cssi/SciSciGPT-SciSciNet huggingface dataset:
A postmodernist take on the human embryo research debate.

This paper is part of a forthcoming symposium that focuses on human embryonic stem cell research, and it responds to the lead paper by Ann Kiessling that urges the use of a more precise scientific definition of the term embryo to include only fertilized ova after implantation in the uterus, which would exclude the conceptus in vitro as well as the products of parthenogenesis and somatic cell nuclear transfer. Although agreeing with her about the desirability of promoting stem cell research, this paper quibbles with Kiessling's account of inconsistencies in definitions of the term embryo found in dictionaries, state statutes, judicial opinions, and advisory panel reports, and it emphasizes the extent to which the question turns on political rather than scientific factors. The paper explains that the federal government uses a definition directly at odds with the one Kiessling recommends, and it criticizes the extent to which the Bush administration has further politicized the issue, but the paper ultimately challenges the premise that semantic games can help to circumvent this debate.
On Trees and Logs

Abstract In this paper we contrast the main workhorse model in asset pricing theory, the Lucas (1978) tree model (LT-Model), to a benchmark model in financial equilibrium theory, the real assets model (RA-Model). It is commonly believed that the two models entail similar conclusions since the LT-Model is a special case of the RA-Model. But this is simply wrong: implications of these models can be strikingly at odds. Indeed, under the widely used log-linear specification of households’ preferences, we show that for a large set of initial endowments the LT-Model—even with potentially complete financial markets—admits only peculiar financial equilibria in which the stock market is completely degenerate, in that all stocks offer the same investment opportunity—and yet, allocation is Pareto optimal. We investigate why the LT-Model is so much at variance with the RA-Model, and uncover new results on uniqueness of financial equilibria and introduction of portfolio constraints obtaining in the LT-Model, but not in the RA-Model.
OPTIMAL FORECAST COMBINATION UNDER REGIME SWITCHING

This article proposes a new forecast combination method that lets the combination weights be driven by regime switching in a latent state variable. An empirical application that combines forecasts from survey data and time series models finds that the proposed regime switching combination scheme performs well for a variety of macroeconomic variables. Monte Carlo simulations shed light on the type of data-generating processes for which the proposed combination method can be expected to perform better than a range of alternative combination schemes. Finally, we show how time variations in the combination weights arise when the target variable and the predictors share a common factor structure driven by a hidden Markov process.
Variable-order fracture mechanics and its application to dynamic fracture

This study presents the formulation, the numerical solution, and the validation of a theoretical framework based on the concept of variable-order mechanics and capable of modeling dynamic fracture in brittle and quasi-brittle solids. More specifically, the reformulation of the elastodynamic problem via variable and fractional-order operators enables a unique and extremely powerful approach to model nucleation and propagation of cracks in solids under dynamic loading. The resulting dynamic fracture formulation is fully evolutionary, hence enabling the analysis of complex crack patterns without requiring any a priori assumption on the damage location and the growth path, and without using any algorithm to numerically track the evolving crack surface. The evolutionary nature of the variable-order formalism also prevents the need for additional partial differential equations to predict the evolution of the damage field, hence suggesting a conspicuous reduction in complexity and computational cost. Remarkably, the variable-order formulation is naturally capable of capturing extremely detailed features characteristic of dynamic crack propagation such as crack surface roughening as well as single and multiple branching. The accuracy and robustness of the proposed variable-order formulation are validated by comparing the results of direct numerical simulations with experimental data of typical benchmark problems available in the literature.
Testing gap k-planarity is NP-complete

Abstract For all k ≥ 1 , we show that deciding whether a graph is k-planar is NP-complete, extending the well-known fact that deciding 1-planarity is NP-complete. Furthermore, we show that the gap version of this decision problem is NP-complete. In particular, given a graph with local crossing number either at most k ≥ 1 or at least 2k, we show that it is NP-complete to decide whether the local crossing number is at most k or at least 2k. This algorithmic lower bound proves the non-existence of a ( 2 − ϵ ) -approximation algorithm for any fixed k ≥ 1 . In addition, we analyze the sometimes competing relationship between the local crossing number (maximum number of crossings per edge) and crossing number (total number of crossings) of a drawing. We present results regarding the non-existence of drawings that simultaneously approximately minimize both the local crossing number and crossing number of a graph.
On the Burer–Monteiro method for general semidefinite programs

Consider a semidefinite program involving an $$n\times n$$ positive semidefinite matrix X. The Burer–Monteiro method uses the substitution $$X=Y Y^T$$ to obtain a nonconvex optimization problem in terms of an $$n\times p$$ matrix Y. Boumal et al. showed that this nonconvex method provably solves equality-constrained semidefinite programs with a generic cost matrix when $$p > rsim \sqrt{2m}$$ , where m is the number of constraints. In this note we extend their result to arbitrary semidefinite programs, possibly involving inequalities or multiple semidefinite constraints. We derive similar guarantees for a fixed cost matrix and generic constraints. We illustrate applications to matrix sensing and integer quadratic minimization.
The Value of Rehabilitation Interventions --Integrating Evidence, Clinical Expertise, Critical Assessment, and , Patient Needs: A Conference Report.

