference strength ratings is able to predict ratings with high accuracy (r = 0.78). We then probe the model’s behavior using manually constructed minimal sentence pairs and corpus data. We first that the model inferred previously established associations between linguistic features and inference strength, suggesting that the model learns to use linguistic features to predict pragmatic inferences.
Learning Spatial Relationships between Samples of Patent Image Shapes.

Binary image based classification and retrieval of documents of an intellectual nature is a very challenging problem. Variations in the binary image generation mechanisms which are subject to the document artisan designer including drawing style, view-point, inclusion of multiple image components are plausible causes for increasing the complexity of the problem. In this work, we propose a method suitable to binary images which bridges some of the successes of deep learning (DL) to alleviate the problems introduced by the aforementioned variations. The method consists on extracting the shape of interest from the binary image and applying a non-Euclidean geometric neural-net architecture to learn the local and global spatial relationships of the shape. Empirical results show that our method is in some sense invariant to the image generation mechanism variations and achieves results outperforming existing methods in a patent image dataset benchmark.
Monogenic and polygenic inheritance become instruments for clonal selection

Clonally expanded blood cells that contain somatic mutations (clonal haematopoiesis) are commonly acquired with age and increase the risk of blood cancer1-9. The blood clones identified so far contain diverse large-scale mosaic chromosomal alterations (deletions, duplications and copy-neutral loss of heterozygosity (CN-LOH)) on all chromosomes1,2,5,6,9, but the sources of selective advantage that drive the expansion of most clones remain unknown. Here, to identify genes, mutations and biological processes that give selective advantage to mutant clones, we analysed genotyping data from the blood-derived DNA of 482,789 participants from the UK Biobank10. We identified 19,632 autosomal mosaic chromosomal alterations and analysed these for relationships to inherited genetic variation. We found 52 inherited, rare, large-effect coding or splice variants in 7 genes that were associated with greatly increased vulnerability to clonal haematopoiesis with specific acquired CN-LOH mutations. Acquired mutations systematically replaced the inherited risk alleles (at MPL) or duplicated them to the homologous chromosome (at FH, NBN, MRE11, ATM, SH2B3 and TM2D3). Three of the genes (MRE11, NBN and ATM) encode components of the MRN-ATM pathway, which limits cell division after DNA damage and telomere attrition11-13; another two (MPL and SH2B3) encode proteins that regulate the self-renewal of stem cells14-16. In addition, we found that CN-LOH mutations across the genome tended to cause chromosomal segments with alleles that promote the expansion of haematopoietic cells to replace their homologous (allelic) counterparts, increasing polygenic drive for blood-cell proliferation traits. Readily acquired mutations that replace chromosomal segments with their homologous counterparts seem to interact with pervasive inherited variation to create a challenge for lifelong cytopoiesis.
Inhibition stabilization is a widespread property of cortical networks

Many cortical network models use recurrent coupling strong enough to require inhibition for stabilization. Yet it has been experimentally unclear whether inhibition-stabilized network (ISN) models describe cortical function well across areas and states. Here, we test several ISN predictions, including the counterintuitive (paradoxical) suppression of inhibitory firing in response to optogenetic inhibitory stimulation. We find clear evidence for ISN operation in mouse visual, somatosensory, and motor cortex. Simple two-population ISN models describe the data well and let us quantify coupling strength. Although some models predict a non-ISN to ISN transition with increasingly strong sensory stimuli, we find ISN effects without sensory stimulation and even during light anesthesia. Additionally, average paradoxical effects result only with transgenic, not viral, opsin expression in parvalbumin (PV)-positive neurons; theory and expression data show this is consistent with ISN operation. Taken together, these results show strong coupling and inhibition stabilization are common features of the cortex.