In order to understand issues related to value, outcomes, and cost-effectiveness of rehabilitation interventions, and to explore how scientific evidence, clinical expertise, and patient needs can be integrated, the Rehabilitation Research and Training Center on Developing Optimal Strategies in Exercise and Survival Skills to Increase Health and Function held a State of the Science (SOS) Symposium on "The Value of Rehabilitation Interventions" at Shirley Ryan AbilityLab in Chicago in 2017. In this conference, the perspectives of 35 invited experts, including people with disabilities, professionals, and consumers, explored the topic of "value" of rehabilitation interventions and discussed their perspectives on the means to integrate best scientific evidence with clinical expertise and patient preferences. This Symposium also resulted in the production of several multifaceted manuscripts providing perspectives on the topic of value and how to use evidence to best determine and demonstrate it. These papers comprise this Supplement. The present paper introduces the key concepts of value, evidence, and knowledge translation, in an effort to provide a context for the papers of the Supplement.
Understanding Quantum Control Processor Capabilities and Limitations through Circuit Characterization

Continuing the scaling of quantum computers hinges on building classical control hardware pipelines that are scalable, extensible, and provide real time response. The instruction set architecture (ISA) of the control processor provides functional abstractions that map high-level semantics of quantum programming languages to low-level pulse generation by hardware. In this paper, we provide a methodology to quantitatively assess the effectiveness of the ISA to encode quantum circuits for intermediate-scale quantum devices with O(102) qubits. The characterization model that we define reflects performance, the ability to meet timing constraint implications, scalability for future quantum chips, and other important considerations making them useful guides for future designs. Using our methodology, we propose scalar (QUASAR) and vector (qV) quantum ISAs as extensions and compare them with other ISAs in metrics such as circuit encoding efficiency, the ability to meet real-time gate cycle requirements of quantum chips, and the ability to scale to more qubits.
A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis

Marginal maximum likelihood (MML) estimation is the preferred approach to fitting item response theory models in psychometrics due to the MML estimator's consistency, normality, and efficiency as the sample size tends to infinity. However, state-of-the-art MML estimation procedures such as the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm as well as approximate MML estimation procedures such as variational inference (VI) are computationally time-consuming when the sample size and the number of latent factors are very large. In this work, we investigate a deep learning-based VI algorithm for exploratory item factor analysis (IFA) that is computationally fast even in large data sets with many latent factors. The proposed approach applies a deep artificial neural network model called an importance-weighted autoencoder (IWAE) for exploratory IFA. The IWAE approximates the MML estimator using an importance sampling technique wherein increasing the number of importance-weighted (IW) samples drawn during fitting improves the approximation, typically at the cost of decreased computational efficiency. We provide a real data application that recovers results aligning with psychological theory across random starts. Via simulation studies, we show that the IWAE yields more accurate estimates as either the sample size or the number of IW samples increases (although factor correlation and intercepts estimates exhibit some bias) and obtains similar results to MH-RM in less time. Our simulations also suggest that the proposed approach performs similarly to and is potentially faster than constrained joint maximum likelihood estimation, a fast procedure that is consistent when the sample size and the number of items simultaneously tend to infinity.
GAMesh: Guided and Augmented Meshing for Deep Point Networks

We present a new meshing algorithm called guided and augmented meshing, GAMesh, which uses a mesh prior to generate a surface for the output points of a point network. By projecting the output points onto this prior and simplifying the resulting mesh, GAMesh ensures a surface with the same topology as the mesh prior but whose geometric fidelity is controlled by the point network. This makes GAMesh independent of both the density and distribution of the output points, a common artifact in traditional surface reconstruction algorithms. We show that such a separation of geometry from topology can have several advantages especially in single-view shape prediction, fair evaluation of point networks and reconstructing surfaces for networks which output sparse point clouds. We further show that by training point networks with GAMesh, we can directly optimize the vertex positions to generate adaptive meshes with arbitrary topologies. Code and data are available on the project webpage1.1https://www.ics.uci.edu/∼agarwal/GAMesh
Sequential Adaptive Compressed Sampling via Huffman Codes

In this paper we introduce an information theoretic approach to construct a sequence of binary sampling vectors to determine a sparse signal. Unlike the standard approaches, ours is adaptive in the sense that each sampling vector depends on the previous sample results. We prove that the expected total cost (number of measurements and reconstruction combined) we need for an s-sparse vector in ℝn is no more than slogn + 2s.
Engineering the light coupling between metalens and photonic crystal resonators for robust on-chip microsystems

We designed an on-chip transformative optic system with a broadband metalens coupler on a foundry compatible silicon photonic platform. By adjusting the on-chip metalens’ focusing length and mode dimension, the insertion loss between the metalens and the photonic crystal waveguide (PhC WG) structures is reduced to 2 dB by matching the mode on the metalens focal plane to the PhC WG mode. Alternatively, the integrated metalens allow for direct coupling from a multi-mode WG to the PhC cavity. The on-resonance transmission in a lens–cavity–lens microsystem achieves 60%. These micro-systems do not involve any single-mode silicon nanowire WG, and even a suspended PhC structure can be mechanically robust against vibrations. The proposed microsystem can be a new platform for miniaturized chemical and biosensor applications operating in air or solution environments.
Beyond deaths per capita: comparative COVID-19 mortality indicators.

OBJECTIVES: Following well-established practices in demography, this article discusses several measures based on the number of COVID-19 deaths to facilitate comparisons over time and across populations. SETTINGS: National populations in 186 United Nations countries and territories and populations in first-level subnational administrative entities in Brazil, China, Italy, Mexico, Peru, Spain and the USA. PARTICIPANTS: None (death statistics only). PRIMARY AND SECONDARY OUTCOME MEASURES: An unstandardised occurrence/exposure rate comparable to the Crude Death Rate; an indirectly age-and-sex standardised rate that can be derived even when the breakdown of COVID-19 deaths by age and sex required for direct standardisation is unavailable; the reduction in life expectancy at birth corresponding to the 2020 number of COVID-19 deaths. RESULTS: To date, the highest unstandardised rate has been in New York, at its peak exceeding the state 2017 crude death rate. Populations compare differently after standardisation: while parts of Italy, Spain and the USA have the highest unstandardised rates, parts of Mexico and Peru have the highest standardised rates. For several populations with the necessary data by age and sex for direct standardisation, we show that direct and indirect standardisation yield similar results. US life expectancy is estimated to have declined this year by more than a year (-1.26 years), far more than during the worst year of the HIV epidemic, or the worst 3 years of the opioid crisis, and to reach its lowest level since 2008. Substantially larger reductions, exceeding 2 years, are estimated for Panama, Peru, and parts of Italy, Spain, the USA and especially, Mexico. CONCLUSIONS: With lesser demand on data than direct standardisation, indirect standardisation is a valid alternative to adjust international comparisons for differences in population distribution by sex and age-groups. A number of populations have experienced reductions in 2020 life expectancies that are substantial by recent historical standards.
Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases

Recent advances in machine learning leverage massive datasets of unlabeled images from the web to learn general-purpose image representations for tasks from image classification to face recognition. But do unsupervised computer vision models automatically learn implicit patterns and embed social biases that could have harmful downstream effects? We develop a novel method for quantifying biased associations between representations of social concepts and attributes in images. We find that state-of-the-art unsupervised models trained on ImageNet, a popular benchmark image dataset curated from internet images, automatically learn racial, gender, and intersectional biases. We replicate 8 previously documented human biases from social psychology, from the innocuous, as with insects and flowers, to the potentially harmful, as with race and gender. Our results closely match three hypotheses about intersectional bias from social psychology. For the first time in unsupervised computer vision, we also quantify implicit human biases about weight, disabilities, and several ethnicities. When compared with statistical patterns in online image datasets, our findings suggest that machine learning models can automatically learn bias from the way people are stereotypically portrayed on the web.
Pancreas transplantation would be easy if the recipients were not diabetic: A practical guide to post-operative management of diabetic complications in pancreas transplant recipients.

Diabetes mellitus remains a major public health problem throughout the United States with over $300 billion spent in total cost of care annually. In addition to being a leading cost of kidney failure, diabetes causes a host of secondary hyperglycemic-related complications including gastroparesis and orthostatic hypotension. While pancreas transplantation has been established as an effective treatment for diabetes, providing long-term normoglycemia in recipients, the secondary complications of diabetes mellitus persist complicating the post-operative course of an otherwise successful pancreas transplantation. This review describes the mechanism and impact of diabetic gastroparesis and orthostatic hypotension in the post-operative course of pancreas transplant patients and analyzes the various treatment modalities, based on current data and extensive experience at our institution, to treat these respective complications. While gastroparesis and orthostatic hypotension remain challenging post-operative conditions, the establishment of institutional protocols and step-up treatment algorithms can help define more effective therapies.
Explicit result on equivalence of rational quadratic forms avoiding primes

Abstract Given a pair of regular quadratic forms over Q which are in the same genus and a finite set of primes P, we show that there is an effective way to determine a rational equivalence between these two quadratic forms which are integral over every prime in P. This answers one of the principal questions posed by Conway and Sloane (1999) [3, page 402] ,.
LRU Is Better than FIFO

In the paging problem we have to manage a two-level memory system, in which the first level has short access time but can hold only up to k pages, while the second level is very large but slow. We use competitive analysis to study the relative performance of the two best known algorithms for paging, LRU and FIFO. Sleator and Tarjan proved that the competitive ratio of LRU and FIFO is k . In practice, however, LRU is known to perform much better than FIFO. It is believed that the superiority of LRU can be attributed to locality of reference exhibited in request sequences. In order to study this phenomenon, Borodin et al. [2] refined the competitive approach by introducing the concept of access graphs. They conjectured that the competitive ratio of LRU on each access graph is less than or equal to the competitive ratio of FIFO. We prove this conjecture in this paper.
Optimal band structure for thermoelectrics with realistic scattering and bands

Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community. Prior models have been limited to simplified bands and/or scattering models. In this study, we apply more rigorous scattering treatments to more realistic model band structures—upward-parabolic bands that inflect to an inverted-parabolic behavior—including cases of multiple bands. In contrast to common descriptors (e.g., quality factor and complexity factor), the degree to which multiple pockets improve thermoelectric performance is bounded by interband scattering and the relative shapes of the bands. We establish that extremely anisotropic “flat-and-dispersive” bands, although best-performing in theory, may not represent a promising design strategy in practice. Critically, we determine optimum bandwidth, dependent on temperature and lattice thermal conductivity, from perfect transport cutoffs that can in theory significantly boost zT beyond the values attainable through intrinsic band structures alone. Our analysis should be widely useful as the thermoelectric research community eyes zT > 3.
Towards an attestation architecture for blockchain networks

If blockchain networks are to become the building blocks of the infrastructure for the future digital economy, then several challenges related to the resiliency and survivability of blockchain networks need to be addressed. The survivability of a blockchain network is influenced by the diversity of its nodes. Trustworthy device-level attestations permits nodes in a blockchain network to provide truthful evidence regarding their current configuration, operational state, keying material and other system attributes. In the current work we review the recent developments towards a standard attestation architecture and evidence conveyance protocols. We explore the applicability and benefits of a standard attestation architecture to blockchain networks. Finally, we discuss a number of open challenges related to node attestations that has arisen due to changing model of blockchain network deployments, such as the use of virtualization and containerization technologies for nodes in cloud infrastructures.
Generative machine learning with tensor networks: Benchmarks on near-term quantum computers

The authors develop a pipeline to train, test, and deploy tensor network-based machine learning models on noisy, intermediate-scale quantum hardware.
Progressive taxation as an automatic stabilizer under nominal wage rigidity and preference shocks

Previous research has shown that in the context of a prototypical New Keynesian model, more progressive income taxation may lead to higher volatilities of hours worked and total output in response to a monetary disturbance. We analytically show that this business-cycle destabilization result is overturned within an otherwise identical macroeconomy subject to impulses to the household's utility formulation. Under a continuously or linearly progressive fiscal policy rule, an increase in the tax progressivity will always raise the degree of equilibrium nominal-wage rigidity, and thus serve as an automatic stabilizer that mitigates cyclical fluctuations driven by preference shocks. Our analysis illustrates that whether a more progressive tax schedule (de)stabilizes the business cycle depends crucially on the underlying driving source.
Racial Sympathy and Support for Capital Punishment: A Case Study in Concept Transfer

Beliefs about race, especially racial resentment, are key predictors of public support for capital punishment and punitiveness generally. Drawing on a conceptual innovation by political scientist J...
An Eigensystem Approach to Anderson Localization for Multi-particle Systems

This paper revisits the proof of Anderson localization for multi-particle systems. We introduce a multi-particle version of the eigensystem multi-scale analysis by Elgart and Klein, which had previously been used for single-particle systems.
Field-based Design of a Resonant Dielectric Antenna for Coherent Spin-Photon Interfaces

We propose a field-based design of dielectric antennas interfacing diamond color centers with a 0.4 numerical aperture far-field Gaussian mode. This enables a highly efficient spin-photon interface with 93.2% mode overlap and 421 Purcell Factor.
Optimal Asset Management Contracts With Hidden Savings

We study the role of hidden savings in optimal contracts for delegated asset management. The principal uses the agent's access to capital to manipulate his precautionary motive and reduce the cost of providing incentives. After bad outcomes, the agent's consumption is somewhat insured, and he is punished instead with less access to capital and lower growth. As a result, in addition to an equity constraint, the optimal contract requires a leverage constraint to be implemented. Hidden investment limits the principal's ability to provide incentives, but doesn't change the contract's qualitative features. We provide a sufficient analytical condition for the validity of the first-order approach: if the agent's precautionary motive falls after bad outcomes, the contract is globally incentive compatible. This condition holds in the optimal contract and in a broader class of contracts.
Good Classifiers are Abundant in the Interpolating Regime

Within the machine learning community, the widely-used uniform convergence framework has been used to answer the question of how complex, over-parameterized models can generalize well to new data. This approach bounds the test error of the worst-case model one could have fit to the data, but it has fundamental limitations. Inspired by the statistical mechanics approach to learning, we formally define and develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers from several model classes. We apply our method to compute this distribution for several real and synthetic datasets, with both linear and random feature classification models. We find that test errors tend to concentrate around a small typical value $\varepsilon^*$, which deviates substantially from the test error of the worst-case interpolating model on the same datasets, indicating that "bad" classifiers are extremely rare. We provide theoretical results in a simple setting in which we characterize the full asymptotic distribution of test errors, and we show that these indeed concentrate around a value $\varepsilon^*$, which we also identify exactly. We then formalize a more general conjecture supported by our empirical findings. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice, and that approaches based on the statistical mechanics of learning may offer a promising alternative.
Proportional-Integral Projected Gradient Method for Model Predictive Control

Recently there has been an increasing interest in primal-dual methods for model predictive control (MPC), which require minimizing the (augmented) Lagrangian at each iteration. We propose a novel first order primal-dual method, termed proportional-integral projected gradient method , for MPC where the underlying finite horizon optimal control problem has both state and input constraints. Instead of minimizing the (augmented) Lagrangian, each iteration of our method only computes a single projection onto the state and input constraint set. Our method ensures that, along a sequence of averaged iterates, both the distance to optimum and the constraint violation converge to zero at a rate of $O{(}1/k{)}$ if the objective function is convex, where $k$ is the iteration number. If the objective function is strongly convex, this rate can be improved to $O{(}1/k^{2}{)}$ for the distance to optimum and $O{(}1/k^{3}{)}$ for the constraint violation. We compare our method against existing methods via a trajectory-planning example with convexified keep-out-zone constraints.
Variance reduced value iteration and faster algorithms for solving Markov decision processes

In this paper we provide faster algorithms for approximately solving discounted Markov Decision Processes in multiple parameter regimes. Given a discounted Markov Decision Process (DMDP) with $|S|$ states, $|A|$ actions, discount factor $\gamma\in(0,1)$, and rewards in the range $[-M, M]$, we show how to compute an $\epsilon$-optimal policy, with probability $1 - \delta$ in time \[ \tilde{O}\left( \left(|S|^2 |A| + \frac{|S| |A|}{(1 - \gamma)^3} \right) \log\left( \frac{M}{\epsilon} \right) \log\left( \frac{1}{\delta} \right) \right) ~ . \] This contribution reflects the first nearly linear time, nearly linearly convergent algorithm for solving DMDPs for intermediate values of $\gamma$. We also show how to obtain improved sublinear time algorithms provided we can sample from the transition function in $O(1)$ time. Under this assumption we provide an algorithm which computes an $\epsilon$-optimal policy with probability $1 - \delta$ in time \[ \tilde{O} \left(\frac{|S| |A| M^2}{(1 - \gamma)^4 \epsilon^2} \log \left(\frac{1}{\delta}\right) \right) ~. \] Lastly, we extend both these algorithms to solve finite horizon MDPs. Our algorithms improve upon the previous best for approximately computing optimal policies for fixed-horizon MDPs in multiple parameter regimes. Interestingly, we obtain our results by a careful modification of approximate value iteration. We show how to combine classic approximate value iteration analysis with new techniques in variance reduction. Our fastest algorithms leverage further insights to ensure that our algorithms make monotonic progress towards the optimal value. This paper is one of few instances in using sampling to obtain a linearly convergent linear programming algorithm and we hope that the analysis may be useful more broadly.
Neurocognitive Heterogeneity in Social Anxiety Disorder: The Role of Self-Referential Processing and Childhood Maltreatment:

Social anxiety disorder (SAD) is characterized by negative self-beliefs and altered brain activation in the default-mode network (DMN). However, the extent to which there is neurocognitive heteroge...
Populism, localism and democratic citizenship:

This article articulates and explores a localist conception of citizenship that stands in contrast to more liberal, neoliberal and cosmopolitan conceptions. A localist orientation, and some real sy...
Hybrid histidine kinase BinK represses Vibrio fischeri biofilm signaling at multiple developmental stages.

The symbiosis between the Hawaiian bobtail squid, Euprymna scolopes, and its exclusive light organ symbiont, Vibrio fischeri, provides a natural system in which to study host-microbe specificity and gene regulation during the establishment of a mutually beneficial symbiosis. Colonization of the host relies on bacterial biofilm-like aggregation in the squid mucus field. Symbiotic biofilm formation is controlled by a two-component signaling (TCS) system consisting of regulators RscS-SypF-SypG, which together direct transcription of the symbiosis polysaccharide Syp. TCS systems are broadly important for bacteria to sense environmental cues and then direct changes in behavior. Previously, we identified the hybrid histidine kinase BinK as a strong negative regulator of V. fischeri biofilm regulation, and here we further explore the function of BinK. To inhibit biofilm formation, BinK requires the predicted phosphorylation sites in both the histidine kinase (H362) and receiver (D794) domains. Furthermore, we show that RscS is not essential for host colonization when binK is deleted from strain ES114, and imaging of aggregate size revealed no benefit to the presence of RscS in a background lacking BinK. Strains lacking RscS still suffered in competition. Finally, we show that BinK functions to inhibit biofilm gene expression in the light organ crypts, providing evidence for biofilm gene regulation at later stages of host colonization. Overall, this study provides direct evidence for opposing activities of RscS and BinK and yields novel insights into biofilm regulation during the maturation of a beneficial symbiosis. IMPORTANCE Bacteria are often in a biofilm state, and transitions between planktonic and biofilm lifestyles are important for pathogenic, beneficial, and environmental microbes. The critical nature of biofilm formation during Vibrio fischeri colonization of the Hawaiian bobtail squid light organ provides an opportunity to study development of this process in vivo using a combination of genetic and imaging approaches. The current work refines the signaling circuitry of the biofilm pathway in V. fischeri, provides evidence that biofilm regulatory changes occur in the host, and identifies BinK as one of the regulators of that process. This study provides information about how bacteria regulate biofilm gene expression in an intact animal host.
Dynamic Modeling and Robust Controller Design of a Two-Stage Parallel Cable Robot

Cable robots have been extensively used for the loading and unloading of cargo in shipping industries. In this paper, we look at a two stage cable robot, i.e., a cable robot with two moving platforms connected in series. The sea condition introduces disturbance into the system. This disturbance is considered while modeling the dynamics of the two stage cable robot. A robust controller is designed which can assure robust tracking of the desired end-effector trajectory in the presence of the disturbance. Simulation results presented show the effectiveness of the controller.
A tighter constraint on Earth-system sensitivity from long-term temperature and carbon-cycle observations.

The long-term temperature response to a given change in CO2 forcing, or Earth-system sensitivity (ESS), is a key parameter quantifying our understanding about the relationship between changes in Earth’s radiative forcing and the resulting long-term Earth-system response. Current ESS estimates are subject to sizable uncertainties. Long-term carbon cycle models can provide a useful avenue to constrain ESS, but previous efforts either use rather informal statistical approaches or focus on discrete paleoevents. Here, we improve on previous ESS estimates by using a Bayesian approach to fuse deep-time CO2 and temperature data over the last 420 Myrs with a long-term carbon cycle model. Our median ESS estimate of 3.4 °C (2.6-4.7 °C; 5-95% range) shows a narrower range than previous assessments. We show that weaker chemical weathering relative to the a priori model configuration via reduced weatherable land area yields better agreement with temperature records during the Cretaceous. Research into improving the understanding about these weathering mechanisms hence provides potentially powerful avenues to further constrain this fundamental Earth-system property. Earth-system sensitivity (ESS) describes the long-term temperature response for a given change in atmospheric CO2 and, as such, is a crucial parameter to assess future climate change. Here, the authors use a Bayesian model with data from the last 420 Myrs to reduce uncertainties and estimate ESS to be around 3.4 °C.
Kronecker products, low-depth circuits, and matrix rigidity

For a matrix M and a positive integer r, the rank r rigidity of M is the smallest number of entries of M which one must change to make its rank at most r. There are many known applications of rigidity lower bounds to a variety of areas in complexity theory, but fewer known applications of rigidity upper bounds. In this paper, we use rigidity upper bounds to prove new upper bounds in a few different models of computation. Our results include: - For any d>1, and over any field F, the N × N Walsh-Hadamard transform has a depth-d linear circuit of size O(d · N1 + 0.96/d). This circumvents a known lower bound of Ω(d · N1 + 1/d) for circuits with bounded coefficients over ℂ by Pudlak (2000), by using coefficients of magnitude polynomial in N. Our construction also generalizes to linear transformations given by a Kronecker power of any fixed 2 × 2 matrix. - The N × N Walsh-Hadamard transform has a linear circuit of size ≤ (1.81 + o(1)) N log2 N, improving on the bound of ≈ 1.88 N log2 N which one obtains from the standard fast Walsh-Hadamard transform. - A new rigidity upper bound, showing that the following classes of matrices are not rigid enough to prove circuit lower bounds using Valiant’s approach: (1) for any field F and any function f : {0,1}n → F, the matrix Vf ∈ F2n × 2n given by, for any x,y ∈ {0,1}n, Vf[x,y] = f(x ∧ y), and (2) for any field F and any fixed-size matrices M1, …, Mn ∈ Fq × q, the Kronecker product M1 ⊗ M2 ⊗ ⋯ ⊗ Mn. This generalizes recent results on non-rigidity, using a simpler approach which avoids needing the polynomial method. - New connections between recursive linear transformations like Fourier and Walsh-Hadamard transforms, and circuits for matrix multiplication.
Re-Evaluating Satellite Solar Power Systems for Earth

The Solar Power Satellite System is a concept to collect solar power in space, and then transport it to the surface of the Earth by microwave (or possibly laser) beam, where if is converted into electrical power for terrestrial use. The recent increase in energy costs, predictions of the near-term exhaustion of oil, and prominence of possible climate change due to the "greenhouse effect" from burning of fossil fuels has again brought alternative energy sources to public attention, and the time is certainly appropriate to reexamine the economics of space based power. Several new concepts for Satellite Power System designs were evaluated to make the concept more economically feasible.
SINDy-BVP: Sparse identification of nonlinear dynamics for boundary value problems

This article develops a data-driven model discovery and system identification technique for spatially-dependent boundary value problems to infer both the operator and spatially-dependent parameters that describe the heterogenous, physical quantities of a system.
Adversarial Robustness Across Representation Spaces

Adversarial robustness corresponds to the susceptibility of deep neural networks to imperceptible perturbations made at test time. In the context of image tasks, many algorithms have been proposed to make neural networks robust to adversarial perturbations made to the input pixels. These perturbations are typically measured in an l p norm. However, robustness often holds only for the specific attack used for training. In this work we extend the above setting to consider the problem of training of deep neural networks that can be made simultaneously robust to perturbations applied in multiple natural representations spaces. For the case of image data, examples include the standard pixel representation as well as the representation in the discrete cosine transform (DCT) basis. We design a theoretically sound algorithm with formal guarantees for the above problem. Furthermore, our guarantees also hold when the goal is to require robustness with respect to multiple l p norm based attacks. We then derive an efficient practical implementation and demonstrate the effectiveness of our approach on standard datasets for image classification.1
Relationship Between Electric Field Strength and Helix Pitch in Induced Cholesteric-Nematic Phase Transitions

A cholesteric-nematic phase transition can be induced by relatively weak electric fields, and the threshold field varies inversely with the pitch of the helix in corroboration of recent theoretical suggestions. The phase change is accompanied by a change in the activation energy for charge-carrier production and can easily be observed by both conductivity measurements and direct optical examination.
Fair Influence Maximization: a Welfare Optimization Approach.

Several behavioral, social, and public health interventions, such as suicide/HIV prevention or community preparedness against natural disasters, leverage social network information to maximize outreach. Algorithmic influence maximization techniques have been proposed to aid with the choice of ``peer leaders'' or ``influencers'' in such interventions. Yet, traditional algorithms for influence maximization have not been designed with these interventions in mind. As a result, they may disproportionately exclude minority communities from the benefits of the intervention. This has motivated research on fair influence maximization. Existing techniques come with two major drawbacks. First, they require committing to a single fairness measure. Second, these measures are typically imposed as strict constraints leading to undesirable properties such as wastage of resources. To address these shortcomings, we provide a principled characterization of the properties that a fair influence maximization algorithm should satisfy. In particular, we propose a framework based on social welfare theory, wherein the cardinal utilities derived by each community are aggregated using the isoelastic social welfare functions. Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter. We then show under what circumstances our proposed principles can be satisfied by a welfare function. The resulting optimization problem is monotone and submodular and can be solved efficiently with optimality guarantees. Our framework encompasses as special cases leximin and proportional fairness. Extensive experiments on synthetic and real world datasets including a case study on landslide risk management demonstrate the efficacy of the proposed framework.
IntelligentPooling: practical Thompson sampling for mHealth

In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time to a user with the goal of helping the user adopt and maintain healthy behaviors. Reinforcement learning appears ideal for learning how to optimally make these sequential treatment decisions. However, significant challenges must be overcome before reinforcement learning can be effectively deployed in a mobile healthcare setting. In this work we are concerned with the following challenges: (1) individuals who are in the same context can exhibit differential response to treatments (2) only a limited amount of data is available for learning on any one individual, and (3) non-stationary responses to treatment. To address these challenges we generalize Thompson-Sampling bandit algorithms to develop IntelligentPooling. IntelligentPooling learns personalized treatment policies thus addressing challenge one. To address the second challenge, IntelligentPooling updates each user’s degree of personalization while making use of available data on other users to speed up learning. Lastly, IntelligentPooling allows responsivity to vary as a function of a user’s time since beginning treatment, thus addressing challenge three.
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a promising direction towards addressing this challenge. Here, we perform a systematic study of the Copy-Paste augmentation (e.g., [13], [12]) for instance segmentation where we randomly paste objects onto an image. Prior studies on Copy-Paste relied on modeling the surrounding visual context for pasting the objects. However, we find that the simple mechanism of pasting objects randomly is good enough and can provide solid gains on top of strong baselines. Furthermore, we show Copy-Paste is additive with semi-supervised methods that leverage extra data through pseudo labeling (e.g. self-training). On COCO instance segmentation, we achieve 49.1 mask AP and 57.3 box AP, an improvement of +0.6 mask AP and +1.5 box AP over the previous state-of-the-art. We further demonstrate that Copy-Paste can lead to significant improvements on the LVIS benchmark. Our baseline model outperforms the LVIS 2020 Challenge winning entry by +3.6 mask AP on rare categories.1
Asset Pricing with Incomplete Information Under Stable Shocks

We study a consumption based asset pricing model with incomplete information and α-stable shocks. Incomplete information leads to a non-Gaussian filtering problem. Bayesian updating generates fluctuating confidence in the agents' estimate of the persistent component of the dividends’ growth rate. Similar results are obtained with alternate distributions exhibiting fat tails (Extreme Value distribution, Pearson Type IV distribution) while they are not with a thin-tail distribution (Binomial distribution). This has the potential to generate time variation in the volatility of model-implied returns, without relying on discrete shifts in the drift rate of dividend growth rates. A test of the model using US consumption data indicates strong support in the sense that the implied returns display significant volatility persistence of a magnitude comparable to that in the data. Key phrases: asset pricing; incomplete information; time-varying volatility; fat tails; stable distributions; JEL classification: G12, G13, E43
General equations for the motions of ice crystals and water drops in gravitational and electric fields

General equations for the Reynolds number of a variety of types of ice crystals and water drops are given in terms of the Davies, Bond, and Knudsen numbers. The equations are in terms of the basic physical parameters of the system and are valid for calculating velocities in gravitational and electric fields over a very wide range of sizes and atmospheric conditions. The equations are asymptotically matched at the bottom and top of the size spectrum, useful when checking large computer codes. A numerical system for specifying the dimensional properties of ice crystals is introduced. Within the limits imposed by such variables as particle density, which have large deviations, the accuracy of velocities appears to be within 10 percent over the entire range of sizes of interest.
Notch signaling represses cone photoreceptor formation through the regulation of retinal progenitor cell states

Notch signaling is required to repress the formation of vertebrate cone photoreceptors and to maintain the proliferative potential of multipotent retinal progenitor cells. However, the mechanism by which Notch signaling controls these processes is unknown. Recently, restricted retinal progenitor cells with limited proliferation capacity and that preferentially generate cone photoreceptors have been identified. Thus, there are several potential steps during cone genesis that Notch signaling could act. Here we use cell type specific cis-regulatory elements to localize the primary role of Notch signaling in cone genesis to the formation of restricted retinal progenitor cells from multipotent retinal progenitor cells. Localized inhibition of Notch signaling in restricted progenitor cells does not alter the number of cones derived from these cells. Cell cycle promotion is not a primary effect of Notch signaling but an indirect effect on progenitor cell state transitions that leads to depletion of the multipotent progenitor cell population. Taken together, this suggests that the role of Notch signaling in cone photoreceptor formation and proliferation are both mediated by a localized function of Notch in multipotent retinal progenitor cells to repress the formation of restricted progenitor cells.
Quantum Euler Relation for Local Measurements

In classical thermodynamics the Euler relation is an expression for the internal energy as a sum of the products of canonical pairs of extensive and intensive variables. For quantum systems the situation is more intricate, since one has to account for the effects of the measurement back action. To this end, we derive a quantum analog of the Euler relation, which is governed by the information retrieved by local quantum measurements. The validity of the relation is demonstrated for the collective dissipation model, where we find that thermodynamic behavior is exhibited in the weak-coupling regime.